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def _a ( __lowercase = 200_0000 ) -> int:
"""simple docstring"""
__UpperCamelCase = [0 for i in range(n + 1 )]
__UpperCamelCase = 1
__UpperCamelCase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , __lowercase ):
__UpperCamelCase = 1
__UpperCamelCase = 0
for i in range(__lowercase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 383
|
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCAmelCase_ ( _lowercase , _lowercase ):
"""simple docstring"""
UpperCAmelCase__ = 1
@register_to_config
def __init__( self , _SCREAMING_SNAKE_CASE = 1_000 , _SCREAMING_SNAKE_CASE = None ) -> Tuple:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(_SCREAMING_SNAKE_CASE )
# standard deviation of the initial noise distribution
__UpperCamelCase = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__UpperCamelCase = 4
# running values
__UpperCamelCase = []
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> int:
__UpperCamelCase = num_inference_steps
__UpperCamelCase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__UpperCamelCase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__UpperCamelCase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__UpperCamelCase = torch.sin(steps * math.pi / 2 ) ** 2
__UpperCamelCase = (1.0 - self.betas**2) ** 0.5
__UpperCamelCase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__UpperCamelCase = timesteps.to(_SCREAMING_SNAKE_CASE )
__UpperCamelCase = []
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )
__UpperCamelCase = (self.timesteps == timestep).nonzero().item()
__UpperCamelCase = timestep_index + 1
__UpperCamelCase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_SCREAMING_SNAKE_CASE )
if len(self.ets ) == 1:
__UpperCamelCase = self.ets[-1]
elif len(self.ets ) == 2:
__UpperCamelCase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__UpperCamelCase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__UpperCamelCase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__UpperCamelCase = self._get_prev_sample(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE )
def __lowercase( self , _SCREAMING_SNAKE_CASE , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> torch.FloatTensor:
return sample
def __lowercase( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
__UpperCamelCase = self.alphas[timestep_index]
__UpperCamelCase = self.betas[timestep_index]
__UpperCamelCase = self.alphas[prev_timestep_index]
__UpperCamelCase = self.betas[prev_timestep_index]
__UpperCamelCase = (sample - sigma * ets) / max(_SCREAMING_SNAKE_CASE , 1e-8 )
__UpperCamelCase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self ) -> int:
return self.config.num_train_timesteps
| 383
| 1
|
from __future__ import annotations
UpperCAmelCase__ = "#"
class __lowerCAmelCase :
def __init__( self : Union[str, Any]) -> None:
"""simple docstring"""
_UpperCAmelCase = {}
def _lowerCamelCase ( self : List[Any] , A : str) -> None:
"""simple docstring"""
_UpperCAmelCase = self._trie
for char in text:
if char not in trie:
_UpperCAmelCase = {}
_UpperCAmelCase = trie[char]
_UpperCAmelCase = True
def _lowerCamelCase ( self : Tuple , A : str) -> tuple | list:
"""simple docstring"""
_UpperCAmelCase = self._trie
for char in prefix:
if char in trie:
_UpperCAmelCase = trie[char]
else:
return []
return self._elements(A)
def _lowerCamelCase ( self : List[str] , A : dict) -> tuple:
"""simple docstring"""
_UpperCAmelCase = []
for c, v in d.items():
_UpperCAmelCase = [' '] if c == END else [(c + s) for s in self._elements(A)]
result.extend(A)
return tuple(A)
UpperCAmelCase__ = Trie()
UpperCAmelCase__ = ("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
trie.insert_word(word)
def A ( _UpperCAmelCase : str ) -> tuple:
'''simple docstring'''
_UpperCAmelCase = trie.find_word(_UpperCAmelCase )
return tuple(string + word for word in suffixes )
def A ( ) -> None:
'''simple docstring'''
print(autocomplete_using_trie('de' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 639
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
UpperCAmelCase__ = None
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase__ = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
UpperCAmelCase__ = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
UpperCAmelCase__ = "▁"
# Segments (not really needed)
UpperCAmelCase__ = 0
UpperCAmelCase__ = 1
UpperCAmelCase__ = 2
UpperCAmelCase__ = 3
UpperCAmelCase__ = 4
class __lowerCAmelCase ( A ):
UpperCamelCase = VOCAB_FILES_NAMES
UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase = '''left'''
UpperCamelCase = XLNetTokenizer
def __init__( self : Any , A : Union[str, Any]=None , A : str=None , A : Tuple=False , A : Tuple=True , A : Any=False , A : List[str]="<s>" , A : List[str]="</s>" , A : Optional[int]="<unk>" , A : Tuple="<sep>" , A : str="<pad>" , A : Dict="<cls>" , A : Dict="<mask>" , A : Optional[Any]=["<eop>", "<eod>"] , **A : Optional[Any] , ) -> str:
"""simple docstring"""
_UpperCAmelCase = AddedToken(A , lstrip=A , rstrip=A) if isinstance(A , A) else mask_token
super().__init__(
vocab_file=A , tokenizer_file=A , do_lower_case=A , remove_space=A , keep_accents=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , additional_special_tokens=A , **A , )
_UpperCAmelCase = 3
_UpperCAmelCase = do_lower_case
_UpperCAmelCase = remove_space
_UpperCAmelCase = keep_accents
_UpperCAmelCase = vocab_file
_UpperCAmelCase = False if not self.vocab_file else True
def _lowerCamelCase ( self : Tuple , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _lowerCamelCase ( self : Tuple , A : List[int] , A : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_UpperCAmelCase = [self.sep_token_id]
_UpperCAmelCase = [2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def _lowerCamelCase ( self : List[str] , A : str , A : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.')
if not os.path.isdir(A):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
_UpperCAmelCase = os.path.join(
A , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
if os.path.abspath(self.vocab_file) != os.path.abspath(A):
copyfile(self.vocab_file , A)
return (out_vocab_file,)
| 639
| 1
|
from maths.prime_check import is_prime
def a_ ( __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
if not isinstance(__magic_name__ , __magic_name__ ):
snake_case : Union[str, Any] = F"Input value of [number={number}] must be an integer"
raise TypeError(__magic_name__ )
if is_prime(__magic_name__ ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 598
|
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
lowerCAmelCase_ = 1_6
lowerCAmelCase_ = 3_2
def _A ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase = 16 ):
'''simple docstring'''
A__ = AutoTokenizer.from_pretrained('bert-base-cased' )
A__ = DatasetDict(
{
'train': dataset['train'].select(UpperCAmelCase ),
'validation': dataset['train'].select(UpperCAmelCase ),
'test': dataset['validation'],
} )
def tokenize_function(UpperCAmelCase ):
# max_length=None => use the model max length (it's actually the default)
A__ = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=UpperCAmelCase ,max_length=UpperCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A__ = datasets.map(
UpperCAmelCase ,batched=UpperCAmelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,)
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(UpperCAmelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A__ = 16
elif accelerator.mixed_precision != "no":
A__ = 8
else:
A__ = None
return tokenizer.pad(
UpperCAmelCase ,padding='longest' ,max_length=UpperCAmelCase ,pad_to_multiple_of=UpperCAmelCase ,return_tensors='pt' ,)
# Instantiate dataloaders.
A__ = DataLoader(
tokenized_datasets['train'] ,shuffle=UpperCAmelCase ,collate_fn=UpperCAmelCase ,batch_size=UpperCAmelCase )
A__ = DataLoader(
tokenized_datasets['validation'] ,shuffle=UpperCAmelCase ,collate_fn=UpperCAmelCase ,batch_size=UpperCAmelCase )
A__ = DataLoader(
tokenized_datasets['test'] ,shuffle=UpperCAmelCase ,collate_fn=UpperCAmelCase ,batch_size=UpperCAmelCase )
return train_dataloader, eval_dataloader, test_dataloader
def _A ( UpperCAmelCase ,UpperCAmelCase ):
'''simple docstring'''
A__ = []
# Download the dataset
A__ = load_dataset('glue' ,'mrpc' )
# Create our splits
A__ = StratifiedKFold(n_splits=int(args.num_folds ) )
# Initialize accelerator
A__ = Accelerator(cpu=args.cpu ,mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ = config['lr']
A__ = int(config['num_epochs'] )
A__ = int(config['seed'] )
A__ = int(config['batch_size'] )
A__ = evaluate.load('glue' ,'mrpc' )
# If the batch size is too big we use gradient accumulation
A__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A__ = batch_size // MAX_GPU_BATCH_SIZE
A__ = MAX_GPU_BATCH_SIZE
set_seed(UpperCAmelCase )
# New Code #
# Create our folds:
A__ = kfold.split(np.zeros(datasets['train'].num_rows ) ,datasets['train']['label'] )
A__ = []
# Iterate over them
for i, (train_idxs, valid_idxs) in enumerate(UpperCAmelCase ):
A__ , A__ , A__ = get_fold_dataloaders(
UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,)
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,return_dict=UpperCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A__ = model.to(accelerator.device )
# Instantiate optimizer
A__ = AdamW(params=model.parameters() ,lr=UpperCAmelCase )
# Instantiate scheduler
A__ = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase ,num_warmup_steps=100 ,num_training_steps=(len(UpperCAmelCase ) * num_epochs) // gradient_accumulation_steps ,)
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__ , A__ , A__ , A__ , A__ = accelerator.prepare(
UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase )
# Now we train the model
for epoch in range(UpperCAmelCase ):
model.train()
for step, batch in enumerate(UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A__ = model(**UpperCAmelCase )
A__ = outputs.loss
A__ = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**UpperCAmelCase )
A__ = outputs.logits.argmax(dim=-1 )
A__ , A__ = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=UpperCAmelCase ,references=UpperCAmelCase ,)
A__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" ,UpperCAmelCase )
# New Code #
# We also run predictions on the test set at the very end
A__ = []
for step, batch in enumerate(UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**UpperCAmelCase )
A__ = outputs.logits
A__ , A__ = accelerator.gather_for_metrics((predictions, batch['labels']) )
fold_predictions.append(predictions.cpu() )
if i == 0:
# We need all of the test predictions
test_references.append(references.cpu() )
# Use accelerator.print to print only on the main process.
test_predictions.append(torch.cat(UpperCAmelCase ,dim=0 ) )
# We now need to release all our memory and get rid of the current model, optimizer, etc
accelerator.free_memory()
# New Code #
# Finally we check the accuracy of our folded results:
A__ = torch.cat(UpperCAmelCase ,dim=0 )
A__ = torch.stack(UpperCAmelCase ,dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
A__ = metric.compute(predictions=UpperCAmelCase ,references=UpperCAmelCase )
accelerator.print('Average test metrics from all folds:' ,UpperCAmelCase )
def _A ( ):
'''simple docstring'''
A__ = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' ,type=UpperCAmelCase ,default=UpperCAmelCase ,choices=['no', 'fp16', 'bf16', 'fp8'] ,help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' ,)
parser.add_argument('--cpu' ,action='store_true' ,help='If passed, will train on the CPU.' )
# New Code #
parser.add_argument('--num_folds' ,type=UpperCAmelCase ,default=3 ,help='The number of splits to perform across the dataset' )
A__ = parser.parse_args()
A__ = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(UpperCAmelCase ,UpperCAmelCase )
if __name__ == "__main__":
main()
| 531
| 0
|
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowerCAmelCase_ ( __lowerCamelCase ):
if not is_accelerate_available():
return method
__snake_case : int = version.parse(accelerate.__version__ ).base_version
if version.parse(__lowerCamelCase ) < version.parse("0.17.0" ):
return method
def wrapper(self , *__lowerCamelCase , **__lowerCamelCase ):
if hasattr(self , "_hf_hook" ) and hasattr(self._hf_hook , "pre_forward" ):
self._hf_hook.pre_forward(self )
return method(self , *__lowerCamelCase , **__lowerCamelCase )
return wrapper
| 203
|
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class a :
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase : str , lowerCamelCase : Tuple=13 , lowerCamelCase : Tuple=32 , lowerCamelCase : Any=2 , lowerCamelCase : Union[str, Any]=3 , lowerCamelCase : Tuple=16 , lowerCamelCase : Any=[32, 64, 128] , lowerCamelCase : str=[1, 2, 1] , lowerCamelCase : Union[str, Any]=[2, 2, 4] , lowerCamelCase : Union[str, Any]=2 , lowerCamelCase : Optional[int]=2.0 , lowerCamelCase : Tuple=True , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=0.0 , lowerCamelCase : Dict=0.1 , lowerCamelCase : Optional[int]="gelu" , lowerCamelCase : Any=False , lowerCamelCase : Any=True , lowerCamelCase : str=0.02 , lowerCamelCase : Tuple=1E-5 , lowerCamelCase : str=True , lowerCamelCase : Optional[Any]=None , lowerCamelCase : List[str]=True , lowerCamelCase : Union[str, Any]=10 , lowerCamelCase : Any=8 , lowerCamelCase : List[str]=["stage1", "stage2"] , lowerCamelCase : Optional[int]=[1, 2] , ) -> Optional[int]:
__snake_case : Any = parent
__snake_case : int = batch_size
__snake_case : Optional[int] = image_size
__snake_case : int = patch_size
__snake_case : Any = num_channels
__snake_case : List[Any] = embed_dim
__snake_case : str = hidden_sizes
__snake_case : int = depths
__snake_case : Any = num_heads
__snake_case : Any = window_size
__snake_case : Optional[Any] = mlp_ratio
__snake_case : Any = qkv_bias
__snake_case : List[Any] = hidden_dropout_prob
__snake_case : Dict = attention_probs_dropout_prob
__snake_case : Tuple = drop_path_rate
__snake_case : Tuple = hidden_act
__snake_case : Optional[int] = use_absolute_embeddings
__snake_case : Optional[int] = patch_norm
__snake_case : int = layer_norm_eps
__snake_case : Any = initializer_range
__snake_case : Any = is_training
__snake_case : Tuple = scope
__snake_case : Tuple = use_labels
__snake_case : Any = type_sequence_label_size
__snake_case : Dict = encoder_stride
__snake_case : Union[str, Any] = out_features
__snake_case : Union[str, Any] = out_indices
def __snake_case ( self : Optional[Any] ) -> str:
__snake_case : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case : Optional[Any] = None
if self.use_labels:
__snake_case : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __snake_case ( self : Union[str, Any] ) -> List[Any]:
return FocalNetConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , hidden_sizes=self.hidden_sizes , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def __snake_case ( self : Optional[int] , lowerCamelCase : Optional[int] , lowerCamelCase : Dict , lowerCamelCase : str ) -> str:
__snake_case : Tuple = FocalNetModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : List[str] = model(lowerCamelCase )
__snake_case : Tuple = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__snake_case : Optional[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def __snake_case ( self : Tuple , lowerCamelCase : Optional[Any] , lowerCamelCase : int , lowerCamelCase : List[Any] ) -> Optional[int]:
__snake_case : Union[str, Any] = FocalNetBackbone(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : int = model(lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
__snake_case : Dict = None
__snake_case : Any = FocalNetBackbone(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[int] = model(lowerCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def __snake_case ( self : Any , lowerCamelCase : Optional[Any] , lowerCamelCase : List[Any] , lowerCamelCase : Any ) -> Optional[int]:
__snake_case : List[Any] = FocalNetForMaskedImageModeling(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : List[str] = model(lowerCamelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__snake_case : List[str] = 1
__snake_case : Any = FocalNetForMaskedImageModeling(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__snake_case : int = model(lowerCamelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __snake_case ( self : str , lowerCamelCase : Dict , lowerCamelCase : Any , lowerCamelCase : str ) -> Optional[Any]:
__snake_case : Tuple = self.type_sequence_label_size
__snake_case : List[Any] = FocalNetForImageClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[Any] = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__snake_case : Optional[int] = 1
__snake_case : str = FocalNetForImageClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
__snake_case : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__snake_case : Optional[Any] = model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __snake_case ( self : Optional[int] ) -> Optional[int]:
__snake_case : str = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case : List[str] = config_and_inputs
__snake_case : Union[str, Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class a (_lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : int = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
__UpperCAmelCase : Optional[int] = (
{"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
if is_torch_available()
else {}
)
__UpperCAmelCase : List[str] = False
__UpperCAmelCase : str = False
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Optional[int] = False
__UpperCAmelCase : List[str] = False
def __snake_case ( self : List[str] ) -> Tuple:
__snake_case : Optional[Any] = FocalNetModelTester(self )
__snake_case : int = ConfigTester(self , config_class=lowerCamelCase , embed_dim=37 , has_text_modality=lowerCamelCase )
def __snake_case ( self : Optional[int] ) -> int:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __snake_case ( self : str ) -> Tuple:
return
def __snake_case ( self : Union[str, Any] ) -> str:
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def __snake_case ( self : str ) -> int:
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowerCamelCase )
def __snake_case ( self : List[str] ) -> Optional[int]:
__snake_case : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase )
def __snake_case ( self : str ) -> List[str]:
__snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def __snake_case ( self : Dict ) -> List[str]:
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def __snake_case ( self : str ) -> int:
pass
def __snake_case ( self : Optional[int] ) -> Tuple:
__snake_case , __snake_case : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__snake_case : Dict = model_class(lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__snake_case : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase , nn.Linear ) )
def __snake_case ( self : List[str] ) -> Optional[int]:
__snake_case , __snake_case : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
__snake_case : List[str] = model_class(lowerCamelCase )
__snake_case : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case : List[Any] = [*signature.parameters.keys()]
__snake_case : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def __snake_case ( self : Optional[int] , lowerCamelCase : List[str] , lowerCamelCase : Tuple , lowerCamelCase : List[Any] , lowerCamelCase : Union[str, Any] ) -> str:
__snake_case : Tuple = model_class(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
with torch.no_grad():
__snake_case : List[Any] = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
__snake_case : List[str] = outputs.hidden_states
__snake_case : Tuple = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
# FocalNet has a different seq_length
__snake_case : Tuple = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__snake_case : int = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
__snake_case : List[Any] = outputs.reshaped_hidden_states
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
__snake_case , __snake_case , __snake_case , __snake_case : Tuple = reshaped_hidden_states[0].shape
__snake_case : Optional[int] = (
reshaped_hidden_states[0].view(lowerCamelCase , lowerCamelCase , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def __snake_case ( self : Tuple ) -> int:
__snake_case , __snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : Dict = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
__snake_case : Optional[Any] = True
self.check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__snake_case : Dict = True
self.check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
def __snake_case ( self : Optional[int] ) -> Any:
__snake_case , __snake_case : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : Optional[int] = 3
__snake_case : List[str] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__snake_case : Any = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__snake_case : Dict = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__snake_case : List[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
__snake_case : List[str] = True
self.check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__snake_case : Tuple = True
self.check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase , (padded_height, padded_width) )
@slow
def __snake_case ( self : List[Any] ) -> Union[str, Any]:
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__snake_case : List[str] = FocalNetModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def __snake_case ( self : Tuple ) -> List[Any]:
__snake_case , __snake_case : str = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case : Optional[Any] = _config_zero_init(lowerCamelCase )
for model_class in self.all_model_classes:
__snake_case : Tuple = model_class(config=lowerCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=F'Parameter {name} of model {model_class} seems not properly initialized' , )
@require_vision
@require_torch
class a (unittest.TestCase ):
"""simple docstring"""
@cached_property
def __snake_case ( self : Union[str, Any] ) -> List[Any]:
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def __snake_case ( self : int ) -> Optional[int]:
__snake_case : List[str] = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(lowerCamelCase )
__snake_case : str = self.default_image_processor
__snake_case : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
__snake_case : Optional[Any] = image_processor(images=lowerCamelCase , return_tensors="pt" ).to(lowerCamelCase )
# forward pass
with torch.no_grad():
__snake_case : int = model(**lowerCamelCase )
# verify the logits
__snake_case : List[str] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
__snake_case : Optional[int] = torch.tensor([0.21_66, -0.43_68, 0.21_91] ).to(lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() , 281 )
@require_torch
class a (_lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : Tuple = (FocalNetBackbone,) if is_torch_available() else ()
__UpperCAmelCase : Dict = FocalNetConfig
__UpperCAmelCase : Any = False
def __snake_case ( self : str ) -> List[str]:
__snake_case : Tuple = FocalNetModelTester(self )
| 203
| 1
|
def a_ (__A , __A , __A , __A ) -> int:
"""simple docstring"""
__a , __a : Any = len(__A ), len(grid[0] )
if (
min(__A , __A ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__a : Dict = 0
count += depth_first_search(__A , row + 1 , __A , __A )
count += depth_first_search(__A , row - 1 , __A , __A )
count += depth_first_search(__A , __A , col + 1 , __A )
count += depth_first_search(__A , __A , col - 1 , __A )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351
|
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class snake_case_ ( __UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case__ = MvpTokenizer
snake_case__ = MvpTokenizerFast
snake_case__ = True
snake_case__ = filter_roberta_detectors
def UpperCAmelCase__ (self: List[str] ) -> Any:
'''simple docstring'''
super().setUp()
__a : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
__a : Optional[int] = dict(zip(__UpperCAmelCase , range(len(__UpperCAmelCase ) ) ) )
__a : Dict = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
__a : Optional[int] = {"unk_token": "<unk>"}
__a : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
__a : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(__UpperCAmelCase ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(__UpperCAmelCase ) )
def UpperCAmelCase__ (self: List[Any] , **__UpperCAmelCase: Dict ) -> int:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCAmelCase__ (self: str , **__UpperCAmelCase: Tuple ) -> Tuple:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__UpperCAmelCase )
def UpperCAmelCase__ (self: Any , __UpperCAmelCase: int ) -> List[str]:
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def UpperCAmelCase__ (self: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
return MvpTokenizer.from_pretrained("RUCAIBox/mvp" )
@cached_property
def UpperCAmelCase__ (self: str ) -> List[Any]:
'''simple docstring'''
return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp" )
@require_torch
def UpperCAmelCase__ (self: Dict ) -> List[Any]:
'''simple docstring'''
__a : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
__a : List[Any] = [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Optional[Any] = tokenizer(__UpperCAmelCase , max_length=len(__UpperCAmelCase ) , padding=__UpperCAmelCase , return_tensors="pt" )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
__a : Optional[int] = batch.input_ids.tolist()[0]
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
# Test that special tokens are reset
@require_torch
def UpperCAmelCase__ (self: Optional[int] ) -> List[Any]:
'''simple docstring'''
__a : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : int = tokenizer(__UpperCAmelCase , padding=__UpperCAmelCase , return_tensors="pt" )
# check if input_ids are returned and no labels
self.assertIn("input_ids" , __UpperCAmelCase )
self.assertIn("attention_mask" , __UpperCAmelCase )
self.assertNotIn("labels" , __UpperCAmelCase )
self.assertNotIn("decoder_attention_mask" , __UpperCAmelCase )
@require_torch
def UpperCAmelCase__ (self: Any ) -> Union[str, Any]:
'''simple docstring'''
__a : List[Any] = [
"Summary of the text.",
"Another summary.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : int = tokenizer(text_target=__UpperCAmelCase , max_length=32 , padding="max_length" , return_tensors="pt" )
self.assertEqual(32 , targets["input_ids"].shape[1] )
@require_torch
def UpperCAmelCase__ (self: int ) -> Union[str, Any]:
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : str = tokenizer(
["I am a small frog" * 1024, "I am a small frog"] , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , return_tensors="pt" )
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase )
self.assertEqual(batch.input_ids.shape , (2, 1024) )
@require_torch
def UpperCAmelCase__ (self: str ) -> Any:
'''simple docstring'''
__a : Optional[int] = ["A long paragraph for summarization."]
__a : Tuple = [
"Summary of the text.",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__a : Dict = tokenizer(__UpperCAmelCase , text_target=__UpperCAmelCase , return_tensors="pt" )
__a : str = inputs["input_ids"]
__a : Optional[Any] = inputs["labels"]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def UpperCAmelCase__ (self: Dict ) -> str:
'''simple docstring'''
pass
def UpperCAmelCase__ (self: Any ) -> Optional[Any]:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__a : Tuple = self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
__a : List[Any] = self.tokenizer_class.from_pretrained(__UpperCAmelCase , **__UpperCAmelCase )
__a : Tuple = "A, <mask> AllenNLP sentence."
__a : Dict = tokenizer_r.encode_plus(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
__a : List[Any] = tokenizer_p.encode_plus(__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["token_type_ids"] ) , sum(tokens_p["token_type_ids"] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["attention_mask"] ) / len(tokens_r["attention_mask"] ) , sum(tokens_p["attention_mask"] ) / len(tokens_p["attention_mask"] ) , )
__a : Optional[int] = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"] )
__a : Union[str, Any] = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["input_ids"] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r["input_ids"] , [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2] )
self.assertSequenceEqual(
__UpperCAmelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
self.assertSequenceEqual(
__UpperCAmelCase , ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
| 351
| 1
|
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase :
def __init__( self : List[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any]=13 , UpperCAmelCase : Optional[Any]=7 , UpperCAmelCase : Dict=True , UpperCAmelCase : Any=True , UpperCAmelCase : List[str]=True , UpperCAmelCase : List[str]=True , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Dict=False , UpperCAmelCase : Dict=False , UpperCAmelCase : List[Any]=False , UpperCAmelCase : Tuple=2 , UpperCAmelCase : Tuple=99 , UpperCAmelCase : List[str]=0 , UpperCAmelCase : Optional[Any]=32 , UpperCAmelCase : Optional[int]=5 , UpperCAmelCase : Dict=4 , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : List[str]=0.1 , UpperCAmelCase : Tuple=512 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : List[str]=0.0_2 , UpperCAmelCase : Optional[Any]=2 , UpperCAmelCase : Union[str, Any]=4 , UpperCAmelCase : Any="last" , UpperCAmelCase : Optional[Any]=True , UpperCAmelCase : Dict=None , UpperCAmelCase : Optional[int]=0 , ) -> List[Any]:
lowerCamelCase__ : List[str] = parent
lowerCamelCase__ : Optional[Any] = batch_size
lowerCamelCase__ : int = seq_length
lowerCamelCase__ : Union[str, Any] = is_training
lowerCamelCase__ : Dict = use_input_lengths
lowerCamelCase__ : str = use_token_type_ids
lowerCamelCase__ : str = use_labels
lowerCamelCase__ : int = gelu_activation
lowerCamelCase__ : Optional[Any] = sinusoidal_embeddings
lowerCamelCase__ : Optional[Any] = causal
lowerCamelCase__ : Any = asm
lowerCamelCase__ : Any = n_langs
lowerCamelCase__ : Tuple = vocab_size
lowerCamelCase__ : int = n_special
lowerCamelCase__ : str = hidden_size
lowerCamelCase__ : List[Any] = num_hidden_layers
lowerCamelCase__ : List[Any] = num_attention_heads
lowerCamelCase__ : str = hidden_dropout_prob
lowerCamelCase__ : str = attention_probs_dropout_prob
lowerCamelCase__ : Union[str, Any] = max_position_embeddings
lowerCamelCase__ : List[Any] = type_sequence_label_size
lowerCamelCase__ : int = initializer_range
lowerCamelCase__ : List[str] = num_labels
lowerCamelCase__ : List[Any] = num_choices
lowerCamelCase__ : Optional[Any] = summary_type
lowerCamelCase__ : Optional[Any] = use_proj
lowerCamelCase__ : Dict = scope
lowerCamelCase__ : List[str] = bos_token_id
def A_ ( self : List[str] ) -> List[Any]:
lowerCamelCase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase__ : Any = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : List[str] = None
if self.use_input_lengths:
lowerCamelCase__ : Optional[int] = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCamelCase__ : str = None
if self.use_token_type_ids:
lowerCamelCase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCamelCase__ : Union[str, Any] = None
lowerCamelCase__ : Union[str, Any] = None
lowerCamelCase__ : Union[str, Any] = None
if self.use_labels:
lowerCamelCase__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase__ : int = ids_tensor([self.batch_size] , 2 ).float()
lowerCamelCase__ : Any = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase__ : Union[str, Any] = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def A_ ( self : str ) -> List[str]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def A_ ( self : Any , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : int , UpperCAmelCase : List[Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : str , UpperCAmelCase : List[Any] , ) -> Union[str, Any]:
lowerCamelCase__ : str = XLMModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : Optional[int] = model(UpperCAmelCase , lengths=UpperCAmelCase , langs=UpperCAmelCase )
lowerCamelCase__ : List[str] = model(UpperCAmelCase , langs=UpperCAmelCase )
lowerCamelCase__ : str = model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A_ ( self : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Dict , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] , ) -> str:
lowerCamelCase__ : int = XLMWithLMHeadModel(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : List[str] = model(UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A_ ( self : List[str] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : Dict , UpperCAmelCase : str , ) -> List[Any]:
lowerCamelCase__ : List[str] = XLMForQuestionAnsweringSimple(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : Tuple = model(UpperCAmelCase )
lowerCamelCase__ : Tuple = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
lowerCamelCase__ : Union[str, Any] = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def A_ ( self : Tuple , UpperCAmelCase : Tuple , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : int , UpperCAmelCase : List[str] , UpperCAmelCase : List[str] , UpperCAmelCase : int , ) -> List[str]:
lowerCamelCase__ : Tuple = XLMForQuestionAnswering(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : List[str] = model(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , p_mask=UpperCAmelCase , )
lowerCamelCase__ : Dict = model(
UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase , cls_index=UpperCAmelCase , is_impossible=UpperCAmelCase , )
((lowerCamelCase__) , ) : str = result_with_labels.to_tuple()
lowerCamelCase__ : List[str] = model(UpperCAmelCase , start_positions=UpperCAmelCase , end_positions=UpperCAmelCase )
((lowerCamelCase__) , ) : Dict = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def A_ ( self : Union[str, Any] , UpperCAmelCase : int , UpperCAmelCase : Dict , UpperCAmelCase : Tuple , UpperCAmelCase : str , UpperCAmelCase : Tuple , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int , ) -> int:
lowerCamelCase__ : Union[str, Any] = XLMForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : Any = model(UpperCAmelCase )
lowerCamelCase__ : Dict = model(UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A_ ( self : str , UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : str , ) -> Optional[Any]:
lowerCamelCase__ : List[Any] = self.num_labels
lowerCamelCase__ : Tuple = XLMForTokenClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : List[Any] = model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A_ ( self : str , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : int , UpperCAmelCase : Dict , UpperCAmelCase : Dict , ) -> List[Any]:
lowerCamelCase__ : Optional[int] = self.num_choices
lowerCamelCase__ : int = XLMForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
lowerCamelCase__ : List[str] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCamelCase__ : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCamelCase__ : Tuple = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowerCamelCase__ : List[str] = model(
UpperCAmelCase , attention_mask=UpperCAmelCase , token_type_ids=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
lowerCamelCase__ : Dict = self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : List[str] = config_and_inputs
lowerCamelCase__ : int = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class lowerCAmelCase ( __UpperCamelCase, __UpperCamelCase, __UpperCamelCase, unittest.TestCase ):
UpperCAmelCase__ = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
UpperCAmelCase__ = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
UpperCAmelCase__ = (
{
"""feature-extraction""": XLMModel,
"""fill-mask""": XLMWithLMHeadModel,
"""question-answering""": XLMForQuestionAnsweringSimple,
"""text-classification""": XLMForSequenceClassification,
"""text-generation""": XLMWithLMHeadModel,
"""token-classification""": XLMForTokenClassification,
"""zero-shot""": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def A_ ( self : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : str , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Tuple ) -> Optional[int]:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def A_ ( self : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Optional[int]=False ) -> Tuple:
lowerCamelCase__ : List[str] = super()._prepare_for_class(UpperCAmelCase , UpperCAmelCase , return_labels=UpperCAmelCase )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
lowerCamelCase__ : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
lowerCamelCase__ : Tuple = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase )
return inputs_dict
def A_ ( self : str ) -> Union[str, Any]:
lowerCamelCase__ : Union[str, Any] = XLMModelTester(self )
lowerCamelCase__ : Any = ConfigTester(self , config_class=UpperCAmelCase , emb_dim=37 )
def A_ ( self : Optional[int] ) -> List[Any]:
self.config_tester.run_common_tests()
def A_ ( self : Any ) -> List[Any]:
lowerCamelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*UpperCAmelCase )
def A_ ( self : str ) -> Any:
lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase )
def A_ ( self : Dict ) -> Optional[Any]:
lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*UpperCAmelCase )
def A_ ( self : Optional[Any] ) -> Optional[Any]:
lowerCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase )
def A_ ( self : Any ) -> Optional[int]:
lowerCamelCase__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase )
def A_ ( self : Any ) -> str:
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*UpperCAmelCase )
def A_ ( self : Any ) -> Optional[int]:
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase )
def A_ ( self : Optional[int] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Any , UpperCAmelCase : List[Any]=False , UpperCAmelCase : str=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_attentions in attentions] , [True] * len(UpperCAmelCase ) )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(UpperCAmelCase ):
# adds PAD dummy token
lowerCamelCase__ : Any = min_length + idx + 1
lowerCamelCase__ : Union[str, Any] = min_length + idx + 1
lowerCamelCase__ : Optional[Any] = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(UpperCAmelCase ) )
def A_ ( self : str , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[int] , UpperCAmelCase : str , UpperCAmelCase : Dict , UpperCAmelCase : List[str]=False , UpperCAmelCase : Tuple=1 ) -> Union[str, Any]:
self.assertIsInstance(UpperCAmelCase , UpperCAmelCase )
self.assertListEqual(
[isinstance(UpperCAmelCase , UpperCAmelCase ) for iter_hidden_states in hidden_states] , [True] * len(UpperCAmelCase ) , )
self.assertEqual(len(UpperCAmelCase ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(UpperCAmelCase ):
# adds PAD dummy token
lowerCamelCase__ : str = min_length + idx + 1
lowerCamelCase__ : Any = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(UpperCAmelCase ) , )
pass
@slow
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ : Dict = XLMModel.from_pretrained(UpperCAmelCase )
self.assertIsNotNone(UpperCAmelCase )
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
@slow
def A_ ( self : Any ) -> List[Any]:
lowerCamelCase__ : Dict = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(UpperCAmelCase )
lowerCamelCase__ : int = torch.tensor([[14, 447]] , dtype=torch.long , device=UpperCAmelCase ) # the president
lowerCamelCase__ : Tuple = [
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
14,
447,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
lowerCamelCase__ : List[Any] = model.generate(UpperCAmelCase , do_sample=UpperCAmelCase )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , UpperCAmelCase )
| 188
|
_UpperCAmelCase : List[Any] = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
_UpperCAmelCase : Dict = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> float:
lowerCamelCase__ : Dict = from_type.lower().strip('s' )
lowerCamelCase__ : Dict = to_type.lower().strip('s' )
lowerCamelCase__ : Dict = UNIT_SYMBOL.get(_UpperCAmelCase , _UpperCAmelCase )
lowerCamelCase__ : str = UNIT_SYMBOL.get(_UpperCAmelCase , _UpperCAmelCase )
if from_sanitized not in METRIC_CONVERSION:
lowerCamelCase__ : List[Any] = (
F"""Invalid 'from_type' value: {from_type!r}.\n"""
F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase )}"""
)
raise ValueError(_UpperCAmelCase )
if to_sanitized not in METRIC_CONVERSION:
lowerCamelCase__ : Optional[Any] = (
F"""Invalid 'to_type' value: {to_type!r}.\n"""
F"""Conversion abbreviations are: {", ".join(_UpperCAmelCase )}"""
)
raise ValueError(_UpperCAmelCase )
lowerCamelCase__ : Any = METRIC_CONVERSION[from_sanitized]
lowerCamelCase__ : Optional[int] = METRIC_CONVERSION[to_sanitized]
lowerCamelCase__ : List[str] = 1
if from_exponent > to_exponent:
lowerCamelCase__ : Dict = from_exponent - to_exponent
else:
lowerCamelCase__ : Dict = -(to_exponent - from_exponent)
return value * pow(10 , _UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 188
| 1
|
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _lowercase ( nn.Module ):
'''simple docstring'''
def __init__( self :Optional[Any] ) -> str:
super().__init__()
__SCREAMING_SNAKE_CASE : Optional[Any] = nn.Linear(3 , 4 )
__SCREAMING_SNAKE_CASE : str = nn.BatchNormad(4 )
__SCREAMING_SNAKE_CASE : List[Any] = nn.Linear(4 , 5 )
def __magic_name__( self :Optional[int] , lowerCAmelCase__ :Tuple ) -> Any:
return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase__ ) ) )
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magic_name__( self :List[Any] ) -> str:
__SCREAMING_SNAKE_CASE : List[Any] = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCAmelCase__ , model.state_dict() )
__SCREAMING_SNAKE_CASE : List[str] = os.path.join(lowerCAmelCase__ , '''index.json''' )
self.assertTrue(os.path.isfile(lowerCAmelCase__ ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(lowerCAmelCase__ , f'''{key}.dat''' )
self.assertTrue(os.path.isfile(lowerCAmelCase__ ) )
# TODO: add tests on the fact weights are properly loaded
def __magic_name__( self :int ) -> Optional[int]:
__SCREAMING_SNAKE_CASE : Optional[Any] = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
__SCREAMING_SNAKE_CASE : str = torch.randn(2 , 3 , dtype=lowerCAmelCase__ )
with TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE : Tuple = offload_weight(lowerCAmelCase__ , '''weight''' , lowerCAmelCase__ , {} )
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(lowerCAmelCase__ , '''weight.dat''' )
self.assertTrue(os.path.isfile(lowerCAmelCase__ ) )
self.assertDictEqual(lowerCAmelCase__ , {'''weight''': {'''shape''': [2, 3], '''dtype''': str(lowerCAmelCase__ ).split('''.''' )[1]}} )
__SCREAMING_SNAKE_CASE : List[Any] = load_offloaded_weight(lowerCAmelCase__ , index['''weight'''] )
self.assertTrue(torch.equal(lowerCAmelCase__ , lowerCAmelCase__ ) )
def __magic_name__( self :int ) -> Dict:
__SCREAMING_SNAKE_CASE : Tuple = ModelForTest()
__SCREAMING_SNAKE_CASE : int = model.state_dict()
__SCREAMING_SNAKE_CASE : List[Any] = {k: v for k, v in state_dict.items() if '''linear2''' not in k}
__SCREAMING_SNAKE_CASE : Optional[int] = {k: v for k, v in state_dict.items() if '''linear2''' in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = OffloadedWeightsLoader(state_dict=lowerCAmelCase__ , save_folder=lowerCAmelCase__ )
# Every key is there with the right value
self.assertEqual(sorted(lowerCAmelCase__ ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(lowerCAmelCase__ , weight_map[key] ) )
__SCREAMING_SNAKE_CASE : Dict = {k: v for k, v in state_dict.items() if '''weight''' in k}
__SCREAMING_SNAKE_CASE : Union[str, Any] = {k: v for k, v in state_dict.items() if '''weight''' not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCAmelCase__ , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = OffloadedWeightsLoader(state_dict=lowerCAmelCase__ , save_folder=lowerCAmelCase__ )
# Every key is there with the right value
self.assertEqual(sorted(lowerCAmelCase__ ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(lowerCAmelCase__ , weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCAmelCase__ , lowerCAmelCase__ )
# Duplicates are removed
__SCREAMING_SNAKE_CASE : Optional[int] = OffloadedWeightsLoader(state_dict=lowerCAmelCase__ , save_folder=lowerCAmelCase__ )
# Every key is there with the right value
self.assertEqual(sorted(lowerCAmelCase__ ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(lowerCAmelCase__ , weight_map[key] ) )
def __magic_name__( self :str ) -> Any:
__SCREAMING_SNAKE_CASE : List[Any] = {'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
__SCREAMING_SNAKE_CASE : Union[str, Any] = extract_submodules_state_dict(lowerCAmelCase__ , ['''a.1''', '''a.2'''] )
self.assertDictEqual(lowerCAmelCase__ , {'''a.1''': 0, '''a.2''': 2} )
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
__SCREAMING_SNAKE_CASE : List[str] = extract_submodules_state_dict(lowerCAmelCase__ , ['''a.1''', '''a.2'''] )
self.assertDictEqual(lowerCAmelCase__ , {'''a.1.a''': 0, '''a.2.a''': 2} )
| 696
|
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
__lowerCAmelCase : int ={
'return_dict': False,
'output_hidden_states': True,
'output_attentions': True,
'torchscript': True,
'torch_dtype': 'float16',
'use_bfloat16': True,
'tf_legacy_loss': True,
'pruned_heads': {'a': 1},
'tie_word_embeddings': False,
'is_decoder': True,
'cross_attention_hidden_size': 1_2_8,
'add_cross_attention': True,
'tie_encoder_decoder': True,
'max_length': 5_0,
'min_length': 3,
'do_sample': True,
'early_stopping': True,
'num_beams': 3,
'num_beam_groups': 3,
'diversity_penalty': 0.5,
'temperature': 2.0,
'top_k': 1_0,
'top_p': 0.7,
'typical_p': 0.2,
'repetition_penalty': 0.8,
'length_penalty': 0.8,
'no_repeat_ngram_size': 5,
'encoder_no_repeat_ngram_size': 5,
'bad_words_ids': [1, 2, 3],
'num_return_sequences': 3,
'chunk_size_feed_forward': 5,
'output_scores': True,
'return_dict_in_generate': True,
'forced_bos_token_id': 2,
'forced_eos_token_id': 3,
'remove_invalid_values': True,
'architectures': ['BertModel'],
'finetuning_task': 'translation',
'id2label': {0: 'label'},
'label2id': {'label': '0'},
'tokenizer_class': 'BertTokenizerFast',
'prefix': 'prefix',
'bos_token_id': 6,
'pad_token_id': 7,
'eos_token_id': 8,
'sep_token_id': 9,
'decoder_start_token_id': 1_0,
'exponential_decay_length_penalty': (5, 1.0_1),
'suppress_tokens': [0, 1],
'begin_suppress_tokens': 2,
'task_specific_params': {'translation': 'some_params'},
'problem_type': 'regression',
}
@is_staging_test
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def __magic_name__( cls :Optional[Any] ) -> Any:
__SCREAMING_SNAKE_CASE : str = TOKEN
HfFolder.save_token(lowerCAmelCase__ )
@classmethod
def __magic_name__( cls :List[str] ) -> List[str]:
try:
delete_repo(token=cls._token , repo_id='''test-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-config-org''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''test-dynamic-config''' )
except HTTPError:
pass
def __magic_name__( self :Any ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE : int = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('''test-config''' , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE : Tuple = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase__ , repo_id='''test-config''' , push_to_hub=lowerCAmelCase__ , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE : Union[str, Any] = BertConfig.from_pretrained(f'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
def __magic_name__( self :int ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE : Union[str, Any] = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub('''valid_org/test-config-org''' , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE : Any = BertConfig.from_pretrained('''valid_org/test-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCAmelCase__ , repo_id='''valid_org/test-config-org''' , push_to_hub=lowerCAmelCase__ , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE : List[Any] = BertConfig.from_pretrained('''valid_org/test-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase__ , getattr(lowerCAmelCase__ , lowerCAmelCase__ ) )
def __magic_name__( self :Dict ) -> Optional[int]:
CustomConfig.register_for_auto_class()
__SCREAMING_SNAKE_CASE : Tuple = CustomConfig(attribute=42 )
config.push_to_hub('''test-dynamic-config''' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'''AutoConfig''': '''custom_configuration.CustomConfig'''} )
__SCREAMING_SNAKE_CASE : Optional[Any] = AutoConfig.from_pretrained(f'''{USER}/test-dynamic-config''' , trust_remote_code=lowerCAmelCase__ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , '''CustomConfig''' )
self.assertEqual(new_config.attribute , 42 )
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def __magic_name__( self :List[str] ) -> Dict:
__SCREAMING_SNAKE_CASE : Optional[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__SCREAMING_SNAKE_CASE : Optional[Any] = c.n_embd + 1 # int
__SCREAMING_SNAKE_CASE : Optional[Any] = c.resid_pdrop + 1.0 # float
__SCREAMING_SNAKE_CASE : Dict = not c.scale_attn_weights # bool
__SCREAMING_SNAKE_CASE : Optional[int] = c.summary_type + '''foo''' # str
c.update_from_string(
f'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(lowerCAmelCase__ , c.n_embd , '''mismatch for key: n_embd''' )
self.assertEqual(lowerCAmelCase__ , c.resid_pdrop , '''mismatch for key: resid_pdrop''' )
self.assertEqual(lowerCAmelCase__ , c.scale_attn_weights , '''mismatch for key: scale_attn_weights''' )
self.assertEqual(lowerCAmelCase__ , c.summary_type , '''mismatch for key: summary_type''' )
def __magic_name__( self :Dict ) -> str:
__SCREAMING_SNAKE_CASE : Dict = PretrainedConfig()
__SCREAMING_SNAKE_CASE : str = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowerCAmelCase__ , ['''is_encoder_decoder''', '''_name_or_path''', '''_commit_hash''', '''transformers_version'''] )
__SCREAMING_SNAKE_CASE : List[Any] = [key for key, value in config_common_kwargs.items() if value == getattr(lowerCAmelCase__ , lowerCAmelCase__ )]
if len(lowerCAmelCase__ ) > 0:
raise ValueError(
'''The following keys are set with the default values in'''
''' `test_configuration_common.config_common_kwargs` pick another value for them:'''
f''' {', '.join(lowerCAmelCase__ )}.''' )
def __magic_name__( self :Union[str, Any] ) -> List[Any]:
with self.assertRaises(lowerCAmelCase__ ):
# config is in subfolder, the following should not work without specifying the subfolder
__SCREAMING_SNAKE_CASE : List[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' )
__SCREAMING_SNAKE_CASE : List[str] = BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert-subfolder''' , subfolder='''bert''' )
self.assertIsNotNone(lowerCAmelCase__ )
def __magic_name__( self :List[Any] ) -> Optional[Any]:
# A mock response for an HTTP head request to emulate server down
__SCREAMING_SNAKE_CASE : Union[str, Any] = mock.Mock()
__SCREAMING_SNAKE_CASE : List[Any] = 500
__SCREAMING_SNAKE_CASE : Union[str, Any] = {}
__SCREAMING_SNAKE_CASE : Optional[Any] = HTTPError
__SCREAMING_SNAKE_CASE : str = {}
# Download this model to make sure it's in the cache.
__SCREAMING_SNAKE_CASE : Union[str, Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('''requests.Session.request''' , return_value=lowerCAmelCase__ ) as mock_head:
__SCREAMING_SNAKE_CASE : Optional[Any] = BertConfig.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
# This check we did call the fake head request
mock_head.assert_called()
def __magic_name__( self :Union[str, Any] ) -> List[Any]:
# This test is for deprecated behavior and can be removed in v5
__SCREAMING_SNAKE_CASE : Optional[int] = BertConfig.from_pretrained(
'''https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json''' )
def __magic_name__( self :str ) -> List[str]:
__SCREAMING_SNAKE_CASE : int = AutoConfig.from_pretrained('''bert-base-cased''' )
__SCREAMING_SNAKE_CASE : Union[str, Any] = ['''config.4.0.0.json''']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[Any] = 2
json.dump(configuration.to_dict() , open(os.path.join(lowerCAmelCase__ , '''config.4.0.0.json''' ) , '''w''' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__SCREAMING_SNAKE_CASE : List[str] = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__SCREAMING_SNAKE_CASE : List[Any] = ['''config.42.0.0.json''']
__SCREAMING_SNAKE_CASE : Tuple = 768
configuration.save_pretrained(lowerCAmelCase__ )
shutil.move(os.path.join(lowerCAmelCase__ , '''config.4.0.0.json''' ) , os.path.join(lowerCAmelCase__ , '''config.42.0.0.json''' ) )
__SCREAMING_SNAKE_CASE : Dict = AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertEqual(new_configuration.hidden_size , 768 )
def __magic_name__( self :List[str] ) -> Union[str, Any]:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''hf-internal-testing/test-two-configs'''
import transformers as new_transformers
__SCREAMING_SNAKE_CASE : int = '''v4.0.0'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = new_transformers.models.auto.AutoConfig.from_pretrained(
lowerCAmelCase__ , return_unused_kwargs=lowerCAmelCase__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowerCAmelCase__ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__SCREAMING_SNAKE_CASE : List[str] = '''v3.0.0'''
__SCREAMING_SNAKE_CASE : Any = old_transformers.models.auto.AutoConfig.from_pretrained(lowerCAmelCase__ )
self.assertEqual(old_configuration.hidden_size , 768 )
| 696
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: Any = logging.get_logger(__name__)
A__: List[str] = {
'''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
'''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
'''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
'''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
'''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
'''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
'''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
'''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
'''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
'''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : str = "xlm"
__UpperCamelCase : List[str] = {
"hidden_size": "emb_dim",
"num_attention_heads": "n_heads",
"num_hidden_layers": "n_layers",
"n_words": "vocab_size", # For backward compatibility
}
def __init__( self :List[str] , SCREAMING_SNAKE_CASE :int=3_0_1_4_5 , SCREAMING_SNAKE_CASE :List[str]=2_0_4_8 , SCREAMING_SNAKE_CASE :str=1_2 , SCREAMING_SNAKE_CASE :Tuple=1_6 , SCREAMING_SNAKE_CASE :Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE :Optional[Any]=0.1 , SCREAMING_SNAKE_CASE :List[Any]=True , SCREAMING_SNAKE_CASE :str=False , SCREAMING_SNAKE_CASE :List[Any]=False , SCREAMING_SNAKE_CASE :Optional[int]=False , SCREAMING_SNAKE_CASE :str=1 , SCREAMING_SNAKE_CASE :List[str]=True , SCREAMING_SNAKE_CASE :int=5_1_2 , SCREAMING_SNAKE_CASE :Any=2_0_4_8**-0.5 , SCREAMING_SNAKE_CASE :Any=1e-12 , SCREAMING_SNAKE_CASE :Union[str, Any]=0.02 , SCREAMING_SNAKE_CASE :Dict=0 , SCREAMING_SNAKE_CASE :Tuple=1 , SCREAMING_SNAKE_CASE :Tuple=2 , SCREAMING_SNAKE_CASE :Optional[int]=3 , SCREAMING_SNAKE_CASE :Dict=5 , SCREAMING_SNAKE_CASE :List[str]=True , SCREAMING_SNAKE_CASE :List[Any]="first" , SCREAMING_SNAKE_CASE :Optional[Any]=True , SCREAMING_SNAKE_CASE :Dict=None , SCREAMING_SNAKE_CASE :List[str]=True , SCREAMING_SNAKE_CASE :Tuple=0.1 , SCREAMING_SNAKE_CASE :List[str]=5 , SCREAMING_SNAKE_CASE :List[str]=5 , SCREAMING_SNAKE_CASE :Tuple=0 , SCREAMING_SNAKE_CASE :Tuple=0 , SCREAMING_SNAKE_CASE :Any=2 , SCREAMING_SNAKE_CASE :Optional[int]=0 , **SCREAMING_SNAKE_CASE :Tuple , ) -> List[str]:
'''simple docstring'''
_a : Tuple =vocab_size
_a : int =emb_dim
_a : Dict =n_layers
_a : List[Any] =n_heads
_a : str =dropout
_a : Tuple =attention_dropout
_a : Dict =gelu_activation
_a : Any =sinusoidal_embeddings
_a : str =causal
_a : str =asm
_a : Tuple =n_langs
_a : str =use_lang_emb
_a : Dict =layer_norm_eps
_a : Union[str, Any] =bos_index
_a : int =eos_index
_a : Optional[int] =pad_index
_a : List[Any] =unk_index
_a : int =mask_index
_a : Any =is_encoder
_a : Tuple =max_position_embeddings
_a : Optional[Any] =embed_init_std
_a : List[Any] =init_std
_a : str =summary_type
_a : Optional[int] =summary_use_proj
_a : List[str] =summary_activation
_a : Tuple =summary_proj_to_labels
_a : List[Any] =summary_first_dropout
_a : Union[str, Any] =start_n_top
_a : Optional[int] =end_n_top
_a : List[Any] =mask_token_id
_a : List[Any] =lang_id
if "n_words" in kwargs:
_a : Dict =kwargs["""n_words"""]
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
class A__ ( UpperCAmelCase__ ):
@property
def __UpperCAmelCase ( self :Optional[int] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
_a : Optional[Any] ={0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_a : Tuple ={0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 716
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
A__: Optional[Any] = logging.get_logger(__name__)
A__: Optional[Any] = {
'''openai/whisper-base''': '''https://huggingface.co/openai/whisper-base/resolve/main/config.json''',
}
# fmt: off
A__: List[str] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1058, 1220, 1267, 1279, 1303, 1343, 1377,
1391, 1635, 1782, 1875, 2162, 2361, 2488, 3467, 4008, 4211,
4600, 4808, 5299, 5855, 6329, 7203, 9609, 9959, 1_0563, 1_0786,
1_1420, 1_1709, 1_1907, 1_3163, 1_3697, 1_3700, 1_4808, 1_5306, 1_6410, 1_6791,
1_7992, 1_9203, 1_9510, 2_0724, 2_2305, 2_2935, 2_7007, 3_0109, 3_0420, 3_3409,
3_4949, 4_0283, 4_0493, 4_0549, 4_7282, 4_9146, 5_0257, 5_0359, 5_0360, 5_0361
]
A__: Any = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1350, 1853, 1982, 2460, 2627,
3246, 3253, 3268, 3536, 3846, 3961, 4183, 4667, 6585, 6647,
7273, 9061, 9383, 1_0428, 1_0929, 1_1938, 1_2033, 1_2331, 1_2562, 1_3793,
1_4157, 1_4635, 1_5265, 1_5618, 1_6553, 1_6604, 1_8362, 1_8956, 2_0075, 2_1675,
2_2520, 2_6130, 2_6161, 2_6435, 2_8279, 2_9464, 3_1650, 3_2302, 3_2470, 3_6865,
4_2863, 4_7425, 4_9870, 5_0254, 5_0258, 5_0360, 5_0361, 5_0362
]
class A__ ( UpperCAmelCase__ ):
__UpperCamelCase : Tuple = "whisper"
__UpperCamelCase : Optional[Any] = ["past_key_values"]
__UpperCamelCase : Union[str, Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self :Union[str, Any] , SCREAMING_SNAKE_CASE :Optional[Any]=5_1_8_6_5 , SCREAMING_SNAKE_CASE :Union[str, Any]=8_0 , SCREAMING_SNAKE_CASE :Any=6 , SCREAMING_SNAKE_CASE :str=4 , SCREAMING_SNAKE_CASE :Dict=6 , SCREAMING_SNAKE_CASE :Union[str, Any]=4 , SCREAMING_SNAKE_CASE :str=1_5_3_6 , SCREAMING_SNAKE_CASE :Optional[Any]=1_5_3_6 , SCREAMING_SNAKE_CASE :Any=0.0 , SCREAMING_SNAKE_CASE :Optional[Any]=0.0 , SCREAMING_SNAKE_CASE :Optional[int]=5_0_2_5_7 , SCREAMING_SNAKE_CASE :Optional[Any]=True , SCREAMING_SNAKE_CASE :Dict=True , SCREAMING_SNAKE_CASE :Union[str, Any]="gelu" , SCREAMING_SNAKE_CASE :List[Any]=2_5_6 , SCREAMING_SNAKE_CASE :List[Any]=0.0 , SCREAMING_SNAKE_CASE :Any=0.0 , SCREAMING_SNAKE_CASE :List[Any]=0.0 , SCREAMING_SNAKE_CASE :Optional[Any]=0.02 , SCREAMING_SNAKE_CASE :int=False , SCREAMING_SNAKE_CASE :Dict=1_5_0_0 , SCREAMING_SNAKE_CASE :Tuple=4_4_8 , SCREAMING_SNAKE_CASE :Optional[Any]=5_0_2_5_6 , SCREAMING_SNAKE_CASE :List[Any]=5_0_2_5_6 , SCREAMING_SNAKE_CASE :int=5_0_2_5_6 , SCREAMING_SNAKE_CASE :str=None , SCREAMING_SNAKE_CASE :Any=[2_2_0, 5_0_2_5_6] , SCREAMING_SNAKE_CASE :List[Any]=False , SCREAMING_SNAKE_CASE :Tuple=2_5_6 , SCREAMING_SNAKE_CASE :List[str]=False , SCREAMING_SNAKE_CASE :Dict=0.05 , SCREAMING_SNAKE_CASE :Optional[int]=1_0 , SCREAMING_SNAKE_CASE :List[Any]=2 , SCREAMING_SNAKE_CASE :List[str]=0.0 , SCREAMING_SNAKE_CASE :Any=1_0 , SCREAMING_SNAKE_CASE :Dict=0 , SCREAMING_SNAKE_CASE :List[Any]=7 , **SCREAMING_SNAKE_CASE :Any , ) -> Dict:
'''simple docstring'''
_a : Tuple =vocab_size
_a : List[str] =num_mel_bins
_a : Optional[Any] =d_model
_a : Any =encoder_layers
_a : Dict =encoder_attention_heads
_a : Dict =decoder_layers
_a : Optional[Any] =decoder_attention_heads
_a : Any =decoder_ffn_dim
_a : List[str] =encoder_ffn_dim
_a : int =dropout
_a : Union[str, Any] =attention_dropout
_a : Union[str, Any] =activation_dropout
_a : List[str] =activation_function
_a : str =init_std
_a : Optional[int] =encoder_layerdrop
_a : Any =decoder_layerdrop
_a : Union[str, Any] =use_cache
_a : Union[str, Any] =encoder_layers
_a : str =scale_embedding # scale factor will be sqrt(d_model) if True
_a : Tuple =max_source_positions
_a : Optional[Any] =max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
_a : Union[str, Any] =classifier_proj_size
_a : Dict =use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_a : List[str] =apply_spec_augment
_a : int =mask_time_prob
_a : Optional[Any] =mask_time_length
_a : Dict =mask_time_min_masks
_a : List[Any] =mask_feature_prob
_a : Optional[int] =mask_feature_length
_a : List[str] =mask_feature_min_masks
_a : List[str] =median_filter_width
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE , bos_token_id=SCREAMING_SNAKE_CASE , eos_token_id=SCREAMING_SNAKE_CASE , is_encoder_decoder=SCREAMING_SNAKE_CASE , decoder_start_token_id=SCREAMING_SNAKE_CASE , suppress_tokens=SCREAMING_SNAKE_CASE , begin_suppress_tokens=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
class A__ ( UpperCAmelCase__ ):
@property
def __UpperCAmelCase ( self :Dict ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
_a : Optional[Any] =OrderedDict(
[
("""input_features""", {0: """batch""", 1: """feature_size""", 2: """encoder_sequence"""}),
] )
if self.use_past:
_a : List[str] ={0: """batch"""}
else:
_a : int ={0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE , direction="""inputs""" )
return common_inputs
def __UpperCAmelCase ( self :Optional[int] , SCREAMING_SNAKE_CASE :Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , SCREAMING_SNAKE_CASE :int = -1 , SCREAMING_SNAKE_CASE :int = -1 , SCREAMING_SNAKE_CASE :bool = False , SCREAMING_SNAKE_CASE :Optional["TensorType"] = None , SCREAMING_SNAKE_CASE :int = 2_2_0_5_0 , SCREAMING_SNAKE_CASE :float = 5.0 , SCREAMING_SNAKE_CASE :int = 2_2_0 , ) -> Mapping[str, Any]:
'''simple docstring'''
_a : int =OrderedDict()
_a : List[str] =OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=SCREAMING_SNAKE_CASE , framework=SCREAMING_SNAKE_CASE , sampling_rate=SCREAMING_SNAKE_CASE , time_duration=SCREAMING_SNAKE_CASE , frequency=SCREAMING_SNAKE_CASE , )
_a : str =encoder_inputs["""input_features"""].shape[2]
_a : str =encoder_sequence_length // 2 if self.use_past else seq_length
_a : List[Any] =super().generate_dummy_inputs(
preprocessor.tokenizer , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
_a : Union[str, Any] =encoder_inputs.pop("""input_features""" )
_a : Optional[Any] =decoder_inputs.pop("""decoder_input_ids""" )
if "past_key_values" in decoder_inputs:
_a : List[Any] =decoder_inputs.pop("""past_key_values""" )
return dummy_inputs
@property
def __UpperCAmelCase ( self :List[str] ) -> float:
'''simple docstring'''
return 1e-3
| 506
| 0
|
def __SCREAMING_SNAKE_CASE ( a__ : list ,a__ : list ,a__ : int ,a__ : int ,a__ : int ) -> int:
if index == number_of_items:
return 0
__A : Optional[int] = 0
__A : List[Any] = 0
__A : int = knapsack(a__ ,a__ ,a__ ,a__ ,index + 1 )
if weights[index] <= max_weight:
__A : Union[str, Any] = values[index] + knapsack(
a__ ,a__ ,a__ ,max_weight - weights[index] ,index + 1 )
return max(a__ ,a__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 17
|
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: Union[str, Any] , *_SCREAMING_SNAKE_CASE: Any , **_SCREAMING_SNAKE_CASE: int) -> List[Any]:
"""simple docstring"""
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Union[str, Any] , *_SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: Union[str, Any]) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: int , *_SCREAMING_SNAKE_CASE: Any , **_SCREAMING_SNAKE_CASE: str) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: Tuple , *_SCREAMING_SNAKE_CASE: Optional[Any] , **_SCREAMING_SNAKE_CASE: List[str]) -> Optional[int]:
"""simple docstring"""
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Dict , *_SCREAMING_SNAKE_CASE: Optional[Any] , **_SCREAMING_SNAKE_CASE: int) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Optional[Any] , *_SCREAMING_SNAKE_CASE: Dict , **_SCREAMING_SNAKE_CASE: Optional[Any]) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: int , *_SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: int) -> List[str]:
"""simple docstring"""
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Dict , *_SCREAMING_SNAKE_CASE: Any , **_SCREAMING_SNAKE_CASE: Any) -> str:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: List[Any] , *_SCREAMING_SNAKE_CASE: List[str] , **_SCREAMING_SNAKE_CASE: int) -> int:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: Tuple , *_SCREAMING_SNAKE_CASE: List[str] , **_SCREAMING_SNAKE_CASE: List[str]) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: List[str] , *_SCREAMING_SNAKE_CASE: Tuple , **_SCREAMING_SNAKE_CASE: int) -> int:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Optional[int] , *_SCREAMING_SNAKE_CASE: str , **_SCREAMING_SNAKE_CASE: Any) -> int:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: Optional[Any] , *_SCREAMING_SNAKE_CASE: Union[str, Any] , **_SCREAMING_SNAKE_CASE: Optional[int]) -> str:
"""simple docstring"""
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Optional[int] , *_SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: Tuple) -> Union[str, Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: int , *_SCREAMING_SNAKE_CASE: str , **_SCREAMING_SNAKE_CASE: Any) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
class A__ ( metaclass=__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = ['torch', 'transformers', 'onnx']
def __init__( self: List[Any] , *_SCREAMING_SNAKE_CASE: List[str] , **_SCREAMING_SNAKE_CASE: List[str]) -> int:
"""simple docstring"""
requires_backends(self , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: Union[str, Any] , *_SCREAMING_SNAKE_CASE: Optional[int] , **_SCREAMING_SNAKE_CASE: Optional[Any]) -> List[Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
@classmethod
def _SCREAMING_SNAKE_CASE ( cls: str , *_SCREAMING_SNAKE_CASE: Optional[int] , **_SCREAMING_SNAKE_CASE: Dict) -> Optional[Any]:
"""simple docstring"""
requires_backends(cls , ["torch", "transformers", "onnx"])
| 293
| 0
|
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
__A : Tuple = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 701
|
'''simple docstring'''
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
from transformers.models.esm.modeling_esm import (
ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
EsmEmbeddings,
create_position_ids_from_input_ids,
)
class __UpperCamelCase :
def __init__( self :Union[str, Any] ,_UpperCamelCase :int ,_UpperCamelCase :str=1_3 ,_UpperCamelCase :Tuple=7 ,_UpperCamelCase :Tuple=False ,_UpperCamelCase :Tuple=True ,_UpperCamelCase :Union[str, Any]=False ,_UpperCamelCase :int=True ,_UpperCamelCase :List[str]=3_3 ,_UpperCamelCase :Any=3_2 ,_UpperCamelCase :Any=5 ,_UpperCamelCase :List[str]=4 ,_UpperCamelCase :Tuple=3_7 ,_UpperCamelCase :Optional[Any]="gelu" ,_UpperCamelCase :Any=0.1 ,_UpperCamelCase :List[Any]=0.1 ,_UpperCamelCase :Any=5_1_2 ,_UpperCamelCase :Tuple=1_6 ,_UpperCamelCase :Any=2 ,_UpperCamelCase :Optional[Any]=0.02 ,_UpperCamelCase :List[str]=3 ,_UpperCamelCase :Union[str, Any]=4 ,_UpperCamelCase :Dict=None ,):
snake_case_ : Tuple = parent
snake_case_ : List[str] = batch_size
snake_case_ : List[str] = seq_length
snake_case_ : Any = is_training
snake_case_ : List[Any] = use_input_mask
snake_case_ : int = use_token_type_ids
snake_case_ : Optional[int] = use_labels
snake_case_ : List[str] = vocab_size
snake_case_ : Dict = hidden_size
snake_case_ : Optional[int] = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : int = intermediate_size
snake_case_ : Optional[Any] = hidden_act
snake_case_ : int = hidden_dropout_prob
snake_case_ : Optional[int] = attention_probs_dropout_prob
snake_case_ : Optional[int] = max_position_embeddings
snake_case_ : List[str] = type_vocab_size
snake_case_ : int = type_sequence_label_size
snake_case_ : Dict = initializer_range
snake_case_ : Any = num_labels
snake_case_ : Any = num_choices
snake_case_ : Tuple = scope
def a__ ( self :str ):
snake_case_ : Dict = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case_ : str = None
if self.use_input_mask:
snake_case_ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Dict = None
snake_case_ : List[str] = None
snake_case_ : Tuple = None
if self.use_labels:
snake_case_ : Dict = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
snake_case_ : Any = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
snake_case_ : Union[str, Any] = ids_tensor([self.batch_size] ,self.num_choices )
snake_case_ : Dict = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def a__ ( self :Optional[Any] ):
return EsmConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,pad_token_id=1 ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,initializer_range=self.initializer_range ,)
def a__ ( self :str ,_UpperCamelCase :List[Any] ,_UpperCamelCase :List[str] ,_UpperCamelCase :Any ,_UpperCamelCase :Optional[int] ,_UpperCamelCase :List[Any] ,_UpperCamelCase :Optional[int] ):
snake_case_ : Union[str, Any] = EsmModel(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
snake_case_ : int = model(_UpperCamelCase ,attention_mask=_UpperCamelCase )
snake_case_ : str = model(_UpperCamelCase )
snake_case_ : Optional[int] = model(_UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape ,(self.batch_size, self.hidden_size) )
def a__ ( self :Any ,_UpperCamelCase :Optional[Any] ,_UpperCamelCase :Optional[Any] ,_UpperCamelCase :Any ,_UpperCamelCase :Dict ,_UpperCamelCase :Union[str, Any] ,_UpperCamelCase :Optional[int] ):
snake_case_ : str = EsmForMaskedLM(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
snake_case_ : Dict = model(_UpperCamelCase ,attention_mask=_UpperCamelCase ,labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def a__ ( self :Optional[int] ,_UpperCamelCase :Optional[Any] ,_UpperCamelCase :List[str] ,_UpperCamelCase :Union[str, Any] ,_UpperCamelCase :Union[str, Any] ,_UpperCamelCase :Union[str, Any] ,_UpperCamelCase :Any ):
snake_case_ : List[Any] = self.num_labels
snake_case_ : int = EsmForTokenClassification(config=_UpperCamelCase )
model.to(_UpperCamelCase )
model.eval()
snake_case_ : int = model(_UpperCamelCase ,attention_mask=_UpperCamelCase ,labels=_UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def a__ ( self :Union[str, Any] ):
snake_case_ : Dict = self.prepare_config_and_inputs()
(
(
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) , (
snake_case_
) ,
) : Optional[int] = config_and_inputs
snake_case_ : List[str] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
lowercase : Optional[int] = False
lowercase : List[str] = (
(
EsmForMaskedLM,
EsmModel,
EsmForSequenceClassification,
EsmForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase : int = ()
lowercase : List[str] = (
{
'feature-extraction': EsmModel,
'fill-mask': EsmForMaskedLM,
'text-classification': EsmForSequenceClassification,
'token-classification': EsmForTokenClassification,
'zero-shot': EsmForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase : str = True
def a__ ( self :Any ):
snake_case_ : Any = EsmModelTester(self )
snake_case_ : str = ConfigTester(self ,config_class=_UpperCamelCase ,hidden_size=3_7 )
def a__ ( self :Optional[Any] ):
self.config_tester.run_common_tests()
def a__ ( self :Optional[Any] ):
snake_case_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def a__ ( self :Dict ):
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
snake_case_ : int = type
self.model_tester.create_and_check_model(*_UpperCamelCase )
def a__ ( self :Dict ):
snake_case_ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase )
def a__ ( self :List[str] ):
snake_case_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
@slow
def a__ ( self :Union[str, Any] ):
for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ : int = EsmModel.from_pretrained(_UpperCamelCase )
self.assertIsNotNone(_UpperCamelCase )
def a__ ( self :Tuple ):
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()[0]
snake_case_ : Optional[int] = EsmEmbeddings(config=_UpperCamelCase )
snake_case_ : List[Any] = torch.as_tensor([[1_2, 3_1, 1_3, model.padding_idx]] )
snake_case_ : Union[str, Any] = torch.as_tensor(
[
[
0 + model.padding_idx + 1,
1 + model.padding_idx + 1,
2 + model.padding_idx + 1,
model.padding_idx,
]
] )
snake_case_ : Optional[Any] = create_position_ids_from_input_ids(_UpperCamelCase ,model.padding_idx )
self.assertEqual(position_ids.shape ,expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_UpperCamelCase ,_UpperCamelCase ) ) )
def a__ ( self :List[Any] ):
snake_case_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()[0]
snake_case_ : List[Any] = EsmEmbeddings(config=_UpperCamelCase )
snake_case_ : Dict = torch.empty(2 ,4 ,3_0 )
snake_case_ : List[Any] = [
0 + embeddings.padding_idx + 1,
1 + embeddings.padding_idx + 1,
2 + embeddings.padding_idx + 1,
3 + embeddings.padding_idx + 1,
]
snake_case_ : Any = torch.as_tensor([expected_single_positions, expected_single_positions] )
snake_case_ : str = embeddings.create_position_ids_from_inputs_embeds(_UpperCamelCase )
self.assertEqual(position_ids.shape ,expected_positions.shape )
self.assertTrue(torch.all(torch.eq(_UpperCamelCase ,_UpperCamelCase ) ) )
@unittest.skip("""Esm does not support embedding resizing""" )
def a__ ( self :Optional[Any] ):
pass
@unittest.skip("""Esm does not support embedding resizing""" )
def a__ ( self :Optional[int] ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def a__ ( self :Optional[int] ):
pass
@require_torch
class __UpperCamelCase ( lowercase__ ):
@slow
def a__ ( self :Any ):
with torch.no_grad():
snake_case_ : Optional[Any] = EsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
snake_case_ : List[Any] = torch.tensor([[0, 1, 2, 3, 4, 5]] )
snake_case_ : Dict = model(_UpperCamelCase )[0]
snake_case_ : Optional[int] = 3_3
snake_case_ : List[Any] = torch.Size((1, 6, vocab_size) )
self.assertEqual(output.shape ,_UpperCamelCase )
snake_case_ : Union[str, Any] = torch.tensor(
[[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,_UpperCamelCase ,atol=1E-4 ) )
@slow
def a__ ( self :List[Any] ):
with torch.no_grad():
snake_case_ : List[Any] = EsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
model.eval()
snake_case_ : str = torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
snake_case_ : Optional[Any] = model(_UpperCamelCase )[0]
# compare the actual values for a slice.
snake_case_ : List[str] = torch.tensor(
[[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,_UpperCamelCase ,atol=1E-4 ) )
| 267
| 0
|
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
'microsoft/deberta-v2-xlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
),
'microsoft/deberta-v2-xxlarge-mnli': (
'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
),
}
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
A_ = '''deberta-v2'''
def __init__( self , lowerCamelCase_=1_2_8_1_0_0 , lowerCamelCase_=1_5_3_6 , lowerCamelCase_=2_4 , lowerCamelCase_=2_4 , lowerCamelCase_=6_1_4_4 , lowerCamelCase_="gelu" , lowerCamelCase_=0.1 , lowerCamelCase_=0.1 , lowerCamelCase_=5_1_2 , lowerCamelCase_=0 , lowerCamelCase_=0.02 , lowerCamelCase_=1e-7 , lowerCamelCase_=False , lowerCamelCase_=-1 , lowerCamelCase_=0 , lowerCamelCase_=True , lowerCamelCase_=None , lowerCamelCase_=0 , lowerCamelCase_="gelu" , **lowerCamelCase_ , ) -> Optional[int]:
super().__init__(**lowerCamelCase_)
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = max_position_embeddings
UpperCamelCase = type_vocab_size
UpperCamelCase = initializer_range
UpperCamelCase = relative_attention
UpperCamelCase = max_relative_positions
UpperCamelCase = pad_token_id
UpperCamelCase = position_biased_input
# Backwards compatibility
if type(lowerCamelCase_) == str:
UpperCamelCase = [x.strip() for x in pos_att_type.lower().split('''|''')]
UpperCamelCase = pos_att_type
UpperCamelCase = vocab_size
UpperCamelCase = layer_norm_eps
UpperCamelCase = kwargs.get('''pooler_hidden_size''' , lowerCamelCase_)
UpperCamelCase = pooler_dropout
UpperCamelCase = pooler_hidden_act
class snake_case_ ( lowerCamelCase_ ):
"""simple docstring"""
@property
def UpperCAmelCase__ ( self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
UpperCamelCase = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
UpperCamelCase = {0: '''batch''', 1: '''sequence'''}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)])
else:
return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)])
@property
def UpperCAmelCase__ ( self) -> int:
return 1_2
def UpperCAmelCase__ ( self , lowerCamelCase_ , lowerCamelCase_ = -1 , lowerCamelCase_ = -1 , lowerCamelCase_ = -1 , lowerCamelCase_ = False , lowerCamelCase_ = None , lowerCamelCase_ = 3 , lowerCamelCase_ = 4_0 , lowerCamelCase_ = 4_0 , lowerCamelCase_ = None , ) -> Mapping[str, Any]:
UpperCamelCase = super().generate_dummy_inputs(preprocessor=lowerCamelCase_ , framework=lowerCamelCase_)
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 34
|
'''simple docstring'''
from math import pi
def _lowerCAmelCase (_lowercase , _lowercase ):
"""simple docstring"""
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 331
| 0
|
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __UpperCAmelCase ( __magic_name__ )-> Tuple:
"""simple docstring"""
snake_case_ : Union[str, Any] = args.pruning_method
snake_case_ : Union[str, Any] = args.threshold
snake_case_ : str = args.model_name_or_path.rstrip("/" )
snake_case_ : int = args.target_model_path
print(F'''Load fine-pruned model from {model_name_or_path}''' )
snake_case_ : Optional[int] = torch.load(os.path.join(__magic_name__ ,"pytorch_model.bin" ) )
snake_case_ : Tuple = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
snake_case_ : List[Any] = tensor
print(F'''Copied layer {name}''' )
elif "classifier" in name or "qa_output" in name:
snake_case_ : Optional[Any] = tensor
print(F'''Copied layer {name}''' )
elif "bias" in name:
snake_case_ : Union[str, Any] = tensor
print(F'''Copied layer {name}''' )
else:
if pruning_method == "magnitude":
snake_case_ : int = MagnitudeBinarizer.apply(inputs=__magic_name__ ,threshold=__magic_name__ )
snake_case_ : int = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
snake_case_ : Any = name[:-6]
snake_case_ : Any = model[F'''{prefix_}mask_scores''']
snake_case_ : Union[str, Any] = TopKBinarizer.apply(__magic_name__ ,__magic_name__ )
snake_case_ : List[Any] = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
snake_case_ : Optional[Any] = name[:-6]
snake_case_ : Any = model[F'''{prefix_}mask_scores''']
snake_case_ : Tuple = ThresholdBinarizer.apply(__magic_name__ ,__magic_name__ ,__magic_name__ )
snake_case_ : List[str] = tensor * mask
print(F'''Pruned layer {name}''' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
snake_case_ : List[str] = name[:-6]
snake_case_ : List[Any] = model[F'''{prefix_}mask_scores''']
snake_case_ : Dict = -0.1, 1.1
snake_case_ : Optional[Any] = torch.sigmoid(__magic_name__ )
snake_case_ : Union[str, Any] = s * (r - l) + l
snake_case_ : Optional[int] = s_bar.clamp(min=0.0 ,max=1.0 )
snake_case_ : Dict = tensor * mask
print(F'''Pruned layer {name}''' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
snake_case_ : int = os.path.join(
os.path.dirname(__magic_name__ ) ,F'''bertarized_{os.path.basename(__magic_name__ )}''' )
if not os.path.isdir(__magic_name__ ):
shutil.copytree(__magic_name__ ,__magic_name__ )
print(F'''\nCreated folder {target_model_path}''' )
torch.save(__magic_name__ ,os.path.join(__magic_name__ ,"pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
__lowerCamelCase : List[str] = argparse.ArgumentParser()
parser.add_argument(
'''--pruning_method''',
choices=['''l0''', '''magnitude''', '''topK''', '''sigmoied_threshold'''],
type=str,
required=True,
help=(
'''Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'''
''' sigmoied_threshold = Soft movement pruning)'''
),
)
parser.add_argument(
'''--threshold''',
type=float,
required=False,
help=(
'''For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
'''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
'''Not needed for `l0`'''
),
)
parser.add_argument(
'''--model_name_or_path''',
type=str,
required=True,
help='''Folder containing the model that was previously fine-pruned''',
)
parser.add_argument(
'''--target_model_path''',
default=None,
type=str,
required=False,
help='''Folder containing the model that was previously fine-pruned''',
)
__lowerCamelCase : Dict = parser.parse_args()
main(args)
| 721
|
'''simple docstring'''
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
raise Exception('''requires fairseq >= 1.0.0a''')
logging.set_verbosity_info()
__lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
__lowerCamelCase : Union[str, Any] = '''Hello world! cécé herlolip'''
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ )-> Optional[Any]:
"""simple docstring"""
snake_case_ : str = FairseqRobertaModel.from_pretrained(__magic_name__ )
roberta.eval() # disable dropout
snake_case_ : Dict = roberta.model.encoder.sentence_encoder
snake_case_ : List[str] = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,hidden_size=roberta.cfg.model.encoder_embed_dim ,num_hidden_layers=roberta.cfg.model.encoder_layers ,num_attention_heads=roberta.cfg.model.encoder_attention_heads ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,max_position_embeddings=514 ,type_vocab_size=1 ,layer_norm_eps=1E-5 ,)
if classification_head:
snake_case_ : List[str] = roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our RoBERTa config:" ,__magic_name__ )
snake_case_ : List[str] = XLMRobertaXLForSequenceClassification(__magic_name__ ) if classification_head else XLMRobertaXLForMaskedLM(__magic_name__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
snake_case_ : List[Any] = roberta_sent_encoder.embed_tokens.weight
snake_case_ : int = roberta_sent_encoder.embed_positions.weight
snake_case_ : Union[str, Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
snake_case_ : Union[str, Any] = roberta_sent_encoder.layer_norm.weight
snake_case_ : str = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
snake_case_ : BertLayer = model.roberta.encoder.layer[i]
snake_case_ : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
snake_case_ : RobertaAttention = layer.attention
snake_case_ : Dict = roberta_layer.self_attn_layer_norm.weight
snake_case_ : Dict = roberta_layer.self_attn_layer_norm.bias
# self attention
snake_case_ : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
snake_case_ : Dict = roberta_layer.self_attn.q_proj.weight
snake_case_ : Any = roberta_layer.self_attn.q_proj.bias
snake_case_ : Optional[Any] = roberta_layer.self_attn.k_proj.weight
snake_case_ : Optional[Any] = roberta_layer.self_attn.k_proj.bias
snake_case_ : Optional[int] = roberta_layer.self_attn.v_proj.weight
snake_case_ : Any = roberta_layer.self_attn.v_proj.bias
# self-attention output
snake_case_ : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
snake_case_ : List[str] = roberta_layer.self_attn.out_proj.weight
snake_case_ : Optional[int] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
snake_case_ : int = roberta_layer.final_layer_norm.weight
snake_case_ : Union[str, Any] = roberta_layer.final_layer_norm.bias
# intermediate
snake_case_ : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
snake_case_ : List[str] = roberta_layer.fca.weight
snake_case_ : List[Any] = roberta_layer.fca.bias
# output
snake_case_ : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
snake_case_ : Any = roberta_layer.fca.weight
snake_case_ : Any = roberta_layer.fca.bias
# end of layer
if classification_head:
snake_case_ : int = roberta.model.classification_heads["mnli"].dense.weight
snake_case_ : Union[str, Any] = roberta.model.classification_heads["mnli"].dense.bias
snake_case_ : Tuple = roberta.model.classification_heads["mnli"].out_proj.weight
snake_case_ : str = roberta.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
snake_case_ : Optional[Any] = roberta.model.encoder.lm_head.dense.weight
snake_case_ : int = roberta.model.encoder.lm_head.dense.bias
snake_case_ : Optional[Any] = roberta.model.encoder.lm_head.layer_norm.weight
snake_case_ : Optional[int] = roberta.model.encoder.lm_head.layer_norm.bias
snake_case_ : int = roberta.model.encoder.lm_head.weight
snake_case_ : List[str] = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
snake_case_ : torch.Tensor = roberta.encode(__magic_name__ ).unsqueeze(0 ) # batch of size 1
snake_case_ : Union[str, Any] = model(__magic_name__ )[0]
if classification_head:
snake_case_ : Optional[Any] = roberta.model.classification_heads["mnli"](roberta.extract_features(__magic_name__ ) )
else:
snake_case_ : List[str] = roberta.model(__magic_name__ )[0]
print(our_output.shape ,their_output.shape )
snake_case_ : str = torch.max(torch.abs(our_output - their_output ) ).item()
print(F'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
snake_case_ : Any = torch.allclose(__magic_name__ ,__magic_name__ ,atol=1E-3 )
print("Do both models output the same tensors?" ,"🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
pathlib.Path(__magic_name__ ).mkdir(parents=__magic_name__ ,exist_ok=__magic_name__ )
print(F'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__magic_name__ )
if __name__ == "__main__":
__lowerCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
__lowerCamelCase : Tuple = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 656
| 0
|
"""simple docstring"""
import re
import string
import numpy as np
import datasets
lowercase_ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
lowercase_ = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
lowercase_ = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCAmelCase ( datasets.Metric ):
'''simple docstring'''
def __UpperCAmelCase ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' , id='''sequence''' ),
'''references''': datasets.Value('''string''' , id='''sequence''' ),
} ) , reference_urls=[] , )
def __UpperCAmelCase ( self , _a , _a , _a=None , _a=False , _a=False , _a=False , ):
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
__a = np.array([re.sub(_a , '''''' , _a ) for x in predictions] )
__a = np.array([re.sub(_a , '''''' , _a ) for x in references] )
else:
__a = np.asarray(_a )
__a = np.asarray(_a )
if ignore_case:
__a = np.char.lower(_a )
__a = np.char.lower(_a )
if ignore_punctuation:
__a = string.punctuation.maketrans('''''' , '''''' , string.punctuation )
__a = np.char.translate(_a , table=_a )
__a = np.char.translate(_a , table=_a )
if ignore_numbers:
__a = string.digits.maketrans('''''' , '''''' , string.digits )
__a = np.char.translate(_a , table=_a )
__a = np.char.translate(_a , table=_a )
__a = predictions == references
return {"exact_match": np.mean(_a ) * 100}
| 695
|
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : str ) -> List[Any]:
# Initialise PyTorch model
__a = RemBertConfig.from_json_file(lowerCAmelCase__ )
print('''Building PyTorch model from configuration: {}'''.format(str(lowerCAmelCase__ ) ) )
__a = RemBertModel(lowerCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Save pytorch-model
print('''Save PyTorch model to {}'''.format(lowerCAmelCase__ ) )
torch.save(model.state_dict() , lowerCAmelCase__ )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
lowercase_ = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 695
| 1
|
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class lowerCAmelCase ( datasets.BeamBasedBuilder ):
def lowercase ( self ):
return datasets.DatasetInfo(
features=datasets.Features({'content': datasets.Value('string' )} ) , supervised_keys=_SCREAMING_SNAKE_CASE , )
def lowercase ( self , snake_case__ , snake_case__ ):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'examples': get_test_dummy_examples()} )]
def lowercase ( self , snake_case__ , snake_case__ ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE )
class lowerCAmelCase ( datasets.BeamBasedBuilder ):
def lowercase ( self ):
return datasets.DatasetInfo(
features=datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string' )} )} ) , supervised_keys=_SCREAMING_SNAKE_CASE , )
def lowercase ( self , snake_case__ , snake_case__ ):
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'examples': get_test_nested_examples()} )
]
def lowercase ( self , snake_case__ , snake_case__ ):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( ) -> Dict:
"""simple docstring"""
return [(i, {"content": content}) for i, content in enumerate(['foo', 'bar', 'foobar'] )]
def __UpperCamelCase ( ) -> Dict:
"""simple docstring"""
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(['foo', 'bar', 'foobar'] )]
class lowerCAmelCase ( lowerCAmelCase__ ):
@require_beam
def lowercase ( self ):
lowerCAmelCase : int = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowerCAmelCase : Optional[Any] = DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE , beam_runner='DirectRunner' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'content': datasets.Value('string' )} ) )
lowerCAmelCase : str = builder.as_dataset()
self.assertEqual(dset['train'].num_rows , _SCREAMING_SNAKE_CASE )
self.assertEqual(dset['train'].info.splits['train'].num_examples , _SCREAMING_SNAKE_CASE )
self.assertDictEqual(dset['train'][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset['train'][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , 'dataset_info.json' ) ) )
del dset
@require_beam
def lowercase ( self ):
import apache_beam as beam
lowerCAmelCase : Any = beam.io.parquetio.WriteToParquet
lowerCAmelCase : int = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowerCAmelCase : Any = DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE , beam_runner='DirectRunner' )
with patch('apache_beam.io.parquetio.WriteToParquet' ) as write_parquet_mock:
lowerCAmelCase : int = partial(_SCREAMING_SNAKE_CASE , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , f"{builder.name}-train-00000-of-00002.arrow" ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({'content': datasets.Value('string' )} ) )
lowerCAmelCase : Tuple = builder.as_dataset()
self.assertEqual(dset['train'].num_rows , _SCREAMING_SNAKE_CASE )
self.assertEqual(dset['train'].info.splits['train'].num_examples , _SCREAMING_SNAKE_CASE )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset['train']['content'] ) , sorted(['foo', 'bar', 'foobar'] ) )
self.assertTrue(
os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , 'dataset_info.json' ) ) )
del dset
@require_beam
def lowercase ( self ):
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowerCAmelCase : str = DummyBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def lowercase ( self ):
lowerCAmelCase : List[str] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
lowerCAmelCase : Optional[int] = NestedBeamDataset(cache_dir=_SCREAMING_SNAKE_CASE , beam_runner='DirectRunner' )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , f"{builder.name}-train.arrow" ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({'a': datasets.Sequence({'b': datasets.Value('string' )} )} ) )
lowerCAmelCase : List[str] = builder.as_dataset()
self.assertEqual(dset['train'].num_rows , _SCREAMING_SNAKE_CASE )
self.assertEqual(dset['train'].info.splits['train'].num_examples , _SCREAMING_SNAKE_CASE )
self.assertDictEqual(dset['train'][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset['train'][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , builder.name , 'default' , '0.0.0' , 'dataset_info.json' ) ) )
del dset
| 709
|
'''simple docstring'''
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase : Union[str, Any] = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( a , unittest.TestCase ):
_lowerCamelCase : Tuple = GPTSwaTokenizer
_lowerCamelCase : str = False
_lowerCamelCase : Dict = True
_lowerCamelCase : Optional[Any] = False
def lowercase ( self ):
super().setUp()
# We have a SentencePiece fixture for testing
lowerCAmelCase : Tuple = GPTSwaTokenizer(snake_case__ , eos_token='<unk>' , bos_token='<unk>' , pad_token='<unk>' )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase ( self , snake_case__ ):
lowerCAmelCase : List[Any] = 'This is a test'
lowerCAmelCase : List[Any] = 'This is a test'
return input_text, output_text
def lowercase ( self ):
lowerCAmelCase : Tuple = '<s>'
lowerCAmelCase : Optional[int] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case__ ) , snake_case__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case__ ) , snake_case__ )
def lowercase ( self ):
lowerCAmelCase : List[Any] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<unk>' )
self.assertEqual(vocab_keys[1] , '<s>' )
self.assertEqual(vocab_keys[-1] , 'j' )
self.assertEqual(len(snake_case__ ) , 2000 )
def lowercase ( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 2000 )
def lowercase ( self ):
lowerCAmelCase : List[Any] = GPTSwaTokenizer(snake_case__ )
lowerCAmelCase : Optional[Any] = tokenizer.tokenize('This is a test' )
self.assertListEqual(snake_case__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , [465, 287, 265, 631, 842] )
lowerCAmelCase : Tuple = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
# fmt: off
self.assertListEqual(
snake_case__ , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.'] , )
# fmt: on
lowerCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(snake_case__ )
self.assertListEqual(
snake_case__ , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260] , )
lowerCAmelCase : int = tokenizer.convert_ids_to_tokens(snake_case__ )
# fmt: off
self.assertListEqual(
snake_case__ , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.'] )
# fmt: on
def lowercase ( self ):
lowerCAmelCase : str = GPTSwaTokenizer(snake_case__ )
lowerCAmelCase : Optional[int] = ['This is a test', 'I was born in 92000, and this is falsé.']
lowerCAmelCase : Tuple = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(snake_case__ , snake_case__ ):
self.assertListEqual(tokenizer.encode_fast(snake_case__ ) , snake_case__ )
# Test that decode_fast returns the input text
for text, token_ids in zip(snake_case__ , snake_case__ ):
self.assertEqual(tokenizer.decode_fast(snake_case__ ) , snake_case__ )
@slow
def lowercase ( self ):
lowerCAmelCase : str = [
'<|python|>def fibonacci(n)\n if n < 0:\n print(\'Incorrect input\')',
'Hey there, how are you doing this fine day?',
'This is a text with a trailing spaces followed by a dot .',
'Häj sväjs lillebrör! =)',
'Det är inget fel på Mr. Cool',
]
# fmt: off
lowerCAmelCase : Tuple = {'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=snake_case__ , model_name='AI-Sweden/gpt-sw3-126m' , sequences=snake_case__ , )
| 646
| 0
|
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def lowercase ( lowerCAmelCase__ ):
lowerCamelCase_ = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
lowerCamelCase_ = True if '''large''' in model_name or '''huge''' in model_name else False
lowerCamelCase_ = True if '''large''' in model_name or '''huge''' in model_name else False
lowerCamelCase_ = True if '''large''' in model_name or '''huge''' in model_name else False
if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
if "fl3" in model_name:
lowerCamelCase_ = [3, 3, 3, 3]
lowerCamelCase_ = [5, 5, 5, 5]
elif "fl4" in model_name:
lowerCamelCase_ = [4, 4, 4, 4]
lowerCamelCase_ = [3, 3, 3, 3]
if "tiny" in model_name or "small" in model_name or "base" in model_name:
lowerCamelCase_ = [3, 3, 3, 3]
if "lrf" in model_name:
lowerCamelCase_ = [3, 3, 3, 3]
else:
lowerCamelCase_ = [2, 2, 2, 2]
if "tiny" in model_name:
lowerCamelCase_ = 96
elif "small" in model_name:
lowerCamelCase_ = 96
elif "base" in model_name:
lowerCamelCase_ = 128
elif "large" in model_name:
lowerCamelCase_ = 192
elif "xlarge" in model_name:
lowerCamelCase_ = 256
elif "huge" in model_name:
lowerCamelCase_ = 352
# set label information
lowerCamelCase_ = '''huggingface/label-files'''
if "large" in model_name or "huge" in model_name:
lowerCamelCase_ = '''imagenet-22k-id2label.json'''
else:
lowerCamelCase_ = '''imagenet-1k-id2label.json'''
lowerCamelCase_ = json.load(open(hf_hub_download(lowerCAmelCase__ ,lowerCAmelCase__ ,repo_type='''dataset''' ) ,'''r''' ) )
lowerCamelCase_ = {int(lowerCAmelCase__ ): v for k, v in idalabel.items()}
lowerCamelCase_ = {v: k for k, v in idalabel.items()}
lowerCamelCase_ = FocalNetConfig(
embed_dim=lowerCAmelCase__ ,depths=lowerCAmelCase__ ,focal_levels=lowerCAmelCase__ ,focal_windows=lowerCAmelCase__ ,use_conv_embed=lowerCAmelCase__ ,idalabel=lowerCAmelCase__ ,labelaid=lowerCAmelCase__ ,use_post_layernorm=lowerCAmelCase__ ,use_layerscale=lowerCAmelCase__ ,)
return config
def lowercase ( lowerCAmelCase__ ):
if "patch_embed.proj" in name:
lowerCamelCase_ = name.replace('''patch_embed.proj''' ,'''embeddings.patch_embeddings.projection''' )
if "patch_embed.norm" in name:
lowerCamelCase_ = name.replace('''patch_embed.norm''' ,'''embeddings.norm''' )
if "layers" in name:
lowerCamelCase_ = '''encoder.''' + name
if "encoder.layers" in name:
lowerCamelCase_ = name.replace('''encoder.layers''' ,'''encoder.stages''' )
if "downsample.proj" in name:
lowerCamelCase_ = name.replace('''downsample.proj''' ,'''downsample.projection''' )
if "blocks" in name:
lowerCamelCase_ = name.replace('''blocks''' ,'''layers''' )
if "modulation.f.weight" in name or "modulation.f.bias" in name:
lowerCamelCase_ = name.replace('''modulation.f''' ,'''modulation.projection_in''' )
if "modulation.h.weight" in name or "modulation.h.bias" in name:
lowerCamelCase_ = name.replace('''modulation.h''' ,'''modulation.projection_context''' )
if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
lowerCamelCase_ = name.replace('''modulation.proj''' ,'''modulation.projection_out''' )
if name == "norm.weight":
lowerCamelCase_ = '''layernorm.weight'''
if name == "norm.bias":
lowerCamelCase_ = '''layernorm.bias'''
if "head" in name:
lowerCamelCase_ = name.replace('''head''' ,'''classifier''' )
else:
lowerCamelCase_ = '''focalnet.''' + name
return name
def lowercase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__=False ):
# fmt: off
lowerCamelCase_ = {
'''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
'''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
'''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
'''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
'''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
'''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
'''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
'''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
'''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
'''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
}
# fmt: on
lowerCamelCase_ = model_name_to_url[model_name]
print('''Checkpoint URL: ''' ,lowerCAmelCase__ )
lowerCamelCase_ = torch.hub.load_state_dict_from_url(lowerCAmelCase__ ,map_location='''cpu''' )['''model''']
# rename keys
for key in state_dict.copy().keys():
lowerCamelCase_ = state_dict.pop(lowerCAmelCase__ )
lowerCamelCase_ = val
lowerCamelCase_ = get_focalnet_config(lowerCAmelCase__ )
lowerCamelCase_ = FocalNetForImageClassification(lowerCAmelCase__ )
model.eval()
# load state dict
model.load_state_dict(lowerCAmelCase__ )
# verify conversion
lowerCamelCase_ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowerCamelCase_ = BitImageProcessor(
do_resize=lowerCAmelCase__ ,size={'''shortest_edge''': 256} ,resample=PILImageResampling.BILINEAR ,do_center_crop=lowerCAmelCase__ ,crop_size=224 ,do_normalize=lowerCAmelCase__ ,image_mean=lowerCAmelCase__ ,image_std=lowerCAmelCase__ ,)
lowerCamelCase_ = Image.open(requests.get(lowerCAmelCase__ ,stream=lowerCAmelCase__ ).raw )
lowerCamelCase_ = processor(images=lowerCAmelCase__ ,return_tensors='''pt''' )
lowerCamelCase_ = transforms.Compose(
[
transforms.Resize(256 ),
transforms.CenterCrop(224 ),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406] ,std=[0.229, 0.224, 0.225] ),
] )
lowerCamelCase_ = image_transforms(lowerCAmelCase__ ).unsqueeze(0 )
# verify pixel_values
assert torch.allclose(inputs.pixel_values ,lowerCAmelCase__ ,atol=1E-4 )
lowerCamelCase_ = model(**lowerCAmelCase__ )
lowerCamelCase_ = outputs.logits.argmax(-1 ).item()
print('''Predicted class:''' ,model.config.idalabel[predicted_class_idx] )
print('''First values of logits:''' ,outputs.logits[0, :3] )
if model_name == "focalnet-tiny":
lowerCamelCase_ = torch.tensor([0.2_166, -0.4_368, 0.2_191] )
elif model_name == "focalnet-tiny-lrf":
lowerCamelCase_ = torch.tensor([1.1_669, 0.0_125, -0.1_695] )
elif model_name == "focalnet-small":
lowerCamelCase_ = torch.tensor([0.4_917, -0.0_430, 0.1_341] )
elif model_name == "focalnet-small-lrf":
lowerCamelCase_ = torch.tensor([-0.2_588, -0.5_342, -0.2_331] )
elif model_name == "focalnet-base":
lowerCamelCase_ = torch.tensor([-0.1_655, -0.4_090, -0.1_730] )
elif model_name == "focalnet-base-lrf":
lowerCamelCase_ = torch.tensor([0.5_306, -0.0_483, -0.3_928] )
assert torch.allclose(outputs.logits[0, :3] ,lowerCAmelCase__ ,atol=1E-4 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f"Saving model and processor of {model_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(lowerCAmelCase__ )
processor.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
print(f"Pushing model and processor of {model_name} to the hub..." )
model.push_to_hub(f"{model_name}" )
processor.push_to_hub(f"{model_name}" )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""focalnet-tiny""",
type=str,
help="""Name of the FocalNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub.""",
)
A_ = parser.parse_args()
convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 29
|
import sys
_a : Any = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def a_ ( __magic_name__ ) -> int:
"""simple docstring"""
snake_case : Dict = 1
for digit in s:
product *= int(__magic_name__ )
return product
def a_ ( __magic_name__ = N ) -> int:
"""simple docstring"""
snake_case : str = -sys.maxsize - 1
snake_case : Optional[int] = n[:13]
snake_case : Tuple = 13
while cur_index < len(__magic_name__ ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
snake_case : str = substr[1:] + n[cur_index]
cur_index += 1
else:
snake_case : List[Any] = max(__magic_name__ , str_eval(__magic_name__ ) )
snake_case : Union[str, Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 598
| 0
|
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_a = logging.getLogger(__name__)
@dataclass
@add_start_docstrings(TrainingArguments.__doc__ )
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = field(default=lowerCamelCase , metadata={'help': 'Whether to use SortishSampler or not.'} )
lowercase__ = field(
default=lowerCamelCase , metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'} )
lowercase__ = field(
default=lowerCamelCase , metadata={
'help': (
'The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '
'to the `max_length` value of the model configuration.'
)
} , )
lowercase__ = field(
default=lowerCamelCase , metadata={
'help': (
'The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '
'to the `num_beams` value of the model configuration.'
)
} , )
lowercase__ = field(
default=lowerCamelCase , metadata={
'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
} , )
def UpperCAmelCase ( self) -> List[str]:
'''simple docstring'''
_UpperCamelCase = super().to_dict()
for k, v in d.items():
if isinstance(__a , __a):
_UpperCamelCase = v.to_dict()
return d
| 78
|
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_a = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
("""input_blocks.0.0.weight""", """conv_in.weight"""),
("""input_blocks.0.0.bias""", """conv_in.bias"""),
("""out.0.weight""", """conv_norm_out.weight"""),
("""out.0.bias""", """conv_norm_out.bias"""),
("""out.2.weight""", """conv_out.weight"""),
("""out.2.bias""", """conv_out.bias"""),
]
_a = [
# (stable-diffusion, HF Diffusers)
("""in_layers.0""", """norm1"""),
("""in_layers.2""", """conv1"""),
("""out_layers.0""", """norm2"""),
("""out_layers.3""", """conv2"""),
("""emb_layers.1""", """time_emb_proj"""),
("""skip_connection""", """conv_shortcut"""),
]
_a = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
_a = F"""down_blocks.{i}.resnets.{j}."""
_a = F"""input_blocks.{3*i + j + 1}.0."""
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
_a = F"""down_blocks.{i}.attentions.{j}."""
_a = F"""input_blocks.{3*i + j + 1}.1."""
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
_a = F"""up_blocks.{i}.resnets.{j}."""
_a = F"""output_blocks.{3*i + j}.0."""
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
_a = F"""up_blocks.{i}.attentions.{j}."""
_a = F"""output_blocks.{3*i + j}.1."""
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
_a = F"""down_blocks.{i}.downsamplers.0.conv."""
_a = F"""input_blocks.{3*(i+1)}.0.op."""
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
_a = F"""up_blocks.{i}.upsamplers.0."""
_a = F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
_a = """mid_block.attentions.0."""
_a = """middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
_a = F"""mid_block.resnets.{j}."""
_a = F"""middle_block.{2*j}."""
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCamelCase__ ( __snake_case ) -> str:
"""simple docstring"""
_UpperCamelCase = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
_UpperCamelCase = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
_UpperCamelCase = v.replace(__snake_case, __snake_case )
_UpperCamelCase = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
_UpperCamelCase = v.replace(__snake_case, __snake_case )
_UpperCamelCase = v
_UpperCamelCase = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
_a = [
# (stable-diffusion, HF Diffusers)
("""nin_shortcut""", """conv_shortcut"""),
("""norm_out""", """conv_norm_out"""),
("""mid.attn_1.""", """mid_block.attentions.0."""),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
_a = F"""encoder.down_blocks.{i}.resnets.{j}."""
_a = F"""encoder.down.{i}.block.{j}."""
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
_a = F"""down_blocks.{i}.downsamplers.0."""
_a = F"""down.{i}.downsample."""
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
_a = F"""up_blocks.{i}.upsamplers.0."""
_a = F"""up.{3-i}.upsample."""
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
_a = F"""decoder.up_blocks.{i}.resnets.{j}."""
_a = F"""decoder.up.{3-i}.block.{j}."""
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
_a = F"""mid_block.resnets.{i}."""
_a = F"""mid.block_{i+1}."""
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
_a = [
# (stable-diffusion, HF Diffusers)
("""norm.""", """group_norm."""),
("""q.""", """query."""),
("""k.""", """key."""),
("""v.""", """value."""),
("""proj_out.""", """proj_attn."""),
]
def lowerCamelCase__ ( __snake_case ) -> List[str]:
"""simple docstring"""
return w.reshape(*w.shape, 1, 1 )
def lowerCamelCase__ ( __snake_case ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
_UpperCamelCase = v.replace(__snake_case, __snake_case )
_UpperCamelCase = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
_UpperCamelCase = v.replace(__snake_case, __snake_case )
_UpperCamelCase = v
_UpperCamelCase = {v: vae_state_dict[k] for k, v in mapping.items()}
_UpperCamelCase = ['''q''', '''k''', '''v''', '''proj_out''']
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if F'''mid.attn_1.{weight_name}.weight''' in k:
print(F'''Reshaping {k} for SD format''' )
_UpperCamelCase = reshape_weight_for_sd(__snake_case )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
_a = [
# (stable-diffusion, HF Diffusers)
("""resblocks.""", """text_model.encoder.layers."""),
("""ln_1""", """layer_norm1"""),
("""ln_2""", """layer_norm2"""),
(""".c_fc.""", """.fc1."""),
(""".c_proj.""", """.fc2."""),
(""".attn""", """.self_attn"""),
("""ln_final.""", """transformer.text_model.final_layer_norm."""),
("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
_a = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
_a = re.compile("""|""".join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
_a = {"""q""": 0, """k""": 1, """v""": 2}
def lowerCamelCase__ ( __snake_case ) -> Any:
"""simple docstring"""
_UpperCamelCase = {}
_UpperCamelCase = {}
_UpperCamelCase = {}
for k, v in text_enc_dict.items():
if (
k.endswith('''.self_attn.q_proj.weight''' )
or k.endswith('''.self_attn.k_proj.weight''' )
or k.endswith('''.self_attn.v_proj.weight''' )
):
_UpperCamelCase = k[: -len('''.q_proj.weight''' )]
_UpperCamelCase = k[-len('''q_proj.weight''' )]
if k_pre not in capture_qkv_weight:
_UpperCamelCase = [None, None, None]
_UpperCamelCase = v
continue
if (
k.endswith('''.self_attn.q_proj.bias''' )
or k.endswith('''.self_attn.k_proj.bias''' )
or k.endswith('''.self_attn.v_proj.bias''' )
):
_UpperCamelCase = k[: -len('''.q_proj.bias''' )]
_UpperCamelCase = k[-len('''q_proj.bias''' )]
if k_pre not in capture_qkv_bias:
_UpperCamelCase = [None, None, None]
_UpperCamelCase = v
continue
_UpperCamelCase = textenc_pattern.sub(lambda __snake_case : protected[re.escape(m.group(0 ) )], __snake_case )
_UpperCamelCase = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
_UpperCamelCase = textenc_pattern.sub(lambda __snake_case : protected[re.escape(m.group(0 ) )], __snake_case )
_UpperCamelCase = torch.cat(__snake_case )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing''' )
_UpperCamelCase = textenc_pattern.sub(lambda __snake_case : protected[re.escape(m.group(0 ) )], __snake_case )
_UpperCamelCase = torch.cat(__snake_case )
return new_state_dict
def lowerCamelCase__ ( __snake_case ) -> Tuple:
"""simple docstring"""
return text_enc_dict
if __name__ == "__main__":
_a = argparse.ArgumentParser()
parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
parser.add_argument(
"""--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
)
_a = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
_a = osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
_a = osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
_a = osp.join(args.model_path, """text_encoder""", """model.safetensors""")
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
_a = load_file(unet_path, device="""cpu""")
else:
_a = osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
_a = torch.load(unet_path, map_location="""cpu""")
if osp.exists(vae_path):
_a = load_file(vae_path, device="""cpu""")
else:
_a = osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
_a = torch.load(vae_path, map_location="""cpu""")
if osp.exists(text_enc_path):
_a = load_file(text_enc_path, device="""cpu""")
else:
_a = osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
_a = torch.load(text_enc_path, map_location="""cpu""")
# Convert the UNet model
_a = convert_unet_state_dict(unet_state_dict)
_a = {"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
_a = convert_vae_state_dict(vae_state_dict)
_a = {"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
_a = """text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
_a = {"""transformer.""" + k: v for k, v in text_enc_dict.items()}
_a = convert_text_enc_state_dict_vaa(text_enc_dict)
_a = {"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
else:
_a = convert_text_enc_state_dict(text_enc_dict)
_a = {"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
_a = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
_a = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
_a = {"""state_dict""": state_dict}
torch.save(state_dict, args.checkpoint_path)
| 78
| 1
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__a = {
'''vocab_file''': {
'''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
'''moussaKam/barthez-orangesum-title''': (
'''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
),
},
}
__a = {
'''moussaKam/mbarthez''': 10_24,
'''moussaKam/barthez''': 10_24,
'''moussaKam/barthez-orangesum-title''': 10_24,
}
__a = '''▁'''
class __SCREAMING_SNAKE_CASE ( A__ ):
A : Optional[int] = VOCAB_FILES_NAMES
A : List[str] = PRETRAINED_VOCAB_FILES_MAP
A : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A : str = ['input_ids', 'attention_mask']
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="</s>" , SCREAMING_SNAKE_CASE__="<s>" , SCREAMING_SNAKE_CASE__="<unk>" , SCREAMING_SNAKE_CASE__="<pad>" , SCREAMING_SNAKE_CASE__="<mask>" , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ):
# Mask token behave like a normal word, i.e. include the space before it
lowercase : Any = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else mask_token
lowercase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=SCREAMING_SNAKE_CASE__ , eos_token=SCREAMING_SNAKE_CASE__ , unk_token=SCREAMING_SNAKE_CASE__ , sep_token=SCREAMING_SNAKE_CASE__ , cls_token=SCREAMING_SNAKE_CASE__ , pad_token=SCREAMING_SNAKE_CASE__ , mask_token=SCREAMING_SNAKE_CASE__ , sp_model_kwargs=self.sp_model_kwargs , **SCREAMING_SNAKE_CASE__ , )
lowercase : Optional[Any] = vocab_file
lowercase : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE__ ) )
lowercase : Union[str, Any] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
lowercase : Optional[int] = len(self.sp_model ) - 1
lowercase : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase : Union[str, Any] = [self.cls_token_id]
lowercase : int = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=SCREAMING_SNAKE_CASE__ , token_ids_a=SCREAMING_SNAKE_CASE__ , already_has_special_tokens=SCREAMING_SNAKE_CASE__ )
if token_ids_a is None:
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
return [1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__ )) + [1]
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
lowercase : Tuple = [self.sep_token_id]
lowercase : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def __lowerCamelCase ( self ):
return len(self.sp_model )
def __lowerCamelCase ( self ):
lowercase : int = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
return self.sp_model.encode(SCREAMING_SNAKE_CASE__ , out_type=SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase : List[str] = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__ )
return spm_id if spm_id else self.unk_token_id
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__ )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Tuple = []
lowercase : Optional[Any] = ''''''
lowercase : Optional[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__ ) + token
lowercase : Optional[Any] = True
lowercase : str = []
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE__ )
lowercase : List[Any] = False
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE__ )
return out_string.strip()
def __getstate__( self ):
lowercase : Optional[int] = self.__dict__.copy()
lowercase : str = None
return state
def __setstate__( self , SCREAMING_SNAKE_CASE__ ):
lowercase : Optional[int] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase : List[str] = {}
lowercase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def __lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ):
if not os.path.isdir(SCREAMING_SNAKE_CASE__ ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowercase : Tuple = os.path.join(
SCREAMING_SNAKE_CASE__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , SCREAMING_SNAKE_CASE__ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE__ , '''wb''' ) as fi:
lowercase : int = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE__ )
return (out_vocab_file,)
| 319
|
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@require_torch
def __lowerCamelCase ( self ):
lowercase : Optional[Any] = pipeline(
task='''zero-shot-audio-classification''' , model='''hf-internal-testing/tiny-clap-htsat-unfused''' )
lowercase : Any = load_dataset('''ashraq/esc50''' )
lowercase : Union[str, Any] = dataset['''train''']['''audio'''][-1]['''array''']
lowercase : Optional[int] = audio_classifier(SCREAMING_SNAKE_CASE__ , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [{'''score''': 0.501, '''label''': '''Sound of a dog'''}, {'''score''': 0.499, '''label''': '''Sound of vaccum cleaner'''}] , )
@unittest.skip('''No models are available in TF''' )
def __lowerCamelCase ( self ):
pass
@slow
@require_torch
def __lowerCamelCase ( self ):
lowercase : List[str] = pipeline(
task='''zero-shot-audio-classification''' , model='''laion/clap-htsat-unfused''' , )
# This is an audio of a dog
lowercase : List[str] = load_dataset('''ashraq/esc50''' )
lowercase : Dict = dataset['''train''']['''audio'''][-1]['''array''']
lowercase : Dict = audio_classifier(SCREAMING_SNAKE_CASE__ , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
] , )
lowercase : Tuple = audio_classifier([audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [
[
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
lowercase : Dict = audio_classifier(
[audio] * 5 , candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] , batch_size=5 )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE__ ) , [
[
{'''score''': 0.999, '''label''': '''Sound of a dog'''},
{'''score''': 0.001, '''label''': '''Sound of vaccum cleaner'''},
],
]
* 5 , )
@unittest.skip('''No models are available in TF''' )
def __lowerCamelCase ( self ):
pass
| 319
| 1
|
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __A ( a ):
__A = """MCTCTFeatureExtractor"""
__A = """AutoTokenizer"""
def __init__( self , UpperCAmelCase_ , UpperCAmelCase_ ):
super().__init__(UpperCAmelCase_ , UpperCAmelCase_ )
lowerCamelCase =self.feature_extractor
lowerCamelCase =False
def __call__( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*UpperCAmelCase_ , **UpperCAmelCase_ )
if "raw_speech" in kwargs:
warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.""" )
lowerCamelCase =kwargs.pop("""raw_speech""" )
else:
lowerCamelCase =kwargs.pop("""audio""" , UpperCAmelCase_ )
lowerCamelCase =kwargs.pop("""sampling_rate""" , UpperCAmelCase_ )
lowerCamelCase =kwargs.pop("""text""" , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
lowerCamelCase =args[0]
lowerCamelCase =args[1:]
if audio is None and text is None:
raise ValueError("""You need to specify either an `audio` or `text` input to process.""" )
if audio is not None:
lowerCamelCase =self.feature_extractor(UpperCAmelCase_ , *UpperCAmelCase_ , sampling_rate=UpperCAmelCase_ , **UpperCAmelCase_ )
if text is not None:
lowerCamelCase =self.tokenizer(UpperCAmelCase_ , **UpperCAmelCase_ )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowerCamelCase =encodings["""input_ids"""]
return inputs
def _snake_case ( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
return self.tokenizer.batch_decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
def _snake_case ( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor.pad(*UpperCAmelCase_ , **UpperCAmelCase_ )
lowerCamelCase =kwargs.pop("""input_features""" , UpperCAmelCase_ )
lowerCamelCase =kwargs.pop("""labels""" , UpperCAmelCase_ )
if len(UpperCAmelCase_ ) > 0:
lowerCamelCase =args[0]
lowerCamelCase =args[1:]
if input_features is not None:
lowerCamelCase =self.feature_extractor.pad(UpperCAmelCase_ , *UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is not None:
lowerCamelCase =self.tokenizer.pad(UpperCAmelCase_ , **UpperCAmelCase_ )
if labels is None:
return input_features
elif input_features is None:
return labels
else:
lowerCamelCase =labels["""input_ids"""]
return input_features
def _snake_case ( self , *UpperCAmelCase_ , **UpperCAmelCase_ ):
return self.tokenizer.decode(*UpperCAmelCase_ , **UpperCAmelCase_ )
@contextmanager
def _snake_case ( self ):
warnings.warn(
"""`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
"""labels by using the argument `text` of the regular `__call__` method (either in the same call as """
"""your audio inputs, or in a separate call.""" )
lowerCamelCase =True
lowerCamelCase =self.tokenizer
yield
lowerCamelCase =self.feature_extractor
lowerCamelCase =False
| 269
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
UpperCAmelCase__ : List[Any] =list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
UpperCAmelCase__ : Dict =[file for file in filepaths if file != file.lower()]
if upper_files:
print(F"{len(upper_files)} files contain uppercase characters:")
print('''\n'''.join(upper_files) + '''\n''')
UpperCAmelCase__ : Dict =[file for file in filepaths if ''' ''' in file]
if space_files:
print(F"{len(space_files)} files contain space characters:")
print('''\n'''.join(space_files) + '''\n''')
UpperCAmelCase__ : Dict =[file for file in filepaths if '''-''' in file]
if hyphen_files:
print(F"{len(hyphen_files)} files contain hyphen characters:")
print('''\n'''.join(hyphen_files) + '''\n''')
UpperCAmelCase__ : Optional[int] =[file for file in filepaths if os.sep not in file]
if nodir_files:
print(F"{len(nodir_files)} files are not in a directory:")
print('''\n'''.join(nodir_files) + '''\n''')
UpperCAmelCase__ : str =len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 269
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple = {
'microsoft/swin-tiny-patch4-window7-224': (
'https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class lowerCamelCase_ ( __A , __A ):
_lowerCAmelCase : Tuple = 'swin'
_lowerCAmelCase : str = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Union[str, Any] , lowerCAmelCase__ : Tuple=2_24 , lowerCAmelCase__ : Optional[Any]=4 , lowerCAmelCase__ : Optional[Any]=3 , lowerCAmelCase__ : Optional[Any]=96 , lowerCAmelCase__ : Union[str, Any]=[2, 2, 6, 2] , lowerCAmelCase__ : Tuple=[3, 6, 12, 24] , lowerCAmelCase__ : Union[str, Any]=7 , lowerCAmelCase__ : List[str]=4.0 , lowerCAmelCase__ : Optional[Any]=True , lowerCAmelCase__ : Union[str, Any]=0.0 , lowerCAmelCase__ : Optional[int]=0.0 , lowerCAmelCase__ : Union[str, Any]=0.1 , lowerCAmelCase__ : List[Any]="gelu" , lowerCAmelCase__ : Union[str, Any]=False , lowerCAmelCase__ : Any=0.02 , lowerCAmelCase__ : str=1e-5 , lowerCAmelCase__ : Any=32 , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : str=None , **lowerCAmelCase__ : Dict , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : int = image_size
SCREAMING_SNAKE_CASE : Any = patch_size
SCREAMING_SNAKE_CASE : Optional[Any] = num_channels
SCREAMING_SNAKE_CASE : Any = embed_dim
SCREAMING_SNAKE_CASE : List[str] = depths
SCREAMING_SNAKE_CASE : Union[str, Any] = len(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE : Dict = num_heads
SCREAMING_SNAKE_CASE : Optional[int] = window_size
SCREAMING_SNAKE_CASE : List[Any] = mlp_ratio
SCREAMING_SNAKE_CASE : int = qkv_bias
SCREAMING_SNAKE_CASE : List[Any] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Optional[Any] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : List[Any] = drop_path_rate
SCREAMING_SNAKE_CASE : int = hidden_act
SCREAMING_SNAKE_CASE : int = use_absolute_embeddings
SCREAMING_SNAKE_CASE : Tuple = layer_norm_eps
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE : Tuple = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
SCREAMING_SNAKE_CASE : int = int(embed_dim * 2 ** (len(lowerCAmelCase__ ) - 1) )
SCREAMING_SNAKE_CASE : Union[str, Any] = ["stem"] + [F"""stage{idx}""" for idx in range(1 , len(lowerCAmelCase__ ) + 1 )]
SCREAMING_SNAKE_CASE : Any = get_aligned_output_features_output_indices(
out_features=lowerCAmelCase__ , out_indices=lowerCAmelCase__ , stage_names=self.stage_names )
class lowerCamelCase_ ( __A ):
_lowerCAmelCase : Optional[Any] = version.parse('1.11' )
@property
def __lowercase ( self : Optional[Any] ):
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowercase ( self : Optional[int] ):
"""simple docstring"""
return 1e-4
| 527
|
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ort.SessionOptions()
SCREAMING_SNAKE_CASE : Union[str, Any] = False
return options
def __UpperCamelCase ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : int = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Tuple = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=10 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[Any] = output.images
SCREAMING_SNAKE_CASE : Union[str, Any] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-inpainting" , subfolder="scheduler" , revision="onnx" )
SCREAMING_SNAKE_CASE : Union[str, Any] = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting" , revision="onnx" , scheduler=a , safety_checker=a , feature_extractor=a , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[Any] = "A red cat sitting on a park bench"
SCREAMING_SNAKE_CASE : Dict = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE : Tuple = pipe(
prompt=a , image=a , mask_image=a , guidance_scale=7.5 , num_inference_steps=20 , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : List[str] = output.images
SCREAMING_SNAKE_CASE : Optional[int] = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE : Any = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 25
| 0
|
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __magic_name__ ( lowercase ) -> Optional[int]:
"""simple docstring"""
lowercase_ : int = SwinConfig(image_size=192 )
if "base" in model_name:
lowercase_ : Dict = 6
lowercase_ : int = 128
lowercase_ : Tuple = (2, 2, 18, 2)
lowercase_ : Tuple = (4, 8, 16, 32)
elif "large" in model_name:
lowercase_ : Optional[Any] = 12
lowercase_ : List[Any] = 192
lowercase_ : Optional[int] = (2, 2, 18, 2)
lowercase_ : str = (6, 12, 24, 48)
else:
raise ValueError("""Model not supported, only supports base and large variants""" )
lowercase_ : int = window_size
lowercase_ : str = embed_dim
lowercase_ : Union[str, Any] = depths
lowercase_ : List[str] = num_heads
return config
def __magic_name__ ( lowercase ) -> Tuple:
"""simple docstring"""
if "encoder.mask_token" in name:
lowercase_ : str = name.replace("""encoder.mask_token""" , """embeddings.mask_token""" )
if "encoder.patch_embed.proj" in name:
lowercase_ : List[Any] = name.replace("""encoder.patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "encoder.patch_embed.norm" in name:
lowercase_ : Tuple = name.replace("""encoder.patch_embed.norm""" , """embeddings.norm""" )
if "attn.proj" in name:
lowercase_ : Optional[int] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
lowercase_ : List[str] = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowercase_ : Optional[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowercase_ : Any = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowercase_ : int = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowercase_ : Any = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "encoder.norm.weight":
lowercase_ : Optional[int] = """layernorm.weight"""
if name == "encoder.norm.bias":
lowercase_ : str = """layernorm.bias"""
if "decoder" in name:
pass
else:
lowercase_ : Optional[int] = """swin.""" + name
return name
def __magic_name__ ( lowercase , lowercase ) -> Union[str, Any]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowercase_ : Tuple = orig_state_dict.pop(lowercase )
if "attn_mask" in key:
pass
elif "qkv" in key:
lowercase_ : Union[str, Any] = key.split(""".""" )
lowercase_ : Any = int(key_split[2] )
lowercase_ : Optional[Any] = int(key_split[4] )
lowercase_ : Union[str, Any] = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowercase_ : Optional[int] = val[:dim, :]
lowercase_ : Optional[int] = val[
dim : dim * 2, :
]
lowercase_ : str = val[-dim:, :]
else:
lowercase_ : List[str] = val[
:dim
]
lowercase_ : str = val[
dim : dim * 2
]
lowercase_ : Optional[int] = val[
-dim:
]
else:
lowercase_ : List[str] = val
return orig_state_dict
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase ) -> List[str]:
"""simple docstring"""
lowercase_ : List[Any] = torch.load(lowercase , map_location="""cpu""" )["""model"""]
lowercase_ : Union[str, Any] = get_swin_config(lowercase )
lowercase_ : Tuple = SwinForMaskedImageModeling(lowercase )
model.eval()
lowercase_ : Tuple = convert_state_dict(lowercase , lowercase )
model.load_state_dict(lowercase )
lowercase_ : Union[str, Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowercase_ : Optional[int] = ViTImageProcessor(size={"""height""": 192, """width""": 192} )
lowercase_ : Dict = Image.open(requests.get(lowercase , stream=lowercase ).raw )
lowercase_ : List[str] = image_processor(images=lowercase , return_tensors="""pt""" )
with torch.no_grad():
lowercase_ : Tuple = model(**lowercase ).logits
print(outputs.keys() )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowercase )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(lowercase )
if push_to_hub:
print(f"""Pushing model and image processor for {model_name} to hub""" )
model.push_to_hub(f"""microsoft/{model_name}""" )
image_processor.push_to_hub(f"""microsoft/{model_name}""" )
if __name__ == "__main__":
UpperCAmelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""swin-base-simmim-window6-192""",
type=str,
choices=["""swin-base-simmim-window6-192""", """swin-large-simmim-window12-192"""],
help="""Name of the Swin SimMIM model you'd like to convert.""",
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth""",
type=str,
help="""Path to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
UpperCAmelCase_ = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 721
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ["""VisionEncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ["""TFVisionEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ = ["""FlaxVisionEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
UpperCAmelCase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 436
| 0
|
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = CodeGenTokenizer
__SCREAMING_SNAKE_CASE = CodeGenTokenizerFast
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = {"""add_prefix_space""": True}
__SCREAMING_SNAKE_CASE = False
def UpperCamelCase__ (self ) -> Optional[Any]:
"""simple docstring"""
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
'<|endoftext|>',
]
UpperCAmelCase__ = dict(zip(__a , range(len(__a ) ) ) )
UpperCAmelCase__ = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
UpperCAmelCase__ = {'unk_token': '<unk>'}
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
UpperCAmelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__a ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__a ) )
def UpperCamelCase__ (self , **__a ) -> List[Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizer.from_pretrained(self.tmpdirname , **__a )
def UpperCamelCase__ (self , **__a ) -> Any:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return CodeGenTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def UpperCamelCase__ (self , __a ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = 'lower newer'
UpperCAmelCase__ = 'lower newer'
return input_text, output_text
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
UpperCAmelCase__ = CodeGenTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
UpperCAmelCase__ = 'lower newer'
UpperCAmelCase__ = ['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
UpperCAmelCase__ = tokenizer.tokenize(__a , add_prefix_space=__a )
self.assertListEqual(__a , __a )
UpperCAmelCase__ = tokens + [tokenizer.unk_token]
UpperCAmelCase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
UpperCAmelCase__ = self.get_tokenizer()
UpperCAmelCase__ = self.get_rust_tokenizer(add_prefix_space=__a )
UpperCAmelCase__ = 'lower newer'
# Testing tokenization
UpperCAmelCase__ = tokenizer.tokenize(__a , add_prefix_space=__a )
UpperCAmelCase__ = rust_tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids without special tokens
UpperCAmelCase__ = tokenizer.encode(__a , add_special_tokens=__a , add_prefix_space=__a )
UpperCAmelCase__ = rust_tokenizer.encode(__a , add_special_tokens=__a )
self.assertListEqual(__a , __a )
# Testing conversion to ids with special tokens
UpperCAmelCase__ = self.get_rust_tokenizer(add_prefix_space=__a )
UpperCAmelCase__ = tokenizer.encode(__a , add_prefix_space=__a )
UpperCAmelCase__ = rust_tokenizer.encode(__a )
self.assertListEqual(__a , __a )
# Testing the unknown token
UpperCAmelCase__ = tokens + [rust_tokenizer.unk_token]
UpperCAmelCase__ = [14, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(__a ) , __a )
def UpperCamelCase__ (self , *__a , **__a ) -> Dict:
"""simple docstring"""
pass
def UpperCamelCase__ (self , __a=15 ) -> List[Any]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# Simple input
UpperCAmelCase__ = 'This is a simple input'
UpperCAmelCase__ = ['This is a simple input 1', 'This is a simple input 2']
UpperCAmelCase__ = ('This is a simple input', 'This is a pair')
UpperCAmelCase__ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = CodeGenTokenizer.from_pretrained(self.tmpdirname , pad_token='<pad>' )
# Simple input
UpperCAmelCase__ = 'This is a simple input'
UpperCAmelCase__ = ['This is a simple input looooooooong', 'This is a simple input']
UpperCAmelCase__ = ('This is a simple input', 'This is a pair')
UpperCAmelCase__ = [
('This is a simple input loooooong', 'This is a simple input'),
('This is a simple pair loooooong', 'This is a simple pair'),
]
UpperCAmelCase__ = tokenizer.pad_token_id
UpperCAmelCase__ = tokenizer(__a , padding='max_length' , max_length=30 , return_tensors='np' )
UpperCAmelCase__ = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
UpperCAmelCase__ = tokenizer(*__a , padding='max_length' , max_length=60 , return_tensors='np' )
UpperCAmelCase__ = tokenizer(__a , padding=__a , truncate=__a , return_tensors='np' )
# s
# test single string max_length padding
self.assertEqual(out_s['input_ids'].shape[-1] , 30 )
self.assertTrue(pad_token_id in out_s['input_ids'] )
self.assertTrue(0 in out_s['attention_mask'] )
# s2
# test automatic padding
self.assertEqual(out_sa['input_ids'].shape[-1] , 33 )
# long slice doesn't have padding
self.assertFalse(pad_token_id in out_sa['input_ids'][0] )
self.assertFalse(0 in out_sa['attention_mask'][0] )
# short slice does have padding
self.assertTrue(pad_token_id in out_sa['input_ids'][1] )
self.assertTrue(0 in out_sa['attention_mask'][1] )
# p
# test single pair max_length padding
self.assertEqual(out_p['input_ids'].shape[-1] , 60 )
self.assertTrue(pad_token_id in out_p['input_ids'] )
self.assertTrue(0 in out_p['attention_mask'] )
# p2
# test automatic padding pair
self.assertEqual(out_pa['input_ids'].shape[-1] , 52 )
# long slice pair doesn't have padding
self.assertFalse(pad_token_id in out_pa['input_ids'][0] )
self.assertFalse(0 in out_pa['attention_mask'][0] )
# short slice pair does have padding
self.assertTrue(pad_token_id in out_pa['input_ids'][1] )
self.assertTrue(0 in out_pa['attention_mask'][1] )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = '$$$'
UpperCAmelCase__ = CodeGenTokenizer.from_pretrained(self.tmpdirname , bos_token=__a , add_bos_token=__a )
UpperCAmelCase__ = 'This is a simple input'
UpperCAmelCase__ = ['This is a simple input 1', 'This is a simple input 2']
UpperCAmelCase__ = tokenizer.bos_token_id
UpperCAmelCase__ = tokenizer(__a )
UpperCAmelCase__ = tokenizer(__a )
self.assertEqual(out_s.input_ids[0] , __a )
self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids ) )
UpperCAmelCase__ = tokenizer.decode(out_s.input_ids )
UpperCAmelCase__ = tokenizer.batch_decode(out_sa.input_ids )
self.assertEqual(decode_s.split()[0] , __a )
self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa ) )
@slow
def UpperCamelCase__ (self ) -> int:
"""simple docstring"""
UpperCAmelCase__ = CodeGenTokenizer.from_pretrained('Salesforce/codegen-350M-mono' )
UpperCAmelCase__ = '\nif len_a > len_b:\n result = a\nelse:\n result = b\n\n\n\n#'
UpperCAmelCase__ = '\nif len_a > len_b: result = a\nelse: result = b'
UpperCAmelCase__ = tokenizer.encode(__a )
UpperCAmelCase__ = ['^#', re.escape('<|endoftext|>' ), '^\'\'\'', '^"""', '\n\n\n']
UpperCAmelCase__ = tokenizer.decode(__a , truncate_before_pattern=__a )
self.assertEqual(__a , __a )
def UpperCamelCase__ (self ) -> List[str]:
"""simple docstring"""
pass
| 146
|
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowercase ( _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = BloomTokenizerFast
__SCREAMING_SNAKE_CASE = BloomTokenizerFast
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = """tokenizer_file"""
__SCREAMING_SNAKE_CASE = {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
def UpperCamelCase__ (self ) -> Any:
"""simple docstring"""
super().setUp()
UpperCAmelCase__ = BloomTokenizerFast.from_pretrained('bigscience/tokenizer' )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase__ (self , **__a ) -> str:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BloomTokenizerFast.from_pretrained(self.tmpdirname , **__a )
def UpperCamelCase__ (self ) -> Tuple:
"""simple docstring"""
UpperCAmelCase__ = self.get_rust_tokenizer()
UpperCAmelCase__ = ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
UpperCAmelCase__ = [[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]
UpperCAmelCase__ = tokenizer.batch_encode_plus(__a )['input_ids']
self.assertListEqual(__a , __a )
UpperCAmelCase__ = tokenizer.batch_decode(__a )
self.assertListEqual(__a , __a )
def UpperCamelCase__ (self , __a=6 ) -> List[str]:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})" ):
UpperCAmelCase__ = self.rust_tokenizer_class.from_pretrained(__a , **__a )
# tokenizer_r.pad_token = None # Hotfixing padding = None
# Simple input
UpperCAmelCase__ = 'This is a simple input'
UpperCAmelCase__ = ['This is a simple input 1', 'This is a simple input 2']
UpperCAmelCase__ = ('This is a simple input', 'This is a pair')
UpperCAmelCase__ = [
('This is a simple input 1', 'This is a simple input 2'),
('This is a simple pair 1', 'This is a simple pair 2'),
]
# Simple input tests
try:
tokenizer_r.encode(__a , max_length=__a )
tokenizer_r.encode_plus(__a , max_length=__a )
tokenizer_r.batch_encode_plus(__a , max_length=__a )
tokenizer_r.encode(__a , max_length=__a )
tokenizer_r.batch_encode_plus(__a , max_length=__a )
except ValueError:
self.fail('Bloom Tokenizer should be able to deal with padding' )
UpperCAmelCase__ = None # Hotfixing padding = None
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Simple input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
# Pair input
self.assertRaises(__a , tokenizer_r.encode , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(__a , tokenizer_r.encode_plus , __a , max_length=__a , padding='max_length' )
# Pair input
self.assertRaises(
__a , tokenizer_r.batch_encode_plus , __a , max_length=__a , padding='max_length' , )
def UpperCamelCase__ (self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase__ = self.get_rust_tokenizer()
UpperCAmelCase__ = load_dataset('xnli' , 'all_languages' , split='test' , streaming=__a )
UpperCAmelCase__ = next(iter(__a ) )['premise'] # pick up one data
UpperCAmelCase__ = list(sample_data.values() )
UpperCAmelCase__ = list(map(tokenizer.encode , __a ) )
UpperCAmelCase__ = [tokenizer.decode(__a , clean_up_tokenization_spaces=__a ) for x in output_tokens]
self.assertListEqual(__a , __a )
def UpperCamelCase__ (self ) -> Dict:
"""simple docstring"""
self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map ) , 1 )
self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values() )[0] ) , 1 )
| 146
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''Helsinki-NLP/opus-mt-en-de''': '''https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json''',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
_UpperCamelCase : Dict = '''marian'''
_UpperCamelCase : List[str] = ['''past_key_values''']
_UpperCamelCase : Tuple = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
def __init__( self : Optional[int] , UpperCamelCase_ : Tuple=58101 , UpperCamelCase_ : int=None , UpperCamelCase_ : str=1024 , UpperCamelCase_ : List[str]=12 , UpperCamelCase_ : List[Any]=4096 , UpperCamelCase_ : Union[str, Any]=16 , UpperCamelCase_ : int=12 , UpperCamelCase_ : Optional[Any]=4096 , UpperCamelCase_ : Union[str, Any]=16 , UpperCamelCase_ : Optional[int]=0.0 , UpperCamelCase_ : Optional[Any]=0.0 , UpperCamelCase_ : Dict=True , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : Optional[int]="gelu" , UpperCamelCase_ : int=1024 , UpperCamelCase_ : Optional[Any]=0.1 , UpperCamelCase_ : Dict=0.0 , UpperCamelCase_ : List[str]=0.0 , UpperCamelCase_ : Optional[int]=0.0_2 , UpperCamelCase_ : Union[str, Any]=58100 , UpperCamelCase_ : Union[str, Any]=False , UpperCamelCase_ : Union[str, Any]=58100 , UpperCamelCase_ : Dict=0 , UpperCamelCase_ : int=0 , UpperCamelCase_ : int=True , **UpperCamelCase_ : Union[str, Any] , ):
lowerCAmelCase_ : Tuple =vocab_size
lowerCAmelCase_ : int =decoder_vocab_size or vocab_size
lowerCAmelCase_ : int =max_position_embeddings
lowerCAmelCase_ : Any =d_model
lowerCAmelCase_ : List[Any] =encoder_ffn_dim
lowerCAmelCase_ : List[Any] =encoder_layers
lowerCAmelCase_ : Any =encoder_attention_heads
lowerCAmelCase_ : Optional[int] =decoder_ffn_dim
lowerCAmelCase_ : List[str] =decoder_layers
lowerCAmelCase_ : Union[str, Any] =decoder_attention_heads
lowerCAmelCase_ : List[str] =dropout
lowerCAmelCase_ : int =attention_dropout
lowerCAmelCase_ : Optional[int] =activation_dropout
lowerCAmelCase_ : Union[str, Any] =activation_function
lowerCAmelCase_ : List[str] =init_std
lowerCAmelCase_ : List[Any] =encoder_layerdrop
lowerCAmelCase_ : Optional[int] =decoder_layerdrop
lowerCAmelCase_ : int =use_cache
lowerCAmelCase_ : Tuple =encoder_layers
lowerCAmelCase_ : Any =scale_embedding # scale factor will be sqrt(d_model) if True
lowerCAmelCase_ : Union[str, Any] =share_encoder_decoder_embeddings
super().__init__(
pad_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , is_encoder_decoder=UpperCamelCase_ , decoder_start_token_id=UpperCamelCase_ , forced_eos_token_id=UpperCamelCase_ , **UpperCamelCase_ , )
class _snake_case ( lowerCAmelCase_ ):
"""simple docstring"""
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def __A ( self : str ):
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase_ : List[str] =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowerCAmelCase_ : Any ={0: '''batch'''}
lowerCAmelCase_ : Any ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
lowerCAmelCase_ : List[Any] ={0: '''batch''', 1: '''decoder_sequence'''}
lowerCAmelCase_ : int ={0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(UpperCamelCase_ , direction='''inputs''' )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowerCAmelCase_ : List[str] =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
] )
if self.use_past:
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] =self.num_layers
for i in range(UpperCamelCase_ ):
lowerCAmelCase_ : int ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowerCAmelCase_ : List[Any] ={0: '''batch''', 2: '''past_sequence + sequence'''}
else:
lowerCAmelCase_ : Optional[Any] =OrderedDict(
[
('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def __A ( self : Union[str, Any] ):
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase_ : List[str] =super().outputs
else:
lowerCAmelCase_ : Optional[Any] =super(UpperCamelCase_ , self ).outputs
if self.use_past:
lowerCAmelCase_ , lowerCAmelCase_ : Dict =self.num_layers
for i in range(UpperCamelCase_ ):
lowerCAmelCase_ : Optional[Any] ={0: '''batch''', 2: '''past_sequence + sequence'''}
lowerCAmelCase_ : Optional[Any] ={0: '''batch''', 2: '''past_sequence + sequence'''}
return common_outputs
def __A ( self : int , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
lowerCAmelCase_ : Optional[Any] =self._generate_dummy_inputs_for_encoder_and_decoder(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# Generate decoder inputs
lowerCAmelCase_ : List[Any] =seq_length if not self.use_past else 1
lowerCAmelCase_ : Dict =self._generate_dummy_inputs_for_encoder_and_decoder(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase_ : Union[str, Any] ={F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
lowerCAmelCase_ : List[Any] =dict(**UpperCamelCase_ , **UpperCamelCase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowerCAmelCase_ , lowerCAmelCase_ : Dict =common_inputs['''input_ids'''].shape
lowerCAmelCase_ : Tuple =common_inputs['''decoder_input_ids'''].shape[1]
lowerCAmelCase_ , lowerCAmelCase_ : Any =self.num_attention_heads
lowerCAmelCase_ : Optional[int] =(
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCAmelCase_ : Optional[int] =decoder_seq_length + 3
lowerCAmelCase_ : List[Any] =(
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowerCAmelCase_ : Dict =torch.cat(
[common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase_ , UpperCamelCase_ )] , dim=1 )
lowerCAmelCase_ : int =[]
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowerCAmelCase_ , lowerCAmelCase_ : Dict =self.num_layers
lowerCAmelCase_ : Union[str, Any] =min(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase_ : Optional[Any] =max(UpperCamelCase_ , UpperCamelCase_ ) - min_num_layers
lowerCAmelCase_ : Union[str, Any] ='''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''
for _ in range(UpperCamelCase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(UpperCamelCase_ ),
torch.zeros(UpperCamelCase_ ),
torch.zeros(UpperCamelCase_ ),
torch.zeros(UpperCamelCase_ ),
) )
# TODO: test this.
lowerCAmelCase_ : List[str] =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
for _ in range(UpperCamelCase_ , UpperCamelCase_ ):
common_inputs["past_key_values"].append((torch.zeros(UpperCamelCase_ ), torch.zeros(UpperCamelCase_ )) )
return common_inputs
def __A ( self : Optional[Any] , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
lowerCAmelCase_ : str =self._generate_dummy_inputs_for_encoder_and_decoder(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] =common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowerCAmelCase_ : int =seqlen + 2
lowerCAmelCase_ , lowerCAmelCase_ : Optional[int] =self.num_layers
lowerCAmelCase_ , lowerCAmelCase_ : List[Any] =self.num_attention_heads
lowerCAmelCase_ : Tuple =(
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowerCAmelCase_ : Any =common_inputs['''attention_mask'''].dtype
lowerCAmelCase_ : List[str] =torch.cat(
[common_inputs['''attention_mask'''], torch.ones(UpperCamelCase_ , UpperCamelCase_ , dtype=UpperCamelCase_ )] , dim=1 )
lowerCAmelCase_ : List[str] =[
(torch.zeros(UpperCamelCase_ ), torch.zeros(UpperCamelCase_ )) for _ in range(UpperCamelCase_ )
]
return common_inputs
def __A ( self : List[Any] , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowerCAmelCase_ : Tuple =compute_effective_axis_dimension(
UpperCamelCase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowerCAmelCase_ : List[Any] =tokenizer.num_special_tokens_to_add(UpperCamelCase_ )
lowerCAmelCase_ : Tuple =compute_effective_axis_dimension(
UpperCamelCase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=UpperCamelCase_ )
# Generate dummy inputs according to compute batch and sequence
lowerCAmelCase_ : List[Any] =[''' '''.join([tokenizer.unk_token] ) * seq_length] * batch_size
lowerCAmelCase_ : Any =dict(tokenizer(UpperCamelCase_ , return_tensors=UpperCamelCase_ ) )
return common_inputs
def __A ( self : List[Any] , UpperCamelCase_ : PreTrainedTokenizer , UpperCamelCase_ : int = -1 , UpperCamelCase_ : int = -1 , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[TensorType] = None , ):
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase_ : Optional[Any] =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_ )
else:
lowerCAmelCase_ : int =self._generate_dummy_inputs_for_causal_lm(
UpperCamelCase_ , batch_size=UpperCamelCase_ , seq_length=UpperCamelCase_ , is_pair=UpperCamelCase_ , framework=UpperCamelCase_ )
return common_inputs
def __A ( self : Any , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : int ):
if self.task in ["default", "seq2seq-lm"]:
lowerCAmelCase_ : Optional[Any] =super()._flatten_past_key_values_(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
else:
lowerCAmelCase_ : Dict =super(UpperCamelCase_ , self )._flatten_past_key_values_(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
@property
def __A ( self : Union[str, Any] ):
return 1E-4
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'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class _snake_case ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : Optional[int] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Any , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : List[Any]=1 , UpperCamelCase_ : Optional[int]=False , **UpperCamelCase_ : List[Any] ):
super().__init__(**UpperCamelCase_ )
lowerCAmelCase_ : Tuple =vocab_size
lowerCAmelCase_ : Optional[Any] =d_embed
lowerCAmelCase_ : int =d_proj
lowerCAmelCase_ : int =cutoffs + [vocab_size]
lowerCAmelCase_ : List[str] =[0] + self.cutoffs
lowerCAmelCase_ : List[str] =div_val
lowerCAmelCase_ : Dict =self.cutoffs[0]
lowerCAmelCase_ : Union[str, Any] =len(self.cutoffs ) - 1
lowerCAmelCase_ : List[str] =self.shortlist_size + self.n_clusters
lowerCAmelCase_ : int =keep_order
lowerCAmelCase_ : str =[]
lowerCAmelCase_ : int =[]
def __A ( self : Union[str, Any] , UpperCamelCase_ : Dict ):
if self.n_clusters > 0:
lowerCAmelCase_ : str =self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer='''zeros''' , trainable=UpperCamelCase_ , name='''cluster_weight''' )
lowerCAmelCase_ : Any =self.add_weight(
shape=(self.n_clusters,) , initializer='''zeros''' , trainable=UpperCamelCase_ , name='''cluster_bias''' )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
lowerCAmelCase_ : List[str] =self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer='''zeros''' , trainable=UpperCamelCase_ , name=F'out_projs_._{i}' , )
self.out_projs.append(UpperCamelCase_ )
else:
self.out_projs.append(UpperCamelCase_ )
lowerCAmelCase_ : Any =self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer='''zeros''' , trainable=UpperCamelCase_ , name=F'out_layers_._{i}_._weight' , )
lowerCAmelCase_ : List[Any] =self.add_weight(
shape=(self.vocab_size,) , initializer='''zeros''' , trainable=UpperCamelCase_ , name=F'out_layers_._{i}_._bias' , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
lowerCAmelCase_ , lowerCAmelCase_ : int =self.cutoff_ends[i], self.cutoff_ends[i + 1]
lowerCAmelCase_ : Any =self.d_embed // (self.div_val**i)
lowerCAmelCase_ : Any =self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer='''zeros''' , trainable=UpperCamelCase_ , name=F'out_projs_._{i}' )
self.out_projs.append(UpperCamelCase_ )
lowerCAmelCase_ : Union[str, Any] =self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer='''zeros''' , trainable=UpperCamelCase_ , name=F'out_layers_._{i}_._weight' , )
lowerCAmelCase_ : Optional[Any] =self.add_weight(
shape=(r_idx - l_idx,) , initializer='''zeros''' , trainable=UpperCamelCase_ , name=F'out_layers_._{i}_._bias' , )
self.out_layers.append((weight, bias) )
super().build(UpperCamelCase_ )
@staticmethod
def __A ( UpperCamelCase_ : int , UpperCamelCase_ : Any , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Any=None ):
lowerCAmelCase_ : Tuple =x
if proj is not None:
lowerCAmelCase_ : List[str] =tf.einsum('''ibd,ed->ibe''' , UpperCamelCase_ , UpperCamelCase_ )
return tf.einsum('''ibd,nd->ibn''' , UpperCamelCase_ , UpperCamelCase_ ) + b
@staticmethod
def __A ( UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Optional[Any] ):
lowerCAmelCase_ : str =shape_list(UpperCamelCase_ )
lowerCAmelCase_ : Tuple =tf.range(lp_size[0] , dtype=target.dtype )
lowerCAmelCase_ : Any =tf.stack([r, target] , 1 )
return tf.gather_nd(UpperCamelCase_ , UpperCamelCase_ )
def __A ( self : str , UpperCamelCase_ : Any , UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int]=True , UpperCamelCase_ : List[str]=False ):
lowerCAmelCase_ : int =0
if self.n_clusters == 0:
lowerCAmelCase_ : Tuple =self._logit(UpperCamelCase_ , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
lowerCAmelCase_ : Dict =tf.nn.sparse_softmax_cross_entropy_with_logits(labels=UpperCamelCase_ , logits=UpperCamelCase_ )
lowerCAmelCase_ : Dict =tf.nn.log_softmax(UpperCamelCase_ , axis=-1 )
else:
lowerCAmelCase_ : Optional[int] =shape_list(UpperCamelCase_ )
lowerCAmelCase_ : List[str] =[]
lowerCAmelCase_ : str =tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
lowerCAmelCase_ , lowerCAmelCase_ : Union[str, Any] =self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
lowerCAmelCase_ : int =(target >= l_idx) & (target < r_idx)
lowerCAmelCase_ : List[Any] =tf.where(UpperCamelCase_ )
lowerCAmelCase_ : Dict =tf.boolean_mask(UpperCamelCase_ , UpperCamelCase_ ) - l_idx
if self.div_val == 1:
lowerCAmelCase_ : int =self.out_layers[0][0][l_idx:r_idx]
lowerCAmelCase_ : Union[str, Any] =self.out_layers[0][1][l_idx:r_idx]
else:
lowerCAmelCase_ : str =self.out_layers[i][0]
lowerCAmelCase_ : Any =self.out_layers[i][1]
if i == 0:
lowerCAmelCase_ : Any =tf.concat([cur_W, self.cluster_weight] , 0 )
lowerCAmelCase_ : str =tf.concat([cur_b, self.cluster_bias] , 0 )
lowerCAmelCase_ : Optional[int] =self._logit(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , self.out_projs[0] )
lowerCAmelCase_ : Tuple =tf.nn.log_softmax(UpperCamelCase_ )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
lowerCAmelCase_ : Union[str, Any] =tf.boolean_mask(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase_ : Dict =self._gather_logprob(UpperCamelCase_ , UpperCamelCase_ )
else:
lowerCAmelCase_ : Dict =self._logit(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , self.out_projs[i] )
lowerCAmelCase_ : List[str] =tf.nn.log_softmax(UpperCamelCase_ )
lowerCAmelCase_ : List[str] =self.cutoffs[0] + i - 1 # No probability for the head cluster
lowerCAmelCase_ : Dict =head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(UpperCamelCase_ )
if target is not None:
lowerCAmelCase_ : Optional[int] =tf.boolean_mask(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase_ : Any =tf.boolean_mask(UpperCamelCase_ , UpperCamelCase_ )
lowerCAmelCase_ : Dict =self._gather_logprob(UpperCamelCase_ , UpperCamelCase_ )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(UpperCamelCase_ , -cur_logprob , shape_list(UpperCamelCase_ ) )
lowerCAmelCase_ : List[Any] =tf.concat(UpperCamelCase_ , axis=-1 )
if target is not None:
if return_mean:
lowerCAmelCase_ : Tuple =tf.reduce_mean(UpperCamelCase_ )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(UpperCamelCase_ )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(UpperCamelCase_ , name=self.name , aggregation='''mean''' if return_mean else '''''' )
return out
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from collections.abc import Callable
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : Tuple , SCREAMING_SNAKE_CASE__ : Callable | None = None ) -> None:
# Stores actual heap items.
A : list =[]
# Stores indexes of each item for supporting updates and deletion.
A : dict ={}
# Stores current size of heap.
A : Tuple =0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
A : Any =key or (lambda SCREAMING_SNAKE_CASE__ : x)
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : int ) -> int | None:
return int((i - 1) / 2 ) if i > 0 else None
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int ) -> int | None:
A : Tuple =int(2 * i + 1 )
return left if 0 < left < self.size else None
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int ) -> int | None:
A : List[str] =int(2 * i + 2 )
return right if 0 < right < self.size else None
def SCREAMING_SNAKE_CASE_ ( self : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
A , A : Tuple =(
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
A , A : Tuple =self.arr[j], self.arr[i]
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> bool:
return self.arr[i][1] < self.arr[j][1]
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : int ) -> int:
A : List[Any] =self._left(SCREAMING_SNAKE_CASE__ )
A : int =self._right(SCREAMING_SNAKE_CASE__ )
A : Dict =i
if left is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
A : Tuple =left
if right is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
A : Dict =right
return valid_parent
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
A : Dict =self._parent(SCREAMING_SNAKE_CASE__ )
while parent is not None and not self._cmp(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A , A : int =parent, self._parent(SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
A : str =self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
while valid_parent != index:
self._swap(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A , A : Optional[int] =valid_parent, self._get_valid_parent(SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
if item not in self.pos_map:
return
A : Any =self.pos_map[item]
A : Any =[item, self.key(SCREAMING_SNAKE_CASE__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : Dict , SCREAMING_SNAKE_CASE__ : int ) -> None:
if item not in self.pos_map:
return
A : Any =self.pos_map[item]
del self.pos_map[item]
A : List[Any] =self.arr[self.size - 1]
A : int =index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(SCREAMING_SNAKE_CASE__ )
self._heapify_down(SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> None:
A : Optional[int] =len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(SCREAMING_SNAKE_CASE__ )] )
else:
A : Optional[int] =[item, self.key(SCREAMING_SNAKE_CASE__ )]
A : Optional[int] =self.size
self.size += 1
self._heapify_up(self.size - 1 )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> tuple | None:
return self.arr[0] if self.size else None
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> tuple | None:
A : Optional[Any] =self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def A__ ( ) -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
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from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class SCREAMING_SNAKE_CASE_ ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : int = 16 , SCREAMING_SNAKE_CASE__ : int = 88 , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : int = 1 , SCREAMING_SNAKE_CASE__ : float = 0.0 , SCREAMING_SNAKE_CASE__ : int = 32 , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : bool = False , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : Optional[int] = None , SCREAMING_SNAKE_CASE__ : str = "geglu" , SCREAMING_SNAKE_CASE__ : Optional[int] = None , ) -> Optional[int]:
super().__init__()
A : Tuple =nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=SCREAMING_SNAKE_CASE__ , attention_head_dim=SCREAMING_SNAKE_CASE__ , in_channels=SCREAMING_SNAKE_CASE__ , num_layers=SCREAMING_SNAKE_CASE__ , dropout=SCREAMING_SNAKE_CASE__ , norm_num_groups=SCREAMING_SNAKE_CASE__ , cross_attention_dim=SCREAMING_SNAKE_CASE__ , attention_bias=SCREAMING_SNAKE_CASE__ , sample_size=SCREAMING_SNAKE_CASE__ , num_vector_embeds=SCREAMING_SNAKE_CASE__ , activation_fn=SCREAMING_SNAKE_CASE__ , num_embeds_ada_norm=SCREAMING_SNAKE_CASE__ , )
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
A : List[Any] =0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
A : str =[77, 2_57]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
A : Optional[int] =[1, 0]
def SCREAMING_SNAKE_CASE_ ( self : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int]=None , SCREAMING_SNAKE_CASE__ : Tuple=None , SCREAMING_SNAKE_CASE__ : Any=None , SCREAMING_SNAKE_CASE__ : bool = True , ) -> Dict:
A : Any =hidden_states
A : int =[]
A : str =0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
A : Optional[int] =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
A : str =self.transformer_index_for_condition[i]
A : str =self.transformers[transformer_index](
SCREAMING_SNAKE_CASE__ , encoder_hidden_states=SCREAMING_SNAKE_CASE__ , timestep=SCREAMING_SNAKE_CASE__ , cross_attention_kwargs=SCREAMING_SNAKE_CASE__ , return_dict=SCREAMING_SNAKE_CASE__ , )[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
A : str =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
A : Any =output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=SCREAMING_SNAKE_CASE__ )
| 305
| 1
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from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__UpperCAmelCase : str = "https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def A__ ( SCREAMING_SNAKE_CASE__ = "mumbai") -> Generator[tuple[str, str], None, None]:
__snake_case: List[Any] = BeautifulSoup(requests.get(url + location).content , """html.parser""")
# This attribute finds out all the specifics listed in a job
for job in soup.find_all("""div""" , attrs={"""data-tn-component""": """organicJob"""}):
__snake_case: int = job.find("""a""" , attrs={"""data-tn-element""": """jobTitle"""}).text.strip()
__snake_case: List[Any] = job.find("""span""" , {"""class""": """company"""}).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("Bangalore"), 1):
print(f'Job {i:>2} is {job[0]} at {job[1]}')
| 707
|
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]:
# load base model
__snake_case: str = StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__ , torch_dtype=torch.floataa)
# load LoRA weight from .safetensors
__snake_case: Dict = load_file(SCREAMING_SNAKE_CASE__)
__snake_case: List[str] = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
__snake_case: Optional[int] = key.split(""".""")[0].split(LORA_PREFIX_TEXT_ENCODER + """_""")[-1].split("""_""")
__snake_case: Union[str, Any] = pipeline.text_encoder
else:
__snake_case: Optional[int] = key.split(""".""")[0].split(LORA_PREFIX_UNET + """_""")[-1].split("""_""")
__snake_case: List[Any] = pipeline.unet
# find the target layer
__snake_case: Optional[Any] = layer_infos.pop(0)
while len(SCREAMING_SNAKE_CASE__) > -1:
try:
__snake_case: Optional[Any] = curr_layer.__getattr__(SCREAMING_SNAKE_CASE__)
if len(SCREAMING_SNAKE_CASE__) > 0:
__snake_case: Optional[int] = layer_infos.pop(0)
elif len(SCREAMING_SNAKE_CASE__) == 0:
break
except Exception:
if len(SCREAMING_SNAKE_CASE__) > 0:
temp_name += "_" + layer_infos.pop(0)
else:
__snake_case: Tuple = layer_infos.pop(0)
__snake_case: Any = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up"""))
pair_keys.append(SCREAMING_SNAKE_CASE__)
else:
pair_keys.append(SCREAMING_SNAKE_CASE__)
pair_keys.append(key.replace("""lora_up""" , """lora_down"""))
# update weight
if len(state_dict[pair_keys[0]].shape) == 4:
__snake_case: Union[str, Any] = state_dict[pair_keys[0]].squeeze(3).squeeze(2).to(torch.floataa)
__snake_case: Dict = state_dict[pair_keys[1]].squeeze(3).squeeze(2).to(torch.floataa)
curr_layer.weight.data += alpha * torch.mm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__).unsqueeze(2).unsqueeze(3)
else:
__snake_case: List[Any] = state_dict[pair_keys[0]].to(torch.floataa)
__snake_case: Dict = state_dict[pair_keys[1]].to(torch.floataa)
curr_layer.weight.data += alpha * torch.mm(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
# update visited list
for item in pair_keys:
visited.append(SCREAMING_SNAKE_CASE__)
return pipeline
if __name__ == "__main__":
__UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
__UpperCAmelCase : str = parser.parse_args()
__UpperCAmelCase : Union[str, Any] = args.base_model_path
__UpperCAmelCase : str = args.checkpoint_path
__UpperCAmelCase : List[str] = args.dump_path
__UpperCAmelCase : Optional[int] = args.lora_prefix_unet
__UpperCAmelCase : Optional[int] = args.lora_prefix_text_encoder
__UpperCAmelCase : int = args.alpha
__UpperCAmelCase : List[Any] = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
__UpperCAmelCase : Union[str, Any] = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 155
| 0
|
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--original_config_file",
type=str,
required=True,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--num_in_channels",
default=None,
type=int,
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
)
parser.add_argument(
"--image_size",
default=512,
type=int,
help=(
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
" Base. Use 768 for Stable Diffusion v2."
),
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--upcast_attention",
action="store_true",
help=(
"Whether the attention computation should always be upcasted. This is necessary when running stable"
" diffusion 2.1."
),
)
parser.add_argument(
"--from_safetensors",
action="store_true",
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
def A ( _lowerCamelCase ):
'''simple docstring'''
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F"could not parse string as bool {string}" )
parser.add_argument(
"--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
)
parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)
_snake_case = parser.parse_args()
_snake_case = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 500
|
def A ( _lowerCamelCase ):
'''simple docstring'''
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = F"Input value of [number={number}] must be an integer"
raise TypeError(_lowerCamelCase )
if number < 1:
_lowerCAmelCase : Tuple = F"Input value of [number={number}] must be > 0"
raise ValueError(_lowerCamelCase )
_lowerCAmelCase : Dict = 1
for i in range(1 , _lowerCamelCase ):
current_number *= 4 * i - 2
current_number //= i + 1
return current_number
if __name__ == "__main__":
import doctest
doctest.testmod()
| 500
| 1
|
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
lowercase__ = datasets.load_iris()
lowercase__ = np.array(data['data'])
lowercase__ = np.array(data['target'])
lowercase__ = data['target_names']
lowercase__ , lowercase__ , lowercase__ , lowercase__ = train_test_split(X, y)
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
return np.linalg.norm(np.array(_SCREAMING_SNAKE_CASE ) - np.array(_SCREAMING_SNAKE_CASE ) )
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=5 ) ->Tuple:
a__: str = zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# List of distances of all points from the point to be classified
a__: Dict = []
for data_point in data:
a__: Optional[int] = euclidean_distance(data_point[0] , _SCREAMING_SNAKE_CASE )
distances.append((distance, data_point[1]) )
# Choosing 'k' points with the least distances.
a__: List[Any] = [i[1] for i in sorted(_SCREAMING_SNAKE_CASE )[:k]]
# Most commonly occurring class among them
# is the class into which the point is classified
a__: Optional[Any] = Counter(_SCREAMING_SNAKE_CASE ).most_common(1 )[0][0]
return classes[result]
if __name__ == "__main__":
print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))
| 709
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}
class __snake_case ( __lowerCAmelCase ):
a__ = """speech_to_text"""
a__ = ["""past_key_values"""]
a__ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , lowercase=1_00_00 , lowercase=12 , lowercase=20_48 , lowercase=4 , lowercase=6 , lowercase=20_48 , lowercase=4 , lowercase=0.0 , lowercase=0.0 , lowercase=True , lowercase=True , lowercase="relu" , lowercase=2_56 , lowercase=0.1 , lowercase=0.0 , lowercase=0.0 , lowercase=0.02 , lowercase=2 , lowercase=True , lowercase=1 , lowercase=0 , lowercase=2 , lowercase=60_00 , lowercase=10_24 , lowercase=2 , lowercase=(5, 5) , lowercase=10_24 , lowercase=80 , lowercase=1 , **lowercase , ) -> List[str]:
'''simple docstring'''
a__: int = vocab_size
a__: Any = d_model
a__: List[str] = encoder_ffn_dim
a__: int = encoder_layers
a__: int = encoder_attention_heads
a__: int = decoder_ffn_dim
a__: Optional[int] = decoder_layers
a__: Optional[Any] = decoder_attention_heads
a__: str = dropout
a__: List[Any] = attention_dropout
a__: Union[str, Any] = activation_dropout
a__: Tuple = activation_function
a__: Optional[Any] = init_std
a__: List[str] = encoder_layerdrop
a__: Optional[int] = decoder_layerdrop
a__: Union[str, Any] = use_cache
a__: Union[str, Any] = encoder_layers
a__: str = scale_embedding # scale factor will be sqrt(d_model) if True
a__: Tuple = max_source_positions
a__: Union[str, Any] = max_target_positions
a__: List[str] = num_conv_layers
a__: Union[str, Any] = list(lowercase)
a__: Dict = conv_channels
a__: List[Any] = input_feat_per_channel
a__: Any = input_channels
if len(self.conv_kernel_sizes) != self.num_conv_layers:
raise ValueError(
'Configuration for convolutional module is incorrect. '
'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
f'but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes)}`, '
f'`config.num_conv_layers = {self.num_conv_layers}`.')
super().__init__(
pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , is_encoder_decoder=lowercase , decoder_start_token_id=lowercase , **lowercase , )
| 217
| 0
|
from __future__ import annotations
from math import pow, sqrt
def lowerCamelCase_ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance == 0:
return {"resistance": sqrt(pow(lowerCamelCase__ , 2 ) - pow(lowerCamelCase__ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(lowerCamelCase__ , 2 ) - pow(lowerCamelCase__ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(lowerCamelCase__ , 2 ) + pow(lowerCamelCase__ , 2 ) )}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 463
|
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _SCREAMING_SNAKE_CASE :
def __init__( self , lowercase , lowercase=13 , lowercase=32 , lowercase=3 , lowercase=4 , lowercase=[10, 20, 30, 40] , lowercase=[2, 2, 3, 2] , lowercase=True , lowercase=True , lowercase=37 , lowercase="gelu" , lowercase=10 , lowercase=0.0_2 , lowercase=["stage2", "stage3", "stage4"] , lowercase=[2, 3, 4] , lowercase=None , ) -> Any:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = num_stages
lowerCamelCase_ = hidden_sizes
lowerCamelCase_ = depths
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = num_labels
lowerCamelCase_ = initializer_range
lowerCamelCase_ = out_features
lowerCamelCase_ = out_indices
lowerCamelCase_ = scope
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_labels )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=lowercase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> Tuple:
lowerCamelCase_ = ConvNextModel(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> str:
lowerCamelCase_ = ConvNextForImageClassification(lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase , labels=lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase , lowercase ) -> str:
lowerCamelCase_ = ConvNextBackbone(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
lowerCamelCase_ = None
lowerCamelCase_ = ConvNextBackbone(config=lowercase )
model.to(lowercase )
model.eval()
lowerCamelCase_ = model(lowercase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
lowerCAmelCase__ = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> Optional[int]:
lowerCamelCase_ = ConvNextModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
return
@unittest.skip(reason="ConvNext does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings" )
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking" )
def SCREAMING_SNAKE_CASE_( self ) -> Tuple:
pass
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(lowercase )
lowerCamelCase_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
def check_hidden_states_output(lowercase , lowercase , lowercase ):
lowerCamelCase_ = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
lowerCamelCase_ = model(**self._prepare_for_class(lowercase , lowercase ) )
lowerCamelCase_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase_ = self.model_tester.num_stages
self.assertEqual(len(lowercase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = True
check_hidden_states_output(lowercase , lowercase , lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase_ = True
check_hidden_states_output(lowercase , lowercase , lowercase )
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase )
@slow
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = ConvNextModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def lowerCamelCase_ ( ):
lowerCamelCase_ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224" ).to(lowercase )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=lowercase , return_tensors="pt" ).to(lowercase )
# forward pass
with torch.no_grad():
lowerCamelCase_ = model(**lowercase )
# verify the logits
lowerCamelCase_ = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , lowercase )
lowerCamelCase_ = torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1] ).to(lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase , atol=1e-4 ) )
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase , snake_case_ ):
lowerCAmelCase__ = (ConvNextBackbone,) if is_torch_available() else ()
lowerCAmelCase__ = ConvNextConfig
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = ConvNextModelTester(self )
| 463
| 1
|
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowercase_ = int(__lowerCAmelCase )
assert noofclusters < len(__lowerCAmelCase )
# Find out the dimensionality
lowercase_ = len(vectors[0] )
# Will help select random centroids from among the available vectors
lowercase_ = list(range(len(__lowerCAmelCase ) ) )
shuffle(__lowerCAmelCase )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
lowercase_ = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
lowercase_ = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
lowercase_ = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(__lowerCAmelCase )
]
##These nodes will assign the centroid Variables the appropriate
##values
lowercase_ = tf.placeholder("""float64""" , [dim] )
lowercase_ = []
for centroid in centroids:
cent_assigns.append(tf.assign(__lowerCAmelCase , __lowerCAmelCase ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
lowercase_ = [tf.Variable(0 ) for i in range(len(__lowerCAmelCase ) )]
##These nodes will assign an assignment Variable the appropriate
##value
lowercase_ = tf.placeholder("""int32""" )
lowercase_ = []
for assignment in assignments:
cluster_assigns.append(tf.assign(__lowerCAmelCase , __lowerCAmelCase ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
lowercase_ = tf.placeholder("""float""" , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
lowercase_ = tf.reduce_mean(__lowerCAmelCase , 0 )
##Node for computing Euclidean distances
# Placeholders for input
lowercase_ = tf.placeholder("""float""" , [dim] )
lowercase_ = tf.placeholder("""float""" , [dim] )
lowercase_ = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(__lowerCAmelCase , __lowerCAmelCase ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
lowercase_ = tf.placeholder("""float""" , [noofclusters] )
lowercase_ = tf.argmin(__lowerCAmelCase , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
lowercase_ = tf.initialize_all_variables()
# Initialize all variables
sess.run(__lowerCAmelCase )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
lowercase_ = 1_00
for _ in range(__lowerCAmelCase ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(__lowerCAmelCase ) ):
lowercase_ = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
lowercase_ = [
sess.run(__lowerCAmelCase , feed_dict={va: vect, va: sess.run(__lowerCAmelCase )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
lowercase_ = sess.run(
__lowerCAmelCase , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(__lowerCAmelCase ):
# Collect all the vectors assigned to this cluster
lowercase_ = [
vectors[i]
for i in range(len(__lowerCAmelCase ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
lowercase_ = sess.run(
__lowerCAmelCase , feed_dict={mean_input: array(__lowerCAmelCase )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
lowercase_ = sess.run(__lowerCAmelCase )
lowercase_ = sess.run(__lowerCAmelCase )
return centroids, assignments
| 712
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Any = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[str] = ["XGLMTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Dict = ["XGLMTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : Tuple = [
"XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XGLMForCausalLM",
"XGLMModel",
"XGLMPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : int = [
"FlaxXGLMForCausalLM",
"FlaxXGLMModel",
"FlaxXGLMPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase : List[str] = [
"TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXGLMForCausalLM",
"TFXGLMModel",
"TFXGLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm import XGLMTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xglm_fast import XGLMTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
TFXGLMPreTrainedModel,
)
else:
import sys
UpperCAmelCase : Any = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 100
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Dict = {
"configuration_maskformer": ["MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "MaskFormerConfig"],
"configuration_maskformer_swin": ["MaskFormerSwinConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : List[Any] = ["MaskFormerFeatureExtractor"]
_lowerCAmelCase : Dict = ["MaskFormerImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase : Any = [
"MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"MaskFormerForInstanceSegmentation",
"MaskFormerModel",
"MaskFormerPreTrainedModel",
]
_lowerCAmelCase : Tuple = [
"MaskFormerSwinBackbone",
"MaskFormerSwinModel",
"MaskFormerSwinPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
from .configuration_maskformer_swin import MaskFormerSwinConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_maskformer import MaskFormerFeatureExtractor
from .image_processing_maskformer import MaskFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskformer import (
MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskFormerForInstanceSegmentation,
MaskFormerModel,
MaskFormerPreTrainedModel,
)
from .modeling_maskformer_swin import (
MaskFormerSwinBackbone,
MaskFormerSwinModel,
MaskFormerSwinPreTrainedModel,
)
else:
import sys
_lowerCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 261
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFLEDForConditionalGeneration, TFLEDModel
@require_tf
class snake_case :
"""simple docstring"""
_lowerCAmelCase = LEDConfig
_lowerCAmelCase = {}
_lowerCAmelCase = 'gelu'
def __init__( self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=99 , lowerCamelCase=32 , lowerCamelCase=2 , lowerCamelCase=4 , lowerCamelCase=37 , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=20 , lowerCamelCase=2 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=4 , ) -> Dict:
"""simple docstring"""
snake_case__ : int = parent
snake_case__ : Union[str, Any] = batch_size
snake_case__ : Optional[Any] = seq_length
snake_case__ : Any = is_training
snake_case__ : Any = use_labels
snake_case__ : Optional[Any] = vocab_size
snake_case__ : List[Any] = hidden_size
snake_case__ : Optional[int] = num_hidden_layers
snake_case__ : List[str] = num_attention_heads
snake_case__ : Tuple = intermediate_size
snake_case__ : List[str] = hidden_dropout_prob
snake_case__ : str = attention_probs_dropout_prob
snake_case__ : Optional[Any] = max_position_embeddings
snake_case__ : List[Any] = eos_token_id
snake_case__ : Optional[Any] = pad_token_id
snake_case__ : Tuple = bos_token_id
snake_case__ : Optional[Any] = attention_window
# `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
# [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
# returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
# because its local attention only attends to `self.attention_window` and one before and one after
snake_case__ : Optional[Any] = self.attention_window + 2
# because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
# the `test_attention_outputs` and `test_hidden_states_output` tests
snake_case__ : Optional[Any] = (
self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
)
def lowercase__ ( self ) -> int:
"""simple docstring"""
snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
snake_case__ : Union[str, Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
snake_case__ : List[str] = tf.concat([input_ids, eos_tensor] , axis=1 )
snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case__ : Union[str, Any] = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , attention_window=self.attention_window , **self.config_updates , )
snake_case__ : str = prepare_led_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase )
snake_case__ : str = tf.concat(
[tf.zeros_like(lowerCamelCase )[:, :-1], tf.ones_like(lowerCamelCase )[:, -1:]] , axis=-1 , )
snake_case__ : List[str] = global_attention_mask
return config, inputs_dict
def lowercase__ ( self , lowerCamelCase , lowerCamelCase ) -> Any:
"""simple docstring"""
snake_case__ : Optional[Any] = TFLEDModel(config=lowerCamelCase ).get_decoder()
snake_case__ : str = inputs_dict['''input_ids''']
snake_case__ : Tuple = input_ids[:1, :]
snake_case__ : List[str] = inputs_dict['''attention_mask'''][:1, :]
snake_case__ : Union[str, Any] = 1
# first forward pass
snake_case__ : Dict = model(lowerCamelCase , attention_mask=lowerCamelCase , use_cache=lowerCamelCase )
snake_case__ ,snake_case__ : List[str] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case__ : Any = ids_tensor((self.batch_size, 3) , config.vocab_size )
snake_case__ : Optional[int] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
snake_case__ : Optional[int] = tf.concat([input_ids, next_tokens] , axis=-1 )
snake_case__ : Dict = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
snake_case__ : List[str] = model(lowerCamelCase , attention_mask=lowerCamelCase )[0]
snake_case__ : int = model(lowerCamelCase , attention_mask=lowerCamelCase , past_key_values=lowerCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
snake_case__ : List[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
snake_case__ : List[str] = output_from_no_past[:, -3:, random_slice_idx]
snake_case__ : Tuple = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCamelCase , lowerCamelCase , rtol=1E-3 )
def _A ( snake_case__ : Tuple , snake_case__ : Dict , snake_case__ : List[Any] , snake_case__ : Tuple=None , snake_case__ : List[str]=None , snake_case__ : Union[str, Any]=None , snake_case__ : List[str]=None , ):
if attention_mask is None:
snake_case__ : Dict = tf.cast(tf.math.not_equal(snake_case__ , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case__ : Tuple = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case__ : List[str] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case__ : Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
}
@require_tf
class snake_case ( __lowerCamelCase , __lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase = (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
_lowerCAmelCase = (TFLEDForConditionalGeneration,) if is_tf_available() else ()
_lowerCAmelCase = (
{
'conversational': TFLEDForConditionalGeneration,
'feature-extraction': TFLEDModel,
'summarization': TFLEDForConditionalGeneration,
'text2text-generation': TFLEDForConditionalGeneration,
'translation': TFLEDForConditionalGeneration,
}
if is_tf_available()
else {}
)
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def lowercase__ ( self ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Union[str, Any] = TFLEDModelTester(self )
snake_case__ : int = ConfigTester(self , config_class=lowerCamelCase )
def lowercase__ ( self ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowercase__ ( self ) -> List[Any]:
"""simple docstring"""
snake_case__ : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase )
def lowercase__ ( self ) -> Optional[Any]:
"""simple docstring"""
snake_case__ ,snake_case__ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ : Dict = tf.zeros_like(inputs_dict['''attention_mask'''] )
snake_case__ : Union[str, Any] = 2
snake_case__ : Tuple = tf.where(
tf.range(self.model_tester.seq_length )[None, :] < num_global_attn_indices , 1 , inputs_dict['''global_attention_mask'''] , )
snake_case__ : Optional[Any] = True
snake_case__ : Optional[int] = self.model_tester.seq_length
snake_case__ : str = self.model_tester.encoder_seq_length
def check_decoder_attentions_output(lowerCamelCase ):
snake_case__ : Optional[Any] = outputs.decoder_attentions
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
def check_encoder_attentions_output(lowerCamelCase ):
snake_case__ : Union[str, Any] = [t.numpy() for t in outputs.encoder_attentions]
snake_case__ : Optional[Any] = [t.numpy() for t in outputs.encoder_global_attentions]
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_length, seq_length] , )
self.assertListEqual(
list(global_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices] , )
for model_class in self.all_model_classes:
snake_case__ : Dict = True
snake_case__ : int = False
snake_case__ : Optional[int] = False
snake_case__ : Optional[int] = model_class(lowerCamelCase )
snake_case__ : Any = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
snake_case__ : Dict = len(lowerCamelCase )
self.assertEqual(config.output_hidden_states , lowerCamelCase )
check_encoder_attentions_output(lowerCamelCase )
if self.is_encoder_decoder:
snake_case__ : List[str] = model_class(lowerCamelCase )
snake_case__ : Optional[int] = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
self.assertEqual(config.output_hidden_states , lowerCamelCase )
check_decoder_attentions_output(lowerCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case__ : Union[str, Any] = True
snake_case__ : str = model_class(lowerCamelCase )
snake_case__ : Union[str, Any] = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
self.assertEqual(config.output_hidden_states , lowerCamelCase )
check_encoder_attentions_output(lowerCamelCase )
# Check attention is always last and order is fine
snake_case__ : Tuple = True
snake_case__ : Any = True
snake_case__ : Any = model_class(lowerCamelCase )
snake_case__ : Any = model(self._prepare_for_class(lowerCamelCase , lowerCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(lowerCamelCase ) )
self.assertEqual(model.config.output_hidden_states , lowerCamelCase )
check_encoder_attentions_output(lowerCamelCase )
@unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
def lowercase__ ( self ) -> List[Any]:
"""simple docstring"""
pass
def lowercase__ ( self ) -> int:
"""simple docstring"""
pass
def _A ( snake_case__ : Optional[int] ):
return tf.constant(snake_case__ , dtype=tf.intaa )
_lowerCAmelCase : Tuple = 1E-4
@slow
@require_tf
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Union[str, Any] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led
# change to intended input here
snake_case__ : Dict = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
snake_case__ : str = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
snake_case__ : str = prepare_led_inputs_dict(model.config , lowerCamelCase , lowerCamelCase )
snake_case__ : List[str] = model(**lowerCamelCase )[0]
snake_case__ : str = (1, 1024, 768)
self.assertEqual(output.shape , lowerCamelCase )
# change to expected output here
snake_case__ : Optional[Any] = tf.convert_to_tensor(
[[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]] , )
tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase , atol=1E-3 )
def lowercase__ ( self ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Optional[int] = TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )
# change to intended input here
snake_case__ : Optional[int] = _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
snake_case__ : str = _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]] )
snake_case__ : str = prepare_led_inputs_dict(model.config , lowerCamelCase , lowerCamelCase )
snake_case__ : Tuple = model(**lowerCamelCase )[0]
snake_case__ : List[str] = (1, 1024, model.config.vocab_size)
self.assertEqual(output.shape , lowerCamelCase )
# change to expected output here
snake_case__ : Any = tf.convert_to_tensor(
[[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]] , )
tf.debugging.assert_near(output[:, :3, :3] , lowerCamelCase , atol=1E-3 , rtol=1E-3 )
| 261
| 1
|
from collections import deque
def snake_case__ ( lowerCamelCase_ ):
A : Any = len(lowerCamelCase_ )
A : Union[str, Any] = deque()
A : Any = [False for _ in range(lowerCamelCase_ )]
A : Any = [-1 for _ in range(lowerCamelCase_ )]
A : str = index_of[:]
def strong_connect(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
A : int = index # the number when this node is seen
A : Union[str, Any] = index # lowest rank node reachable from here
index += 1
stack.append(lowerCamelCase_ )
A : Optional[Any] = True
for w in g[v]:
if index_of[w] == -1:
A : List[Any] = strong_connect(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
A : Tuple = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
A : List[str] = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
A : Tuple = []
A : int = stack.pop()
A : Tuple = False
component.append(lowerCamelCase_ )
while w != v:
A : Tuple = stack.pop()
A : Tuple = False
component.append(lowerCamelCase_ )
components.append(lowerCamelCase_ )
return index
A : Optional[int] = []
for v in range(lowerCamelCase_ ):
if index_of[v] == -1:
strong_connect(lowerCamelCase_ , 0 , lowerCamelCase_ )
return components
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : int = [[] for _ in range(lowerCamelCase_ )]
for u, v in edges:
g[u].append(lowerCamelCase_ )
return g
if __name__ == "__main__":
# Test
lowercase : Any = 7
lowercase : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
lowercase : Dict = [1, 3, 2, 0, 1, 4, 5, 6, 5]
lowercase : str = [(u, v) for u, v in zip(source, target)]
lowercase : Optional[Any] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 719
|
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : Optional[int] = len(lowerCamelCase_ )
A : List[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
A : Tuple = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
A : List[str] = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
A : str = subset[i - 1][j]
if arr[i - 1] <= j:
A : str = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 423
| 0
|
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class A ( UpperCamelCase__ ):
__snake_case = 'gpt_neo'
__snake_case = ['past_key_values']
__snake_case = {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}
def __init__( self, UpperCamelCase__=5_0257, UpperCamelCase__=2048, UpperCamelCase__=2048, UpperCamelCase__=24, UpperCamelCase__=[[["global", "local"], 12]], UpperCamelCase__=16, UpperCamelCase__=None, UpperCamelCase__=256, UpperCamelCase__="gelu_new", UpperCamelCase__=0.0, UpperCamelCase__=0.0, UpperCamelCase__=0.0, UpperCamelCase__=0.1, UpperCamelCase__=1E-5, UpperCamelCase__=0.02, UpperCamelCase__=True, UpperCamelCase__=5_0256, UpperCamelCase__=5_0256, **UpperCamelCase__, ):
"""simple docstring"""
lowerCAmelCase_ = vocab_size
lowerCAmelCase_ = max_position_embeddings
lowerCAmelCase_ = hidden_size
lowerCAmelCase_ = num_layers
lowerCAmelCase_ = num_heads
lowerCAmelCase_ = intermediate_size
lowerCAmelCase_ = window_size
lowerCAmelCase_ = activation_function
lowerCAmelCase_ = resid_dropout
lowerCAmelCase_ = embed_dropout
lowerCAmelCase_ = attention_dropout
lowerCAmelCase_ = classifier_dropout
lowerCAmelCase_ = layer_norm_epsilon
lowerCAmelCase_ = initializer_range
lowerCAmelCase_ = use_cache
lowerCAmelCase_ = bos_token_id
lowerCAmelCase_ = eos_token_id
lowerCAmelCase_ = attention_types
lowerCAmelCase_ = self.expand_attention_types_params(snake_case_ )
if len(self.attention_layers ) != self.num_layers:
raise ValueError(
'''Configuration for convolutional module is incorrect. '''
'''It is required that `len(config.attention_layers)` == `config.num_layers` '''
f"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
f"`config.num_layers = {self.num_layers}`. "
'''`config.attention_layers` is prepared using `config.attention_types`. '''
'''Please verify the value of `config.attention_types` argument.''' )
super().__init__(bos_token_id=snake_case_, eos_token_id=snake_case_, **snake_case_ )
@staticmethod
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = []
for item in attention_types:
for _ in range(item[1] ):
attentions.extend(item[0] )
return attentions
def __UpperCamelCase ( _A , _A , _A , _A ):
import torch
lowerCAmelCase_ = input.size()
lowerCAmelCase_ = len(__a )
lowerCAmelCase_ = shape[dimension]
lowerCAmelCase_ = torch.arange(0 , __a , __a )
lowerCAmelCase_ = torch.div(sizedim - size , __a , rounding_mode='''floor''' ) + 1
lowerCAmelCase_ = torch.arange(__a ) + low_indices[:min_length][:, None]
lowerCAmelCase_ = [slice(__a )] * rank
lowerCAmelCase_ = indices
lowerCAmelCase_ = input[s]
lowerCAmelCase_ = list(range(0 , rank + 1 ) )
perm.append(perm.pop(dimension + 1 ) )
return sliced.permute(__a )
def __UpperCamelCase ( _A , _A ):
import torch
lowerCAmelCase_ = torch.arange(1 , __a )
lowerCAmelCase_ = torch.remainder(__a , __a )
lowerCAmelCase_ = remainders == 0
lowerCAmelCase_ = candidates[divisor_indices]
lowerCAmelCase_ = torch.max(__a )
return largest_divisor, torch.div(__a , __a , rounding_mode='''floor''' )
class A ( UpperCamelCase__ ):
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
lowerCAmelCase_ = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
if self.use_past:
self.fill_with_past_key_values_(snake_case_, direction='''inputs''' )
lowerCAmelCase_ = {0: '''batch''', 1: '''past_sequence + sequence'''}
else:
lowerCAmelCase_ = {0: '''batch''', 1: '''sequence'''}
return common_inputs
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return self._config.num_heads
def SCREAMING_SNAKE_CASE__ ( self, UpperCamelCase__, UpperCamelCase__ = -1, UpperCamelCase__ = -1, UpperCamelCase__ = False, UpperCamelCase__ = None, ):
"""simple docstring"""
lowerCAmelCase_ = super(snake_case_, self ).generate_dummy_inputs(
snake_case_, batch_size=snake_case_, seq_length=snake_case_, is_pair=snake_case_, framework=snake_case_ )
# We need to order the input in the way they appears in the forward()
lowerCAmelCase_ = OrderedDict({'''input_ids''': common_inputs['''input_ids''']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.''' )
else:
import torch
lowerCAmelCase_ = common_inputs['''input_ids'''].shape
# Not using the same length for past_key_values
lowerCAmelCase_ = seqlen + 2
lowerCAmelCase_ = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
lowerCAmelCase_ = [
(torch.zeros(snake_case_ ), torch.zeros(snake_case_ )) for _ in range(self.num_layers )
]
lowerCAmelCase_ = common_inputs['''attention_mask''']
if self.use_past:
lowerCAmelCase_ = ordered_inputs['''attention_mask'''].dtype
lowerCAmelCase_ = torch.cat(
[ordered_inputs['''attention_mask'''], torch.ones(snake_case_, snake_case_, dtype=snake_case_ )], dim=1 )
return ordered_inputs
@property
def SCREAMING_SNAKE_CASE__ ( self ):
"""simple docstring"""
return 13
| 431
|
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=UpperCamelCase__ ):
lowerCamelCase__ = ['torch', 'torchsde']
def __init__( self , *snake_case_ , **snake_case_ ) -> Optional[int]:
requires_backends(self , ['''torch''', '''torchsde'''] )
@classmethod
def __a ( cls , *snake_case_ , **snake_case_ ) -> Optional[Any]:
requires_backends(cls , ['''torch''', '''torchsde'''] )
@classmethod
def __a ( cls , *snake_case_ , **snake_case_ ) -> Union[str, Any]:
requires_backends(cls , ['''torch''', '''torchsde'''] )
| 258
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ : Any = {
"""configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
"""tokenization_roc_bert""": ["""RoCBertTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ : Any = [
"""ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RoCBertForCausalLM""",
"""RoCBertForMaskedLM""",
"""RoCBertForMultipleChoice""",
"""RoCBertForPreTraining""",
"""RoCBertForQuestionAnswering""",
"""RoCBertForSequenceClassification""",
"""RoCBertForTokenClassification""",
"""RoCBertLayer""",
"""RoCBertModel""",
"""RoCBertPreTrainedModel""",
"""load_tf_weights_in_roc_bert""",
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
UpperCamelCase__ : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 708
|
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A_( A , A ):
assert isinstance(A , A )
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def A_( A , A , A ):
UpperCAmelCase_ = tmp_path / """cache"""
UpperCAmelCase_ = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase_ = TextDatasetReader(A , cache_dir=A , keep_in_memory=A ).read()
_check_text_dataset(A , A )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""text""": """string"""},
{"""text""": """int32"""},
{"""text""": """float32"""},
] , )
def A_( A , A , A ):
UpperCAmelCase_ = tmp_path / """cache"""
UpperCAmelCase_ = {"""text""": """string"""}
UpperCAmelCase_ = features.copy() if features else default_expected_features
UpperCAmelCase_ = (
Features({feature: Value(A ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase_ = TextDatasetReader(A , features=A , cache_dir=A ).read()
_check_text_dataset(A , A )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def A_( A , A , A ):
UpperCAmelCase_ = tmp_path / """cache"""
UpperCAmelCase_ = {"""text""": """string"""}
UpperCAmelCase_ = TextDatasetReader(A , cache_dir=A , split=A ).read()
_check_text_dataset(A , A )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def A_( A , A , A ):
if issubclass(A , A ):
UpperCAmelCase_ = text_path
elif issubclass(A , A ):
UpperCAmelCase_ = [text_path]
UpperCAmelCase_ = tmp_path / """cache"""
UpperCAmelCase_ = {"""text""": """string"""}
UpperCAmelCase_ = TextDatasetReader(A , cache_dir=A ).read()
_check_text_dataset(A , A )
def A_( A , A , A=("train",) ):
assert isinstance(A , A )
for split in splits:
UpperCAmelCase_ = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 1
assert dataset.column_names == ["text"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def A_( A , A , A ):
UpperCAmelCase_ = tmp_path / """cache"""
UpperCAmelCase_ = {"""text""": """string"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCAmelCase_ = TextDatasetReader({"""train""": text_path} , cache_dir=A , keep_in_memory=A ).read()
_check_text_datasetdict(A , A )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""text""": """string"""},
{"""text""": """int32"""},
{"""text""": """float32"""},
] , )
def A_( A , A , A ):
UpperCAmelCase_ = tmp_path / """cache"""
# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
UpperCAmelCase_ = {"""text""": """string"""}
UpperCAmelCase_ = features.copy() if features else default_expected_features
UpperCAmelCase_ = (
Features({feature: Value(A ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCAmelCase_ = TextDatasetReader({"""train""": text_path} , features=A , cache_dir=A ).read()
_check_text_datasetdict(A , A )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def A_( A , A , A ):
if split:
UpperCAmelCase_ = {split: text_path}
else:
UpperCAmelCase_ = """train"""
UpperCAmelCase_ = {"""train""": text_path, """test""": text_path}
UpperCAmelCase_ = tmp_path / """cache"""
UpperCAmelCase_ = {"""text""": """string"""}
UpperCAmelCase_ = TextDatasetReader(A , cache_dir=A ).read()
_check_text_datasetdict(A , A , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
| 486
| 0
|
'''simple docstring'''
UpperCamelCase__ : Any = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cookiecutter==1.7.3''',
'''dataclasses''': '''dataclasses''',
'''datasets''': '''datasets!=2.5.0''',
'''decord''': '''decord==0.6.0''',
'''deepspeed''': '''deepspeed>=0.9.3''',
'''diffusers''': '''diffusers''',
'''dill''': '''dill<0.3.5''',
'''evaluate''': '''evaluate>=0.2.0''',
'''fairscale''': '''fairscale>0.3''',
'''faiss-cpu''': '''faiss-cpu''',
'''fastapi''': '''fastapi''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1,<=0.7.0''',
'''ftfy''': '''ftfy''',
'''fugashi''': '''fugashi>=1.0''',
'''GitPython''': '''GitPython<3.1.19''',
'''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
'''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
'''importlib_metadata''': '''importlib_metadata''',
'''ipadic''': '''ipadic>=1.0.0,<2.0''',
'''isort''': '''isort>=5.5.4''',
'''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
'''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
'''jieba''': '''jieba''',
'''kenlm''': '''kenlm''',
'''keras-nlp''': '''keras-nlp>=0.3.1''',
'''librosa''': '''librosa''',
'''nltk''': '''nltk''',
'''natten''': '''natten>=0.14.6''',
'''numpy''': '''numpy>=1.17''',
'''onnxconverter-common''': '''onnxconverter-common''',
'''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
'''onnxruntime''': '''onnxruntime>=1.4.0''',
'''opencv-python''': '''opencv-python''',
'''optuna''': '''optuna''',
'''optax''': '''optax>=0.0.8,<=0.1.4''',
'''packaging''': '''packaging>=20.0''',
'''parameterized''': '''parameterized''',
'''phonemizer''': '''phonemizer''',
'''protobuf''': '''protobuf''',
'''psutil''': '''psutil''',
'''pyyaml''': '''pyyaml>=5.1''',
'''pydantic''': '''pydantic<2''',
'''pytest''': '''pytest>=7.2.0''',
'''pytest-timeout''': '''pytest-timeout''',
'''pytest-xdist''': '''pytest-xdist''',
'''python''': '''python>=3.8.0''',
'''ray[tune]''': '''ray[tune]''',
'''regex''': '''regex!=2019.12.17''',
'''requests''': '''requests''',
'''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
'''rjieba''': '''rjieba''',
'''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
'''ruff''': '''ruff>=0.0.241,<=0.0.259''',
'''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
'''sacremoses''': '''sacremoses''',
'''safetensors''': '''safetensors>=0.3.1''',
'''sagemaker''': '''sagemaker>=2.31.0''',
'''scikit-learn''': '''scikit-learn''',
'''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
'''sigopt''': '''sigopt''',
'''starlette''': '''starlette''',
'''sudachipy''': '''sudachipy>=0.6.6''',
'''sudachidict_core''': '''sudachidict_core>=20220729''',
'''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
'''tensorflow''': '''tensorflow>=2.6,<2.14''',
'''tensorflow-text''': '''tensorflow-text<2.14''',
'''tf2onnx''': '''tf2onnx''',
'''timeout-decorator''': '''timeout-decorator''',
'''timm''': '''timm''',
'''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
'''torch''': '''torch>=1.9,!=1.12.0''',
'''torchaudio''': '''torchaudio''',
'''torchvision''': '''torchvision''',
'''pyctcdecode''': '''pyctcdecode>=0.4.0''',
'''tqdm''': '''tqdm>=4.27''',
'''unidic''': '''unidic>=1.0.2''',
'''unidic_lite''': '''unidic_lite>=1.0.7''',
'''urllib3''': '''urllib3<2.0.0''',
'''uvicorn''': '''uvicorn''',
}
| 578
|
'''simple docstring'''
import math
def lowerCAmelCase_ ( _lowerCamelCase: int ):
__SCREAMING_SNAKE_CASE : Dict = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCamelCase )
def lowerCAmelCase_ ( _lowerCamelCase: float = 1 / 1_23_45 ):
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : List[str] = 3
while True:
__SCREAMING_SNAKE_CASE : int = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : str = int(_lowerCamelCase )
total_partitions += 1
if check_partition_perfect(_lowerCamelCase ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(_lowerCamelCase )
integer += 1
if __name__ == "__main__":
print(f"{solution() = }")
| 578
| 1
|
'''simple docstring'''
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_lowerCAmelCase = False
try:
_lowerCAmelCase = _is_package_available('''google.colab''')
except ModuleNotFoundError:
pass
@input.register
class lowerCAmelCase_:
'''simple docstring'''
def __init__( self ,__UpperCAmelCase = None ,__UpperCAmelCase = [] ) -> str:
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCase__ : Dict = choices
lowerCAmelCase__ : str = prompt
if sys.platform == "win32":
lowerCAmelCase__ : Optional[int] = """*"""
else:
lowerCAmelCase__ : Any = """➔ """
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = "" ) -> int:
if sys.platform != "win32":
writeColor(self.choices[index] ,32 ,__UpperCAmelCase )
else:
forceWrite(self.choices[index] ,__UpperCAmelCase )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> str:
if index == self.position:
forceWrite(F""" {self.arrow_char} """ )
self.write_choice(__UpperCAmelCase )
else:
forceWrite(F""" {self.choices[index]}""" )
reset_cursor()
def UpperCAmelCase_ ( self ,__UpperCAmelCase ,__UpperCAmelCase = 1 ) -> List[Any]:
lowerCAmelCase__ : int = self.position
if direction == Direction.DOWN:
if self.position + 1 >= len(self.choices ):
return
self.position += num_spaces
else:
if self.position - 1 < 0:
return
self.position -= num_spaces
clear_line()
self.print_choice(__UpperCAmelCase )
move_cursor(__UpperCAmelCase ,direction.name )
self.print_choice(self.position )
@input.mark(KEYMAP["""up"""] )
def UpperCAmelCase_ ( self ) -> List[str]:
self.move_direction(Direction.UP )
@input.mark(KEYMAP["""down"""] )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
self.move_direction(Direction.DOWN )
@input.mark(KEYMAP["""newline"""] )
def UpperCAmelCase_ ( self ) -> int:
move_cursor(len(self.choices ) - self.position ,"""DOWN""" )
return self.position
@input.mark(KEYMAP["""interrupt"""] )
def UpperCAmelCase_ ( self ) -> int:
move_cursor(len(self.choices ) - self.position ,"""DOWN""" )
raise KeyboardInterrupt
@input.mark_multiple(*[KEYMAP[str(__UpperCAmelCase )] for number in range(10 )] )
def UpperCAmelCase_ ( self ) -> str:
lowerCAmelCase__ : Tuple = int(chr(self.current_selection ) )
lowerCAmelCase__ : Optional[int] = index - self.position
if index == self.position:
return
if index < len(self.choices ):
if self.position > index:
self.move_direction(Direction.UP ,-movement )
elif self.position < index:
self.move_direction(Direction.DOWN ,__UpperCAmelCase )
else:
return
else:
return
def UpperCAmelCase_ ( self ,__UpperCAmelCase = 0 ) -> int:
if self.prompt:
linebreak()
forceWrite(self.prompt ,"""\n""" )
if in_colab:
forceWrite("""Please input a choice index (starting from 0), and press enter""" ,"""\n""" )
else:
forceWrite("""Please select a choice using the arrow or number keys, and selecting with enter""" ,"""\n""" )
lowerCAmelCase__ : List[Any] = default_choice
for i in range(len(self.choices ) ):
self.print_choice(__UpperCAmelCase )
forceWrite("""\n""" )
move_cursor(len(self.choices ) - self.position ,"""UP""" )
with cursor.hide():
while True:
if in_colab:
try:
lowerCAmelCase__ : int = int(builtins.input() )
except ValueError:
lowerCAmelCase__ : Optional[Any] = default_choice
else:
lowerCAmelCase__ : List[str] = self.handle_input()
if choice is not None:
reset_cursor()
for _ in range(len(self.choices ) + 1 ):
move_cursor(1 ,"""UP""" )
clear_line()
self.write_choice(__UpperCAmelCase ,"""\n""" )
return choice
| 710
|
'''simple docstring'''
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase="attention" ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase__ : Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase__ : Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase__ : Dict = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase=False ):
"""simple docstring"""
if split_mlp_wi:
lowerCAmelCase__ : Any = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase__ : Any = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase__ : Dict = (wi_a, wi_a)
else:
lowerCAmelCase__ : Union[str, Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase__ : int = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , *, UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : List[str] = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase__ : Optional[Any] = {"""/""".join(UpperCamelCase ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase__ : Optional[int] = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , UpperCamelCase )
lowerCAmelCase__ : List[str] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase__ : str = old["""token_embedder/embedding"""]
# Encoder.
for i in range(UpperCamelCase ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase__ : Optional[Any] = tax_layer_norm_lookup(UpperCamelCase , UpperCamelCase , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : str = tax_attention_lookup(UpperCamelCase , UpperCamelCase , """encoder""" , """attention""" )
lowerCAmelCase__ : Tuple = layer_norm
lowerCAmelCase__ : Optional[Any] = k.T
lowerCAmelCase__ : Dict = o.T
lowerCAmelCase__ : Any = q.T
lowerCAmelCase__ : Dict = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase__ : Optional[Any] = tax_layer_norm_lookup(UpperCamelCase , UpperCamelCase , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = tax_mlp_lookup(UpperCamelCase , UpperCamelCase , """encoder""" , UpperCamelCase )
lowerCAmelCase__ : List[str] = layer_norm
if split_mlp_wi:
lowerCAmelCase__ : Tuple = wi[0].T
lowerCAmelCase__ : Optional[Any] = wi[1].T
else:
lowerCAmelCase__ : Dict = wi.T
lowerCAmelCase__ : List[Any] = wo.T
lowerCAmelCase__ : Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase__ : List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(UpperCamelCase ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase__ : Optional[Any] = tax_layer_norm_lookup(UpperCamelCase , UpperCamelCase , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = tax_attention_lookup(UpperCamelCase , UpperCamelCase , """decoder""" , """self_attention""" )
lowerCAmelCase__ : Tuple = layer_norm
lowerCAmelCase__ : List[Any] = k.T
lowerCAmelCase__ : Dict = o.T
lowerCAmelCase__ : Union[str, Any] = q.T
lowerCAmelCase__ : Union[str, Any] = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase__ : Optional[int] = tax_layer_norm_lookup(UpperCamelCase , UpperCamelCase , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Tuple = tax_attention_lookup(UpperCamelCase , UpperCamelCase , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase__ : Any = layer_norm
lowerCAmelCase__ : Tuple = k.T
lowerCAmelCase__ : int = o.T
lowerCAmelCase__ : Union[str, Any] = q.T
lowerCAmelCase__ : str = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase__ : Any = tax_layer_norm_lookup(UpperCamelCase , UpperCamelCase , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase__ , lowerCAmelCase__ : Dict = tax_mlp_lookup(UpperCamelCase , UpperCamelCase , """decoder""" , UpperCamelCase )
lowerCAmelCase__ : Optional[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase__ : str = wi[0].T
lowerCAmelCase__ : List[Any] = wi[1].T
else:
lowerCAmelCase__ : List[str] = wi.T
lowerCAmelCase__ : int = wo.T
lowerCAmelCase__ : Union[str, Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase__ : Dict = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase__ : Optional[Any] = old["""decoder/logits_dense/kernel"""].T
return new
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Tuple = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase__ : Optional[Any] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase__ : List[str] = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase__ : Union[str, Any] = state_dict["""shared.weight"""]
return state_dict
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : int = checkpoints.load_tax_checkpoint(UpperCamelCase )
lowerCAmelCase__ : List[str] = convert_tax_to_pytorch(UpperCamelCase , num_layers=config.num_layers , is_encoder_only=UpperCamelCase )
lowerCAmelCase__ : Union[str, Any] = make_state_dict(UpperCamelCase , UpperCamelCase )
model.load_state_dict(UpperCamelCase , strict=UpperCamelCase )
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = False ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = TaConfig.from_json_file(UpperCamelCase )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase__ : List[Any] = TaEncoderModel(UpperCamelCase )
else:
lowerCAmelCase__ : Union[str, Any] = TaForConditionalGeneration(UpperCamelCase )
# Load weights from tf checkpoint
load_tax_weights_in_ta(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(UpperCamelCase )
# Verify that we can load the checkpoint.
model.from_pretrained(UpperCamelCase )
print("""Done""" )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
# Required parameters
parser.add_argument(
'''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
)
parser.add_argument(
'''--config_file''',
default=None,
type=str,
required=True,
help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
)
parser.add_argument(
'''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
)
_lowerCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 160
| 0
|
'''simple docstring'''
import datasets
from .evaluate import evaluate
__snake_case : Tuple = '''\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n'''
__snake_case : Tuple = '''\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'''
__snake_case : Tuple = '''\nComputes SQuAD scores (F1 and EM).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': the text of the answer\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the SQuAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> squad_metric = datasets.load_metric(\"squad\")\n >>> results = squad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0}\n'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase_ ( datasets.Metric ):
def lowerCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": {"id": datasets.Value("string" ), "prediction_text": datasets.Value("string" )},
"references": {
"id": datasets.Value("string" ),
"answers": datasets.features.Sequence(
{
"text": datasets.Value("string" ),
"answer_start": datasets.Value("int32" ),
} ),
},
} ) , codebase_urls=["https://rajpurkar.github.io/SQuAD-explorer/"] , reference_urls=["https://rajpurkar.github.io/SQuAD-explorer/"] , )
def lowerCamelCase_ ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase_ = {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
UpperCAmelCase_ = [
{
"paragraphs": [
{
"qas": [
{
"answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
"id": ref["id"],
}
for ref in references
]
}
]
}
]
UpperCAmelCase_ = evaluate(dataset=_UpperCamelCase , predictions=_UpperCamelCase )
return score
| 660
|
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
lowerCamelCase_ = {
"facebook/maskformer-swin-base-ade": (
"https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
)
# See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}
lowerCamelCase_ = logging.get_logger(__name__)
class __a ( __lowerCamelCase ):
"""simple docstring"""
_A : Optional[int] = "maskformer"
_A : List[str] = {"hidden_size": "mask_feature_size"}
_A : Tuple = ["resnet", "swin"]
_A : Optional[Any] = ["detr"]
def __init__( self : List[str] ,_UpperCamelCase : int = 2_5_6 ,_UpperCamelCase : int = 2_5_6 ,_UpperCamelCase : float = 0.1 ,_UpperCamelCase : bool = False ,_UpperCamelCase : Optional[Dict] = None ,_UpperCamelCase : Optional[Dict] = None ,_UpperCamelCase : float = 0.02 ,_UpperCamelCase : float = 1.0 ,_UpperCamelCase : float = 1.0 ,_UpperCamelCase : float = 1.0 ,_UpperCamelCase : float = 20.0 ,_UpperCamelCase : Optional[bool] = None ,**_UpperCamelCase : str ,) -> Tuple:
'''simple docstring'''
if backbone_config is None:
# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
SCREAMING_SNAKE_CASE__ =SwinConfig(
image_size=3_8_4 ,in_channels=3 ,patch_size=4 ,embed_dim=1_2_8 ,depths=[2, 2, 1_8, 2] ,num_heads=[4, 8, 1_6, 3_2] ,window_size=1_2 ,drop_path_rate=0.3 ,out_features=["""stage1""", """stage2""", """stage3""", """stage4"""] ,)
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
SCREAMING_SNAKE_CASE__ =backbone_config.pop("""model_type""" )
SCREAMING_SNAKE_CASE__ =CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ =config_class.from_dict(_UpperCamelCase )
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
f"""Supported model types: {",".join(self.backbones_supported )}""" )
if decoder_config is None:
# fall back to https://huggingface.co/facebook/detr-resnet-50
SCREAMING_SNAKE_CASE__ =DetrConfig()
else:
# verify that the decoder is supported
SCREAMING_SNAKE_CASE__ =(
decoder_config.pop("""model_type""" ) if isinstance(_UpperCamelCase ,_UpperCamelCase ) else decoder_config.model_type
)
if decoder_type not in self.decoders_supported:
raise ValueError(
f"""Transformer Decoder {decoder_type} not supported, please use one of"""
f""" {",".join(self.decoders_supported )}""" )
if isinstance(_UpperCamelCase ,_UpperCamelCase ):
SCREAMING_SNAKE_CASE__ =CONFIG_MAPPING[decoder_type]
SCREAMING_SNAKE_CASE__ =config_class.from_dict(_UpperCamelCase )
SCREAMING_SNAKE_CASE__ =backbone_config
SCREAMING_SNAKE_CASE__ =decoder_config
# main feature dimension for the model
SCREAMING_SNAKE_CASE__ =fpn_feature_size
SCREAMING_SNAKE_CASE__ =mask_feature_size
# initializer
SCREAMING_SNAKE_CASE__ =init_std
SCREAMING_SNAKE_CASE__ =init_xavier_std
# Hungarian matcher && loss
SCREAMING_SNAKE_CASE__ =cross_entropy_weight
SCREAMING_SNAKE_CASE__ =dice_weight
SCREAMING_SNAKE_CASE__ =mask_weight
SCREAMING_SNAKE_CASE__ =use_auxiliary_loss
SCREAMING_SNAKE_CASE__ =no_object_weight
SCREAMING_SNAKE_CASE__ =output_auxiliary_logits
SCREAMING_SNAKE_CASE__ =self.decoder_config.encoder_attention_heads
SCREAMING_SNAKE_CASE__ =self.decoder_config.num_hidden_layers
super().__init__(**_UpperCamelCase )
@classmethod
def __A ( cls : int ,_UpperCamelCase : PretrainedConfig ,_UpperCamelCase : PretrainedConfig ,**_UpperCamelCase : Any ) -> Union[str, Any]:
'''simple docstring'''
return cls(
backbone_config=_UpperCamelCase ,decoder_config=_UpperCamelCase ,**_UpperCamelCase ,)
def __A ( self : int ) -> Dict[str, any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ =copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE__ =self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ =self.decoder_config.to_dict()
SCREAMING_SNAKE_CASE__ =self.__class__.model_type
return output
| 151
| 0
|
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase__ :int = logging.get_logger(__name__)
lowercase__ :Optional[Any] = {
'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : Optional[int] = 'blip_2_vision_model'
def __init__( self : Any , __lowercase : List[str]=1_408 , __lowercase : str=6_144 , __lowercase : Optional[int]=39 , __lowercase : List[str]=16 , __lowercase : Optional[int]=224 , __lowercase : str=14 , __lowercase : List[Any]="gelu" , __lowercase : Optional[Any]=0.0_0_0_0_1 , __lowercase : Dict=0.0 , __lowercase : List[str]=1e-10 , __lowercase : Union[str, Any]=True , **__lowercase : Optional[int] , ):
'''simple docstring'''
super().__init__(**__lowercase )
__UpperCAmelCase : Optional[Any] = hidden_size
__UpperCAmelCase : Optional[int] = intermediate_size
__UpperCAmelCase : Any = num_hidden_layers
__UpperCAmelCase : Dict = num_attention_heads
__UpperCAmelCase : Optional[Any] = patch_size
__UpperCAmelCase : str = image_size
__UpperCAmelCase : str = initializer_range
__UpperCAmelCase : Optional[int] = attention_dropout
__UpperCAmelCase : str = layer_norm_eps
__UpperCAmelCase : List[str] = hidden_act
__UpperCAmelCase : str = qkv_bias
@classmethod
def A_ ( cls : Optional[Any] , __lowercase : Union[str, os.PathLike] , **__lowercase : Optional[Any] ):
'''simple docstring'''
cls._set_token_in_kwargs(__lowercase )
__UpperCAmelCase : Optional[Any] = cls.get_config_dict(__lowercase , **__lowercase )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
__UpperCAmelCase : str = config_dict['''vision_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__lowercase , **__lowercase )
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : int = 'blip_2_qformer'
def __init__( self : Dict , __lowercase : Optional[int]=30_522 , __lowercase : int=768 , __lowercase : str=12 , __lowercase : Union[str, Any]=12 , __lowercase : int=3_072 , __lowercase : List[Any]="gelu" , __lowercase : Dict=0.1 , __lowercase : Optional[int]=0.1 , __lowercase : Dict=512 , __lowercase : Any=0.0_2 , __lowercase : Any=1e-12 , __lowercase : Dict=0 , __lowercase : Tuple="absolute" , __lowercase : str=2 , __lowercase : Union[str, Any]=1_408 , **__lowercase : str , ):
'''simple docstring'''
super().__init__(pad_token_id=__lowercase , **__lowercase )
__UpperCAmelCase : List[str] = vocab_size
__UpperCAmelCase : List[Any] = hidden_size
__UpperCAmelCase : Union[str, Any] = num_hidden_layers
__UpperCAmelCase : Tuple = num_attention_heads
__UpperCAmelCase : Any = hidden_act
__UpperCAmelCase : Optional[int] = intermediate_size
__UpperCAmelCase : Tuple = hidden_dropout_prob
__UpperCAmelCase : Dict = attention_probs_dropout_prob
__UpperCAmelCase : Tuple = max_position_embeddings
__UpperCAmelCase : Dict = initializer_range
__UpperCAmelCase : int = layer_norm_eps
__UpperCAmelCase : List[Any] = position_embedding_type
__UpperCAmelCase : Union[str, Any] = cross_attention_frequency
__UpperCAmelCase : Union[str, Any] = encoder_hidden_size
@classmethod
def A_ ( cls : int , __lowercase : Union[str, os.PathLike] , **__lowercase : Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(__lowercase )
__UpperCAmelCase : List[str] = cls.get_config_dict(__lowercase , **__lowercase )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get('''model_type''' ) == "blip-2":
__UpperCAmelCase : Union[str, Any] = config_dict['''qformer_config''']
if "model_type" in config_dict and hasattr(cls , '''model_type''' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(__lowercase , **__lowercase )
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : Union[str, Any] = 'blip-2'
_A : str = True
def __init__( self : Any , __lowercase : Union[str, Any]=None , __lowercase : Union[str, Any]=None , __lowercase : Any=None , __lowercase : List[str]=32 , **__lowercase : List[Any] ):
'''simple docstring'''
super().__init__(**__lowercase )
if vision_config is None:
__UpperCAmelCase : Any = {}
logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''' )
if qformer_config is None:
__UpperCAmelCase : int = {}
logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''' )
if text_config is None:
__UpperCAmelCase : Dict = {}
logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )
__UpperCAmelCase : Any = BlipaVisionConfig(**__lowercase )
__UpperCAmelCase : List[str] = BlipaQFormerConfig(**__lowercase )
__UpperCAmelCase : Any = text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
__UpperCAmelCase : Optional[Any] = CONFIG_MAPPING[text_model_type](**__lowercase )
__UpperCAmelCase : str = self.text_config.tie_word_embeddings
__UpperCAmelCase : Dict = self.text_config.is_encoder_decoder
__UpperCAmelCase : Union[str, Any] = num_query_tokens
__UpperCAmelCase : Union[str, Any] = self.vision_config.hidden_size
__UpperCAmelCase : Optional[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__UpperCAmelCase : Union[str, Any] = 1.0
__UpperCAmelCase : Optional[Any] = 0.0_2
@classmethod
def A_ ( cls : str , __lowercase : BlipaVisionConfig , __lowercase : BlipaQFormerConfig , __lowercase : PretrainedConfig , **__lowercase : Dict , ):
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__lowercase , )
def A_ ( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
__UpperCAmelCase : str = self.vision_config.to_dict()
__UpperCAmelCase : str = self.qformer_config.to_dict()
__UpperCAmelCase : Any = self.text_config.to_dict()
__UpperCAmelCase : Union[str, Any] = self.__class__.model_type
return output
| 708
|
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class snake_case ( unittest.TestCase ):
'''simple docstring'''
def A_ ( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = tempfile.mkdtemp()
# fmt: off
__UpperCAmelCase : Optional[int] = ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
# fmt: on
__UpperCAmelCase : List[str] = dict(zip(__lowercase , range(len(__lowercase ) ) ) )
__UpperCAmelCase : Any = ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
__UpperCAmelCase : Dict = {'''unk_token''': '''<unk>'''}
__UpperCAmelCase : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(__lowercase ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__lowercase ) )
__UpperCAmelCase : List[str] = {
'''do_resize''': True,
'''size''': 20,
'''do_center_crop''': True,
'''crop_size''': 18,
'''do_normalize''': True,
'''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
'''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
}
__UpperCAmelCase : Any = os.path.join(self.tmpdirname , __lowercase )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(__lowercase , __lowercase )
def A_ ( self : Optional[Any] , **__lowercase : Tuple ):
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__lowercase )
def A_ ( self : Any , **__lowercase : Optional[Any] ):
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__lowercase )
def A_ ( self : Dict , **__lowercase : str ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__lowercase )
def A_ ( self : List[str] ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A_ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : str = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
__UpperCAmelCase : int = [Image.fromarray(np.moveaxis(__lowercase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A_ ( self : Optional[Any] ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.get_tokenizer()
__UpperCAmelCase : Dict = self.get_rust_tokenizer()
__UpperCAmelCase : Optional[Any] = self.get_image_processor()
__UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
processor_slow.save_pretrained(self.tmpdirname )
__UpperCAmelCase : List[Any] = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=__lowercase )
__UpperCAmelCase : Tuple = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
processor_fast.save_pretrained(self.tmpdirname )
__UpperCAmelCase : str = CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , __lowercase )
self.assertIsInstance(processor_fast.tokenizer , __lowercase )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , __lowercase )
self.assertIsInstance(processor_fast.image_processor , __lowercase )
def A_ ( self : Optional[int] ):
'''simple docstring'''
__UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__UpperCAmelCase : Any = self.get_tokenizer(bos_token='''(BOS)''' , eos_token='''(EOS)''' )
__UpperCAmelCase : Tuple = self.get_image_processor(do_normalize=__lowercase , padding_value=1.0 )
__UpperCAmelCase : List[str] = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token='''(BOS)''' , eos_token='''(EOS)''' , do_normalize=__lowercase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __lowercase )
def A_ ( self : int ):
'''simple docstring'''
__UpperCAmelCase : int = self.get_image_processor()
__UpperCAmelCase : List[str] = self.get_tokenizer()
__UpperCAmelCase : Optional[Any] = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__UpperCAmelCase : List[Any] = self.prepare_image_inputs()
__UpperCAmelCase : Dict = image_processor(__lowercase , return_tensors='''np''' )
__UpperCAmelCase : Optional[int] = processor(images=__lowercase , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def A_ ( self : Dict ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.get_image_processor()
__UpperCAmelCase : Any = self.get_tokenizer()
__UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__UpperCAmelCase : int = '''lower newer'''
__UpperCAmelCase : int = processor(text=__lowercase )
__UpperCAmelCase : Any = tokenizer(__lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A_ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = self.get_image_processor()
__UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
__UpperCAmelCase : List[Any] = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__UpperCAmelCase : str = '''lower newer'''
__UpperCAmelCase : int = self.prepare_image_inputs()
__UpperCAmelCase : str = processor(text=__lowercase , images=__lowercase )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowercase ):
processor()
def A_ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : Dict = self.get_image_processor()
__UpperCAmelCase : str = self.get_tokenizer()
__UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__UpperCAmelCase : Union[str, Any] = self.prepare_image_inputs()
__UpperCAmelCase : Optional[Any] = self.prepare_image_inputs()
__UpperCAmelCase : Tuple = processor(images=__lowercase , visual_prompt=__lowercase )
self.assertListEqual(list(inputs.keys() ) , ['''pixel_values''', '''conditional_pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(__lowercase ):
processor()
def A_ ( self : str ):
'''simple docstring'''
__UpperCAmelCase : Optional[int] = self.get_image_processor()
__UpperCAmelCase : Any = self.get_tokenizer()
__UpperCAmelCase : Any = CLIPSegProcessor(tokenizer=__lowercase , image_processor=__lowercase )
__UpperCAmelCase : Dict = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__UpperCAmelCase : int = processor.batch_decode(__lowercase )
__UpperCAmelCase : int = tokenizer.batch_decode(__lowercase )
self.assertListEqual(__lowercase , __lowercase )
| 374
| 0
|
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__A : Tuple = logging.get_logger(__name__)
__A : int = {
'''b0''': efficientnet.EfficientNetBa,
'''b1''': efficientnet.EfficientNetBa,
'''b2''': efficientnet.EfficientNetBa,
'''b3''': efficientnet.EfficientNetBa,
'''b4''': efficientnet.EfficientNetBa,
'''b5''': efficientnet.EfficientNetBa,
'''b6''': efficientnet.EfficientNetBa,
'''b7''': efficientnet.EfficientNetBa,
}
__A : int = {
'''b0''': {
'''hidden_dim''': 1280,
'''width_coef''': 1.0,
'''depth_coef''': 1.0,
'''image_size''': 224,
'''dropout_rate''': 0.2,
'''dw_padding''': [],
},
'''b1''': {
'''hidden_dim''': 1280,
'''width_coef''': 1.0,
'''depth_coef''': 1.1,
'''image_size''': 240,
'''dropout_rate''': 0.2,
'''dw_padding''': [16],
},
'''b2''': {
'''hidden_dim''': 1408,
'''width_coef''': 1.1,
'''depth_coef''': 1.2,
'''image_size''': 260,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 8, 16],
},
'''b3''': {
'''hidden_dim''': 1536,
'''width_coef''': 1.2,
'''depth_coef''': 1.4,
'''image_size''': 300,
'''dropout_rate''': 0.3,
'''dw_padding''': [5, 18],
},
'''b4''': {
'''hidden_dim''': 1792,
'''width_coef''': 1.4,
'''depth_coef''': 1.8,
'''image_size''': 380,
'''dropout_rate''': 0.4,
'''dw_padding''': [6],
},
'''b5''': {
'''hidden_dim''': 2048,
'''width_coef''': 1.6,
'''depth_coef''': 2.2,
'''image_size''': 456,
'''dropout_rate''': 0.4,
'''dw_padding''': [13, 27],
},
'''b6''': {
'''hidden_dim''': 2304,
'''width_coef''': 1.8,
'''depth_coef''': 2.6,
'''image_size''': 528,
'''dropout_rate''': 0.5,
'''dw_padding''': [31],
},
'''b7''': {
'''hidden_dim''': 2560,
'''width_coef''': 2.0,
'''depth_coef''': 3.1,
'''image_size''': 600,
'''dropout_rate''': 0.5,
'''dw_padding''': [18],
},
}
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Any:
'''simple docstring'''
lowerCAmelCase : str = EfficientNetConfig()
lowerCAmelCase : Any = CONFIG_MAP[model_name]['hidden_dim']
lowerCAmelCase : List[str] = CONFIG_MAP[model_name]['width_coef']
lowerCAmelCase : Optional[Any] = CONFIG_MAP[model_name]['depth_coef']
lowerCAmelCase : Dict = CONFIG_MAP[model_name]['image_size']
lowerCAmelCase : List[Any] = CONFIG_MAP[model_name]['dropout_rate']
lowerCAmelCase : Any = CONFIG_MAP[model_name]['dw_padding']
lowerCAmelCase : Union[str, Any] = 'huggingface/label-files'
lowerCAmelCase : Dict = 'imagenet-1k-id2label.json'
lowerCAmelCase : Dict = 1_000
lowerCAmelCase : Dict = json.load(open(hf_hub_download(_UpperCAmelCase, _UpperCAmelCase, repo_type='dataset' ), 'r' ) )
lowerCAmelCase : List[str] = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
lowerCAmelCase : str = idalabel
lowerCAmelCase : List[Any] = {v: k for k, v in idalabel.items()}
return config
def SCREAMING_SNAKE_CASE__ ( ) -> Any:
'''simple docstring'''
lowerCAmelCase : Dict = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowerCAmelCase : List[str] = Image.open(requests.get(_UpperCAmelCase, stream=_UpperCAmelCase ).raw )
return im
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase : str = CONFIG_MAP[model_name]['image_size']
lowerCAmelCase : Dict = EfficientNetImageProcessor(
size={'height': size, 'width': size}, image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6], image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3], do_center_crop=_UpperCAmelCase, )
return preprocessor
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase : Dict = [v.split('_' )[0].split('block' )[1] for v in original_param_names if v.startswith('block' )]
lowerCAmelCase : str = sorted(set(_UpperCAmelCase ) )
lowerCAmelCase : Dict = len(_UpperCAmelCase )
lowerCAmelCase : Union[str, Any] = {b: str(_UpperCAmelCase ) for b, i in zip(_UpperCAmelCase, range(_UpperCAmelCase ) )}
lowerCAmelCase : Optional[int] = []
rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight') )
rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight') )
rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias') )
rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean') )
rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var') )
for b in block_names:
lowerCAmelCase : int = block_name_mapping[b]
rename_keys.append((f"block{b}_expand_conv/kernel:0", f"encoder.blocks.{hf_b}.expansion.expand_conv.weight") )
rename_keys.append((f"block{b}_expand_bn/gamma:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.weight") )
rename_keys.append((f"block{b}_expand_bn/beta:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.bias") )
rename_keys.append(
(f"block{b}_expand_bn/moving_mean:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_mean") )
rename_keys.append(
(f"block{b}_expand_bn/moving_variance:0", f"encoder.blocks.{hf_b}.expansion.expand_bn.running_var") )
rename_keys.append(
(f"block{b}_dwconv/depthwise_kernel:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight") )
rename_keys.append((f"block{b}_bn/gamma:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight") )
rename_keys.append((f"block{b}_bn/beta:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias") )
rename_keys.append(
(f"block{b}_bn/moving_mean:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean") )
rename_keys.append(
(f"block{b}_bn/moving_variance:0", f"encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var") )
rename_keys.append((f"block{b}_se_reduce/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.weight") )
rename_keys.append((f"block{b}_se_reduce/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.reduce.bias") )
rename_keys.append((f"block{b}_se_expand/kernel:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.weight") )
rename_keys.append((f"block{b}_se_expand/bias:0", f"encoder.blocks.{hf_b}.squeeze_excite.expand.bias") )
rename_keys.append(
(f"block{b}_project_conv/kernel:0", f"encoder.blocks.{hf_b}.projection.project_conv.weight") )
rename_keys.append((f"block{b}_project_bn/gamma:0", f"encoder.blocks.{hf_b}.projection.project_bn.weight") )
rename_keys.append((f"block{b}_project_bn/beta:0", f"encoder.blocks.{hf_b}.projection.project_bn.bias") )
rename_keys.append(
(f"block{b}_project_bn/moving_mean:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_mean") )
rename_keys.append(
(f"block{b}_project_bn/moving_variance:0", f"encoder.blocks.{hf_b}.projection.project_bn.running_var") )
rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight') )
rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight') )
rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias') )
rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean') )
rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var') )
lowerCAmelCase : Union[str, Any] = {}
for item in rename_keys:
if item[0] in original_param_names:
lowerCAmelCase : Dict = 'efficientnet.' + item[1]
lowerCAmelCase : Tuple = 'classifier.weight'
lowerCAmelCase : Optional[Any] = 'classifier.bias'
return key_mapping
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> Any:
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
lowerCAmelCase : str = key_mapping[key]
if "_conv" in key and "kernel" in key:
lowerCAmelCase : List[Any] = torch.from_numpy(_UpperCAmelCase ).permute(3, 2, 0, 1 )
elif "depthwise_kernel" in key:
lowerCAmelCase : int = torch.from_numpy(_UpperCAmelCase ).permute(2, 3, 0, 1 )
elif "kernel" in key:
lowerCAmelCase : str = torch.from_numpy(np.transpose(_UpperCAmelCase ) )
else:
lowerCAmelCase : int = torch.from_numpy(_UpperCAmelCase )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(_UpperCAmelCase )
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
lowerCAmelCase : Optional[int] = model_classes[model_name](
include_top=_UpperCAmelCase, weights='imagenet', input_tensor=_UpperCAmelCase, input_shape=_UpperCAmelCase, pooling=_UpperCAmelCase, classes=1_000, classifier_activation='softmax', )
lowerCAmelCase : int = original_model.trainable_variables
lowerCAmelCase : Union[str, Any] = original_model.non_trainable_variables
lowerCAmelCase : Union[str, Any] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
lowerCAmelCase : str = param.numpy()
lowerCAmelCase : str = list(tf_params.keys() )
# Load HuggingFace model
lowerCAmelCase : Union[str, Any] = get_efficientnet_config(_UpperCAmelCase )
lowerCAmelCase : Dict = EfficientNetForImageClassification(_UpperCAmelCase ).eval()
lowerCAmelCase : str = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('Converting parameters...' )
lowerCAmelCase : str = rename_keys(_UpperCAmelCase )
replace_params(_UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase )
# Initialize preprocessor and preprocess input image
lowerCAmelCase : int = convert_image_processor(_UpperCAmelCase )
lowerCAmelCase : Union[str, Any] = preprocessor(images=prepare_img(), return_tensors='pt' )
# HF model inference
hf_model.eval()
with torch.no_grad():
lowerCAmelCase : str = hf_model(**_UpperCAmelCase )
lowerCAmelCase : Dict = outputs.logits.detach().numpy()
# Original model inference
lowerCAmelCase : str = False
lowerCAmelCase : List[Any] = CONFIG_MAP[model_name]['image_size']
lowerCAmelCase : int = prepare_img().resize((image_size, image_size), resample=PIL.Image.NEAREST )
lowerCAmelCase : Dict = image.img_to_array(_UpperCAmelCase )
lowerCAmelCase : Optional[Any] = np.expand_dims(_UpperCAmelCase, axis=0 )
lowerCAmelCase : Optional[Any] = original_model.predict(_UpperCAmelCase )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(_UpperCAmelCase, _UpperCAmelCase, atol=1e-3 ), "The predicted logits are not the same."
print('Model outputs match!' )
if save_model:
# Create folder to save model
if not os.path.isdir(_UpperCAmelCase ):
os.mkdir(_UpperCAmelCase )
# Save converted model and image processor
hf_model.save_pretrained(_UpperCAmelCase )
preprocessor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
# Push model and image processor to hub
print(f"Pushing converted {model_name} to the hub..." )
lowerCAmelCase : Any = f"efficientnet-{model_name}"
preprocessor.push_to_hub(_UpperCAmelCase )
hf_model.push_to_hub(_UpperCAmelCase )
if __name__ == "__main__":
__A : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''b0''',
type=str,
help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''hf_model''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
__A : str = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 343
|
import math
import sys
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : str = ''
try:
with open(_UpperCAmelCase, 'rb' ) as binary_file:
lowerCAmelCase : Any = binary_file.read()
for dat in data:
lowerCAmelCase : int = f"{dat:08b}"
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : Tuple = {'0': '0', '1': '1'}
lowerCAmelCase , lowerCAmelCase : Tuple = '', ''
lowerCAmelCase : Any = len(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
lowerCAmelCase : List[Any] = lexicon[curr_string]
result += last_match_id
lowerCAmelCase : int = last_match_id + '0'
if math.loga(_UpperCAmelCase ).is_integer():
lowerCAmelCase : List[str] = {}
for curr_key in list(_UpperCAmelCase ):
lowerCAmelCase : List[Any] = lexicon.pop(_UpperCAmelCase )
lowerCAmelCase : Optional[int] = new_lex
lowerCAmelCase : Tuple = last_match_id + '1'
index += 1
lowerCAmelCase : List[Any] = ''
return result
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> None:
'''simple docstring'''
lowerCAmelCase : Dict = 8
try:
with open(_UpperCAmelCase, 'wb' ) as opened_file:
lowerCAmelCase : List[Any] = [
to_write[i : i + byte_length]
for i in range(0, len(_UpperCAmelCase ), _UpperCAmelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(_UpperCAmelCase, 2 ).to_bytes(1, byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : int = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
lowerCAmelCase : int = data_bits[counter:]
lowerCAmelCase : Optional[int] = data_bits[counter + 1 :]
return data_bits
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase ) -> None:
'''simple docstring'''
lowerCAmelCase : Tuple = read_file_binary(_UpperCAmelCase )
lowerCAmelCase : int = remove_prefix(_UpperCAmelCase )
lowerCAmelCase : List[str] = decompress_data(_UpperCAmelCase )
write_file_binary(_UpperCAmelCase, _UpperCAmelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 343
| 1
|
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCamelCase : int = logging.get_logger(__name__)
__lowerCamelCase : List[Any] = "https://openaipublic.azureedge.net/jukebox/models/"
__lowerCamelCase : Optional[Any] = {
"jukebox-1b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"1b_lyrics/prior_level_2.pth.tar",
],
"jukebox-5b-lyrics": [
"5b/vqvae.pth.tar",
"5b/prior_level_0.pth.tar",
"5b/prior_level_1.pth.tar",
"5b_lyrics/prior_level_2.pth.tar",
],
}
def __UpperCAmelCase ( __magic_name__ )-> List[Any]:
"""simple docstring"""
if key.endswith(".model.1.bias" ) and len(key.split("." ) ) > 10:
snake_case_ : List[str] = key.replace(".model.1.bias" ,".conv1d_1.bias" )
elif key.endswith(".model.1.weight" ) and len(key.split("." ) ) > 10:
snake_case_ : str = key.replace(".model.1.weight" ,".conv1d_1.weight" )
elif key.endswith(".model.3.bias" ) and len(key.split("." ) ) > 10:
snake_case_ : List[Any] = key.replace(".model.3.bias" ,".conv1d_2.bias" )
elif key.endswith(".model.3.weight" ) and len(key.split("." ) ) > 10:
snake_case_ : Any = key.replace(".model.3.weight" ,".conv1d_2.weight" )
if "conditioner_blocks.0." in key:
snake_case_ : str = key.replace("conditioner_blocks.0" ,"conditioner_blocks" )
if "prime_prior" in key:
snake_case_ : Any = key.replace("prime_prior" ,"encoder" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
snake_case_ : int = key.replace(".emb." ,"." )
if key.endswith("k" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(".k" ,".codebook" )
if "y_emb." in key:
return key.replace("y_emb." ,"metadata_embedding." )
if "x_emb.emb." in key:
snake_case_ : Optional[int] = key.replace("0.x_emb.emb" ,"embed_tokens" )
if "prime_state_ln" in key:
return key.replace("prime_state_ln" ,"encoder.final_layer_norm" )
if ".ln" in key:
return key.replace(".ln" ,".layer_norm" )
if "_ln" in key:
return key.replace("_ln" ,"_layer_norm" )
if "prime_state_proj" in key:
return key.replace("prime_state_proj" ,"encoder.proj_in" )
if "prime_x_out" in key:
return key.replace("prime_x_out" ,"encoder.lm_head" )
if "prior.x_out" in key:
return key.replace("x_out" ,"fc_proj_out" )
if "x_emb" in key:
return key.replace("x_emb" ,"embed_tokens" )
return key
def __UpperCAmelCase ( __magic_name__ ,__magic_name__ ,__magic_name__ ,__magic_name__ )-> int:
"""simple docstring"""
snake_case_ : Tuple = {}
import re
snake_case_ : Optional[int] = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
snake_case_ : Dict = re.compile(
r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
snake_case_ : str = re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
snake_case_ : List[Any] = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)" )
snake_case_ : List[Any] = re.compile(
r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
snake_case_ : str = re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)" )
snake_case_ : Any = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)" )
snake_case_ : Optional[Any] = re.compile(
r"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)" )
snake_case_ : Any = re.compile(r"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(__magic_name__ ):
snake_case_ : Any = re_encoder_block_conv_in.match(__magic_name__ )
snake_case_ : Optional[Any] = regex_match.groups()
snake_case_ : List[str] = int(groups[2] ) * 2 + int(groups[3] )
snake_case_ : List[str] = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
snake_case_ : int = re_encoder_block_conv_in.sub(__magic_name__ ,__magic_name__ )
elif re_encoder_block_resnet.fullmatch(__magic_name__ ):
snake_case_ : Dict = re_encoder_block_resnet.match(__magic_name__ )
snake_case_ : Dict = regex_match.groups()
snake_case_ : List[Any] = int(groups[2] ) * 2 + int(groups[3] )
snake_case_ : Tuple = {"1": 1, "3": 2}[groups[-2]]
snake_case_ : Tuple = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
snake_case_ : Union[str, Any] = F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
snake_case_ : Dict = prefix + resnet_block
snake_case_ : List[str] = re_encoder_block_resnet.sub(__magic_name__ ,__magic_name__ )
elif re_encoder_block_proj_out.fullmatch(__magic_name__ ):
snake_case_ : List[str] = re_encoder_block_proj_out.match(__magic_name__ )
snake_case_ : Optional[int] = regex_match.groups()
snake_case_ : List[str] = F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
snake_case_ : Union[str, Any] = re_encoder_block_proj_out.sub(__magic_name__ ,__magic_name__ )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(__magic_name__ ):
snake_case_ : List[Any] = re_decoder_block_conv_out.match(__magic_name__ )
snake_case_ : int = regex_match.groups()
snake_case_ : Tuple = int(groups[2] ) * 2 + int(groups[3] ) - 2
snake_case_ : int = F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
snake_case_ : Dict = re_decoder_block_conv_out.sub(__magic_name__ ,__magic_name__ )
elif re_decoder_block_resnet.fullmatch(__magic_name__ ):
snake_case_ : Dict = re_decoder_block_resnet.match(__magic_name__ )
snake_case_ : List[Any] = regex_match.groups()
snake_case_ : Optional[int] = int(groups[2] ) * 2 + int(groups[3] ) - 2
snake_case_ : List[str] = {"1": 1, "3": 2}[groups[-2]]
snake_case_ : Union[str, Any] = F'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
snake_case_ : Union[str, Any] = F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
snake_case_ : Dict = prefix + resnet_block
snake_case_ : Optional[int] = re_decoder_block_resnet.sub(__magic_name__ ,__magic_name__ )
elif re_decoder_block_proj_in.fullmatch(__magic_name__ ):
snake_case_ : Optional[Any] = re_decoder_block_proj_in.match(__magic_name__ )
snake_case_ : Dict = regex_match.groups()
snake_case_ : List[Any] = F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
snake_case_ : Tuple = re_decoder_block_proj_in.sub(__magic_name__ ,__magic_name__ )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(__magic_name__ ):
snake_case_ : str = re_prior_cond_conv_out.match(__magic_name__ )
snake_case_ : List[Any] = regex_match.groups()
snake_case_ : Optional[Any] = int(groups[1] ) * 2 + int(groups[2] ) - 2
snake_case_ : List[Any] = F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
snake_case_ : List[str] = re_prior_cond_conv_out.sub(__magic_name__ ,__magic_name__ )
elif re_prior_cond_resnet.fullmatch(__magic_name__ ):
snake_case_ : List[Any] = re_prior_cond_resnet.match(__magic_name__ )
snake_case_ : int = regex_match.groups()
snake_case_ : Dict = int(groups[1] ) * 2 + int(groups[2] ) - 2
snake_case_ : Any = {"1": 1, "3": 2}[groups[-2]]
snake_case_ : List[str] = F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
snake_case_ : Union[str, Any] = F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
snake_case_ : List[Any] = prefix + resnet_block
snake_case_ : Tuple = re_prior_cond_resnet.sub(__magic_name__ ,__magic_name__ )
elif re_prior_cond_proj_in.fullmatch(__magic_name__ ):
snake_case_ : List[Any] = re_prior_cond_proj_in.match(__magic_name__ )
snake_case_ : List[str] = regex_match.groups()
snake_case_ : Optional[Any] = F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
snake_case_ : Dict = re_prior_cond_proj_in.sub(__magic_name__ ,__magic_name__ )
# keep original key
else:
snake_case_ : Union[str, Any] = original_key
snake_case_ : List[Any] = replace_key(__magic_name__ )
if F'''{key_prefix}.{key}''' not in model_state_dict or key is None:
print(F'''failed converting {original_key} to {key}, does not match''' )
# handle missmatched shape
elif value.shape != model_state_dict[F'''{key_prefix}.{key}'''].shape:
snake_case_ : Dict = model_state_dict[F'''{key_prefix}.{key}''']
print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match''' )
snake_case_ : List[str] = original_key
snake_case_ : str = original_key
snake_case_ : Union[str, Any] = value
return new_dict
@torch.no_grad()
def __UpperCAmelCase ( __magic_name__=None ,__magic_name__=None )-> Any:
"""simple docstring"""
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/' )[-1]}''' ):
snake_case_ : Tuple = requests.get(F'''{PREFIX}{file}''' ,allow_redirects=__magic_name__ )
os.makedirs(F'''{pytorch_dump_folder_path}/''' ,exist_ok=__magic_name__ )
open(F'''{pytorch_dump_folder_path}/{file.split('/' )[-1]}''' ,"wb" ).write(r.content )
snake_case_ : int = MODEL_MAPPING[model_name.split("/" )[-1]]
snake_case_ : List[str] = JukeboxConfig.from_pretrained(__magic_name__ )
snake_case_ : Tuple = JukeboxModel(__magic_name__ )
snake_case_ : Dict = []
snake_case_ : Optional[int] = {}
for i, dict_name in enumerate(__magic_name__ ):
snake_case_ : int = torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}''' )["model"]
snake_case_ : List[Any] = {}
for k in old_dic.keys():
if k.endswith(".b" ):
snake_case_ : Any = old_dic[k]
elif k.endswith(".w" ):
snake_case_ : int = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
snake_case_ : Union[str, Any] = old_dic[k]
else:
snake_case_ : Dict = old_dic[k]
snake_case_ : Optional[int] = "vqvae" if i == 0 else F'''priors.{3 - i}'''
snake_case_ : Union[str, Any] = fix_jukebox_keys(__magic_name__ ,model.state_dict() ,__magic_name__ ,__magic_name__ )
weight_dict.append(__magic_name__ )
snake_case_ : Dict = weight_dict.pop(0 )
model.vqvae.load_state_dict(__magic_name__ )
for i in range(len(__magic_name__ ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
with open(F'''{pytorch_dump_folder_path}/mapping.json''' ,"w" ) as txtfile:
json.dump(__magic_name__ ,__magic_name__ )
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__magic_name__ )
return weight_dict
if __name__ == "__main__":
__lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''jukebox-5b-lyrics''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''jukebox-5b-lyrics-converted''',
type=str,
help='''Path to the output PyTorch model directory.''',
)
__lowerCamelCase : Dict = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 705
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : int = {
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class A_ (a_ ):
"""simple docstring"""
a__ = '''cvt'''
def __init__( self :List[Any] , lowerCAmelCase__ :Optional[int]=3 , lowerCAmelCase__ :Any=[7, 3, 3] , lowerCAmelCase__ :Dict=[4, 2, 2] , lowerCAmelCase__ :Union[str, Any]=[2, 1, 1] , lowerCAmelCase__ :Any=[64, 192, 384] , lowerCAmelCase__ :List[str]=[1, 3, 6] , lowerCAmelCase__ :str=[1, 2, 10] , lowerCAmelCase__ :Any=[4.0, 4.0, 4.0] , lowerCAmelCase__ :int=[0.0, 0.0, 0.0] , lowerCAmelCase__ :Optional[Any]=[0.0, 0.0, 0.0] , lowerCAmelCase__ :Dict=[0.0, 0.0, 0.1] , lowerCAmelCase__ :List[Any]=[True, True, True] , lowerCAmelCase__ :List[Any]=[False, False, True] , lowerCAmelCase__ :Dict=["dw_bn", "dw_bn", "dw_bn"] , lowerCAmelCase__ :Any=[3, 3, 3] , lowerCAmelCase__ :Tuple=[1, 1, 1] , lowerCAmelCase__ :Optional[int]=[2, 2, 2] , lowerCAmelCase__ :Union[str, Any]=[1, 1, 1] , lowerCAmelCase__ :Any=[1, 1, 1] , lowerCAmelCase__ :List[str]=0.0_2 , lowerCAmelCase__ :Dict=1E-1_2 , **lowerCAmelCase__ :Optional[Any] , ) -> str:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ )
snake_case_ : int = num_channels
snake_case_ : int = patch_sizes
snake_case_ : Optional[Any] = patch_stride
snake_case_ : Dict = patch_padding
snake_case_ : Tuple = embed_dim
snake_case_ : Optional[int] = num_heads
snake_case_ : Union[str, Any] = depth
snake_case_ : Optional[int] = mlp_ratio
snake_case_ : Tuple = attention_drop_rate
snake_case_ : str = drop_rate
snake_case_ : Tuple = drop_path_rate
snake_case_ : Any = qkv_bias
snake_case_ : Union[str, Any] = cls_token
snake_case_ : int = qkv_projection_method
snake_case_ : Any = kernel_qkv
snake_case_ : Union[str, Any] = padding_kv
snake_case_ : str = stride_kv
snake_case_ : Dict = padding_q
snake_case_ : Tuple = stride_q
snake_case_ : Any = initializer_range
snake_case_ : Any = layer_norm_eps
| 656
| 0
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
__lowerCamelCase : str = None
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
__lowerCamelCase : Dict = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCamelCase : Any = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
__lowerCamelCase : List[str] = {
'''camembert-base''': 512,
}
__lowerCamelCase : int = '''▁'''
class A_ (a_ ):
"""simple docstring"""
a__ = VOCAB_FILES_NAMES
a__ = PRETRAINED_VOCAB_FILES_MAP
a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = ['''input_ids''', '''attention_mask''']
a__ = CamembertTokenizer
def __init__( self :int , lowerCAmelCase__ :Tuple=None , lowerCAmelCase__ :List[str]=None , lowerCAmelCase__ :Tuple="<s>" , lowerCAmelCase__ :Tuple="</s>" , lowerCAmelCase__ :Any="</s>" , lowerCAmelCase__ :Optional[int]="<s>" , lowerCAmelCase__ :Optional[Any]="<unk>" , lowerCAmelCase__ :int="<pad>" , lowerCAmelCase__ :List[str]="<mask>" , lowerCAmelCase__ :Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"] , **lowerCAmelCase__ :Tuple , ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ : str = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
snake_case_ : int = vocab_file
snake_case_ : str = False if not self.vocab_file else True
def _A ( self :int , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case_ : Dict = [self.cls_token_id]
snake_case_ : Dict = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _A ( self :int , lowerCAmelCase__ :List[int] , lowerCAmelCase__ :Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
snake_case_ : List[Any] = [self.sep_token_id]
snake_case_ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _A ( self :Dict , lowerCAmelCase__ :str , lowerCAmelCase__ :Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
snake_case_ : List[str] = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 653
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Any = {
'''configuration_longformer''': [
'''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''LongformerConfig''',
'''LongformerOnnxConfig''',
],
'''tokenization_longformer''': ['''LongformerTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = ['''LongformerTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
'''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LongformerForMaskedLM''',
'''LongformerForMultipleChoice''',
'''LongformerForQuestionAnswering''',
'''LongformerForSequenceClassification''',
'''LongformerForTokenClassification''',
'''LongformerModel''',
'''LongformerPreTrainedModel''',
'''LongformerSelfAttention''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Any = [
'''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFLongformerForMaskedLM''',
'''TFLongformerForMultipleChoice''',
'''TFLongformerForQuestionAnswering''',
'''TFLongformerForSequenceClassification''',
'''TFLongformerForTokenClassification''',
'''TFLongformerModel''',
'''TFLongformerPreTrainedModel''',
'''TFLongformerSelfAttention''',
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
__lowerCamelCase : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 653
| 1
|
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class UpperCAmelCase ( __lowercase ):
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = SMALL_MODEL_IDENTIFIER
UpperCamelCase = """pt"""
UpperCamelCase = """tf"""
def lowerCamelCase_ ( self : Any , __magic_name__ : Tuple ):
"""simple docstring"""
UpperCamelCase = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_A )
def lowerCamelCase_ ( self : Any , __magic_name__ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = TFAutoModel.from_pretrained(self.test_model , from_pt=_A )
model_tf.save_pretrained(_A )
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = """mock_framework"""
# Framework provided - return whatever the user provides
UpperCamelCase = FeaturesManager.determine_framework(self.test_model , _A )
self.assertEqual(_A , _A )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_A )
UpperCamelCase = FeaturesManager.determine_framework(_A , _A )
self.assertEqual(_A , _A )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_A )
UpperCamelCase = FeaturesManager.determine_framework(_A , _A )
self.assertEqual(_A , _A )
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_A )
UpperCamelCase = FeaturesManager.determine_framework(_A )
self.assertEqual(_A , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_A )
UpperCamelCase = FeaturesManager.determine_framework(_A )
self.assertEqual(_A , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_A ):
UpperCamelCase = FeaturesManager.determine_framework(_A )
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = MagicMock(return_value=_A )
with patch("""transformers.onnx.features.is_tf_available""" , _A ):
UpperCamelCase = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_A , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
UpperCamelCase = MagicMock(return_value=_A )
with patch("""transformers.onnx.features.is_torch_available""" , _A ):
UpperCamelCase = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_A , self.framework_tf )
# Both in environment -> use PyTorch
UpperCamelCase = MagicMock(return_value=_A )
UpperCamelCase = MagicMock(return_value=_A )
with patch("""transformers.onnx.features.is_tf_available""" , _A ), patch(
"""transformers.onnx.features.is_torch_available""" , _A ):
UpperCamelCase = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_A , self.framework_pt )
# Both not in environment -> raise error
UpperCamelCase = MagicMock(return_value=_A )
UpperCamelCase = MagicMock(return_value=_A )
with patch("""transformers.onnx.features.is_tf_available""" , _A ), patch(
"""transformers.onnx.features.is_torch_available""" , _A ):
with self.assertRaises(_A ):
UpperCamelCase = FeaturesManager.determine_framework(self.test_model )
| 705
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class UpperCAmelCase ( __snake_case , unittest.TestCase ):
lowercase = KandinskyInpaintPipeline
lowercase = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
lowercase = [
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
lowercase = [
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
lowercase = False
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
return 3_2
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return 3_2
@property
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
return self.time_input_dim
@property
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def lowerCamelCase_ ( self : str ):
"""simple docstring"""
return 1_0_0
@property
def lowerCamelCase_ ( self : Optional[Any] ):
"""simple docstring"""
UpperCamelCase = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = MCLIPConfig(
numDims=self.cross_attention_dim , transformerDimensions=self.text_embedder_hidden_size , hidden_size=self.text_embedder_hidden_size , intermediate_size=3_7 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=1_0_0_5 , )
UpperCamelCase = MultilingualCLIP(__magic_name__ )
UpperCamelCase = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = {
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
UpperCamelCase = UNetaDConditionModel(**__magic_name__ )
return model
@property
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
return {
"block_out_channels": [3_2, 6_4],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
UpperCamelCase = self.dummy_text_encoder
UpperCamelCase = self.dummy_tokenizer
UpperCamelCase = self.dummy_unet
UpperCamelCase = self.dummy_movq
UpperCamelCase = DDIMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule="""linear""" , beta_start=0.00_085 , beta_end=0.012 , clip_sample=__magic_name__ , set_alpha_to_one=__magic_name__ , steps_offset=1 , prediction_type="""epsilon""" , thresholding=__magic_name__ , )
UpperCamelCase = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCamelCase_ ( self : Tuple , __magic_name__ : Tuple , __magic_name__ : Optional[int]=0 ):
"""simple docstring"""
UpperCamelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
UpperCamelCase = floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1 ) ).to(__magic_name__ )
# create init_image
UpperCamelCase = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(__magic_name__ ) ).to(__magic_name__ )
UpperCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
UpperCamelCase = Image.fromarray(np.uinta(__magic_name__ ) ).convert("""RGB""" ).resize((2_5_6, 2_5_6) )
# create mask
UpperCamelCase = np.ones((6_4, 6_4) , dtype=np.floataa )
UpperCamelCase = 0
if str(__magic_name__ ).startswith("""mps""" ):
UpperCamelCase = torch.manual_seed(__magic_name__ )
else:
UpperCamelCase = torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
UpperCamelCase = {
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 6_4,
"""width""": 6_4,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = """cpu"""
UpperCamelCase = self.get_dummy_components()
UpperCamelCase = self.pipeline_class(**__magic_name__ )
UpperCamelCase = pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
UpperCamelCase = pipe(**self.get_dummy_inputs(__magic_name__ ) )
UpperCamelCase = output.images
UpperCamelCase = pipe(
**self.get_dummy_inputs(__magic_name__ ) , return_dict=__magic_name__ , )[0]
UpperCamelCase = image[0, -3:, -3:, -1]
UpperCamelCase = image_from_tuple[0, -3:, -3:, -1]
print(F'image.shape {image.shape}' )
assert image.shape == (1, 6_4, 6_4, 3)
UpperCamelCase = np.array(
[0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
UpperCamelCase = np.ones((7_6_8, 7_6_8) , dtype=np.floataa )
UpperCamelCase = 0
UpperCamelCase = """a hat"""
UpperCamelCase = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__magic_name__ )
UpperCamelCase = KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""" , torch_dtype=torch.floataa )
UpperCamelCase = pipeline.to(__magic_name__ )
pipeline.set_progress_bar_config(disable=__magic_name__ )
UpperCamelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
UpperCamelCase , UpperCamelCase = pipe_prior(
__magic_name__ , generator=__magic_name__ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
UpperCamelCase = pipeline(
__magic_name__ , image=__magic_name__ , mask_image=__magic_name__ , image_embeds=__magic_name__ , negative_image_embeds=__magic_name__ , generator=__magic_name__ , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , output_type="""np""" , )
UpperCamelCase = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(__magic_name__ , __magic_name__ )
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'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : Tuple = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class lowercase__ ( lowercase_ ):
lowercase__ = "xlm-roberta-xl"
def __init__( self : Tuple ,lowerCamelCase__ : Optional[int]=250880 ,lowerCamelCase__ : Tuple=2560 ,lowerCamelCase__ : Dict=36 ,lowerCamelCase__ : str=32 ,lowerCamelCase__ : Dict=10240 ,lowerCamelCase__ : Dict="gelu" ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : List[str]=514 ,lowerCamelCase__ : Tuple=1 ,lowerCamelCase__ : int=0.0_2 ,lowerCamelCase__ : Union[str, Any]=1E-05 ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=0 ,lowerCamelCase__ : str=2 ,lowerCamelCase__ : Tuple="absolute" ,lowerCamelCase__ : int=True ,lowerCamelCase__ : int=None ,**lowerCamelCase__ : List[str] ,):
'''simple docstring'''
super().__init__(pad_token_id=a__ ,bos_token_id=a__ ,eos_token_id=a__ ,**a__ )
_UpperCamelCase : List[str] = vocab_size
_UpperCamelCase : List[Any] = hidden_size
_UpperCamelCase : List[Any] = num_hidden_layers
_UpperCamelCase : int = num_attention_heads
_UpperCamelCase : List[str] = hidden_act
_UpperCamelCase : Optional[int] = intermediate_size
_UpperCamelCase : Tuple = hidden_dropout_prob
_UpperCamelCase : List[str] = attention_probs_dropout_prob
_UpperCamelCase : List[str] = max_position_embeddings
_UpperCamelCase : str = type_vocab_size
_UpperCamelCase : str = initializer_range
_UpperCamelCase : str = layer_norm_eps
_UpperCamelCase : Union[str, Any] = position_embedding_type
_UpperCamelCase : str = use_cache
_UpperCamelCase : List[Any] = classifier_dropout
class lowercase__ ( lowercase_ ):
@property
def UpperCamelCase_ ( self : Dict ):
'''simple docstring'''
if self.task == "multiple-choice":
_UpperCamelCase : str = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCamelCase : List[Any] = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
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'''simple docstring'''
def UpperCamelCase_( snake_case : Dict , snake_case : str , snake_case : Optional[int] , snake_case : Optional[Any] ):
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
snake_case_ = mf_knapsack(i - 1 , snake_case , snake_case , snake_case )
else:
snake_case_ = max(
mf_knapsack(i - 1 , snake_case , snake_case , snake_case ) , mf_knapsack(i - 1 , snake_case , snake_case , j - wt[i - 1] ) + val[i - 1] , )
snake_case_ = val
return f[i][j]
def UpperCamelCase_( snake_case : Dict , snake_case : Tuple , snake_case : Dict , snake_case : int ):
'''simple docstring'''
snake_case_ = [[0] * (w + 1) for _ in range(n + 1 )]
for i in range(1 , n + 1 ):
for w_ in range(1 , w + 1 ):
if wt[i - 1] <= w_:
snake_case_ = max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] , dp[i - 1][w_] )
else:
snake_case_ = dp[i - 1][w_]
return dp[n][w_], dp
def UpperCamelCase_( snake_case : int , snake_case : list , snake_case : list ):
'''simple docstring'''
if not (isinstance(snake_case , (list, tuple) ) and isinstance(snake_case , (list, tuple) )):
raise ValueError(
"Both the weights and values vectors must be either lists or tuples" )
snake_case_ = len(snake_case )
if num_items != len(snake_case ):
snake_case_ = (
"The number of weights must be the same as the number of values.\n"
f'But got {num_items} weights and {len(snake_case )} values'
)
raise ValueError(snake_case )
for i in range(snake_case ):
if not isinstance(wt[i] , snake_case ):
snake_case_ = (
"All weights must be integers but got weight of "
f'type {type(wt[i] )} at index {i}'
)
raise TypeError(snake_case )
snake_case_ , snake_case_ = knapsack(snake_case , snake_case , snake_case , snake_case )
snake_case_ = set()
_construct_solution(snake_case , snake_case , snake_case , snake_case , snake_case )
return optimal_val, example_optional_set
def UpperCamelCase_( snake_case : list , snake_case : list , snake_case : int , snake_case : int , snake_case : set ):
'''simple docstring'''
if i > 0 and j > 0:
if dp[i - 1][j] == dp[i][j]:
_construct_solution(snake_case , snake_case , i - 1 , snake_case , snake_case )
else:
optimal_set.add(snake_case )
_construct_solution(snake_case , snake_case , i - 1 , j - wt[i - 1] , snake_case )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Tuple = [3, 2, 4, 4]
_SCREAMING_SNAKE_CASE : int = [4, 3, 2, 3]
_SCREAMING_SNAKE_CASE : List[Any] = 4
_SCREAMING_SNAKE_CASE : List[Any] = 6
_SCREAMING_SNAKE_CASE : List[str] = [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[str] = knapsack(w, wt, val, n)
print(optimal_solution)
print(mf_knapsack(n, wt, val, w)) # switched the n and w
# testing the dynamic programming problem with example
# the optimal subset for the above example are items 3 and 4
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[Any] = knapsack_with_example_solution(w, wt, val)
assert optimal_solution == 8
assert optimal_subset == {3, 4}
print("optimal_value = ", optimal_solution)
print("An optimal subset corresponding to the optimal value", optimal_subset)
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|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ : Optional[Any] = {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Tuple = ['''ViTFeatureExtractor''']
lowerCamelCase_ : Union[str, Any] = ['''ViTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Optional[int] = [
'''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTForImageClassification''',
'''ViTForMaskedImageModeling''',
'''ViTModel''',
'''ViTPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Dict = [
'''TFViTForImageClassification''',
'''TFViTModel''',
'''TFViTPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Any = [
'''FlaxViTForImageClassification''',
'''FlaxViTModel''',
'''FlaxViTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
lowerCamelCase_ : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
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|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase_ : List[str] = TypeVar('''T''')
lowerCamelCase_ : Optional[int] = TypeVar('''U''')
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict , lowercase : T | None , lowercase : U | None ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ = key
UpperCamelCase__ = val
UpperCamelCase__ = None
UpperCamelCase__ = None
def __repr__( self : List[Any] ) -> str:
'''simple docstring'''
return (
f"Node: key: {self.key}, val: {self.val}, "
f"has next: {bool(self.next )}, has prev: {bool(self.prev )}"
)
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
'''simple docstring'''
UpperCamelCase__ = DoubleLinkedListNode(lowercase , lowercase )
UpperCamelCase__ = DoubleLinkedListNode(lowercase , lowercase )
UpperCamelCase__ , UpperCamelCase__ = self.rear, self.head
def __repr__( self : int ) -> str:
'''simple docstring'''
UpperCamelCase__ = ["""DoubleLinkedList"""]
UpperCamelCase__ = self.head
while node.next is not None:
rep.append(str(lowercase ) )
UpperCamelCase__ = node.next
rep.append(str(self.rear ) )
return ",\n ".join(lowercase )
def A ( self : str , lowercase : DoubleLinkedListNode[T, U] ) -> None:
'''simple docstring'''
UpperCamelCase__ = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
UpperCamelCase__ = node
UpperCamelCase__ = previous
UpperCamelCase__ = node
UpperCamelCase__ = self.rear
def A ( self : Any , lowercase : DoubleLinkedListNode[T, U] ) -> DoubleLinkedListNode[T, U] | None:
'''simple docstring'''
if node.prev is None or node.next is None:
return None
UpperCamelCase__ = node.next
UpperCamelCase__ = node.prev
UpperCamelCase__ = None
UpperCamelCase__ = None
return node
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
'''simple docstring'''
__a : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : int , lowercase : int ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = DoubleLinkedList()
UpperCamelCase__ = capacity
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = {}
def __repr__( self : Any ) -> str:
'''simple docstring'''
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__( self : Any , lowercase : T ) -> bool:
'''simple docstring'''
return key in self.cache
def A ( self : Tuple , lowercase : T ) -> U | None:
'''simple docstring'''
if key in self.cache:
self.hits += 1
UpperCamelCase__ = self.cache[key]
UpperCamelCase__ = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowercase )
return node.val
self.miss += 1
return None
def A ( self : Dict , lowercase : T , lowercase : U ) -> None:
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
UpperCamelCase__ = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowercase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
UpperCamelCase__ = DoubleLinkedListNode(lowercase , lowercase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
UpperCamelCase__ = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
UpperCamelCase__ = value
self.list.add(lowercase )
@classmethod
def A ( cls : Optional[int] , lowercase : int = 1_2_8 ) -> Callable[[Callable[[T], U]], Callable[..., U]]:
'''simple docstring'''
def cache_decorator_inner(lowercase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*lowercase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
UpperCamelCase__ = LRUCache(lowercase )
UpperCamelCase__ = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
UpperCamelCase__ = func(*lowercase )
cls.decorator_function_to_instance_map[func].put(args[0] , lowercase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowercase , """cache_info""" , lowercase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
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'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_lowercase = 10
def lowerCamelCase__ ( a , a , a , a ):
for i in range(_A , _A ):
if array[i] == target:
return i
return -1
def lowerCamelCase__ ( a , a ):
__snake_case = 0
__snake_case = len(_A )
while left <= right:
if right - left < precision:
return lin_search(_A , _A , _A , _A )
__snake_case = (left + right) // 3 + 1
__snake_case = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
__snake_case = one_third - 1
elif array[two_third] < target:
__snake_case = two_third + 1
else:
__snake_case = one_third + 1
__snake_case = two_third - 1
else:
return -1
def lowerCamelCase__ ( a , a , a , a ):
if left < right:
if right - left < precision:
return lin_search(_A , _A , _A , _A )
__snake_case = (left + right) // 3 + 1
__snake_case = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(_A , one_third - 1 , _A , _A )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , _A , _A , _A )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , _A , _A )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase = input("""Enter numbers separated by comma:\n""").strip()
_lowercase = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_lowercase = int(input("""Enter the number to be found in the list:\n""").strip())
_lowercase = ite_ternary_search(collection, target)
_lowercase = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'''Iterative search: {target} found at positions: {resulta}''')
print(f'''Recursive search: {target} found at positions: {resulta}''')
else:
print("""Not found""")
| 356
|
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
SCREAMING_SNAKE_CASE : Union[str, Any] = namedtuple(
"""_TestCommandArgs""",
[
"""dataset""",
"""name""",
"""cache_dir""",
"""data_dir""",
"""all_configs""",
"""save_infos""",
"""ignore_verifications""",
"""force_redownload""",
"""clear_cache""",
],
defaults=[None, None, None, False, False, False, False, False],
)
def __A ( _A , _A ):
"""simple docstring"""
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def __A ( _A ):
"""simple docstring"""
__a = _TestCommandArgs(dataset=_A , all_configs=_A , save_infos=_A )
__a = TestCommand(*_A )
test_command.run()
__a = os.path.join(_A , "README.md" )
assert os.path.exists(_A )
__a = DatasetInfosDict.from_directory(_A )
__a = DatasetInfosDict(
{
"default": DatasetInfo(
features=Features(
{
"tokens": Sequence(Value("string" ) ),
"ner_tags": Sequence(
ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"] ) ),
"langs": Sequence(Value("string" ) ),
"spans": Sequence(Value("string" ) ),
} ) , splits=[
{
"name": "train",
"num_bytes": 235_1563,
"num_examples": 1_0000,
},
{
"name": "validation",
"num_bytes": 23_8418,
"num_examples": 1000,
},
] , download_size=394_0680 , dataset_size=258_9981 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
__a , __a = getattr(dataset_infos["default"] , _A ), getattr(expected_dataset_infos["default"] , _A )
if key == "num_bytes":
assert is_apercent_close(_A , _A )
elif key == "splits":
assert list(_A ) == list(_A )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 197
| 0
|
"""simple docstring"""
from __future__ import annotations
__lowercase : List[str] = tuple[int, int, int]
__lowercase : List[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__lowercase : int = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# -------------------------- default selection --------------------------
# rotors --------------------------
__lowercase : int = """EGZWVONAHDCLFQMSIPJBYUKXTR"""
__lowercase : int = """FOBHMDKEXQNRAULPGSJVTYICZW"""
__lowercase : Optional[int] = """ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
__lowercase : Dict = {
"""A""": """N""",
"""N""": """A""",
"""B""": """O""",
"""O""": """B""",
"""C""": """P""",
"""P""": """C""",
"""D""": """Q""",
"""Q""": """D""",
"""E""": """R""",
"""R""": """E""",
"""F""": """S""",
"""S""": """F""",
"""G""": """T""",
"""T""": """G""",
"""H""": """U""",
"""U""": """H""",
"""I""": """V""",
"""V""": """I""",
"""J""": """W""",
"""W""": """J""",
"""K""": """X""",
"""X""": """K""",
"""L""": """Y""",
"""Y""": """L""",
"""M""": """Z""",
"""Z""": """M""",
}
# -------------------------- extra rotors --------------------------
__lowercase : Any = """RMDJXFUWGISLHVTCQNKYPBEZOA"""
__lowercase : List[str] = """SGLCPQWZHKXAREONTFBVIYJUDM"""
__lowercase : Any = """HVSICLTYKQUBXDWAJZOMFGPREN"""
__lowercase : List[str] = """RZWQHFMVDBKICJLNTUXAGYPSOE"""
__lowercase : List[str] = """LFKIJODBEGAMQPXVUHYSTCZRWN"""
__lowercase : Dict = """KOAEGVDHXPQZMLFTYWJNBRCIUS"""
def lowerCamelCase_ ( _lowerCamelCase : RotorPositionT , _lowerCamelCase : RotorSelectionT , _lowerCamelCase : str ):
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(_lowerCamelCase ) )) < 3:
lowerCamelCase_ = F"""Please use 3 unique rotors (not {unique_rotsel})"""
raise Exception(_lowerCamelCase )
# Checks if rotor positions are valid
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = rotpos
if not 0 < rotorposa <= len(_lowerCamelCase ):
lowerCamelCase_ = F"""First rotor position is not within range of 1..26 ({rotorposa}"""
raise ValueError(_lowerCamelCase )
if not 0 < rotorposa <= len(_lowerCamelCase ):
lowerCamelCase_ = F"""Second rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(_lowerCamelCase )
if not 0 < rotorposa <= len(_lowerCamelCase ):
lowerCamelCase_ = F"""Third rotor position is not within range of 1..26 ({rotorposa})"""
raise ValueError(_lowerCamelCase )
# Validates string and returns dict
lowerCamelCase_ = _plugboard(_lowerCamelCase )
return rotpos, rotsel, pbdict
def lowerCamelCase_ ( _lowerCamelCase : str ):
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
lowerCamelCase_ = F"""Plugboard setting isn't type string ({type(_lowerCamelCase )})"""
raise TypeError(_lowerCamelCase )
elif len(_lowerCamelCase ) % 2 != 0:
lowerCamelCase_ = F"""Odd number of symbols ({len(_lowerCamelCase )})"""
raise Exception(_lowerCamelCase )
elif pbstring == "":
return {}
pbstring.replace(''' ''' , '''''' )
# Checks if all characters are unique
lowerCamelCase_ = set()
for i in pbstring:
if i not in abc:
lowerCamelCase_ = F"""'{i}' not in list of symbols"""
raise Exception(_lowerCamelCase )
elif i in tmppbl:
lowerCamelCase_ = F"""Duplicate symbol ({i})"""
raise Exception(_lowerCamelCase )
else:
tmppbl.add(_lowerCamelCase )
del tmppbl
# Created the dictionary
lowerCamelCase_ = {}
for j in range(0 , len(_lowerCamelCase ) - 1 , 2 ):
lowerCamelCase_ = pbstring[j + 1]
lowerCamelCase_ = pbstring[j]
return pb
def lowerCamelCase_ ( _lowerCamelCase : str , _lowerCamelCase : RotorPositionT , _lowerCamelCase : RotorSelectionT = (rotora, rotora, rotora) , _lowerCamelCase : str = "" , ):
lowerCamelCase_ = text.upper()
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = _validator(
_lowerCamelCase , _lowerCamelCase , plugb.upper() )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = rotor_position
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
lowerCamelCase_ = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
lowerCamelCase_ = plugboard[symbol]
# rotor ra --------------------------
lowerCamelCase_ = abc.index(_lowerCamelCase ) + rotorposa
lowerCamelCase_ = rotora[index % len(_lowerCamelCase )]
# rotor rb --------------------------
lowerCamelCase_ = abc.index(_lowerCamelCase ) + rotorposa
lowerCamelCase_ = rotora[index % len(_lowerCamelCase )]
# rotor rc --------------------------
lowerCamelCase_ = abc.index(_lowerCamelCase ) + rotorposa
lowerCamelCase_ = rotora[index % len(_lowerCamelCase )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
lowerCamelCase_ = reflector[symbol]
# 2nd rotors
lowerCamelCase_ = abc[rotora.index(_lowerCamelCase ) - rotorposa]
lowerCamelCase_ = abc[rotora.index(_lowerCamelCase ) - rotorposa]
lowerCamelCase_ = abc[rotora.index(_lowerCamelCase ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
lowerCamelCase_ = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(_lowerCamelCase ):
lowerCamelCase_ = 0
rotorposa += 1
if rotorposa >= len(_lowerCamelCase ):
lowerCamelCase_ = 0
rotorposa += 1
if rotorposa >= len(_lowerCamelCase ):
lowerCamelCase_ = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(_lowerCamelCase )
return "".join(_lowerCamelCase )
if __name__ == "__main__":
__lowercase : Union[str, Any] = """This is my Python script that emulates the Enigma machine from WWII."""
__lowercase : Optional[Any] = (1, 1, 1)
__lowercase : Union[str, Any] = """pictures"""
__lowercase : str = (rotora, rotora, rotora)
__lowercase : Optional[int] = enigma(message, rotor_pos, rotor_sel, pb)
print("""Encrypted message:""", en)
print("""Decrypted message:""", enigma(en, rotor_pos, rotor_sel, pb))
| 710
|
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCAmelCase ( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = None ) -> List[Any]:
'''simple docstring'''
super().__init__()
lowerCamelCase_ = pad_token_id
lowerCamelCase_ = max_length
lowerCamelCase_ = vocab
lowerCamelCase_ = merges
lowerCamelCase_ = BytePairTokenizer(UpperCamelCase__ , UpperCamelCase__ , sequence_length=UpperCamelCase__ )
@classmethod
def _lowerCAmelCase ( cls , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = [''' '''.join(UpperCamelCase__ ) for m in tokenizer.bpe_ranks.keys()]
lowerCamelCase_ = tokenizer.get_vocab()
return cls(UpperCamelCase__ , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
@classmethod
def _lowerCAmelCase ( cls , UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ ) -> str:
'''simple docstring'''
lowerCamelCase_ = GPTaTokenizer.from_pretrained(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
return cls.from_tokenizer(UpperCamelCase__ , *UpperCamelCase__ , **UpperCamelCase__ )
@classmethod
def _lowerCAmelCase ( cls , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
return cls(**UpperCamelCase__ )
def _lowerCAmelCase ( self ) -> int:
'''simple docstring'''
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _lowerCAmelCase ( self , UpperCamelCase__ , UpperCamelCase__ = None ) -> Any:
'''simple docstring'''
lowerCamelCase_ = self.tf_tokenizer(UpperCamelCase__ )
lowerCamelCase_ = tf.ones_like(UpperCamelCase__ )
if self.pad_token_id is not None:
# pad the tokens up to max length
lowerCamelCase_ = max_length if max_length is not None else self.max_length
if max_length is not None:
lowerCamelCase_ , lowerCamelCase_ = pad_model_inputs(
UpperCamelCase__ , max_seq_length=UpperCamelCase__ , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 66
| 0
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCAmelCase__ :Tuple = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
def __init__( self : Optional[Any] , *A__ : Optional[Any] , **A__ : Optional[int] ):
"""simple docstring"""
warnings.warn(
"""The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use SegformerImageProcessor instead.""" , A__ , )
super().__init__(*A__ , **A__ )
| 150
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ :List[str] = logging.get_logger(__name__)
UpperCAmelCase__ :Union[str, Any] = {
"""BAAI/AltCLIP""": """https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json""",
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : str = 'altclip_text_model'
def __init__( self : List[Any] , A__ : Optional[int]=250002 , A__ : Any=1024 , A__ : List[Any]=24 , A__ : Dict=16 , A__ : Union[str, Any]=4096 , A__ : Union[str, Any]="gelu" , A__ : str=0.1 , A__ : int=0.1 , A__ : str=514 , A__ : Optional[int]=1 , A__ : Optional[Any]=0.02 , A__ : int=0.02 , A__ : Optional[Any]=1e-0_5 , A__ : int=1 , A__ : Optional[Any]=0 , A__ : Dict=2 , A__ : Optional[int]="absolute" , A__ : Optional[int]=True , A__ : List[str]=768 , **A__ : List[str] , ):
"""simple docstring"""
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
__lowerCamelCase : List[str] = vocab_size
__lowerCamelCase : Optional[Any] = hidden_size
__lowerCamelCase : List[str] = num_hidden_layers
__lowerCamelCase : Tuple = num_attention_heads
__lowerCamelCase : List[Any] = hidden_act
__lowerCamelCase : Union[str, Any] = intermediate_size
__lowerCamelCase : Dict = hidden_dropout_prob
__lowerCamelCase : Any = attention_probs_dropout_prob
__lowerCamelCase : List[str] = max_position_embeddings
__lowerCamelCase : Optional[Any] = type_vocab_size
__lowerCamelCase : int = initializer_range
__lowerCamelCase : Optional[int] = initializer_factor
__lowerCamelCase : List[Any] = layer_norm_eps
__lowerCamelCase : List[str] = position_embedding_type
__lowerCamelCase : str = use_cache
__lowerCamelCase : Optional[Any] = project_dim
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : List[Any] = 'altclip_vision_model'
def __init__( self : Optional[int] , A__ : str=768 , A__ : str=3072 , A__ : str=512 , A__ : Optional[int]=12 , A__ : List[Any]=12 , A__ : Union[str, Any]=3 , A__ : Dict=224 , A__ : List[Any]=32 , A__ : List[Any]="quick_gelu" , A__ : Dict=1e-5 , A__ : List[str]=0.0 , A__ : Dict=0.02 , A__ : List[str]=1.0 , **A__ : Union[str, Any] , ):
"""simple docstring"""
super().__init__(**A__ )
__lowerCamelCase : Optional[int] = hidden_size
__lowerCamelCase : Optional[int] = intermediate_size
__lowerCamelCase : Optional[Any] = projection_dim
__lowerCamelCase : Union[str, Any] = num_hidden_layers
__lowerCamelCase : Optional[Any] = num_attention_heads
__lowerCamelCase : str = num_channels
__lowerCamelCase : Any = patch_size
__lowerCamelCase : Any = image_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : List[str] = initializer_factor
__lowerCamelCase : List[str] = attention_dropout
__lowerCamelCase : Any = layer_norm_eps
__lowerCamelCase : Any = hidden_act
@classmethod
def a_ ( cls : str , A__ : Union[str, os.PathLike] , **A__ : List[str] ):
"""simple docstring"""
cls._set_token_in_kwargs(A__ )
__lowerCamelCase , __lowerCamelCase : str = cls.get_config_dict(A__ , **A__ )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("""model_type""" ) == "altclip":
__lowerCamelCase : Optional[Any] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(A__ , **A__ )
class SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
snake_case__ : int = 'altclip'
snake_case__ : Dict = True
def __init__( self : Optional[Any] , A__ : Optional[Any]=None , A__ : Union[str, Any]=None , A__ : Union[str, Any]=768 , A__ : Tuple=2.6592 , **A__ : List[Any] ):
"""simple docstring"""
__lowerCamelCase : str = kwargs.pop("""text_config_dict""" , A__ )
__lowerCamelCase : Dict = kwargs.pop("""vision_config_dict""" , A__ )
super().__init__(**A__ )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
__lowerCamelCase : Any = {}
# This is the complete result when using `text_config_dict`.
__lowerCamelCase : Tuple = AltCLIPTextConfig(**A__ ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
__lowerCamelCase : Optional[Any] = (
f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
f"The value `text_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
__lowerCamelCase : int = (
f"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
f"value `text_config[\"{key}\"]` will be overriden."
)
logger.warning(A__ )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
__lowerCamelCase : Dict = {}
# This is the complete result when using `vision_config_dict`.
__lowerCamelCase : List[str] = AltCLIPVisionConfig(**A__ ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
__lowerCamelCase : str = {
str(A__ ): value for key, value in _vision_config_dict["""id2label"""].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
__lowerCamelCase : List[Any] = (
f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
f"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
__lowerCamelCase : Optional[Any] = (
f"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
f"The value `vision_config[\"{key}\"]` will be overriden."
)
logger.warning(A__ )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
__lowerCamelCase : List[Any] = {}
logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.""" )
if vision_config is None:
__lowerCamelCase : List[str] = {}
logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.""" )
__lowerCamelCase : Union[str, Any] = AltCLIPTextConfig(**A__ )
__lowerCamelCase : Optional[int] = AltCLIPVisionConfig(**A__ )
__lowerCamelCase : Optional[Any] = projection_dim
__lowerCamelCase : List[str] = logit_scale_init_value
__lowerCamelCase : Union[str, Any] = 1.0
@classmethod
def a_ ( cls : Optional[Any] , A__ : AltCLIPTextConfig , A__ : AltCLIPVisionConfig , **A__ : List[str] ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **A__ )
def a_ ( self : Optional[int] ):
"""simple docstring"""
__lowerCamelCase : List[Any] = copy.deepcopy(self.__dict__ )
__lowerCamelCase : Optional[Any] = self.text_config.to_dict()
__lowerCamelCase : Tuple = self.vision_config.to_dict()
__lowerCamelCase : Tuple = self.__class__.model_type
return output
| 150
| 1
|
"""simple docstring"""
from PIL import Image
def snake_case ( UpperCamelCase__ : Image , UpperCamelCase__ : float ) -> Image:
def brightness(UpperCamelCase__ : int ) -> float:
return 128 + level + (c - 128)
if not -2_5_5.0 <= level <= 2_5_5.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(UpperCamelCase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__lowerCamelCase :Optional[Any] = change_brightness(img, 100)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 42
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
__lowerCamelCase :Any = False
@skip_mps
class A__ ( __lowercase , __lowercase , __lowercase , unittest.TestCase):
"""simple docstring"""
snake_case__ : Optional[Any] =StableDiffusionAttendAndExcitePipeline
snake_case__ : Any =False
snake_case__ : Dict =TEXT_TO_IMAGE_PARAMS
snake_case__ : Any =TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''})
snake_case__ : Dict =TEXT_TO_IMAGE_IMAGE_PARAMS
snake_case__ : str =TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def a__ ( cls: Dict )-> Tuple:
super().setUpClass()
torch.use_deterministic_algorithms(__a )
@classmethod
def a__ ( cls: Union[str, Any] )-> Any:
super().tearDownClass()
torch.use_deterministic_algorithms(__a )
def a__ ( self: Tuple )-> Union[str, Any]:
torch.manual_seed(0 )
lowerCamelCase : str = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__a , )
lowerCamelCase : Union[str, Any] = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , clip_sample=__a , set_alpha_to_one=__a , )
torch.manual_seed(0 )
lowerCamelCase : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
lowerCamelCase : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act="""gelu""" , projection_dim=512 , )
lowerCamelCase : Optional[int] = CLIPTextModel(__a )
lowerCamelCase : str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCamelCase : List[str] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def a__ ( self: Tuple , __a: int , __a: Union[str, Any]=0 )-> Optional[Any]:
if str(__a ).startswith("""mps""" ):
lowerCamelCase : Tuple = torch.manual_seed(__a )
else:
lowerCamelCase : str = torch.Generator(device=__a ).manual_seed(__a )
lowerCamelCase : Dict = {
"""prompt""": """a cat and a frog""",
"""token_indices""": [2, 5],
"""generator""": generator,
"""num_inference_steps""": 1,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""max_iter_to_alter""": 2,
"""thresholds""": {0: 0.7},
}
return inputs
def a__ ( self: Dict )-> str:
lowerCamelCase : Tuple = """cpu"""
lowerCamelCase : List[str] = self.get_dummy_components()
lowerCamelCase : List[Any] = self.pipeline_class(**__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
lowerCamelCase : Any = self.get_dummy_inputs(__a )
lowerCamelCase : Union[str, Any] = pipe(**__a ).images
lowerCamelCase : Tuple = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
lowerCamelCase : Optional[Any] = np.array(
[0.63_90_53_64, 0.62_89_73_07, 0.48_59_90_17, 0.5_13_36_24, 0.5_55_00_48, 0.45_76_95_16, 0.50_32_69_73, 0.5_02_31_39, 0.45_38_44_96] )
lowerCamelCase : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__a , 1e-3 )
def a__ ( self: int )-> Optional[Any]:
super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )
def a__ ( self: Union[str, Any] )-> Optional[int]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def a__ ( self: Tuple )-> int:
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7e-4 )
def a__ ( self: Dict )-> List[Any]:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def a__ ( self: Optional[int] )-> Dict:
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )
def a__ ( self: Any )-> Tuple:
super().test_save_load_local(expected_max_difference=5e-4 )
def a__ ( self: str )-> str:
super().test_save_load_optional_components(expected_max_difference=4e-4 )
@require_torch_gpu
@slow
class A__ ( unittest.TestCase):
"""simple docstring"""
@classmethod
def a__ ( cls: Any )-> Tuple:
super().setUpClass()
torch.use_deterministic_algorithms(__a )
@classmethod
def a__ ( cls: Dict )-> Optional[int]:
super().tearDownClass()
torch.use_deterministic_algorithms(__a )
def a__ ( self: int )-> Optional[int]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a__ ( self: int )-> Optional[Any]:
lowerCamelCase : List[Any] = torch.manual_seed(51 )
lowerCamelCase : List[str] = StableDiffusionAttendAndExcitePipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , safety_checker=__a , torch_dtype=torch.floataa )
pipe.to("""cuda""" )
lowerCamelCase : Dict = """a painting of an elephant with glasses"""
lowerCamelCase : Any = [5, 7]
lowerCamelCase : Tuple = pipe(
prompt=__a , token_indices=__a , guidance_scale=7.5 , generator=__a , num_inference_steps=5 , max_iter_to_alter=5 , output_type="""numpy""" , ).images[0]
lowerCamelCase : Union[str, Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy""" )
assert np.abs((expected_image - image).max() ) < 5e-1
| 42
| 1
|
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
A_ = [0, 25, 50]
A_ = [25, 50, 75]
A_ = fuzz.membership.trimf(X, abca)
A_ = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
A_ = np.ones(75)
A_ = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
A_ = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
A_ = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
A_ = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
A_ = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
A_ = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
A_ = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
A_ = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
A_ = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("Young")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("Middle aged")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("union")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("intersection")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("complement_a")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("difference a/b")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("alg_sum")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("alg_product")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("bdd_sum")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("bdd_difference")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 42
|
import math
import sys
def lowerCamelCase_ ( lowerCAmelCase__ : str ) -> str:
'''simple docstring'''
A = ''
try:
with open(lowerCAmelCase__ , 'rb' ) as binary_file:
A = binary_file.read()
for dat in data:
A = F'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('File not accessible' )
sys.exit()
def lowerCamelCase_ ( lowerCAmelCase__ : str ) -> str:
'''simple docstring'''
A = {'0': '0', '1': '1'}
A , A = '', ''
A = len(lowerCAmelCase__ )
for i in range(len(lowerCAmelCase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
A = lexicon[curr_string]
result += last_match_id
A = last_match_id + '0'
if math.loga(lowerCAmelCase__ ).is_integer():
A = {}
for curr_key in list(lowerCAmelCase__ ):
A = lexicon.pop(lowerCAmelCase__ )
A = new_lex
A = last_match_id + '1'
index += 1
A = ''
return result
def lowerCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
A = 8
try:
with open(lowerCAmelCase__ , 'wb' ) as opened_file:
A = [
to_write[i : i + byte_length]
for i in range(0 , len(lowerCAmelCase__ ) , lowerCAmelCase__ )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('10000000' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array[:-1]:
opened_file.write(int(lowerCAmelCase__ , 2 ).to_bytes(1 , byteorder='big' ) )
except OSError:
print('File not accessible' )
sys.exit()
def lowerCamelCase_ ( lowerCAmelCase__ : str ) -> str:
'''simple docstring'''
A = 0
for letter in data_bits:
if letter == "1":
break
counter += 1
A = data_bits[counter:]
A = data_bits[counter + 1 :]
return data_bits
def lowerCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : str ) -> None:
'''simple docstring'''
A = read_file_binary(lowerCAmelCase__ )
A = remove_prefix(lowerCAmelCase__ )
A = decompress_data(lowerCAmelCase__ )
write_file_binary(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 106
| 0
|
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowerCAmelCase : List[str] = {
"""169M""": 12,
"""430M""": 24,
"""1B5""": 24,
"""3B""": 32,
"""7B""": 32,
"""14B""": 40,
}
lowerCAmelCase : Optional[Any] = {
"""169M""": 768,
"""430M""": 1024,
"""1B5""": 2048,
"""3B""": 2560,
"""7B""": 4096,
"""14B""": 5120,
}
def a__ ( snake_case__ ) -> Tuple:
lowerCamelCase = list(state_dict.keys() )
for name in state_dict_keys:
lowerCamelCase = state_dict.pop(snake_case__ )
# emb -> embedding
if name.startswith("""emb.""" ):
lowerCamelCase = name.replace("""emb.""" , """embeddings.""" )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith("""blocks.0.ln0""" ):
lowerCamelCase = name.replace("""blocks.0.ln0""" , """blocks.0.pre_ln""" )
# att -> attention
lowerCamelCase = re.sub(R"""blocks\.(\d+)\.att""" , R"""blocks.\1.attention""" , snake_case__ )
# ffn -> feed_forward
lowerCamelCase = re.sub(R"""blocks\.(\d+)\.ffn""" , R"""blocks.\1.feed_forward""" , snake_case__ )
# time_mix_k -> time_mix_key and reshape
if name.endswith(""".time_mix_k""" ):
lowerCamelCase = name.replace(""".time_mix_k""" , """.time_mix_key""" )
# time_mix_v -> time_mix_value and reshape
if name.endswith(""".time_mix_v""" ):
lowerCamelCase = name.replace(""".time_mix_v""" , """.time_mix_value""" )
# time_mix_r -> time_mix_key and reshape
if name.endswith(""".time_mix_r""" ):
lowerCamelCase = name.replace(""".time_mix_r""" , """.time_mix_receptance""" )
if name != "head.weight":
lowerCamelCase = """rwkv.""" + name
lowerCamelCase = weight
return state_dict
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=None , snake_case__=None , snake_case__=False , snake_case__=None ) -> int:
# 1. If possible, build the tokenizer.
if tokenizer_file is None:
print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
lowerCamelCase = 5_02_77
lowerCamelCase = AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
else:
lowerCamelCase = PreTrainedTokenizerFast(tokenizer_file=snake_case__ )
lowerCamelCase = len(snake_case__ )
tokenizer.save_pretrained(snake_case__ )
# 2. Build the config
lowerCamelCase = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
lowerCamelCase = candidate
break
if size is None:
raise ValueError("""Could not infer the size, please provide it with the `--size` argument.""" )
if size not in possible_sizes:
raise ValueError(F'`size` should be one of {possible_sizes}, got {size}.' )
lowerCamelCase = RwkvConfig(
vocab_size=snake_case__ , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(snake_case__ )
# 3. Download model file then convert state_dict
lowerCamelCase = hf_hub_download(snake_case__ , snake_case__ )
lowerCamelCase = torch.load(snake_case__ , map_location="""cpu""" )
lowerCamelCase = convert_state_dict(snake_case__ )
# 4. Split in shards and save
lowerCamelCase , lowerCamelCase = shard_checkpoint(snake_case__ )
for shard_file, shard in shards.items():
torch.save(snake_case__ , os.path.join(snake_case__ , snake_case__ ) )
if index is not None:
lowerCamelCase = os.path.join(snake_case__ , snake_case__ )
# Save the index as well
with open(snake_case__ , """w""" , encoding="""utf-8""" ) as f:
lowerCamelCase = json.dumps(snake_case__ , indent=2 , sort_keys=snake_case__ ) + """\n"""
f.write(snake_case__ )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
"""Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model.""" )
lowerCamelCase = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
lowerCamelCase = torch.load(os.path.join(snake_case__ , snake_case__ ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(snake_case__ , snake_case__ ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError("""Please provide a `model_name` to push the model to the Hub.""" )
lowerCamelCase = AutoModelForCausalLM.from_pretrained(snake_case__ )
model.push_to_hub(snake_case__ , max_shard_size="""2GB""" )
tokenizer.push_to_hub(snake_case__ )
if __name__ == "__main__":
lowerCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
)
parser.add_argument(
"""--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
)
parser.add_argument(
"""--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
)
parser.add_argument(
"""--tokenizer_file""",
default=None,
type=str,
help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
)
parser.add_argument(
"""--size""",
default=None,
type=str,
help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Push to the Hub the converted model.""",
)
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help="""Name of the pushed model on the Hub, including the username / organization.""",
)
lowerCAmelCase : Optional[Any] = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 710
|
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if n_term == "":
return []
lowerCamelCase = []
for temp in range(int(snake_case__ ) ):
series.append(F'1/{temp + 1}' if series else """1""" )
return series
if __name__ == "__main__":
lowerCAmelCase : Optional[int] = input("""Enter the last number (nth term) of the Harmonic Series""")
print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
print(harmonic_series(nth_term))
| 533
| 0
|
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCAmelCase__ :
def __init__( self , UpperCamelCase__ , UpperCamelCase__=3 , UpperCamelCase__=32 , UpperCamelCase__=3 , UpperCamelCase__=10 , UpperCamelCase__=[10, 20, 30, 40] , UpperCamelCase__=[1, 1, 2, 1] , UpperCamelCase__=True , UpperCamelCase__=True , UpperCamelCase__="relu" , UpperCamelCase__=3 , UpperCamelCase__=None , ):
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = embeddings_size
A__ = hidden_sizes
A__ = depths
A__ = is_training
A__ = use_labels
A__ = hidden_act
A__ = num_labels
A__ = scope
A__ = len(UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.num_labels )
A__ = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self ):
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
A__ = TFRegNetModel(config=UpperCamelCase__ )
A__ = model(UpperCamelCase__ , training=UpperCamelCase__ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowercase_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
A__ = self.num_labels
A__ = TFRegNetForImageClassification(UpperCamelCase__ )
A__ = model(UpperCamelCase__ , labels=UpperCamelCase__ , training=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class lowerCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
lowercase__ : str = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
lowercase__ : List[str] = (
{"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
lowercase__ : List[str] = False
lowercase__ : List[str] = False
lowercase__ : Tuple = False
lowercase__ : Any = False
lowercase__ : Tuple = False
def lowercase_ ( self ):
'''simple docstring'''
A__ = TFRegNetModelTester(self )
A__ = ConfigTester(self , config_class=UpperCamelCase__ , has_text_modality=UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def lowercase_ ( self ):
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def lowercase_ ( self ):
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def lowercase_ ( self ):
'''simple docstring'''
pass
def lowercase_ ( self ):
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase__ )
A__ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
def check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
A__ = model_class(UpperCamelCase__ )
A__ = model(**self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ ) , training=UpperCamelCase__ )
A__ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
A__ = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase__ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
A__ = layer_type
A__ = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
check_hidden_states_output(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def lowercase_ ( self ):
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__={} ):
A__ = model(UpperCamelCase__ , return_dict=UpperCamelCase__ , **UpperCamelCase__ )
A__ = model(UpperCamelCase__ , return_dict=UpperCamelCase__ , **UpperCamelCase__ ).to_tuple()
def recursive_check(UpperCamelCase__ , UpperCamelCase__ ):
if isinstance(UpperCamelCase__ , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(UpperCamelCase__ , UpperCamelCase__ ):
recursive_check(UpperCamelCase__ , UpperCamelCase__ )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(UpperCamelCase__ , UpperCamelCase__ ) ) , msg=(
"Tuple and dict output are not equal. Difference:"
f""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(UpperCamelCase__ , UpperCamelCase__ )
for model_class in self.all_model_classes:
A__ = model_class(UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , {"output_hidden_states": True} )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
A__ = self._prepare_for_class(UpperCamelCase__ , UpperCamelCase__ , return_labels=UpperCamelCase__ )
check_equivalence(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , {"output_hidden_states": True} )
def lowercase_ ( self ):
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )
@slow
def lowercase_ ( self ):
'''simple docstring'''
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = TFRegNetModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
def __a ( ) -> int:
'''simple docstring'''
A__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class lowerCAmelCase__ ( unittest.TestCase ):
@cached_property
def lowercase_ ( self ):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowercase_ ( self ):
'''simple docstring'''
A__ = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=UpperCamelCase__ , return_tensors="tf" )
# forward pass
A__ = model(**UpperCamelCase__ , training=UpperCamelCase__ )
# verify the logits
A__ = tf.TensorShape((1, 10_00) )
self.assertEqual(outputs.logits.shape , UpperCamelCase__ )
A__ = tf.constant([-0.4180, -1.5051, -3.4836] )
tf.debugging.assert_near(outputs.logits[0, :3] , UpperCamelCase__ , atol=1e-4 )
| 337
|
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
def __a ( A=None , A=None ) -> int:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=A )
@dataclass
class lowerCAmelCase__ :
lowercase__ : List[str] = list_field(
default=[] , metadata={
"""help""": (
"""Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
""" of all available models"""
)
} , )
lowercase__ : List[int] = list_field(
default=[8] , metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""} )
lowercase__ : List[int] = list_field(
default=[8, 32, 1_28, 5_12] , metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""} , )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""} , )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""} , )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""} )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Use FP16 to accelerate inference."""} )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Benchmark training of model"""} )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Verbose memory tracing"""} )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""} , )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={
"""help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
} , )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Trace memory line by line"""} )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Save result to a CSV file"""} )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Save all print statements in a log file"""} )
lowercase__ : bool = field(default=UpperCAmelCase_ , metadata={"""help""": """Whether to print environment information"""} )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
""" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
""" for debugging / testing and on TPU."""
)
} , )
lowercase__ : str = field(
default=f'inference_time_{round(time() )}.csv' , metadata={"""help""": """CSV filename used if saving time results to csv."""} , )
lowercase__ : str = field(
default=f'inference_memory_{round(time() )}.csv' , metadata={"""help""": """CSV filename used if saving memory results to csv."""} , )
lowercase__ : str = field(
default=f'train_time_{round(time() )}.csv' , metadata={"""help""": """CSV filename used if saving time results to csv for training."""} , )
lowercase__ : str = field(
default=f'train_memory_{round(time() )}.csv' , metadata={"""help""": """CSV filename used if saving memory results to csv for training."""} , )
lowercase__ : str = field(
default=f'env_info_{round(time() )}.csv' , metadata={"""help""": """CSV filename used if saving environment information."""} , )
lowercase__ : str = field(
default=f'log_{round(time() )}.csv' , metadata={"""help""": """Log filename used if print statements are saved in log."""} , )
lowercase__ : int = field(default=3 , metadata={"""help""": """Times an experiment will be run."""} )
lowercase__ : bool = field(
default=UpperCAmelCase_ , metadata={
"""help""": (
"""Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
""" model weights."""
)
} , )
def lowercase_ ( self ):
'''simple docstring'''
warnings.warn(
f"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
" are deprecated in general and it is advised to use external Benchmarking libraries "
" to benchmark Transformer models." , UpperCamelCase__ , )
def lowercase_ ( self ):
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) , indent=2 )
@property
def lowercase_ ( self ):
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
"Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
" bert-base-cased` or `args.models = ['bert-base-cased']." )
return self.models
@property
def lowercase_ ( self ):
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("Multiprocessing is currently not possible on TPU." )
return False
else:
return True
| 337
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class lowerCamelCase ( lowercase__, lowercase__, lowercase__, unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase_ : Dict = StableUnCLIPImgaImgPipeline
lowerCAmelCase_ : List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS
lowerCAmelCase_ : int = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCAmelCase_ : Any = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCAmelCase_ : Optional[int] = frozenset([] )
def A__ ( self ):
UpperCAmelCase_ = 32
UpperCAmelCase_ = embedder_hidden_size
# image encoding components
UpperCAmelCase_ = CLIPImageProcessor(crop_size=32 , size=32 )
torch.manual_seed(0 )
UpperCAmelCase_ = CLIPVisionModelWithProjection(
CLIPVisionConfig(
hidden_size=lowerCAmelCase , projection_dim=lowerCAmelCase , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , ) )
# regular denoising components
torch.manual_seed(0 )
UpperCAmelCase_ = StableUnCLIPImageNormalizer(embedding_dim=lowerCAmelCase )
UpperCAmelCase_ = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
UpperCAmelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
UpperCAmelCase_ = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=lowerCAmelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
UpperCAmelCase_ = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=lowerCAmelCase , layers_per_block=1 , upcast_attention=lowerCAmelCase , use_linear_projection=lowerCAmelCase , )
torch.manual_seed(0 )
UpperCAmelCase_ = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.00085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=lowerCAmelCase , steps_offset=1 , )
torch.manual_seed(0 )
UpperCAmelCase_ = AutoencoderKL()
UpperCAmelCase_ = {
# image encoding components
"feature_extractor": feature_extractor,
"image_encoder": image_encoder.eval(),
# image noising components
"image_normalizer": image_normalizer.eval(),
"image_noising_scheduler": image_noising_scheduler,
# regular denoising components
"tokenizer": tokenizer,
"text_encoder": text_encoder.eval(),
"unet": unet.eval(),
"scheduler": scheduler,
"vae": vae.eval(),
}
return components
def A__ ( self , lowerCAmelCase , lowerCAmelCase=0 , lowerCAmelCase=True ):
if str(lowerCAmelCase ).startswith("mps" ):
UpperCAmelCase_ = torch.manual_seed(lowerCAmelCase )
else:
UpperCAmelCase_ = torch.Generator(device=lowerCAmelCase ).manual_seed(lowerCAmelCase )
UpperCAmelCase_ = floats_tensor((1, 3, 32, 32) , rng=random.Random(lowerCAmelCase ) ).to(lowerCAmelCase )
if pil_image:
UpperCAmelCase_ = input_image * 0.5 + 0.5
UpperCAmelCase_ = input_image.clamp(0 , 1 )
UpperCAmelCase_ = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCAmelCase_ = DiffusionPipeline.numpy_to_pil(lowerCAmelCase )[0]
return {
"prompt": "An anime racoon running a marathon",
"image": input_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "np",
}
@skip_mps
def A__ ( self ):
UpperCAmelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
UpperCAmelCase_ = self.get_dummy_components()
UpperCAmelCase_ = StableUnCLIPImgaImgPipeline(**lowerCAmelCase )
UpperCAmelCase_ = sd_pipe.to(lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=lowerCAmelCase )
UpperCAmelCase_ = self.get_dummy_inputs(lowerCAmelCase )
inputs.update({"image_embeds": None} )
UpperCAmelCase_ = sd_pipe(**lowerCAmelCase ).images
UpperCAmelCase_ = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase_ = np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def A__ ( self ):
UpperCAmelCase_ = torch_device in ["cpu", "mps"]
self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase )
def A__ ( self ):
UpperCAmelCase_ = torch_device in ["cpu", "mps"]
self._test_inference_batch_single_identical(test_max_difference=lowerCAmelCase )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def A__ ( self ):
self._test_xformers_attention_forwardGenerator_pass(test_max_difference=lowerCAmelCase )
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ):
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
UpperCAmelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy" )
UpperCAmelCase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-l-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase_ = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCAmelCase_ = pipe(lowerCAmelCase , "anime turle" , generator=lowerCAmelCase , output_type="np" )
UpperCAmelCase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
def A__ ( self ):
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
UpperCAmelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy" )
UpperCAmelCase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase_ = torch.Generator(device="cpu" ).manual_seed(0 )
UpperCAmelCase_ = pipe(lowerCAmelCase , "anime turle" , generator=lowerCAmelCase , output_type="np" )
UpperCAmelCase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(lowerCAmelCase , lowerCAmelCase )
def A__ ( self ):
UpperCAmelCase_ = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png" )
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
UpperCAmelCase_ = StableUnCLIPImgaImgPipeline.from_pretrained(
"fusing/stable-unclip-2-1-h-img2img" , torch_dtype=torch.floataa )
UpperCAmelCase_ = pipe.to(lowerCAmelCase )
pipe.set_progress_bar_config(disable=lowerCAmelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
UpperCAmelCase_ = pipe(
lowerCAmelCase , "anime turtle" , num_inference_steps=2 , output_type="np" , )
UpperCAmelCase_ = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 704
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE = {
"vocab_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"
),
"distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt",
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json",
"distilbert-base-uncased-distilled-squad": (
"https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json",
"distilbert-base-cased-distilled-squad": (
"https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"
),
"distilbert-base-german-cased": (
"https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"
),
"distilbert-base-multilingual-cased": (
"https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE = {
"distilbert-base-uncased": 512,
"distilbert-base-uncased-distilled-squad": 512,
"distilbert-base-cased": 512,
"distilbert-base-cased-distilled-squad": 512,
"distilbert-base-german-cased": 512,
"distilbert-base-multilingual-cased": 512,
}
SCREAMING_SNAKE_CASE = {
"distilbert-base-uncased": {"do_lower_case": True},
"distilbert-base-uncased-distilled-squad": {"do_lower_case": True},
"distilbert-base-cased": {"do_lower_case": False},
"distilbert-base-cased-distilled-squad": {"do_lower_case": False},
"distilbert-base-german-cased": {"do_lower_case": False},
"distilbert-base-multilingual-cased": {"do_lower_case": False},
}
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
lowerCAmelCase_ : Any = VOCAB_FILES_NAMES
lowerCAmelCase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ : Union[str, Any] = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ : int = ['input_ids', 'attention_mask']
lowerCAmelCase_ : str = DistilBertTokenizer
def __init__( self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=True , lowerCAmelCase="[UNK]" , lowerCAmelCase="[SEP]" , lowerCAmelCase="[PAD]" , lowerCAmelCase="[CLS]" , lowerCAmelCase="[MASK]" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , ):
super().__init__(
lowerCAmelCase , tokenizer_file=lowerCAmelCase , do_lower_case=lowerCAmelCase , unk_token=lowerCAmelCase , sep_token=lowerCAmelCase , pad_token=lowerCAmelCase , cls_token=lowerCAmelCase , mask_token=lowerCAmelCase , tokenize_chinese_chars=lowerCAmelCase , strip_accents=lowerCAmelCase , **lowerCAmelCase , )
UpperCAmelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , lowerCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , lowerCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , lowerCAmelCase ) != tokenize_chinese_chars
):
UpperCAmelCase_ = getattr(lowerCAmelCase , normalizer_state.pop("type" ) )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = strip_accents
UpperCAmelCase_ = tokenize_chinese_chars
UpperCAmelCase_ = normalizer_class(**lowerCAmelCase )
UpperCAmelCase_ = do_lower_case
def A__ ( self , lowerCAmelCase , lowerCAmelCase=None ):
UpperCAmelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A__ ( self , lowerCAmelCase , lowerCAmelCase = None ):
UpperCAmelCase_ = [self.sep_token_id]
UpperCAmelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A__ ( self , lowerCAmelCase , lowerCAmelCase = None ):
UpperCAmelCase_ = self._tokenizer.model.save(lowerCAmelCase , name=lowerCAmelCase )
return tuple(lowerCAmelCase )
| 23
| 0
|
"""simple docstring"""
from math import sqrt
def lowerCAmelCase_ ( lowercase_ : str ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = 0
for i in range(1 , int(sqrt(__A ) + 1 ) ):
if n % i == 0 and i != sqrt(__A ):
total += i + n // i
elif i == sqrt(__A ):
total += i
return total - n
def lowerCAmelCase_ ( lowercase_ : Union[str, Any] = 1_0000 ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = sum(
i
for i in range(1 , __A )
if sum_of_divisors(sum_of_divisors(__A ) ) == i and sum_of_divisors(__A ) != i )
return total
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 674
|
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = ['''image_processor''', '''tokenizer''']
__SCREAMING_SNAKE_CASE = '''Pix2StructImageProcessor'''
__SCREAMING_SNAKE_CASE = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self , lowercase , lowercase ) -> Any:
__UpperCamelCase = False
super().__init__(lowercase , lowercase )
def __call__( self , lowercase=None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 2_0_4_8 , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None and not self.image_processor.is_vqa:
__UpperCamelCase = self.tokenizer
__UpperCamelCase = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
__UpperCamelCase = self.image_processor(
lowercase , return_tensors=lowercase , max_patches=lowercase , **lowercase )
else:
# add pixel_values and bbox
__UpperCamelCase = self.image_processor(
lowercase , return_tensors=lowercase , max_patches=lowercase , header_text=lowercase , **lowercase )
if text is not None and not self.image_processor.is_vqa:
__UpperCamelCase = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
if "attention_mask" in text_encoding:
__UpperCamelCase = text_encoding.pop("""attention_mask""" )
if "input_ids" in text_encoding:
__UpperCamelCase = text_encoding.pop("""input_ids""" )
else:
__UpperCamelCase = None
if text_encoding is not None:
encoding_image_processor.update(lowercase )
return encoding_image_processor
def __lowerCamelCase ( self , *lowercase , **lowercase ) -> Tuple:
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def __lowerCamelCase ( self , *lowercase , **lowercase ) -> List[str]:
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.tokenizer.model_input_names
__UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 601
| 0
|
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import platform
import sys
a = '3'
print('Python version:', sys.version)
print('OS platform:', platform.platform())
print('OS architecture:', platform.machine())
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
except ImportError:
print('Torch version:', None)
try:
import transformers
print('transformers version:', transformers.__version__)
except ImportError:
print('transformers version:', None)
| 708
|
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
a = False
a = logging.get_logger(__name__)
a = 'ybelkada/fonts'
def UpperCAmelCase_ ( ):
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
"""Pix2StructImageProcessor. Please upgrade torch.""" )
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
requires_backends(UpperCAmelCase__ , ["""torch"""] )
_check_torch_version()
lowercase_ = image_tensor.unsqueeze(0 )
lowercase_ = torch.nn.functional.unfold(UpperCAmelCase__ , (patch_height, patch_width) , stride=(patch_height, patch_width) )
lowercase_ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , UpperCAmelCase__ , UpperCAmelCase__ , -1 )
lowercase_ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ = 3_6 , UpperCAmelCase__ = "black" , UpperCAmelCase__ = "white" , UpperCAmelCase__ = 5 , UpperCAmelCase__ = 5 , UpperCAmelCase__ = 5 , UpperCAmelCase__ = 5 , UpperCAmelCase__ = None , UpperCAmelCase__ = None , ):
requires_backends(UpperCAmelCase__ , """vision""" )
# Add new lines so that each line is no more than 80 characters.
lowercase_ = textwrap.TextWrapper(width=8_0 )
lowercase_ = wrapper.wrap(text=UpperCAmelCase__ )
lowercase_ = """\n""".join(UpperCAmelCase__ )
if font_bytes is not None and font_path is None:
lowercase_ = io.BytesIO(UpperCAmelCase__ )
elif font_path is not None:
lowercase_ = font_path
else:
lowercase_ = hf_hub_download(UpperCAmelCase__ , """Arial.TTF""" )
lowercase_ = ImageFont.truetype(UpperCAmelCase__ , encoding="""UTF-8""" , size=UpperCAmelCase__ )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
lowercase_ = ImageDraw.Draw(Image.new("""RGB""" , (1, 1) , UpperCAmelCase__ ) )
lowercase_ , lowercase_ , lowercase_ , lowercase_ = temp_draw.textbbox((0, 0) , UpperCAmelCase__ , UpperCAmelCase__ )
# Create the actual image with a bit of padding around the text.
lowercase_ = text_width + left_padding + right_padding
lowercase_ = text_height + top_padding + bottom_padding
lowercase_ = Image.new("""RGB""" , (image_width, image_height) , UpperCAmelCase__ )
lowercase_ = ImageDraw.Draw(UpperCAmelCase__ )
draw.text(xy=(left_padding, top_padding) , text=UpperCAmelCase__ , fill=UpperCAmelCase__ , font=UpperCAmelCase__ )
return image
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ ):
requires_backends(UpperCAmelCase__ , """vision""" )
# Convert to PIL image if necessary
lowercase_ = to_pil_image(UpperCAmelCase__ )
lowercase_ = render_text(UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase_ = max(header_image.width , image.width )
lowercase_ = int(image.height * (new_width / image.width) )
lowercase_ = int(header_image.height * (new_width / header_image.width) )
lowercase_ = Image.new("""RGB""" , (new_width, new_height + new_header_height) , """white""" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
lowercase_ = to_numpy_array(UpperCAmelCase__ )
if infer_channel_dimension_format(UpperCAmelCase__ ) == ChannelDimension.LAST:
lowercase_ = to_channel_dimension_format(UpperCAmelCase__ , ChannelDimension.LAST )
return new_image
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : Tuple = ['flattened_patches']
def __init__( self : str , UpperCamelCase__ : bool = True , UpperCamelCase__ : bool = True , UpperCamelCase__ : Dict[str, int] = None , UpperCamelCase__ : int = 2_048 , UpperCamelCase__ : bool = False , **UpperCamelCase__ : Optional[int] , ):
'''simple docstring'''
super().__init__(**UpperCamelCase__ )
lowercase_ = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
lowercase_ = do_normalize
lowercase_ = do_convert_rgb
lowercase_ = max_patches
lowercase_ = is_vqa
def UpperCAmelCase__ ( self : Optional[Any] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : int , UpperCamelCase__ : dict , **UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
requires_backends(self.extract_flattened_patches , """torch""" )
_check_torch_version()
# convert to torch
lowercase_ = to_channel_dimension_format(UpperCamelCase__ , ChannelDimension.FIRST )
lowercase_ = torch.from_numpy(UpperCamelCase__ )
lowercase_ , lowercase_ = patch_size["""height"""], patch_size["""width"""]
lowercase_ , lowercase_ = get_image_size(UpperCamelCase__ )
# maximize scale s.t.
lowercase_ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
lowercase_ = max(min(math.floor(scale * image_height / patch_height ) , UpperCamelCase__ ) , 1 )
lowercase_ = max(min(math.floor(scale * image_width / patch_width ) , UpperCamelCase__ ) , 1 )
lowercase_ = max(num_feasible_rows * patch_height , 1 )
lowercase_ = max(num_feasible_cols * patch_width , 1 )
lowercase_ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="""bilinear""" , align_corners=UpperCamelCase__ , antialias=UpperCamelCase__ , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
lowercase_ = torch_extract_patches(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowercase_ = patches.shape
lowercase_ = patches_shape[1]
lowercase_ = patches_shape[2]
lowercase_ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
lowercase_ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
lowercase_ = torch.arange(UpperCamelCase__ ).reshape([rows, 1] ).repeat(1 , UpperCamelCase__ ).reshape([rows * columns, 1] )
lowercase_ = torch.arange(UpperCamelCase__ ).reshape([1, columns] ).repeat(UpperCamelCase__ , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
lowercase_ = row_ids.to(torch.floataa )
lowercase_ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
lowercase_ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
lowercase_ = torch.nn.functional.pad(UpperCamelCase__ , [0, 0, 0, max_patches - (rows * columns)] ).float()
lowercase_ = to_numpy_array(UpperCamelCase__ )
return result
def UpperCAmelCase__ ( self : List[Any] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : Dict ):
'''simple docstring'''
if image.dtype == np.uinta:
lowercase_ = image.astype(np.floataa )
# take mean across the whole `image`
lowercase_ = np.mean(UpperCamelCase__ )
lowercase_ = np.std(UpperCamelCase__ )
lowercase_ = max(UpperCamelCase__ , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ , **UpperCamelCase__ )
def UpperCAmelCase__ ( self : str , UpperCamelCase__ : ImageInput , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[Dict[str, int]] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase__ : Union[str, Any] , ):
'''simple docstring'''
lowercase_ = do_normalize if do_normalize is not None else self.do_normalize
lowercase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowercase_ = patch_size if patch_size is not None else self.patch_size
lowercase_ = max_patches if max_patches is not None else self.max_patches
lowercase_ = self.is_vqa
if kwargs.get("""data_format""" , UpperCamelCase__ ) is not None:
raise ValueError("""data_format is not an accepted input as the outputs are """ )
lowercase_ = make_list_of_images(UpperCamelCase__ )
if not valid_images(UpperCamelCase__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowercase_ = [convert_to_rgb(UpperCamelCase__ ) for image in images]
# All transformations expect numpy arrays.
lowercase_ = [to_numpy_array(UpperCamelCase__ ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("""A header text must be provided for VQA models.""" )
lowercase_ = kwargs.pop("""font_bytes""" , UpperCamelCase__ )
lowercase_ = kwargs.pop("""font_path""" , UpperCamelCase__ )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowercase_ = [header_text] * len(UpperCamelCase__ )
lowercase_ = [
render_header(UpperCamelCase__ , header_text[i] , font_bytes=UpperCamelCase__ , font_path=UpperCamelCase__ )
for i, image in enumerate(UpperCamelCase__ )
]
if do_normalize:
lowercase_ = [self.normalize(image=UpperCamelCase__ ) for image in images]
# convert to torch tensor and permute
lowercase_ = [
self.extract_flattened_patches(image=UpperCamelCase__ , max_patches=UpperCamelCase__ , patch_size=UpperCamelCase__ )
for image in images
]
# create attention mask in numpy
lowercase_ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
lowercase_ = BatchFeature(
data={"""flattened_patches""": images, """attention_mask""": attention_masks} , tensor_type=UpperCamelCase__ )
return encoded_outputs
| 650
| 0
|
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCamelCase_ (UpperCamelCase__ : List[str] , UpperCamelCase__ : List[str]=() , UpperCamelCase__ : List[str]=None , UpperCamelCase__ : Tuple="no" , UpperCamelCase__ : Any="29500" ):
_UpperCAmelCase : List[str] = False
_UpperCAmelCase : Union[str, Any] = False
if any(key.startswith('''KAGGLE''' ) for key in os.environ.keys() ):
_UpperCAmelCase : int = True
elif "IPython" in sys.modules:
_UpperCAmelCase : Optional[int] = '''google.colab''' in str(sys.modules['''IPython'''].get_ipython() )
try:
_UpperCAmelCase : Any = PrecisionType(mixed_precision.lower() )
except ValueError:
raise ValueError(
F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.' )
if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME''' , UpperCamelCase__ ) is not None):
# TPU launch
import torch_xla.distributed.xla_multiprocessing as xmp
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
'''your training function. Restart your notebook and make sure no cells initializes an '''
'''`Accelerator`.''' )
if num_processes is None:
_UpperCAmelCase : Any = 8
_UpperCAmelCase : Optional[int] = PrepareForLaunch(UpperCamelCase__ , distributed_type='''TPU''' )
print(F'Launching a training on {num_processes} TPU cores.' )
xmp.spawn(UpperCamelCase__ , args=UpperCamelCase__ , nprocs=UpperCamelCase__ , start_method='''fork''' )
elif in_colab:
# No need for a distributed launch otherwise as it's either CPU or one GPU.
if torch.cuda.is_available():
print('''Launching training on one GPU.''' )
else:
print('''Launching training on one CPU.''' )
function(*UpperCamelCase__ )
else:
if num_processes is None:
raise ValueError(
'''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''' )
if num_processes > 1:
# Multi-GPU launch
from torch.multiprocessing import start_processes
from torch.multiprocessing.spawn import ProcessRaisedException
if len(AcceleratorState._shared_state ) > 0:
raise ValueError(
'''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
'''inside your training function. Restart your notebook and make sure no cells initializes an '''
'''`Accelerator`.''' )
if torch.cuda.is_initialized():
raise ValueError(
'''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
'''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
'''function.''' )
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=UpperCamelCase__ , master_addr='''127.0.01''' , master_port=UpperCamelCase__ , mixed_precision=UpperCamelCase__ ):
_UpperCAmelCase : List[str] = PrepareForLaunch(UpperCamelCase__ , distributed_type='''MULTI_GPU''' )
print(F'Launching training on {num_processes} GPUs.' )
try:
start_processes(UpperCamelCase__ , args=UpperCamelCase__ , nprocs=UpperCamelCase__ , start_method='''fork''' )
except ProcessRaisedException as e:
if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
raise RuntimeError(
'''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
'''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
'''Please review your imports and test them when running the `notebook_launcher()` to identify '''
'''which one is problematic.''' ) from e
else:
# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
if is_mps_available():
_UpperCAmelCase : List[Any] = '''1'''
print('''Launching training on MPS.''' )
elif torch.cuda.is_available():
print('''Launching training on one GPU.''' )
else:
print('''Launching training on CPU.''' )
function(*UpperCamelCase__ )
def lowerCamelCase_ (UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int=() , UpperCamelCase__ : Tuple=2 ):
from torch.multiprocessing import start_processes
with tempfile.NamedTemporaryFile() as tmp_file:
# torch.distributed will expect a few environment variable to be here. We set the ones common to each
# process here (the other ones will be set be the launcher).
with patch_environment(
world_size=UpperCamelCase__ , master_addr='''127.0.01''' , master_port='''29500''' , accelerate_mixed_precision='''no''' , accelerate_debug_rdv_file=tmp_file.name , accelerate_use_cpu='''yes''' , ):
_UpperCAmelCase : Dict = PrepareForLaunch(UpperCamelCase__ , debug=UpperCamelCase__ )
start_processes(UpperCamelCase__ , args=UpperCamelCase__ , nprocs=UpperCamelCase__ , start_method='''fork''' )
| 506
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _UpperCAmelCase ( a ):
'''simple docstring'''
def __init__( self , A , A=1_3 , A=7 , A=True , A=True , A=False , A=True , A=9_9 , A=3_2 , A=5 , A=4 , A=3_7 , A="gelu" , A=0.1 , A=0.1 , A=5_1_2 , A=1_6 , A=2 , A=0.02 , A=3 , A=4 , A=None , ) -> Tuple:
_UpperCAmelCase : int = parent
_UpperCAmelCase : List[Any] = batch_size
_UpperCAmelCase : Union[str, Any] = seq_length
_UpperCAmelCase : Any = is_training
_UpperCAmelCase : str = use_input_mask
_UpperCAmelCase : Tuple = use_token_type_ids
_UpperCAmelCase : Tuple = use_labels
_UpperCAmelCase : Tuple = vocab_size
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : Tuple = hidden_dropout_prob
_UpperCAmelCase : Optional[Any] = attention_probs_dropout_prob
_UpperCAmelCase : List[Any] = max_position_embeddings
_UpperCAmelCase : Optional[Any] = type_vocab_size
_UpperCAmelCase : Dict = type_sequence_label_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : int = num_choices
_UpperCAmelCase : Any = scope
def __lowerCAmelCase ( self ) -> int:
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Dict = None
if self.use_input_mask:
_UpperCAmelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : str = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Any = None
if self.use_labels:
_UpperCAmelCase : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : Optional[int] = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCAmelCase ( self ) -> Dict:
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> int:
_UpperCAmelCase : List[str] = DistilBertModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : List[Any] = model(A , A )
_UpperCAmelCase : Tuple = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Tuple = DistilBertForMaskedLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[str]:
_UpperCAmelCase : Optional[Any] = DistilBertForQuestionAnswering(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : str = model(
A , attention_mask=A , start_positions=A , end_positions=A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> List[Any]:
_UpperCAmelCase : Optional[Any] = self.num_labels
_UpperCAmelCase : Dict = DistilBertForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Dict = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> int:
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : int = DistilBertForTokenClassification(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : int = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , A , A , A , A , A , A ) -> str:
_UpperCAmelCase : List[str] = self.num_choices
_UpperCAmelCase : Optional[int] = DistilBertForMultipleChoice(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_UpperCAmelCase : Optional[Any] = model(
A , attention_mask=A , labels=A , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
((_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase) , (_UpperCAmelCase)) : List[str] = config_and_inputs
_UpperCAmelCase : Tuple = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( a ,a ,unittest.TestCase ):
'''simple docstring'''
a__ =(
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
a__ =(
{
'''feature-extraction''': DistilBertModel,
'''fill-mask''': DistilBertForMaskedLM,
'''question-answering''': DistilBertForQuestionAnswering,
'''text-classification''': DistilBertForSequenceClassification,
'''token-classification''': DistilBertForTokenClassification,
'''zero-shot''': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
a__ =True
a__ =True
a__ =True
a__ =True
def __lowerCAmelCase ( self ) -> Tuple:
_UpperCAmelCase : Optional[Any] = DistilBertModelTester(self )
_UpperCAmelCase : Optional[int] = ConfigTester(self , config_class=A , dim=3_7 )
def __lowerCAmelCase ( self ) -> Tuple:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Optional[int]:
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*A )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*A )
def __lowerCAmelCase ( self ) -> Any:
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*A )
def __lowerCAmelCase ( self ) -> List[Any]:
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*A )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*A )
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*A )
@slow
def __lowerCAmelCase ( self ) -> str:
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[Any] = DistilBertModel.from_pretrained(A )
self.assertIsNotNone(A )
@slow
@require_torch_gpu
def __lowerCAmelCase ( self ) -> List[str]:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Dict = model_class(config=A )
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : List[Any] = torch.jit.trace(
A , (inputs_dict['''input_ids'''].to('''cpu''' ), inputs_dict['''attention_mask'''].to('''cpu''' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(A , os.path.join(A , '''traced_model.pt''' ) )
_UpperCAmelCase : Optional[Any] = torch.jit.load(os.path.join(A , '''traced_model.pt''' ) , map_location=A )
loaded(inputs_dict['''input_ids'''].to(A ) , inputs_dict['''attention_mask'''].to(A ) )
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCAmelCase ( self ) -> Dict:
_UpperCAmelCase : Optional[int] = DistilBertModel.from_pretrained('''distilbert-base-uncased''' )
_UpperCAmelCase : int = torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_UpperCAmelCase : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_UpperCAmelCase : Optional[Any] = model(A , attention_mask=A )[0]
_UpperCAmelCase : int = torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , A , atol=1E-4 ) )
| 506
| 1
|
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def A_ ( UpperCAmelCase__ ) -> Optional[int]:
a : Optional[int] = SwinConfig()
a : Dict = swin_name.split('_' )
a : Dict = name_split[1]
a : Tuple = int(name_split[4] )
a : Tuple = int(name_split[3][-1] )
if model_size == "tiny":
a : Any = 96
a : List[str] = (2, 2, 6, 2)
a : List[str] = (3, 6, 12, 24)
elif model_size == "small":
a : Any = 96
a : Tuple = (2, 2, 18, 2)
a : Dict = (3, 6, 12, 24)
elif model_size == "base":
a : Optional[Any] = 128
a : List[str] = (2, 2, 18, 2)
a : Tuple = (4, 8, 16, 32)
else:
a : List[str] = 192
a : Optional[Any] = (2, 2, 18, 2)
a : List[Any] = (6, 12, 24, 48)
if "in22k" in swin_name:
a : Union[str, Any] = 2_1841
else:
a : Optional[int] = 1000
a : int = 'huggingface/label-files'
a : Union[str, Any] = 'imagenet-1k-id2label.json'
a : List[Any] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
a : Optional[Any] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
a : Optional[Any] = idalabel
a : str = {v: k for k, v in idalabel.items()}
a : List[str] = img_size
a : List[Any] = num_classes
a : int = embed_dim
a : Tuple = depths
a : Dict = num_heads
a : List[Any] = window_size
return config
def A_ ( UpperCAmelCase__ ) -> List[str]:
if "patch_embed.proj" in name:
a : Optional[Any] = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
a : Any = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
a : Optional[Any] = 'encoder.' + name
if "attn.proj" in name:
a : Optional[int] = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
a : str = name.replace('attn' , 'attention.self' )
if "norm1" in name:
a : Optional[Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
a : Dict = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
a : str = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
a : Union[str, Any] = name.replace('mlp.fc2' , 'output.dense' )
if name == "norm.weight":
a : Tuple = 'layernorm.weight'
if name == "norm.bias":
a : Dict = 'layernorm.bias'
if "head" in name:
a : List[str] = name.replace('head' , 'classifier' )
else:
a : List[Any] = 'swin.' + name
return name
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ ) -> Any:
for key in orig_state_dict.copy().keys():
a : Any = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
a : List[str] = key.split('.' )
a : Dict = int(key_split[1] )
a : List[Any] = int(key_split[3] )
a : Any = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
a : List[str] = val[:dim, :]
a : Tuple = val[
dim : dim * 2, :
]
a : Union[str, Any] = val[-dim:, :]
else:
a : Tuple = val[
:dim
]
a : str = val[
dim : dim * 2
]
a : Any = val[
-dim:
]
else:
a : Dict = val
return orig_state_dict
def A_ ( UpperCAmelCase__ , UpperCAmelCase__ ) -> List[Any]:
a : Any = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
a : List[str] = get_swin_config(_SCREAMING_SNAKE_CASE )
a : List[Any] = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
a : Optional[int] = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
a : List[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
a : Dict = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' , '-' ) ) )
a : Union[str, Any] = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
a : str = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
a : List[str] = timm_model(inputs['pixel_values'] )
a : Optional[Any] = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-3 )
print(F'Saving model {swin_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE__ : Tuple = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 707
|
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
skip_first_batches,
)
class A_ ( _UpperCAmelCase ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase=0.01 , __UpperCAmelCase=10_00 ) -> int:
a : Dict = p_stop
a : Tuple = max_length
def __iter__( self ) -> str:
a : Optional[Any] = 0
a : Union[str, Any] = False
while not stop and count < self.max_length:
yield count
count += 1
a : Optional[int] = random.random() < self.p_stop
class A_ ( unittest.TestCase ):
"""simple docstring"""
def lowercase_ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=True ) -> List[str]:
a : Optional[Any] = [
BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
for i in range(2 )
]
a : str = [list(__UpperCAmelCase ) for batch_sampler_shard in batch_sampler_shards]
if not split_batches:
self.assertListEqual([len(__UpperCAmelCase ) for shard in batch_sampler_shards] , [len(__UpperCAmelCase ) for e in expected] )
self.assertListEqual(__UpperCAmelCase , __UpperCAmelCase )
def lowercase_ ( self ) -> List[str]:
# Check the shards when the dataset is a round multiple of total batch size.
a : Tuple = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
a : int = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
a : Dict = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
a : Union[str, Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Union[str, Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
a : Optional[int] = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Tuple = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
a : str = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
a : Optional[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : List[Any] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
a : List[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
# Check the shards when the dataset is very small.
a : Optional[Any] = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : List[str] = [[[0, 1, 0]], [[1, 0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
a : List[str] = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : int = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase )
def lowercase_ ( self ) -> Tuple:
# Check the shards when the dataset is a round multiple of batch size.
a : List[str] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
a : Union[str, Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
a : int = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : str = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
a : Any = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : int = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
a : List[str] = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Optional[Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
a : Any = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
a : Tuple = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : int = [[[0, 1]], [[0, 1]]]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
a : List[Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Union[str, Any] = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowercase_ ( self ) -> Optional[int]:
# Check the shards when the dataset is a round multiple of total batch size.
a : Union[str, Any] = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : str = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
a : Optional[int] = BatchSampler(range(24 ) , batch_size=3 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is a round multiple of batch size but not total batch size.
a : Optional[Any] = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
a : int = BatchSampler(range(21 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but has a multiple of
# num_processes batch.
a : Optional[int] = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
a : int = BatchSampler(range(22 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Any = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
# num_processes batch.
a : List[Any] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Optional[int] = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
a : List[str] = BatchSampler(range(20 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Dict = [
[[0, 1, 2], [6, 7, 8], [12, 13, 14]],
[[3, 4, 5], [9, 10, 11], [15, 16, 17]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
a : Union[str, Any] = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : str = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
a : Dict = BatchSampler(range(2 ) , batch_size=3 , drop_last=__UpperCAmelCase )
a : Dict = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowercase_ ( self ) -> List[str]:
# Check the shards when the dataset is a round multiple of batch size.
a : Union[str, Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Any = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
a : Optional[Any] = BatchSampler(range(24 ) , batch_size=4 , drop_last=__UpperCAmelCase )
# Expected shouldn't change
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size.
a : List[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Dict = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
a : Optional[Any] = BatchSampler(range(22 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : List[str] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is not a round multiple of batch size or num_processes.
a : List[str] = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : str = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
a : Optional[Any] = BatchSampler(range(21 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Union[str, Any] = [
[[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
[[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
# Check the shards when the dataset is very small.
a : Dict = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Dict = [[[0, 1]], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
a : Optional[Any] = BatchSampler(range(2 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Tuple = [[], []]
self.check_batch_sampler_shards(__UpperCAmelCase , __UpperCAmelCase , split_batches=__UpperCAmelCase , even_batches=__UpperCAmelCase )
def lowercase_ ( self ) -> List[Any]:
a : int = [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
a : Dict = [BatchSamplerShard(__UpperCAmelCase , 2 , __UpperCAmelCase , even_batches=__UpperCAmelCase ) for i in range(2 )]
self.assertEqual(len(batch_sampler_shards[0] ) , 3 )
self.assertEqual(len(batch_sampler_shards[1] ) , 2 )
self.assertListEqual(list(batch_sampler_shards[0] ) , [[0, 1, 2], [5, 6, 7, 8], [12, 13]] )
self.assertListEqual(list(batch_sampler_shards[1] ) , [[3, 4], [9, 10, 11]] )
def lowercase_ ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False , __UpperCAmelCase=2 , __UpperCAmelCase=False ) -> Tuple:
random.seed(__UpperCAmelCase )
a : Dict = list(__UpperCAmelCase )
a : Any = [
IterableDatasetShard(
__UpperCAmelCase , batch_size=__UpperCAmelCase , drop_last=__UpperCAmelCase , num_processes=__UpperCAmelCase , process_index=__UpperCAmelCase , split_batches=__UpperCAmelCase , )
for i in range(__UpperCAmelCase )
]
a : int = []
for iterable_dataset_shard in iterable_dataset_shards:
# Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
random.seed(__UpperCAmelCase )
iterable_dataset_lists.append(list(__UpperCAmelCase ) )
a : Dict = batch_size // num_processes if split_batches else batch_size
# All iterable dataset shard should have the same length, a round multiple of shard_batch_size
a : Optional[int] = iterable_dataset_lists[0]
for l in iterable_dataset_lists[1:]:
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
self.assertTrue(len(__UpperCAmelCase ) % shard_batch_size == 0 )
a : Optional[Any] = []
for idx in range(0 , len(__UpperCAmelCase ) , __UpperCAmelCase ):
for l in iterable_dataset_lists:
observed += l[idx : idx + shard_batch_size]
if not drop_last:
while len(__UpperCAmelCase ) < len(__UpperCAmelCase ):
reference += reference
self.assertListEqual(__UpperCAmelCase , reference[: len(__UpperCAmelCase )] )
def lowercase_ ( self ) -> int:
a : Any = 42
a : Union[str, Any] = RandomIterableDataset()
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
# Edge case with a very small dataset
a : Dict = RandomIterableDataset(max_length=2 )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
self.check_iterable_dataset_shards(__UpperCAmelCase , __UpperCAmelCase , batch_size=4 , drop_last=__UpperCAmelCase , split_batches=__UpperCAmelCase )
def lowercase_ ( self ) -> List[Any]:
a : str = BatchSampler(range(16 ) , batch_size=4 , drop_last=__UpperCAmelCase )
a : Any = SkipBatchSampler(__UpperCAmelCase , 2 )
self.assertListEqual(list(__UpperCAmelCase ) , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowercase_ ( self ) -> str:
a : Optional[Any] = SkipDataLoader(list(range(16 ) ) , batch_size=4 , skip_batches=2 )
self.assertListEqual([t.tolist() for t in dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowercase_ ( self ) -> int:
a : List[str] = DataLoader(list(range(16 ) ) , batch_size=4 )
a : Dict = skip_first_batches(__UpperCAmelCase , num_batches=2 )
self.assertListEqual([t.tolist() for t in new_dataloader] , [[8, 9, 10, 11], [12, 13, 14, 15]] )
def lowercase_ ( self ) -> Any:
a : Union[str, Any] = DataLoaderShard(list(range(16 ) ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
def lowercase_ ( self ) -> List[Any]:
Accelerator()
a : Union[str, Any] = DataLoaderDispatcher(range(16 ) , batch_size=4 )
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
# Test it also works on the second iteration
for idx, _ in enumerate(__UpperCAmelCase ):
self.assertEqual(dataloader.end_of_dataloader , idx == 3 )
| 509
| 0
|
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__a : Dict = logging.getLogger()
@unittest.skip("Temporarily disable the doc tests." )
@require_torch
@require_tf
@slow
class __lowercase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Optional[int] , UpperCamelCase_ : Path , UpperCamelCase_ : Union[str, None] = None , UpperCamelCase_ : Union[List[str], None] = None , UpperCamelCase_ : Union[str, List[str], None] = None , UpperCamelCase_ : bool = True , ):
"""simple docstring"""
__A = [file for file in os.listdir(UpperCamelCase_ ) if os.path.isfile(os.path.join(UpperCamelCase_ , UpperCamelCase_ ) )]
if identifier is not None:
__A = [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
for n_ in n_identifier:
__A = [file for file in files if n_ not in file]
else:
__A = [file for file in files if n_identifier not in file]
__A = ignore_files or []
ignore_files.append("""__init__.py""" )
__A = [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("""Testing""" , UpperCamelCase_ )
if only_modules:
__A = file.split(""".""" )[0]
try:
__A = getattr(UpperCamelCase_ , UpperCamelCase_ )
__A = doctest.DocTestSuite(UpperCamelCase_ )
__A = unittest.TextTestRunner().run(UpperCamelCase_ )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(F"{module_identifier} is not a module." )
else:
__A = doctest.testfile(str("""..""" / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def lowerCAmelCase_ ( self : Union[str, Any] ):
"""simple docstring"""
__A = Path("""src/transformers""" )
__A = """modeling"""
__A = [
"""modeling_ctrl.py""",
"""modeling_tf_ctrl.py""",
]
self.analyze_directory(UpperCamelCase_ , identifier=UpperCamelCase_ , ignore_files=UpperCamelCase_ )
def lowerCAmelCase_ ( self : Dict ):
"""simple docstring"""
__A = Path("""src/transformers""" )
__A = """tokenization"""
self.analyze_directory(UpperCamelCase_ , identifier=UpperCamelCase_ )
def lowerCAmelCase_ ( self : str ):
"""simple docstring"""
__A = Path("""src/transformers""" )
__A = """configuration"""
self.analyze_directory(UpperCamelCase_ , identifier=UpperCamelCase_ )
def lowerCAmelCase_ ( self : Any ):
"""simple docstring"""
__A = Path("""src/transformers""" )
__A = ["""configuration""", """modeling""", """tokenization"""]
self.analyze_directory(UpperCamelCase_ , n_identifier=UpperCamelCase_ )
def lowerCAmelCase_ ( self : int ):
"""simple docstring"""
__A = Path("""docs/source""" )
__A = ["""favicon.ico"""]
self.analyze_directory(UpperCamelCase_ , ignore_files=UpperCamelCase_ , only_modules=UpperCamelCase_ )
| 637
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Dict = logging.get_logger(__name__)
__a : Dict = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class __lowercase ( lowercase_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = "gpt_bigcode"
SCREAMING_SNAKE_CASE = ["past_key_values"]
SCREAMING_SNAKE_CASE = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : List[str] , UpperCamelCase_ : Dict=50_257 , UpperCamelCase_ : int=1_024 , UpperCamelCase_ : List[Any]=768 , UpperCamelCase_ : str=12 , UpperCamelCase_ : Union[str, Any]=12 , UpperCamelCase_ : Optional[Any]=None , UpperCamelCase_ : Tuple="gelu_pytorch_tanh" , UpperCamelCase_ : Optional[Any]=0.1 , UpperCamelCase_ : List[str]=0.1 , UpperCamelCase_ : Optional[int]=0.1 , UpperCamelCase_ : int=1e-5 , UpperCamelCase_ : Tuple=0.02 , UpperCamelCase_ : Any=True , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : Dict=50_256 , UpperCamelCase_ : Optional[Any]=50_256 , UpperCamelCase_ : int=True , UpperCamelCase_ : List[Any]=True , UpperCamelCase_ : List[Any]=True , **UpperCamelCase_ : int , ):
"""simple docstring"""
__A = vocab_size
__A = n_positions
__A = n_embd
__A = n_layer
__A = n_head
__A = n_inner
__A = activation_function
__A = resid_pdrop
__A = embd_pdrop
__A = attn_pdrop
__A = layer_norm_epsilon
__A = initializer_range
__A = scale_attn_weights
__A = use_cache
__A = attention_softmax_in_fpaa
__A = scale_attention_softmax_in_fpaa
__A = multi_query
__A = bos_token_id
__A = eos_token_id
super().__init__(bos_token_id=UpperCamelCase_ , eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
| 637
| 1
|
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def UpperCamelCase ( _A : Union[str, Any] )-> Optional[Any]:
"""simple docstring"""
def wrapper(*_A : Tuple , **_A : Dict ):
A__ = timeit.default_timer()
A__ = func(*_A , **_A )
A__ = timeit.default_timer() - starttime
return delta
A__ = func.__name__
return wrapper
def UpperCamelCase ( _A : dict , _A : str=100 , _A : Any=None )-> Union[str, Any]:
"""simple docstring"""
A__ = []
A__ = seq_shapes or {}
for i in range(_A ):
A__ = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(_A , _ArrayXD ):
A__ = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(_A , datasets.Value ):
if v.dtype == "string":
A__ = "The small grey turtle was surprisingly fast when challenged."
else:
A__ = np.random.randint(10 , size=1 ).astype(v.dtype ).item()
elif isinstance(_A , datasets.Sequence ):
while isinstance(_A , datasets.Sequence ):
A__ = v.feature
A__ = seq_shapes[k]
A__ = np.random.rand(*_A ).astype(v.dtype )
A__ = data
dummy_data.append((i, example) )
return dummy_data
def UpperCamelCase ( _A : Optional[Any] , _A : Optional[int] , _A : Tuple=100 , _A : str=None )-> int:
"""simple docstring"""
A__ = generate_examples(_A , num_examples=_A , seq_shapes=_A )
with ArrowWriter(features=_A , path=_A ) as writer:
for key, record in dummy_data:
A__ = features.encode_example(_A )
writer.write(_A )
A__ , A__ = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.""" )
A__ = datasets.Dataset.from_file(filename=_A , info=datasets.DatasetInfo(features=_A ) )
return dataset
| 232
|
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class UpperCamelCase ( _UpperCAmelCase , unittest.TestCase ):
lowerCAmelCase : List[Any] = FlaxAutoencoderKL
@property
def __A ( self ):
A__ = 4
A__ = 3
A__ = (32, 32)
A__ = jax.random.PRNGKey(0 )
A__ = jax.random.uniform(UpperCAmelCase__ , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def __A ( self ):
A__ = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
A__ = self.dummy_input
return init_dict, inputs_dict
| 232
| 1
|
def _lowercase ( a__ : int , a__ : int ) -> Dict:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
_UpperCamelCase = str(bin(snake_case__ ) )[2:] # remove the leading "0b"
_UpperCamelCase = str(bin(snake_case__ ) )[2:] # remove the leading "0b"
_UpperCamelCase = max(len(snake_case__ ) , len(snake_case__ ) )
return "0b" + "".join(
str(int(char_a == "1" and char_b == "1" ) )
for char_a, char_b in zip(a_binary.zfill(snake_case__ ) , b_binary.zfill(snake_case__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 147
|
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Optional[Any] = str(bin(snake_case__ ) )
binary_number += "0" * shift_amount
return binary_number
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
_snake_case : Any = str(bin(snake_case__ ) )[2:]
if shift_amount >= len(snake_case__ ):
return "0b0"
_snake_case : Tuple = binary_number[: len(snake_case__ ) - shift_amount]
return "0b" + shifted_binary_number
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
if number >= 0: # Get binary representation of positive number
_snake_case : Any = """0""" + str(bin(snake_case__ ) ).strip("""-""" )[2:]
else: # Get binary (2's complement) representation of negative number
_snake_case : Dict = len(bin(snake_case__ )[3:] ) # Find 2's complement of number
_snake_case : Tuple = bin(abs(snake_case__ ) - (1 << binary_number_length) )[3:]
_snake_case : Union[str, Any] = (
"""1""" + """0""" * (binary_number_length - len(snake_case__ )) + binary_number
)
if shift_amount >= len(snake_case__ ):
return "0b" + binary_number[0] * len(snake_case__ )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(snake_case__ ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 609
| 0
|
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging
logging.set_verbosity_info()
# should not include what is already done by the `from_pt` argument
_a: Optional[Any] = {
"""/attention/""": """/0/SelfAttention/""",
"""/self_attention/""": """/0/SelfAttention/""",
"""/encoder_decoder_attention/""": """/1/EncDecAttention/""",
"""value""": """v""",
"""query""": """q""",
"""key""": """k""",
"""out""": """o""",
"""pre_self_attention_layer_norm""": """0/layer_norm""",
"""pre_cross_attention_layer_norm""": """1/layer_norm""",
"""pre_attention_layer_norm""": """0/layer_norm""", # previously 1, but seems wrong
"""token_embedder""": """shared""",
"""encoder_norm""": """final_layer_norm""",
"""decoder_norm""": """final_layer_norm""",
"""relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
"""router/router_weights/w/""": """router/classifier/""",
"""roer/roer_weights/w/""": """router/classifier/""",
"""logits_dense""": """lm_head""",
}
def __lowerCAmelCase ( A ):
# 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
# the original model
UpperCAmelCase_ = list(s_dict.keys() )
for key in keys:
UpperCAmelCase_ = r".*/layers_(\d+)"
UpperCAmelCase_ = key
if re.match(A , A ):
UpperCAmelCase_ = re.sub(r"layers_(\d+)" , r"block/\1/layer" , A )
UpperCAmelCase_ = r"(encoder|decoder)\/"
if re.match(A , A ):
UpperCAmelCase_ = re.match(A , A ).groups()
if groups[0] == "encoder":
UpperCAmelCase_ = re.sub(r"/mlp/" , r"/1/mlp/" , A )
UpperCAmelCase_ = re.sub(r"/pre_mlp_layer_norm/" , r"/1/layer_norm/" , A )
elif groups[0] == "decoder":
UpperCAmelCase_ = re.sub(r"/mlp/" , r"/2/mlp/" , A )
UpperCAmelCase_ = re.sub(r"/pre_mlp_layer_norm/" , r"/2/layer_norm/" , A )
# 2. Convert other classic mappings
for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
if old_key in new_key:
UpperCAmelCase_ = new_key.replace(A , A )
print(F"{key} -> {new_key}" )
UpperCAmelCase_ = s_dict.pop(A )
if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase_ = s_dict[
"encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
UpperCAmelCase_ = s_dict[
"decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
].T
# 3. Take extra care of the EXPERTS layer
for key in list(s_dict.keys() ):
if "expert" in key:
UpperCAmelCase_ = s_dict[key].shape[0]
UpperCAmelCase_ = s_dict[key]
for idx in range(A ):
UpperCAmelCase_ = expert_weihts[idx]
print(F"{key} -> {key.replace('expert/' , 'nested fstring' )}" )
s_dict.pop(A )
return s_dict
_a: Any = {
"""NUM_ENCODER_LAYERS""": """num_layers""",
"""NUM_DECODER_LAYERS""": """num_decoder_layers""",
"""NUM_HEADS""": """num_heads""",
"""HEAD_DIM""": """d_kv""",
"""EMBED_DIM""": """d_model""",
"""MLP_DIM""": """d_ff""",
"""NUM_SELECTED_EXPERTS""": """num_selected_experts""",
"""NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
"""NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
"""dense.MlpBlock.activations""": """feed_forward_proj""",
}
def __lowerCAmelCase ( A , A ):
# Convert a google style config to the hugging face fromat
import regex as re
with open(A , "r" ) as f:
UpperCAmelCase_ = f.read()
UpperCAmelCase_ = re.findall(r"(.*) = ([0-9.]*)" , A )
UpperCAmelCase_ = {}
for param, value in regex_match:
if param in GIN_TO_CONFIG_MAPPING and value != "":
UpperCAmelCase_ = float(A ) if "." in value else int(A )
UpperCAmelCase_ = re.findall(r"(.*activations) = \(\'(.*)\',\)" , A )[0]
UpperCAmelCase_ = str(activation[1] )
UpperCAmelCase_ = num_experts
UpperCAmelCase_ = SwitchTransformersConfig(**A )
return config
def __lowerCAmelCase ( A , A , A=None , A="./" , A=8 ):
# Initialise PyTorch model
print(F"Loading flax weights from : {flax_checkpoint_path}" )
UpperCAmelCase_ = checkpoints.load_tax_checkpoint(A )
if gin_file is not None:
UpperCAmelCase_ = convert_gin_to_config(A , A )
else:
UpperCAmelCase_ = SwitchTransformersConfig.from_pretrained(A )
UpperCAmelCase_ = SwitchTransformersForConditionalGeneration(A )
UpperCAmelCase_ = flax_params["target"]
UpperCAmelCase_ = flatten_dict(A , sep="/" )
UpperCAmelCase_ = rename_keys(A )
UpperCAmelCase_ = unflatten_dict(A , sep="/" )
# Load the flax params in the PT model
load_flax_weights_in_pytorch_model(A , A )
print(F"Save PyTorch model to {pytorch_dump_path}" )
pt_model.save_pretrained(A )
if __name__ == "__main__":
_a: List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--switch_t5x_checkpoint_path""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
""" model architecture. If not provided, a `gin_file` has to be provided."""
),
)
parser.add_argument(
"""--gin_file""",
default=None,
type=str,
required=False,
help="""Path to the gin config file. If not provided, a `config_file` has to be passed """,
)
parser.add_argument(
"""--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
)
parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
_a: Any = parser.parse_args()
convert_flax_checkpoint_to_pytorch(
args.switch_tax_checkpoint_path,
args.config_name,
args.gin_file,
args.pytorch_dump_folder_path,
args.num_experts,
)
| 268
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_a: Optional[Any] = logging.get_logger(__name__)
def __lowerCAmelCase ( A ):
# initialize config
if "resnet-50" in model_name:
UpperCAmelCase_ = ResNetConfig.from_pretrained("microsoft/resnet-50" )
elif "resnet-101" in model_name:
UpperCAmelCase_ = ResNetConfig.from_pretrained("microsoft/resnet-101" )
else:
raise ValueError("Model name should include either resnet50 or resnet101" )
UpperCAmelCase_ = DetrConfig(use_timm_backbone=A , backbone_config=A )
# set label attributes
UpperCAmelCase_ = "panoptic" in model_name
if is_panoptic:
UpperCAmelCase_ = 250
else:
UpperCAmelCase_ = 91
UpperCAmelCase_ = "huggingface/label-files"
UpperCAmelCase_ = "coco-detection-id2label.json"
UpperCAmelCase_ = json.load(open(hf_hub_download(A , A , repo_type="dataset" ) , "r" ) )
UpperCAmelCase_ = {int(A ): v for k, v in idalabel.items()}
UpperCAmelCase_ = idalabel
UpperCAmelCase_ = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def __lowerCAmelCase ( A ):
# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_ = []
# stem
# fmt: off
rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight") )
rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight") )
rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias") )
rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean") )
rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean",
) )
rename_keys.append(
(
F"backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var",
F"backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F"transformer.encoder.layers.{i}.self_attn.out_proj.weight",
F"encoder.layers.{i}.self_attn.out_proj.weight",
) )
rename_keys.append(
(F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias") )
rename_keys.append(
(F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append(
(F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append(
(F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F"transformer.decoder.layers.{i}.self_attn.out_proj.weight",
F"decoder.layers.{i}.self_attn.out_proj.weight",
) )
rename_keys.append(
(F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias") )
rename_keys.append(
(
F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
F"decoder.layers.{i}.encoder_attn.out_proj.weight",
) )
rename_keys.append(
(
F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
F"decoder.layers.{i}.encoder_attn.out_proj.bias",
) )
rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias") )
rename_keys.append(
(F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight") )
rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
] )
return rename_keys
def __lowerCAmelCase ( A , A , A ):
UpperCAmelCase_ = state_dict.pop(A )
UpperCAmelCase_ = val
def __lowerCAmelCase ( A , A=False ):
UpperCAmelCase_ = ""
if is_panoptic:
UpperCAmelCase_ = "detr."
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase_ = state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight" )
UpperCAmelCase_ = state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ = in_proj_weight[:256, :]
UpperCAmelCase_ = in_proj_bias[:256]
UpperCAmelCase_ = in_proj_weight[256:512, :]
UpperCAmelCase_ = in_proj_bias[256:512]
UpperCAmelCase_ = in_proj_weight[-256:, :]
UpperCAmelCase_ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
UpperCAmelCase_ = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight" )
UpperCAmelCase_ = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase_ = in_proj_weight[:256, :]
UpperCAmelCase_ = in_proj_bias[:256]
UpperCAmelCase_ = in_proj_weight[256:512, :]
UpperCAmelCase_ = in_proj_bias[256:512]
UpperCAmelCase_ = in_proj_weight[-256:, :]
UpperCAmelCase_ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase_ = state_dict.pop(
F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight" )
UpperCAmelCase_ = state_dict.pop(F"{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
UpperCAmelCase_ = in_proj_weight_cross_attn[:256, :]
UpperCAmelCase_ = in_proj_bias_cross_attn[:256]
UpperCAmelCase_ = in_proj_weight_cross_attn[256:512, :]
UpperCAmelCase_ = in_proj_bias_cross_attn[256:512]
UpperCAmelCase_ = in_proj_weight_cross_attn[-256:, :]
UpperCAmelCase_ = in_proj_bias_cross_attn[-256:]
def __lowerCAmelCase ( ):
UpperCAmelCase_ = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase_ = Image.open(requests.get(A , stream=A ).raw )
return im
@torch.no_grad()
def __lowerCAmelCase ( A , A=None , A=False ):
UpperCAmelCase_ , UpperCAmelCase_ = get_detr_config(A )
# load original model from torch hub
UpperCAmelCase_ = {
"detr-resnet-50": "detr_resnet50",
"detr-resnet-101": "detr_resnet101",
}
logger.info(F"Converting model {model_name}..." )
UpperCAmelCase_ = torch.hub.load("facebookresearch/detr" , model_name_to_original_name[model_name] , pretrained=A ).eval()
UpperCAmelCase_ = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(A ):
if is_panoptic:
UpperCAmelCase_ = "detr." + src
rename_key(A , A , A )
# query, key and value matrices need special treatment
read_in_q_k_v(A , is_panoptic=A )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase_ = "detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("detr" )
and not key.startswith("class_labels_classifier" )
and not key.startswith("bbox_predictor" )
):
UpperCAmelCase_ = state_dict.pop(A )
UpperCAmelCase_ = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
UpperCAmelCase_ = state_dict.pop(A )
UpperCAmelCase_ = val
elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
continue
else:
UpperCAmelCase_ = state_dict.pop(A )
UpperCAmelCase_ = val
else:
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
UpperCAmelCase_ = state_dict.pop(A )
UpperCAmelCase_ = val
# finally, create HuggingFace model and load state dict
UpperCAmelCase_ = DetrForSegmentation(A ) if is_panoptic else DetrForObjectDetection(A )
model.load_state_dict(A )
model.eval()
# verify our conversion on an image
UpperCAmelCase_ = "coco_panoptic" if is_panoptic else "coco_detection"
UpperCAmelCase_ = DetrImageProcessor(format=A )
UpperCAmelCase_ = processor(images=prepare_img() , return_tensors="pt" )
UpperCAmelCase_ = encoding["pixel_values"]
UpperCAmelCase_ = detr(A )
UpperCAmelCase_ = model(A )
assert torch.allclose(outputs.logits , original_outputs["pred_logits"] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["pred_boxes"] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["pred_masks"] , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..." )
Path(A ).mkdir(exist_ok=A )
model.save_pretrained(A )
processor.save_pretrained(A )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("Uploading PyTorch model and image processor to the hub..." )
model.push_to_hub(F"nielsr/{model_name}" )
processor.push_to_hub(F"nielsr/{model_name}" )
if __name__ == "__main__":
_a: List[str] = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""detr-resnet-50""",
type=str,
choices=["""detr-resnet-50""", """detr-resnet-101"""],
help="""Name of the DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
_a: str = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 268
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase: Optional[int] = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase: str = ["""ConvNextFeatureExtractor"""]
__UpperCamelCase: Tuple = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase: Optional[int] = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase: Optional[Any] = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
__UpperCamelCase: Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 266
|
def lowerCamelCase__ ( __A :int ,__A :float ,__A :float ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (volume) ) )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
return round(float((moles * 0.0_821 * temperature) / (pressure) ) )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
return round(float((pressure * volume) / (0.0_821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 268
| 0
|
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def a__ ( ) -> Tuple:
lowerCamelCase = """https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
lowerCamelCase = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw ).convert("""RGB""" )
return image
def a__ ( snake_case__ ) -> Optional[Any]:
lowerCamelCase = []
# fmt: off
# vision encoder
rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""") )
rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""") )
rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""") )
rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""") )
rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""") )
rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',) )
rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias') )
# QFormer
rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""") )
rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""") )
# fmt: on
return rename_keys
def a__ ( snake_case__ , snake_case__ , snake_case__ ) -> List[str]:
lowerCamelCase = dct.pop(snake_case__ )
lowerCamelCase = val
def a__ ( snake_case__ , snake_case__ ) -> Tuple:
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowerCamelCase = state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias' )
lowerCamelCase = state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias' )
# next, set bias in the state dict
lowerCamelCase = torch.cat((q_bias, torch.zeros_like(snake_case__ , requires_grad=snake_case__ ), v_bias) )
lowerCamelCase = qkv_bias
def a__ ( snake_case__ , snake_case__ ) -> int:
lowerCamelCase = 3_64 if """coco""" in model_name else 2_24
lowerCamelCase = BlipaVisionConfig(image_size=snake_case__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
lowerCamelCase = OPTConfig.from_pretrained("""facebook/opt-2.7b""" , eos_token_id=snake_case__ ).to_dict()
elif "opt-6.7b" in model_name:
lowerCamelCase = OPTConfig.from_pretrained("""facebook/opt-6.7b""" , eos_token_id=snake_case__ ).to_dict()
elif "t5-xl" in model_name:
lowerCamelCase = TaConfig.from_pretrained("""google/flan-t5-xl""" , dense_act_fn="""gelu""" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowerCamelCase = TaConfig.from_pretrained("""google/flan-t5-xxl""" , dense_act_fn="""gelu""" , bos_token_id=1 ).to_dict()
lowerCamelCase = BlipaConfig(vision_config=snake_case__ , text_config=snake_case__ )
return config, image_size
@torch.no_grad()
def a__ ( snake_case__ , snake_case__=None , snake_case__=False ) -> str:
lowerCamelCase = (
AutoTokenizer.from_pretrained("""facebook/opt-2.7b""" )
if """opt""" in model_name
else AutoTokenizer.from_pretrained("""google/flan-t5-xl""" )
)
lowerCamelCase = tokenizer("""\n""" , add_special_tokens=snake_case__ ).input_ids[0]
lowerCamelCase , lowerCamelCase = get_blipa_config(snake_case__ , eos_token_id=snake_case__ )
lowerCamelCase = BlipaForConditionalGeneration(snake_case__ ).eval()
lowerCamelCase = {
"""blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
"""blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
"""blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
"""blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
"""blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
"""blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
"""blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
}
lowerCamelCase , lowerCamelCase = model_name_to_original[model_name]
# load original model
print("""Loading original model...""" )
lowerCamelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
lowerCamelCase , lowerCamelCase , lowerCamelCase = load_model_and_preprocess(
name=snake_case__ , model_type=snake_case__ , is_eval=snake_case__ , device=snake_case__ )
original_model.eval()
print("""Done!""" )
# update state dict keys
lowerCamelCase = original_model.state_dict()
lowerCamelCase = create_rename_keys(snake_case__ )
for src, dest in rename_keys:
rename_key(snake_case__ , snake_case__ , snake_case__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowerCamelCase = state_dict.pop(snake_case__ )
if key.startswith("""Qformer.bert""" ):
lowerCamelCase = key.replace("""Qformer.bert""" , """qformer""" )
if "attention.self" in key:
lowerCamelCase = key.replace("""self""" , """attention""" )
if "opt_proj" in key:
lowerCamelCase = key.replace("""opt_proj""" , """language_projection""" )
if "t5_proj" in key:
lowerCamelCase = key.replace("""t5_proj""" , """language_projection""" )
if key.startswith("""opt""" ):
lowerCamelCase = key.replace("""opt""" , """language""" )
if key.startswith("""t5""" ):
lowerCamelCase = key.replace("""t5""" , """language""" )
lowerCamelCase = val
# read in qv biases
read_in_q_v_bias(snake_case__ , snake_case__ )
lowerCamelCase , lowerCamelCase = hf_model.load_state_dict(snake_case__ , strict=snake_case__ )
assert len(snake_case__ ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
lowerCamelCase = load_demo_image()
lowerCamelCase = vis_processors["""eval"""](snake_case__ ).unsqueeze(0 ).to(snake_case__ )
lowerCamelCase = tokenizer(["""\n"""] , return_tensors="""pt""" ).input_ids.to(snake_case__ )
# create processor
lowerCamelCase = BlipImageProcessor(
size={"""height""": image_size, """width""": image_size} , image_mean=snake_case__ , image_std=snake_case__ )
lowerCamelCase = BlipaProcessor(image_processor=snake_case__ , tokenizer=snake_case__ )
lowerCamelCase = processor(images=snake_case__ , return_tensors="""pt""" ).pixel_values.to(snake_case__ )
# make sure processor creates exact same pixel values
assert torch.allclose(snake_case__ , snake_case__ )
original_model.to(snake_case__ )
hf_model.to(snake_case__ )
with torch.no_grad():
if "opt" in model_name:
lowerCamelCase = original_model({"""image""": original_pixel_values, """text_input""": [""""""]} ).logits
lowerCamelCase = hf_model(snake_case__ , snake_case__ ).logits
else:
lowerCamelCase = original_model(
{"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]} ).logits
lowerCamelCase = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -1_00 )
lowerCamelCase = hf_model(snake_case__ , snake_case__ , labels=snake_case__ ).logits
assert original_logits.shape == logits.shape
print("""First values of original logits:""" , original_logits[0, :3, :3] )
print("""First values of HF logits:""" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
lowerCamelCase = torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] , device=snake_case__ )
assert torch.allclose(logits[0, :3, :3] , snake_case__ , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
lowerCamelCase = torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] , device=snake_case__ )
else:
# cast to same type
lowerCamelCase = logits.dtype
assert torch.allclose(original_logits.to(snake_case__ ) , snake_case__ , atol=1E-2 )
print("""Looks ok!""" )
print("""Generating a caption...""" )
lowerCamelCase = """"""
lowerCamelCase = tokenizer(snake_case__ , return_tensors="""pt""" ).input_ids.to(snake_case__ )
lowerCamelCase = original_model.generate({"""image""": original_pixel_values} )
lowerCamelCase = hf_model.generate(
snake_case__ , snake_case__ , do_sample=snake_case__ , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("""Original generation:""" , snake_case__ )
lowerCamelCase = input_ids.shape[1]
lowerCamelCase = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=snake_case__ )
lowerCamelCase = [text.strip() for text in output_text]
print("""HF generation:""" , snake_case__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(snake_case__ )
hf_model.save_pretrained(snake_case__ )
if push_to_hub:
processor.push_to_hub(F'nielsr/{model_name}' )
hf_model.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
lowerCAmelCase : Any = argparse.ArgumentParser()
lowerCAmelCase : List[Any] = [
"""blip2-opt-2.7b""",
"""blip2-opt-6.7b""",
"""blip2-opt-2.7b-coco""",
"""blip2-opt-6.7b-coco""",
"""blip2-flan-t5-xl""",
"""blip2-flan-t5-xl-coco""",
"""blip2-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""blip2-opt-2.7b""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
lowerCAmelCase : Dict = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 702
|
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def a__ ( snake_case__ ) -> List[str]:
return getitem, k
def a__ ( snake_case__ , snake_case__ ) -> Optional[Any]:
return setitem, k, v
def a__ ( snake_case__ ) -> str:
return delitem, k
def a__ ( snake_case__ , snake_case__ , *snake_case__ ) -> Union[str, Any]:
try:
return fun(snake_case__ , *snake_case__ ), None
except Exception as e:
return None, e
lowerCAmelCase : Optional[int] = (
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
)
lowerCAmelCase : List[Any] = [
_set("""key_a""", """val_a"""),
_set("""key_a""", """val_b"""),
]
lowerCAmelCase : List[Any] = [
_set("""key_a""", """val_a"""),
_set("""key_b""", """val_b"""),
_del("""key_a"""),
_del("""key_b"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
]
lowerCAmelCase : List[Any] = [
_get("""key_a"""),
_del("""key_a"""),
_set("""key_a""", """val_a"""),
_del("""key_a"""),
_del("""key_a"""),
_get("""key_a"""),
]
lowerCAmelCase : Tuple = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
lowerCAmelCase : Tuple = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("""key_a""", """val_b"""),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def a__ ( snake_case__ ) -> Any:
lowerCamelCase = HashMap(initial_block_size=4 )
lowerCamelCase = {}
for _, (fun, *args) in enumerate(snake_case__ ):
lowerCamelCase , lowerCamelCase = _run_operation(snake_case__ , snake_case__ , *snake_case__ )
lowerCamelCase , lowerCamelCase = _run_operation(snake_case__ , snake_case__ , *snake_case__ )
assert my_res == py_res
assert str(snake_case__ ) == str(snake_case__ )
assert set(snake_case__ ) == set(snake_case__ )
assert len(snake_case__ ) == len(snake_case__ )
assert set(my.items() ) == set(py.items() )
def a__ ( ) -> int:
def is_public(snake_case__ ) -> bool:
return not name.startswith("""_""" )
lowerCamelCase = {name for name in dir({} ) if is_public(snake_case__ )}
lowerCamelCase = {name for name in dir(HashMap() ) if is_public(snake_case__ )}
assert dict_public_names > hash_public_names
| 533
| 0
|
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
A = logging.get_logger(__name__)
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = ['''pixel_values''']
def __init__( self , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = PILImageResampling.BICUBIC , _UpperCAmelCase = True , _UpperCAmelCase = None , _UpperCAmelCase = True , _UpperCAmelCase = 1 / 255 , _UpperCAmelCase = True , _UpperCAmelCase = IMAGENET_DEFAULT_MEAN , _UpperCAmelCase = IMAGENET_DEFAULT_STD , **_UpperCAmelCase , ):
super().__init__(**_UpperCAmelCase )
__a : int = size if size is not None else {'''shortest_edge''': 224}
__a : str = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
__a : List[Any] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__a : Tuple = get_size_dict(_UpperCAmelCase , param_name='''crop_size''' )
__a : Tuple = do_resize
__a : Optional[int] = size
__a : List[Any] = resample
__a : List[str] = do_center_crop
__a : Dict = crop_size
__a : Union[str, Any] = do_rescale
__a : int = rescale_factor
__a : int = do_normalize
__a : Optional[int] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
__a : Any = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = PILImageResampling.BICUBIC , _UpperCAmelCase = None , **_UpperCAmelCase , ):
__a : str = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
__a : str = int((256 / 224) * size['''shortest_edge'''] )
__a : Tuple = get_resize_output_image_size(_UpperCAmelCase , size=_UpperCAmelCase , default_to_square=_UpperCAmelCase )
__a : Tuple = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""" )
return resize(
_UpperCAmelCase , size=(size_dict['''height'''], size_dict['''width''']) , resample=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , **_UpperCAmelCase , ):
__a : List[Any] = get_size_dict(_UpperCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""" )
return center_crop(_UpperCAmelCase , size=(size['''height'''], size['''width''']) , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , **_UpperCAmelCase , ):
return rescale(_UpperCAmelCase , scale=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = None , **_UpperCAmelCase , ):
return normalize(_UpperCAmelCase , mean=_UpperCAmelCase , std=_UpperCAmelCase , data_format=_UpperCAmelCase , **_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = None , _UpperCAmelCase = ChannelDimension.FIRST , **_UpperCAmelCase , ):
__a : str = do_resize if do_resize is not None else self.do_resize
__a : List[str] = resample if resample is not None else self.resample
__a : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
__a : Any = do_rescale if do_rescale is not None else self.do_rescale
__a : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
__a : str = do_normalize if do_normalize is not None else self.do_normalize
__a : List[str] = image_mean if image_mean is not None else self.image_mean
__a : List[Any] = image_std if image_std is not None else self.image_std
__a : Optional[Any] = size if size is not None else self.size
__a : Optional[Any] = get_size_dict(_UpperCAmelCase , default_to_square=_UpperCAmelCase )
__a : Optional[int] = crop_size if crop_size is not None else self.crop_size
__a : int = get_size_dict(_UpperCAmelCase , param_name='''crop_size''' )
__a : int = make_list_of_images(_UpperCAmelCase )
if not valid_images(_UpperCAmelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__a : Union[str, Any] = [to_numpy_array(_UpperCAmelCase ) for image in images]
if do_resize:
__a : Union[str, Any] = [self.resize(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) for image in images]
if do_center_crop:
__a : str = [self.center_crop(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
if do_rescale:
__a : Optional[int] = [self.rescale(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
if do_normalize:
__a : Union[str, Any] = [self.normalize(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) for image in images]
__a : Tuple = [to_channel_dimension_format(_UpperCAmelCase , _UpperCAmelCase ) for image in images]
__a : Optional[Any] = {'''pixel_values''': images}
return BatchFeature(data=_UpperCAmelCase , tensor_type=_UpperCAmelCase )
| 52
|
'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
_snake_case = get_logger(__name__)
_snake_case = Path(__file__).parent / 'model_card_template.md'
_snake_case = uuida().hex
_snake_case = os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
_snake_case = os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
_snake_case = HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'
def _A ( snake_case = None ) -> str:
_lowercase : Dict = F'''diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'''
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += F'''; torch/{_torch_version}'''
if is_flax_available():
ua += F'''; jax/{_jax_version}'''
ua += F'''; flax/{_flax_version}'''
if is_onnx_available():
ua += F'''; onnxruntime/{_onnxruntime_version}'''
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" , "" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(snake_case , snake_case ):
ua += "; " + "; ".join(F'''{k}/{v}''' for k, v in user_agent.items() )
elif isinstance(snake_case , snake_case ):
ua += "; " + user_agent
return ua
def _A ( snake_case , snake_case = None , snake_case = None ) -> Optional[Any]:
if token is None:
_lowercase : List[Any] = HfFolder.get_token()
if organization is None:
_lowercase : Tuple = whoami(snake_case )["name"]
return F'''{username}/{model_id}'''
else:
return F'''{organization}/{model_id}'''
def _A ( snake_case , snake_case ) -> Tuple:
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(snake_case , "local_rank" ) and args.local_rank not in [-1, 0]:
return
_lowercase : Tuple = args.hub_token if hasattr(snake_case , "hub_token" ) else None
_lowercase : Optional[int] = get_full_repo_name(snake_case , token=snake_case )
_lowercase : List[Any] = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" , license="apache-2.0" , library_name="diffusers" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=snake_case , model_name=snake_case , repo_name=snake_case , dataset_name=args.dataset_name if hasattr(snake_case , "dataset_name" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(snake_case , "gradient_accumulation_steps" ) else None
) , adam_betaa=args.adam_betaa if hasattr(snake_case , "adam_beta1" ) else None , adam_betaa=args.adam_betaa if hasattr(snake_case , "adam_beta2" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(snake_case , "adam_weight_decay" ) else None , adam_epsilon=args.adam_epsilon if hasattr(snake_case , "adam_epsilon" ) else None , lr_scheduler=args.lr_scheduler if hasattr(snake_case , "lr_scheduler" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(snake_case , "lr_warmup_steps" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(snake_case , "ema_inv_gamma" ) else None , ema_power=args.ema_power if hasattr(snake_case , "ema_power" ) else None , ema_max_decay=args.ema_max_decay if hasattr(snake_case , "ema_max_decay" ) else None , mixed_precision=args.mixed_precision , )
_lowercase : List[str] = os.path.join(args.output_dir , "README.md" )
model_card.save(snake_case )
def _A ( snake_case , snake_case = None ) -> Union[str, Any]:
if resolved_file is None or commit_hash is not None:
return commit_hash
_lowercase : Optional[int] = str(Path(snake_case ).as_posix() )
_lowercase : Dict = re.search(r"snapshots/([^/]+)/" , snake_case )
if search is None:
return None
_lowercase : Union[str, Any] = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(snake_case ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_snake_case = os.path.expanduser(
os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
_snake_case = os.path.join(hf_cache_home, 'diffusers')
def _A ( snake_case = None , snake_case = None ) -> None:
if new_cache_dir is None:
_lowercase : Optional[int] = DIFFUSERS_CACHE
if old_cache_dir is None:
_lowercase : Any = old_diffusers_cache
_lowercase : int = Path(snake_case ).expanduser()
_lowercase : int = Path(snake_case ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
_lowercase : int = new_cache_dir / old_blob_path.relative_to(snake_case )
new_blob_path.parent.mkdir(parents=snake_case , exist_ok=snake_case )
os.replace(snake_case , snake_case )
try:
os.symlink(snake_case , snake_case )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
_snake_case = os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
_snake_case = 0
else:
with open(cache_version_file) as f:
try:
_snake_case = int(f.read())
except ValueError:
_snake_case = 0
if cache_version < 1:
_snake_case = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
'existing cached models. This is a one-time operation, you can interrupt it or run it '
'later by calling `diffusers.utils.hub_utils.move_cache()`.'
)
try:
move_cache()
except Exception as e:
_snake_case = '\n'.join(traceback.format_tb(e.__traceback__))
logger.error(
F'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
'message and we will do our best to help.'
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, 'w') as f:
f.write('1')
except Exception:
logger.warning(
F'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
'the directory exists and can be written to.'
)
def _A ( snake_case , snake_case = None ) -> str:
if variant is not None:
_lowercase : Any = weights_name.split("." )
_lowercase : str = splits[:-1] + [variant] + splits[-1:]
_lowercase : List[str] = ".".join(snake_case )
return weights_name
def _A ( snake_case , *,
snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=None , ) -> Optional[Any]:
_lowercase : Tuple = str(snake_case )
if os.path.isfile(snake_case ):
return pretrained_model_name_or_path
elif os.path.isdir(snake_case ):
if os.path.isfile(os.path.join(snake_case , snake_case ) ):
# Load from a PyTorch checkpoint
_lowercase : Any = os.path.join(snake_case , snake_case )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(snake_case , snake_case , snake_case ) ):
_lowercase : List[Any] = os.path.join(snake_case , snake_case , snake_case )
return model_file
else:
raise EnvironmentError(
F'''Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.''' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(snake_case ).base_version ) >= version.parse("0.20.0" )
):
try:
_lowercase : List[str] = hf_hub_download(
snake_case , filename=_add_variant(snake_case , snake_case ) , cache_dir=snake_case , force_download=snake_case , proxies=snake_case , resume_download=snake_case , local_files_only=snake_case , use_auth_token=snake_case , user_agent=snake_case , subfolder=snake_case , revision=revision or commit_hash , )
warnings.warn(
F'''Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.''' , snake_case , )
return model_file
except: # noqa: E722
warnings.warn(
F'''You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(snake_case , snake_case )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(snake_case , snake_case )}\' so that the correct variant file can be added.''' , snake_case , )
try:
# 2. Load model file as usual
_lowercase : Tuple = hf_hub_download(
snake_case , filename=snake_case , cache_dir=snake_case , force_download=snake_case , proxies=snake_case , resume_download=snake_case , local_files_only=snake_case , use_auth_token=snake_case , user_agent=snake_case , subfolder=snake_case , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
F'''{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '''
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
F'''{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '''
"this model name. Check the model page at "
F'''\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.''' )
except EntryNotFoundError:
raise EnvironmentError(
F'''{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.''' )
except HTTPError as err:
raise EnvironmentError(
F'''There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}''' )
except ValueError:
raise EnvironmentError(
F'''We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'''
F''' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'''
F''' directory containing a file named {weights_name} or'''
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
F'''Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '''
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
F'''Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '''
F'''containing a file named {weights_name}''' )
| 245
| 0
|
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
__UpperCAmelCase : str = int(__UpperCAmelCase )
__UpperCAmelCase : Any = int(__UpperCAmelCase )
__UpperCAmelCase : List[str] = []
for temp in range(int(__UpperCAmelCase ) ):
series.append(f"""1 / {pow(temp + 1 , int(__UpperCAmelCase ) )}""" if series else "1" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
_a : List[str] = int(input("Enter the last number (nth term) of the P-Series"))
_a : Any = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 712
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : List[Any] = {
"microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class __A (__magic_name__ ):
snake_case :Any = "cvt"
def __init__( self , UpperCamelCase_=3 , UpperCamelCase_=[7, 3, 3] , UpperCamelCase_=[4, 2, 2] , UpperCamelCase_=[2, 1, 1] , UpperCamelCase_=[64, 1_92, 3_84] , UpperCamelCase_=[1, 3, 6] , UpperCamelCase_=[1, 2, 10] , UpperCamelCase_=[4.0, 4.0, 4.0] , UpperCamelCase_=[0.0, 0.0, 0.0] , UpperCamelCase_=[0.0, 0.0, 0.0] , UpperCamelCase_=[0.0, 0.0, 0.1] , UpperCamelCase_=[True, True, True] , UpperCamelCase_=[False, False, True] , UpperCamelCase_=["dw_bn", "dw_bn", "dw_bn"] , UpperCamelCase_=[3, 3, 3] , UpperCamelCase_=[1, 1, 1] , UpperCamelCase_=[2, 2, 2] , UpperCamelCase_=[1, 1, 1] , UpperCamelCase_=[1, 1, 1] , UpperCamelCase_=0.0_2 , UpperCamelCase_=1E-12 , **UpperCamelCase_ , ):
super().__init__(**UpperCamelCase_ )
__UpperCAmelCase : Optional[int] = num_channels
__UpperCAmelCase : Optional[Any] = patch_sizes
__UpperCAmelCase : List[str] = patch_stride
__UpperCAmelCase : Tuple = patch_padding
__UpperCAmelCase : int = embed_dim
__UpperCAmelCase : str = num_heads
__UpperCAmelCase : Any = depth
__UpperCAmelCase : List[str] = mlp_ratio
__UpperCAmelCase : List[str] = attention_drop_rate
__UpperCAmelCase : Dict = drop_rate
__UpperCAmelCase : Dict = drop_path_rate
__UpperCAmelCase : str = qkv_bias
__UpperCAmelCase : Optional[int] = cls_token
__UpperCAmelCase : Optional[Any] = qkv_projection_method
__UpperCAmelCase : Tuple = kernel_qkv
__UpperCAmelCase : Optional[Any] = padding_kv
__UpperCAmelCase : Optional[int] = stride_kv
__UpperCAmelCase : Any = padding_q
__UpperCAmelCase : List[Any] = stride_q
__UpperCAmelCase : Union[str, Any] = initializer_range
__UpperCAmelCase : Any = layer_norm_eps
| 10
| 0
|
"""simple docstring"""
from manim import *
class _lowerCAmelCase ( lowerCamelCase ):
def _a ( self ) -> List[Any]:
_UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
_UpperCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_UpperCAmelCase = Rectangle(height=0.25 , width=0.25 )
_UpperCAmelCase = [mem.copy() for i in range(6 )]
_UpperCAmelCase = [mem.copy() for i in range(6 )]
_UpperCAmelCase = VGroup(*a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = VGroup(*a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = VGroup(a_ , a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = Text("CPU" , font_size=24 )
_UpperCAmelCase = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(a_ )
_UpperCAmelCase = [mem.copy() for i in range(4 )]
_UpperCAmelCase = VGroup(*a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = Text("GPU" , font_size=24 )
_UpperCAmelCase = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
gpu.move_to([-1, -1, 0] )
self.add(a_ )
_UpperCAmelCase = [mem.copy() for i in range(6 )]
_UpperCAmelCase = VGroup(*a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = Text("Model" , font_size=24 )
_UpperCAmelCase = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
model.move_to([3, -1.0, 0] )
self.add(a_ )
_UpperCAmelCase = []
_UpperCAmelCase = []
for i, rect in enumerate(a_ ):
_UpperCAmelCase = fill.copy().set_fill(a_ , opacity=0.8 )
target.move_to(a_ )
model_arr.append(a_ )
_UpperCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0.0 ).set_fill(a_ , opacity=0.8 )
cpu_target.move_to(cpu_left_col_base[i] )
model_cpu_arr.append(a_ )
self.add(*a_ , *a_ )
_UpperCAmelCase = [meta_mem.copy() for i in range(6 )]
_UpperCAmelCase = [meta_mem.copy() for i in range(6 )]
_UpperCAmelCase = VGroup(*a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = VGroup(*a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = VGroup(a_ , a_ ).arrange(a_ , buff=0 )
_UpperCAmelCase = Text("Disk" , font_size=24 )
_UpperCAmelCase = Group(a_ , a_ ).arrange(a_ , buff=0.5 , aligned_edge=a_ )
disk.move_to([-4, -1.25, 0] )
self.add(a_ , a_ )
_UpperCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_UpperCAmelCase = MarkupText(
f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(a_ , a_ )
_UpperCAmelCase = MarkupText(
f"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(a_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(a_ )
_UpperCAmelCase = MarkupText(
f"Now watch as an input is passed through the model\nand how the memory is utilized and handled." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(a_ ) )
_UpperCAmelCase = Square(0.3 )
input.set_fill(a_ , opacity=1.0 )
input.set_stroke(width=0.0 )
input.next_to(model_base[0] , a_ , buff=0.5 )
self.play(Write(a_ ) )
input.generate_target()
input.target.next_to(model_arr[0] , direction=a_ , buff=0.02 )
self.play(MoveToTarget(a_ ) )
self.play(FadeOut(a_ ) )
_UpperCAmelCase = Arrow(start=a_ , end=a_ , color=a_ , buff=0.5 )
a.next_to(model_arr[0].get_left() , a_ , buff=0.2 )
model_cpu_arr[0].generate_target()
model_cpu_arr[0].target.move_to(gpu_rect[0] )
_UpperCAmelCase = MarkupText(
f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(a_ , run_time=3 ) )
_UpperCAmelCase = {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.02}
self.play(
Write(a_ ) , Circumscribe(model_arr[0] , color=a_ , **a_ ) , Circumscribe(model_cpu_arr[0] , color=a_ , **a_ ) , Circumscribe(gpu_rect[0] , color=a_ , **a_ ) , )
self.play(MoveToTarget(model_cpu_arr[0] ) )
_UpperCAmelCase = a.copy()
for i in range(6 ):
a_c.next_to(model_arr[i].get_right() + 0.02 , a_ , buff=0.2 )
input.generate_target()
input.target.move_to(model_arr[i].get_right() + 0.02 )
_UpperCAmelCase = AnimationGroup(
FadeOut(a_ , run_time=0.5 ) , MoveToTarget(a_ , run_time=0.5 ) , FadeIn(a_ , run_time=0.5 ) , lag_ratio=0.2 )
self.play(a_ )
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[i] )
if i < 5:
model_cpu_arr[i + 1].generate_target()
model_cpu_arr[i + 1].target.move_to(gpu_rect[0] )
if i >= 1:
_UpperCAmelCase = 0.7
self.play(
Circumscribe(model_arr[i] , **a_ ) , Circumscribe(cpu_left_col_base[i] , **a_ ) , Circumscribe(cpu_left_col_base[i + 1] , color=a_ , **a_ ) , Circumscribe(gpu_rect[0] , color=a_ , **a_ ) , Circumscribe(model_arr[i + 1] , color=a_ , **a_ ) , )
if i < 1:
self.play(
MoveToTarget(model_cpu_arr[i] ) , MoveToTarget(model_cpu_arr[i + 1] ) , )
else:
self.play(
MoveToTarget(model_cpu_arr[i] , run_time=0.7 ) , MoveToTarget(model_cpu_arr[i + 1] , run_time=0.7 ) , )
else:
model_cpu_arr[i].generate_target()
model_cpu_arr[i].target.move_to(cpu_left_col_base[-1] )
input.generate_target()
input.target.next_to(model_arr[-1].get_right() , RIGHT + 0.02 , buff=0.2 )
self.play(
Circumscribe(model_arr[-1] , color=a_ , **a_ ) , Circumscribe(cpu_left_col_base[-1] , color=a_ , **a_ ) , Circumscribe(gpu_rect[0] , color=a_ , **a_ ) , )
self.play(MoveToTarget(model_cpu_arr[i] ) )
_UpperCAmelCase = a_c
_UpperCAmelCase = a_c.copy()
input.generate_target()
input.target.next_to(model_base[-1] , RIGHT + 0.02 , buff=0.5 )
self.play(
FadeOut(a_ ) , FadeOut(a_ , run_time=0.5 ) , )
_UpperCAmelCase = MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed." , font_size=24 )
step_a.move_to([2, 2, 0] )
self.play(Write(a_ , run_time=3 ) , MoveToTarget(a_ ) )
self.wait()
| 657
|
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def merge(UpperCamelCase__ , UpperCamelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(UpperCamelCase__ ) <= 1:
return collection
_UpperCAmelCase = len(UpperCamelCase__ ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
__magic_name__ = input('''Enter numbers separated by a comma:\n''').strip()
__magic_name__ = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 657
| 1
|
"""simple docstring"""
from __future__ import annotations
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE ) < 2:
raise ValueError("Monogons and Digons are not polygons in the Euclidean space" )
if any(i <= 0 for i in nums ):
raise ValueError("All values must be greater than 0" )
__snake_case = nums.copy()
copy_nums.sort()
return copy_nums[-1] < sum(copy_nums[:-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700
|
"""simple docstring"""
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertModel,
)
class __magic_name__ :
def __init__( self : Optional[int] , snake_case_ : List[str] , snake_case_ : str=13 , snake_case_ : Optional[int]=7 , snake_case_ : Optional[Any]=True , snake_case_ : Union[str, Any]=True , snake_case_ : Tuple=True , snake_case_ : int=True , snake_case_ : int=99 , snake_case_ : Optional[int]=64 , snake_case_ : Dict=32 , snake_case_ : Dict=5 , snake_case_ : List[str]=4 , snake_case_ : List[Any]=37 , snake_case_ : Optional[Any]="gelu" , snake_case_ : Optional[Any]=0.1 , snake_case_ : List[Any]=0.1 , snake_case_ : Union[str, Any]=512 , snake_case_ : int=16 , snake_case_ : List[str]=2 , snake_case_ : Union[str, Any]=0.02 , snake_case_ : Union[str, Any]=3 , snake_case_ : str=4 , snake_case_ : int=None , ):
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_input_mask
__snake_case = use_token_type_ids
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = embedding_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = type_vocab_size
__snake_case = type_sequence_label_size
__snake_case = initializer_range
__snake_case = num_labels
__snake_case = num_choices
__snake_case = scope
def lowerCAmelCase ( self : Union[str, Any] ):
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_input_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case = None
if self.use_token_type_ids:
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case = None
__snake_case = None
__snake_case = None
if self.use_labels:
__snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__snake_case = ids_tensor([self.batch_size] , self.num_choices )
__snake_case = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase ( self : Union[str, Any] ):
return MobileBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , embedding_size=self.embedding_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
def lowerCAmelCase ( self : Optional[Any] , snake_case_ : Any , snake_case_ : Optional[Any] , snake_case_ : Tuple , snake_case_ : Any , snake_case_ : str , snake_case_ : Dict , snake_case_ : List[str] ):
__snake_case = MobileBertModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ )
__snake_case = model(snake_case_ , token_type_ids=snake_case_ )
__snake_case = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def lowerCAmelCase ( self : List[str] , snake_case_ : List[Any] , snake_case_ : Any , snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : int , snake_case_ : str , snake_case_ : str ):
__snake_case = MobileBertForMaskedLM(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self : Optional[Any] , snake_case_ : Optional[int] , snake_case_ : Union[str, Any] , snake_case_ : List[str] , snake_case_ : int , snake_case_ : int , snake_case_ : int , snake_case_ : Any ):
__snake_case = MobileBertForNextSentencePrediction(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def lowerCAmelCase ( self : Optional[int] , snake_case_ : List[str] , snake_case_ : List[str] , snake_case_ : str , snake_case_ : Optional[Any] , snake_case_ : List[str] , snake_case_ : List[str] , snake_case_ : Dict ):
__snake_case = MobileBertForPreTraining(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , next_sentence_label=snake_case_ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def lowerCAmelCase ( self : Any , snake_case_ : List[str] , snake_case_ : int , snake_case_ : Any , snake_case_ : Union[str, Any] , snake_case_ : Dict , snake_case_ : int , snake_case_ : int ):
__snake_case = MobileBertForQuestionAnswering(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase ( self : str , snake_case_ : Tuple , snake_case_ : int , snake_case_ : str , snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : str , snake_case_ : List[str] ):
__snake_case = self.num_labels
__snake_case = MobileBertForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : Optional[Any] , snake_case_ : Tuple , snake_case_ : str , snake_case_ : Union[str, Any] , snake_case_ : Tuple , snake_case_ : Optional[int] , snake_case_ : Tuple , snake_case_ : List[Any] ):
__snake_case = self.num_labels
__snake_case = MobileBertForTokenClassification(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = model(snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase ( self : Tuple , snake_case_ : Union[str, Any] , snake_case_ : Tuple , snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : Any , snake_case_ : Dict , snake_case_ : Union[str, Any] ):
__snake_case = self.num_choices
__snake_case = MobileBertForMultipleChoice(config=snake_case_ )
model.to(snake_case_ )
model.eval()
__snake_case = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__snake_case = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase ( self : List[Any] ):
__snake_case = self.prepare_config_and_inputs()
(
(
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) , (
__snake_case
) ,
) = config_and_inputs
__snake_case = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __magic_name__ ( lowercase__ , lowercase__ , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : Dict = (
(
MobileBertModel,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
)
if is_torch_available()
else ()
)
_SCREAMING_SNAKE_CASE : Optional[int] = (
{
'feature-extraction': MobileBertModel,
'fill-mask': MobileBertForMaskedLM,
'question-answering': MobileBertForQuestionAnswering,
'text-classification': MobileBertForSequenceClassification,
'token-classification': MobileBertForTokenClassification,
'zero-shot': MobileBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_SCREAMING_SNAKE_CASE : int = True
def lowerCAmelCase ( self : List[str] , snake_case_ : List[str] , snake_case_ : str , snake_case_ : str=False ):
__snake_case = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class in get_values(snake_case_ ):
__snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=snake_case_ )
__snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case_ )
return inputs_dict
def lowerCAmelCase ( self : Optional[Any] ):
__snake_case = MobileBertModelTester(self )
__snake_case = ConfigTester(self , config_class=snake_case_ , hidden_size=37 )
def lowerCAmelCase ( self : List[Any] ):
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Tuple ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*snake_case_ )
def lowerCAmelCase ( self : Optional[Any] ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_ )
def lowerCAmelCase ( self : Tuple ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_ )
def lowerCAmelCase ( self : Any ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_ )
def lowerCAmelCase ( self : Any ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_ )
def lowerCAmelCase ( self : List[str] ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_ )
def lowerCAmelCase ( self : List[Any] ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_ )
def lowerCAmelCase ( self : str ):
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_ )
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
return torch.tensor(
SCREAMING_SNAKE_CASE , dtype=torch.long , device=SCREAMING_SNAKE_CASE , )
_SCREAMING_SNAKE_CASE = 1E-3
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
@slow
def lowerCAmelCase ( self : Tuple ):
__snake_case = MobileBertModel.from_pretrained("google/mobilebert-uncased" ).to(snake_case_ )
__snake_case = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]] )
with torch.no_grad():
__snake_case = model(snake_case_ )[0]
__snake_case = torch.Size((1, 9, 512) )
self.assertEqual(output.shape , snake_case_ )
__snake_case = torch.tensor(
[
[
[-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
[-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
[2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
]
] , device=snake_case_ , )
# MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
# ~1 difference, it's therefore not a good idea to measure using addition.
# Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
# result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
__snake_case = torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE )
__snake_case = torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE )
self.assertTrue(lower_bound and upper_bound )
| 614
| 0
|
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing import APIRoute
from pydantic import BaseModel
from starlette.responses import JSONResponse
from uvicorn import run
A_ = True
except (ImportError, AttributeError):
A_ = object
def _UpperCamelCase ( *__UpperCamelCase ,**__UpperCamelCase ) -> Optional[int]:
pass
A_ = False
A_ = logging.get_logger("transformers-cli/serving")
def _UpperCamelCase ( __UpperCamelCase ) -> int:
lowerCamelCase_ = pipeline(
task=args.task ,model=args.model if args.model else None ,config=args.config ,tokenizer=args.tokenizer ,device=args.device ,)
return ServeCommand(__UpperCamelCase ,args.host ,args.port ,args.workers )
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = 42
class UpperCAmelCase ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def UpperCamelCase( SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ = parser.add_parser(
'serve' , help='CLI tool to run inference requests through REST and GraphQL endpoints.' )
serve_parser.add_argument(
'--task' , type=SCREAMING_SNAKE_CASE_ , choices=get_supported_tasks() , help='The task to run the pipeline on' , )
serve_parser.add_argument('--host' , type=SCREAMING_SNAKE_CASE_ , default='localhost' , help='Interface the server will listen on.' )
serve_parser.add_argument('--port' , type=SCREAMING_SNAKE_CASE_ , default=8888 , help='Port the serving will listen to.' )
serve_parser.add_argument('--workers' , type=SCREAMING_SNAKE_CASE_ , default=1 , help='Number of http workers' )
serve_parser.add_argument('--model' , type=SCREAMING_SNAKE_CASE_ , help='Model\'s name or path to stored model.' )
serve_parser.add_argument('--config' , type=SCREAMING_SNAKE_CASE_ , help='Model\'s config name or path to stored model.' )
serve_parser.add_argument('--tokenizer' , type=SCREAMING_SNAKE_CASE_ , help='Tokenizer name to use.' )
serve_parser.add_argument(
'--device' , type=SCREAMING_SNAKE_CASE_ , default=-1 , help='Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)' , )
serve_parser.set_defaults(func=SCREAMING_SNAKE_CASE_ )
def __init__( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = pipeline
lowerCamelCase_ = host
lowerCamelCase_ = port
lowerCamelCase_ = workers
if not _serve_dependencies_installed:
raise RuntimeError(
'Using serve command requires FastAPI and uvicorn. '
'Please install transformers with [serving]: pip install "transformers[serving]".'
'Or install FastAPI and uvicorn separately.' )
else:
logger.info(f'''Serving model over {host}:{port}''' )
lowerCamelCase_ = FastAPI(
routes=[
APIRoute(
'/' , self.model_info , response_model=SCREAMING_SNAKE_CASE_ , response_class=SCREAMING_SNAKE_CASE_ , methods=['GET'] , ),
APIRoute(
'/tokenize' , self.tokenize , response_model=SCREAMING_SNAKE_CASE_ , response_class=SCREAMING_SNAKE_CASE_ , methods=['POST'] , ),
APIRoute(
'/detokenize' , self.detokenize , response_model=SCREAMING_SNAKE_CASE_ , response_class=SCREAMING_SNAKE_CASE_ , methods=['POST'] , ),
APIRoute(
'/forward' , self.forward , response_model=SCREAMING_SNAKE_CASE_ , response_class=SCREAMING_SNAKE_CASE_ , methods=['POST'] , ),
] , timeout=600 , )
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
run(self._app , host=self.host , port=self.port , workers=self.workers )
def UpperCamelCase( self ) -> int:
'''simple docstring'''
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = Body(SCREAMING_SNAKE_CASE_ , embed=SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ = Body(SCREAMING_SNAKE_CASE_ , embed=SCREAMING_SNAKE_CASE_ ) ) -> Dict:
'''simple docstring'''
try:
lowerCamelCase_ = self._pipeline.tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
if return_ids:
lowerCamelCase_ = self._pipeline.tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
return ServeTokenizeResult(tokens=SCREAMING_SNAKE_CASE_ , tokens_ids=SCREAMING_SNAKE_CASE_ )
else:
return ServeTokenizeResult(tokens=SCREAMING_SNAKE_CASE_ )
except Exception as e:
raise HTTPException(status_code=500 , detail={'model': '', 'error': str(SCREAMING_SNAKE_CASE_ )} )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ = Body(SCREAMING_SNAKE_CASE_ , embed=SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ = Body(SCREAMING_SNAKE_CASE_ , embed=SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ = Body(SCREAMING_SNAKE_CASE_ , embed=SCREAMING_SNAKE_CASE_ ) , ) -> Tuple:
'''simple docstring'''
try:
lowerCamelCase_ = self._pipeline.tokenizer.decode(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
return ServeDeTokenizeResult(model='' , text=SCREAMING_SNAKE_CASE_ )
except Exception as e:
raise HTTPException(status_code=500 , detail={'model': '', 'error': str(SCREAMING_SNAKE_CASE_ )} )
async def UpperCamelCase( self , SCREAMING_SNAKE_CASE_=Body(SCREAMING_SNAKE_CASE_ , embed=SCREAMING_SNAKE_CASE_ ) ) -> int:
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE_ ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
lowerCamelCase_ = self._pipeline(SCREAMING_SNAKE_CASE_ )
return ServeForwardResult(output=SCREAMING_SNAKE_CASE_ )
except Exception as e:
raise HTTPException(500 , {'error': str(SCREAMING_SNAKE_CASE_ )} )
| 42
|
'''simple docstring'''
A_ = "Input must be a string of 8 numbers plus letter"
A_ = "TRWAGMYFPDXBNJZSQVHLCKE"
def _UpperCamelCase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
lowerCamelCase_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
lowerCamelCase_ = spanish_id.replace('-' ,'' ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
lowerCamelCase_ = int(spanish_id_clean[0:8] )
lowerCamelCase_ = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(__UpperCamelCase ) from ex
if letter.isdigit():
raise ValueError(__UpperCamelCase )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42
| 1
|
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class lowerCamelCase (unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
SCREAMING_SNAKE_CASE__ = XLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
SCREAMING_SNAKE_CASE__ = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
SCREAMING_SNAKE_CASE__ = torch.Size((1, 1_2, 7_6_8) ) # batch_size, sequence_length, embedding_vector_dim
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-0.0_101, 0.1_218, -0.0_803, 0.0_801, 0.1_327, 0.0_776, -0.1_215, 0.2_383, 0.3_338, 0.3_106, 0.0_300, 0.0_252]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(__UpperCAmelCase )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , __UpperCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , __UpperCAmelCase , atol=1e-3 ) )
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE__ = XLMRobertaModel.from_pretrained("""xlm-roberta-large""" )
SCREAMING_SNAKE_CASE__ = torch.tensor([[0, 5_8_1, 1_0_2_6_9, 8_3, 9_9_9_4_2, 1_3_6, 6_0_7_4_2, 2_3, 7_0, 8_0_5_8_3, 1_8_2_7_6, 2]] )
# The dog is cute and lives in the garden house
SCREAMING_SNAKE_CASE__ = torch.Size((1, 1_2, 1_0_2_4) ) # batch_size, sequence_length, embedding_vector_dim
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-0.0_699, -0.0_318, 0.0_705, -0.1_241, 0.0_999, -0.0_520, 0.1_004, -0.1_838, -0.4_704, 0.1_437, 0.0_821, 0.0_126]] )
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
# xlmr.eval()
# expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
with torch.no_grad():
SCREAMING_SNAKE_CASE__ = model(__UpperCAmelCase )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , __UpperCAmelCase )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , __UpperCAmelCase , atol=1e-3 ) )
| 616
|
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(snake_case__ ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def A ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def A ( ):
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(snake_case__ ):
http_head("""https://huggingface.co""" )
| 616
| 1
|
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class UpperCamelCase :
# setable values
a__ :Optional[int] = None
a__ :Optional[jnp.ndarray] = None
a__ :Optional[jnp.ndarray] = None # sigma(t_i)
@classmethod
def A_ (cls ) -> str:
return cls()
@dataclass
class UpperCamelCase ( __a ):
a__ :jnp.ndarray
a__ :jnp.ndarray
a__ :KarrasVeSchedulerState
class UpperCamelCase ( __a , __a ):
@property
def A_ (self ) -> Union[str, Any]:
return True
@register_to_config
def __init__(self , __UpperCamelCase = 0.02 , __UpperCamelCase = 100 , __UpperCamelCase = 1.007 , __UpperCamelCase = 80 , __UpperCamelCase = 0.05 , __UpperCamelCase = 50 , ) -> Any:
pass
def A_ (self ) -> Dict:
return KarrasVeSchedulerState.create()
def A_ (self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = () ) -> KarrasVeSchedulerState:
UpperCamelCase_ : str = jnp.arange(0 , __UpperCamelCase )[::-1].copy()
UpperCamelCase_ : Optional[int] = [
(
self.config.sigma_max**2
* (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
)
for i in timesteps
]
return state.replace(
num_inference_steps=__UpperCamelCase , schedule=jnp.array(__UpperCamelCase , dtype=jnp.floataa ) , timesteps=__UpperCamelCase , )
def A_ (self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ) -> Tuple[jnp.ndarray, float]:
if self.config.s_min <= sigma <= self.config.s_max:
UpperCamelCase_ : Optional[Any] = min(self.config.s_churn / state.num_inference_steps , 2**0.5 - 1 )
else:
UpperCamelCase_ : List[Any] = 0
# sample eps ~ N(0, S_noise^2 * I)
UpperCamelCase_ : Tuple = random.split(__UpperCamelCase , num=1 )
UpperCamelCase_ : Optional[int] = self.config.s_noise * random.normal(key=__UpperCamelCase , shape=sample.shape )
UpperCamelCase_ : int = sigma + gamma * sigma
UpperCamelCase_ : int = sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)
return sample_hat, sigma_hat
def A_ (self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , ) -> Union[FlaxKarrasVeOutput, Tuple]:
UpperCamelCase_ : Optional[int] = sample_hat + sigma_hat * model_output
UpperCamelCase_ : Dict = (sample_hat - pred_original_sample) / sigma_hat
UpperCamelCase_ : List[str] = sample_hat + (sigma_prev - sigma_hat) * derivative
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase , derivative=__UpperCamelCase , state=__UpperCamelCase )
def A_ (self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = True , ) -> Union[FlaxKarrasVeOutput, Tuple]:
UpperCamelCase_ : Union[str, Any] = sample_prev + sigma_prev * model_output
UpperCamelCase_ : str = (sample_prev - pred_original_sample) / sigma_prev
UpperCamelCase_ : Any = sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)
if not return_dict:
return (sample_prev, derivative, state)
return FlaxKarrasVeOutput(prev_sample=__UpperCamelCase , derivative=__UpperCamelCase , state=__UpperCamelCase )
def A_ (self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Dict:
raise NotImplementedError()
| 635
|
from heapq import heappop, heappush
import numpy as np
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE : np.ndarray , _SCREAMING_SNAKE_CASE : tuple[int, int] , _SCREAMING_SNAKE_CASE : tuple[int, int] , _SCREAMING_SNAKE_CASE : bool , ):
UpperCamelCase_,UpperCamelCase_ : Union[str, Any] = grid.shape
UpperCamelCase_ : List[str] = [-1, 1, 0, 0]
UpperCamelCase_ : Dict = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
UpperCamelCase_,UpperCamelCase_ : List[Any] = [(0, source)], set()
UpperCamelCase_ : Any = np.full((rows, cols) , np.inf )
UpperCamelCase_ : List[str] = 0
UpperCamelCase_ : Union[str, Any] = np.empty((rows, cols) , dtype=_SCREAMING_SNAKE_CASE )
UpperCamelCase_ : Optional[int] = None
while queue:
((UpperCamelCase_),(UpperCamelCase_)) : Any = heappop(_SCREAMING_SNAKE_CASE )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
UpperCamelCase_ : Tuple = []
while (x, y) != source:
path.append((x, y) )
UpperCamelCase_,UpperCamelCase_ : Union[str, Any] = predecessors[x, y]
path.append(_SCREAMING_SNAKE_CASE ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
UpperCamelCase_,UpperCamelCase_ : Optional[Any] = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
UpperCamelCase_ : Union[str, Any] = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(_SCREAMING_SNAKE_CASE , (dist + 1, (nx, ny)) )
UpperCamelCase_ : Optional[Any] = dist + 1
UpperCamelCase_ : List[Any] = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 635
| 1
|
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_lowercase = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=128, activation='relu'))
classifier.add(layers.Dense(units=1, activation='sigmoid'))
# Compiling the CNN
classifier.compile(
optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
_lowercase = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
_lowercase = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)
_lowercase = train_datagen.flow_from_directory(
'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
)
_lowercase = test_datagen.flow_from_directory(
'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
)
classifier.save('cnn.h5')
# Part 3 - Making new predictions
_lowercase = tf.keras.preprocessing.image.load_img(
'dataset/single_prediction/image.png', target_size=(64, 64)
)
_lowercase = tf.keras.preprocessing.image.img_to_array(test_image)
_lowercase = np.expand_dims(test_image, axis=0)
_lowercase = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
_lowercase = 'Normal'
if result[0][0] == 1:
_lowercase = 'Abnormality detected'
| 242
|
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class lowerCamelCase__ :
def __init__( self : Any , __a : str , __a : int = 13 , __a : int = 64 , __a : int = 2 , __a : int = 3 , __a : int = 3 , __a : bool = True , __a : bool = True , __a : int = 128 , __a : Any=[16, 32, 64, 128] , __a : int = 7 , __a : int = 4 , __a : int = 37 , __a : str = "gelu" , __a : float = 0.1 , __a : float = 0.1 , __a : int = 10 , __a : float = 0.02 , __a : int = 2 , __a : int = 1 , __a : int = 128 , __a : List[int] = [2, 2, 2, 2] , __a : int = 2 , __a : int = 2 , ):
'''simple docstring'''
lowerCamelCase__: Any = parent
lowerCamelCase__: Optional[int] = batch_size
lowerCamelCase__: List[Any] = image_size
lowerCamelCase__: Dict = patch_size
lowerCamelCase__: int = num_channels
lowerCamelCase__: Any = is_training
lowerCamelCase__: List[Any] = use_labels
lowerCamelCase__: List[Any] = hidden_size
lowerCamelCase__: Optional[Any] = num_hidden_layers
lowerCamelCase__: Optional[Any] = num_attention_heads
lowerCamelCase__: int = intermediate_size
lowerCamelCase__: Dict = hidden_act
lowerCamelCase__: Any = hidden_dropout_prob
lowerCamelCase__: Dict = attention_probs_dropout_prob
lowerCamelCase__: Union[str, Any] = type_sequence_label_size
lowerCamelCase__: List[str] = initializer_range
lowerCamelCase__: List[str] = encoder_stride
lowerCamelCase__: Dict = num_attention_outputs
lowerCamelCase__: Dict = embed_dim
lowerCamelCase__: Optional[int] = embed_dim + 1
lowerCamelCase__: str = resolution
lowerCamelCase__: Dict = depths
lowerCamelCase__: Optional[int] = hidden_sizes
lowerCamelCase__: Any = dim
lowerCamelCase__: Optional[Any] = mlp_expansion_ratio
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__: Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__: Any = None
if self.use_labels:
lowerCamelCase__: List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__: Dict = self.get_config()
return config, pixel_values, labels
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
return EfficientFormerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def lowerCamelCase_ ( self : Optional[int] , __a : str , __a : Optional[Any] , __a : Dict ):
'''simple docstring'''
lowerCamelCase__: int = TFEfficientFormerModel(config=__a )
lowerCamelCase__: List[str] = model(__a , training=__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase_ ( self : int , __a : Optional[Any] , __a : List[str] , __a : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__: Optional[int] = self.type_sequence_label_size
lowerCamelCase__: List[str] = TFEfficientFormerForImageClassification(__a )
lowerCamelCase__: List[Any] = model(__a , labels=__a , training=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase__: Any = 1
lowerCamelCase__: int = TFEfficientFormerForImageClassification(__a )
lowerCamelCase__: Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase__: int = model(__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowerCamelCase__: Any = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__: Tuple = config_and_inputs
lowerCamelCase__: List[str] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase__ ( A__ , A__ , unittest.TestCase ):
__lowerCamelCase = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__lowerCamelCase = (
{
"""feature-extraction""": TFEfficientFormerModel,
"""image-classification""": (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowerCamelCase__: Optional[int] = TFEfficientFormerModelTester(self )
lowerCamelCase__: Union[str, Any] = ConfigTester(
self , config_class=__a , has_text_modality=__a , hidden_size=37 )
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
pass
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__: Optional[int] = model_class(__a )
lowerCamelCase__: int = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__: Dict = [*signature.parameters.keys()]
lowerCamelCase__: str = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __a )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
def check_hidden_states_output(__a : List[str] , __a : str , __a : Tuple ):
lowerCamelCase__: List[Any] = model_class(__a )
lowerCamelCase__: Optional[Any] = model(**self._prepare_for_class(__a , __a ) , training=__a )
lowerCamelCase__: Tuple = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
lowerCamelCase__: Union[str, Any] = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__a ) , __a )
if hasattr(self.model_tester , """encoder_seq_length""" ):
lowerCamelCase__: int = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
lowerCamelCase__: Optional[Any] = seq_length * self.model_tester.chunk_length
else:
lowerCamelCase__: Union[str, Any] = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
lowerCamelCase__: Optional[Any] = outputs.decoder_hidden_states
self.asseretIsInstance(__a , (list, tuple) )
self.assertEqual(len(__a ) , __a )
lowerCamelCase__: List[Any] = getattr(self.model_tester , """seq_length""" , __a )
lowerCamelCase__: Optional[int] = getattr(self.model_tester , """decoder_seq_length""" , __a )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
lowerCamelCase__ , lowerCamelCase__: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__: Any = True
check_hidden_states_output(__a , __a , __a )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__: str = True
check_hidden_states_output(__a , __a , __a )
def lowerCamelCase_ ( self : List[Any] , __a : int , __a : Tuple , __a : str=False ):
'''simple docstring'''
lowerCamelCase__: List[str] = super()._prepare_for_class(__a , __a , return_labels=__a )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
lowerCamelCase__: List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__a )
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
lowerCamelCase__: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__a )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__: List[Any] = TFEfficientFormerModel.from_pretrained(__a )
self.assertIsNotNone(__a )
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: str = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__: str = True
lowerCamelCase__: Tuple = getattr(self.model_tester , """seq_length""" , __a )
lowerCamelCase__: Tuple = getattr(self.model_tester , """encoder_seq_length""" , __a )
lowerCamelCase__: Optional[Any] = getattr(self.model_tester , """key_length""" , __a )
lowerCamelCase__: Tuple = getattr(self.model_tester , """chunk_length""" , __a )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
lowerCamelCase__: Tuple = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
lowerCamelCase__: List[str] = True
lowerCamelCase__: Dict = False
lowerCamelCase__: str = True
lowerCamelCase__: int = model_class(__a )
lowerCamelCase__: Any = model(**self._prepare_for_class(__a , __a ) , training=__a )
lowerCamelCase__: Optional[int] = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__a ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCamelCase__: Optional[Any] = True
lowerCamelCase__: str = model_class(__a )
lowerCamelCase__: Optional[Any] = model(**self._prepare_for_class(__a , __a ) , training=__a )
lowerCamelCase__: Tuple = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__a ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__: Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
lowerCamelCase__: List[str] = model_class(__a )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
lowerCamelCase__: str = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=__a )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
lowerCamelCase__: Optional[int] = model(__a )
self.assertTrue(outputs_dict is not None )
def __lowerCAmelCase ( ) -> Any:
'''simple docstring'''
lowerCamelCase__: str = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class lowerCamelCase__ ( unittest.TestCase ):
@cached_property
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__: Any = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
lowerCamelCase__: str = self.default_image_processor
lowerCamelCase__: List[Any] = prepare_img()
lowerCamelCase__: List[str] = image_processor(images=__a , return_tensors="""tf""" )
# forward pass
lowerCamelCase__: int = model(**__a , training=__a )
# verify the logits
lowerCamelCase__: Union[str, Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
lowerCamelCase__: str = tf.constant([-0.0_555, 0.4_825, -0.0_852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , __a , atol=1e-4 ) )
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
lowerCamelCase__: Tuple = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
lowerCamelCase__: Union[str, Any] = self.default_image_processor
lowerCamelCase__: List[Any] = prepare_img()
lowerCamelCase__: Any = image_processor(images=__a , return_tensors="""tf""" )
# forward pass
lowerCamelCase__: Union[str, Any] = model(**__a , training=__a )
# verify the logits
lowerCamelCase__: Any = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __a )
lowerCamelCase__: Optional[int] = tf.constant([-0.1_312, 0.4_353, -1.0_499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , __a , atol=1e-4 ) )
| 242
| 1
|
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel
@require_tf
class _A :
'''simple docstring'''
__lowerCamelCase : List[Any] = MBartConfig
__lowerCamelCase : Dict = {}
__lowerCamelCase : str = '''gelu'''
def __init__( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=13 ,SCREAMING_SNAKE_CASE_=7 ,SCREAMING_SNAKE_CASE_=True ,SCREAMING_SNAKE_CASE_=False ,SCREAMING_SNAKE_CASE_=99 ,SCREAMING_SNAKE_CASE_=32 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=4 ,SCREAMING_SNAKE_CASE_=37 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=0.1 ,SCREAMING_SNAKE_CASE_=20 ,SCREAMING_SNAKE_CASE_=2 ,SCREAMING_SNAKE_CASE_=1 ,SCREAMING_SNAKE_CASE_=0 ,):
'''simple docstring'''
snake_case : int = parent
snake_case : List[str] = batch_size
snake_case : List[Any] = seq_length
snake_case : Optional[int] = is_training
snake_case : int = use_labels
snake_case : Any = vocab_size
snake_case : str = hidden_size
snake_case : Union[str, Any] = num_hidden_layers
snake_case : int = num_attention_heads
snake_case : Any = intermediate_size
snake_case : Tuple = hidden_dropout_prob
snake_case : str = attention_probs_dropout_prob
snake_case : List[str] = max_position_embeddings
snake_case : List[Any] = eos_token_id
snake_case : Optional[int] = pad_token_id
snake_case : Optional[Any] = bos_token_id
def snake_case_ ( self ):
'''simple docstring'''
snake_case : str = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
snake_case : Optional[Any] = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
snake_case : Optional[Any] = tf.concat([input_ids, eos_tensor] ,axis=1 )
snake_case : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case : int = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
snake_case : Tuple = prepare_mbart_inputs_dict(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
return config, inputs_dict
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Dict = TFMBartModel(config=_UpperCAmelCase ).get_decoder()
snake_case : int = inputs_dict["""input_ids"""]
snake_case : Tuple = input_ids[:1, :]
snake_case : List[str] = inputs_dict["""attention_mask"""][:1, :]
snake_case : Union[str, Any] = inputs_dict["""head_mask"""]
snake_case : Tuple = 1
# first forward pass
snake_case : Union[str, Any] = model(_UpperCAmelCase ,attention_mask=_UpperCAmelCase ,head_mask=_UpperCAmelCase ,use_cache=_UpperCAmelCase )
snake_case , snake_case : str = outputs.to_tuple()
snake_case : Tuple = past_key_values[1]
def lowercase ( __A : Optional[int] , __A : Tuple , __A : str , __A : int=None , __A : List[Any]=None , __A : str=None , __A : str=None , __A : Optional[Any]=None , ) -> List[str]:
'''simple docstring'''
if attention_mask is None:
snake_case : int = tf.cast(tf.math.not_equal(__lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case : Tuple = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case : Any = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case : Dict = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
snake_case : Optional[int] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _A ( UpperCAmelCase__ , UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : List[str] = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
__lowerCamelCase : List[str] = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
__lowerCamelCase : Optional[int] = (
{
'''conversational''': TFMBartForConditionalGeneration,
'''feature-extraction''': TFMBartModel,
'''summarization''': TFMBartForConditionalGeneration,
'''text2text-generation''': TFMBartForConditionalGeneration,
'''translation''': TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
__lowerCamelCase : Optional[Any] = True
__lowerCamelCase : Tuple = False
__lowerCamelCase : Optional[Any] = False
def snake_case_ ( self ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = TFMBartModelTester(self )
snake_case : str = ConfigTester(self ,config_class=_UpperCAmelCase )
def snake_case_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class _A ( unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Tuple = [
''' UN Chief Says There Is No Military Solution in Syria''',
]
__lowerCamelCase : List[Any] = [
'''Şeful ONU declară că nu există o soluţie militară în Siria''',
]
__lowerCamelCase : Optional[Any] = '''facebook/mbart-large-en-ro'''
@cached_property
def snake_case_ ( self ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def snake_case_ ( self ):
'''simple docstring'''
snake_case : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def snake_case_ ( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Any = self.translate_src_text(**_UpperCAmelCase )
self.assertListEqual(self.expected_text ,_UpperCAmelCase )
def snake_case_ ( self ,**SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case : Optional[int] = self.tokenizer(self.src_text ,**_UpperCAmelCase ,return_tensors="""tf""" )
snake_case : Optional[int] = self.model.generate(
model_inputs.input_ids ,attention_mask=model_inputs.attention_mask ,num_beams=2 )
snake_case : Union[str, Any] = self.tokenizer.batch_decode(_UpperCAmelCase ,skip_special_tokens=_UpperCAmelCase )
return generated_words
@slow
def snake_case_ ( self ):
'''simple docstring'''
self._assert_generated_batch_equal_expected()
| 36
|
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
snake_case__ : List[str] = TypeVar("""T""")
def _snake_case (__lowercase):
return (position - 1) // 2
def _snake_case (__lowercase):
return (2 * position) + 1
def _snake_case (__lowercase):
return (2 * position) + 2
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> None:
UpperCamelCase_ = []
UpperCamelCase_ = {}
UpperCamelCase_ = 0
def __len__( self ) -> int:
return self.elements
def __repr__( self ) -> str:
return str(self.heap )
def _UpperCAmelCase ( self ) -> bool:
# Check if the priority queue is empty
return self.elements == 0
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> None:
# Add an element with given priority to the queue
self.heap.append((elem, weight) )
UpperCamelCase_ = self.elements
self.elements += 1
self._bubble_up(_UpperCAmelCase )
def _UpperCAmelCase ( self ) -> T:
# Remove and return the element with lowest weight (highest priority)
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
UpperCamelCase_ , UpperCamelCase_ = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
UpperCamelCase_ , UpperCamelCase_ = self.heap[0]
self._bubble_down(_UpperCAmelCase )
return elem
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> None:
# Update the weight of the given key
UpperCamelCase_ = self.position_map[elem]
UpperCamelCase_ = (elem, weight)
if position > 0:
UpperCamelCase_ = get_parent_position(_UpperCAmelCase )
UpperCamelCase_ , UpperCamelCase_ = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(_UpperCAmelCase )
else:
self._bubble_down(_UpperCAmelCase )
else:
self._bubble_down(_UpperCAmelCase )
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> None:
# Place a node at the proper position (upward movement) [to be used internally
# only]
UpperCamelCase_ = self.position_map[elem]
if curr_pos == 0:
return None
UpperCamelCase_ = get_parent_position(_UpperCAmelCase )
UpperCamelCase_ , UpperCamelCase_ = self.heap[curr_pos]
UpperCamelCase_ , UpperCamelCase_ = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(_UpperCAmelCase , _UpperCAmelCase )
return self._bubble_up(_UpperCAmelCase )
return None
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> None:
# Place a node at the proper position (downward movement) [to be used
# internally only]
UpperCamelCase_ = self.position_map[elem]
UpperCamelCase_ , UpperCamelCase_ = self.heap[curr_pos]
UpperCamelCase_ = get_child_left_position(_UpperCAmelCase )
UpperCamelCase_ = get_child_right_position(_UpperCAmelCase )
if child_left_position < self.elements and child_right_position < self.elements:
UpperCamelCase_ , UpperCamelCase_ = self.heap[child_left_position]
UpperCamelCase_ , UpperCamelCase_ = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(_UpperCAmelCase , _UpperCAmelCase )
return self._bubble_down(_UpperCAmelCase )
if child_left_position < self.elements:
UpperCamelCase_ , UpperCamelCase_ = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(_UpperCAmelCase , _UpperCAmelCase )
return self._bubble_down(_UpperCAmelCase )
else:
return None
if child_right_position < self.elements:
UpperCamelCase_ , UpperCamelCase_ = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(_UpperCAmelCase , _UpperCAmelCase )
return self._bubble_down(_UpperCAmelCase )
return None
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> None:
# Swap the nodes at the given positions
UpperCamelCase_ = self.heap[nodea_pos][0]
UpperCamelCase_ = self.heap[nodea_pos][0]
UpperCamelCase_ , UpperCamelCase_ = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
UpperCamelCase_ = nodea_pos
UpperCamelCase_ = nodea_pos
class _a ( Generic[T] ):
"""simple docstring"""
def __init__( self ) -> None:
UpperCamelCase_ = {}
UpperCamelCase_ = 0
def __repr__( self ) -> str:
return str(self.connections )
def __len__( self ) -> int:
return self.nodes
def _UpperCAmelCase ( self , _UpperCAmelCase ) -> None:
# Add a node in the graph if it is not in the graph
if node not in self.connections:
UpperCamelCase_ = {}
self.nodes += 1
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> None:
# Add an edge between 2 nodes in the graph
self.add_node(_UpperCAmelCase )
self.add_node(_UpperCAmelCase )
UpperCamelCase_ = weight
UpperCamelCase_ = weight
def _snake_case (__lowercase , ):
UpperCamelCase_ = {node: maxsize for node in graph.connections}
UpperCamelCase_ = {node: None for node in graph.connections}
UpperCamelCase_ = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(__lowercase , __lowercase)
if priority_queue.is_empty():
return dist, parent
# initialization
UpperCamelCase_ = priority_queue.extract_min()
UpperCamelCase_ = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCamelCase_ = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__lowercase , dist[neighbour])
UpperCamelCase_ = node
# running prim's algorithm
while not priority_queue.is_empty():
UpperCamelCase_ = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
UpperCamelCase_ = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(__lowercase , dist[neighbour])
UpperCamelCase_ = node
return dist, parent
| 23
| 0
|
"""simple docstring"""
import qiskit
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : int ):
__a : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : int = qiskit.QuantumCircuit(_lowerCamelCase , _lowerCamelCase )
# Map the quantum measurement to the classical bits
circuit.measure([0] , [0] )
# Execute the circuit on the simulator
__a : str = qiskit.execute(_lowerCamelCase , _lowerCamelCase , shots=1_0_0_0 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(_lowerCamelCase )
if __name__ == "__main__":
print(f'Total count for various states are: {single_qubit_measure(1, 1)}')
| 63
|
"""simple docstring"""
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument(
"--original_config_file",
type=str,
required=True,
help="The YAML config file corresponding to the original architecture.",
)
parser.add_argument(
"--num_in_channels",
default=None,
type=int,
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
)
parser.add_argument(
"--image_size",
default=512,
type=int,
help=(
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
" Base. Use 768 for Stable Diffusion v2."
),
)
parser.add_argument(
"--extract_ema",
action="store_true",
help=(
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
),
)
parser.add_argument(
"--upcast_attention",
action="store_true",
help=(
"Whether the attention computation should always be upcasted. This is necessary when running stable"
" diffusion 2.1."
),
)
parser.add_argument(
"--from_safetensors",
action="store_true",
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
)
parser.add_argument(
"--to_safetensors",
action="store_true",
help="Whether to store pipeline in safetensors format or not.",
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
def __magic_name__ ( _lowerCamelCase : Optional[Any] ):
if string == "True":
return True
elif string == "False":
return False
else:
raise ValueError(F'''could not parse string as bool {string}''' )
parser.add_argument(
"--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
)
parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)
lowercase__ = parser.parse_args()
lowercase__ = download_controlnet_from_original_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
extract_ema=args.extract_ema,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
use_linear_projection=args.use_linear_projection,
cross_attention_dim=args.cross_attention_dim,
)
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 63
| 1
|
'''simple docstring'''
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A_ (lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
a__ = TransfoXLTokenizer
a__ = False
a__ = False
def _A ( self :Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
super().setUp()
snake_case_ : Dict = [
"<unk>",
"[CLS]",
"[SEP]",
"want",
"unwanted",
"wa",
"un",
"running",
",",
"low",
"l",
]
snake_case_ : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )
def _A ( self :Union[str, Any] , **lowerCAmelCase__ :Union[str, Any] ) -> Tuple:
'''simple docstring'''
snake_case_ : List[Any] = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **a__ )
def _A ( self :Optional[int] , lowerCAmelCase__ :int ) -> Any:
'''simple docstring'''
snake_case_ : Dict = "<unk> UNwanted , running"
snake_case_ : Any = "<unk> unwanted, running"
return input_text, output_text
def _A ( self :int ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : List[str] = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=a__ )
snake_case_ : Tuple = tokenizer.tokenize("<unk> UNwanted , running" )
self.assertListEqual(a__ , ["<unk>", "unwanted", ",", "running"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a__ ) , [0, 4, 8, 7] )
def _A ( self :int ) -> List[Any]:
'''simple docstring'''
snake_case_ : Union[str, Any] = TransfoXLTokenizer(lower_case=a__ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo ! how \n Are yoU ? " ) , ["hello", "!", "how", "are", "you", "?"] )
def _A ( self :Any ) -> List[Any]:
'''simple docstring'''
snake_case_ : List[str] = TransfoXLTokenizer(lower_case=a__ )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo ! how \n Are yoU ? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def _A ( self :Union[str, Any] ) -> List[str]:
'''simple docstring'''
snake_case_ : List[str] = TransfoXLTokenizer(lower_case=a__ )
snake_case_ : List[Any] = "Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?"
snake_case_ : Tuple = [
"Hello",
"(",
"bracket",
")",
"and",
"side",
"@-@",
"scrolled",
"[",
"and",
"]",
"Henry",
"\'s",
"$",
"5",
"@,@",
"000",
"with",
"3",
"@.@",
"34",
"m",
".",
"What",
"\'s",
"up",
"!",
"?",
]
self.assertListEqual(tokenizer.tokenize(a__ ) , a__ )
self.assertEqual(tokenizer.convert_tokens_to_string(a__ ) , a__ )
def _A ( self :Dict ) -> Dict:
'''simple docstring'''
snake_case_ : Union[str, Any] = self.get_tokenizer()
snake_case_ : int = len(a__ )
tokenizer.add_tokens(["new1", "new2"] )
tokenizer.move_added_token("new1" , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(a__ ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode("new1" ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , "new1" )
| 653
|
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig
A_ : Dict =[
"""openmmlab/upernet-convnext-tiny""",
# See all UperNet models at https://huggingface.co/models?filter=upernet
]
# General docstring
A_ : Dict ="""UperNetConfig"""
class __a ( nn.Module ):
def __init__( self , a__ , a__ , a__ , a__ = 0 , a__ = False , a__ = 1 , ):
super().__init__()
_lowerCamelCase = nn.Convad(
in_channels=a__ , out_channels=a__ , kernel_size=a__ , padding=a__ , bias=a__ , dilation=a__ , )
_lowerCamelCase = nn.BatchNormad(a__ )
_lowerCamelCase = nn.ReLU()
def snake_case_ ( self , a__ ):
_lowerCamelCase = self.conv(a__ )
_lowerCamelCase = self.batch_norm(a__ )
_lowerCamelCase = self.activation(a__ )
return output
class __a ( nn.Module ):
def __init__( self , a__ , a__ , a__ ):
super().__init__()
_lowerCamelCase = [
nn.AdaptiveAvgPoolad(a__ ),
UperNetConvModule(a__ , a__ , kernel_size=1 ),
]
for i, layer in enumerate(self.layers ):
self.add_module(str(a__ ) , a__ )
def snake_case_ ( self , a__ ):
_lowerCamelCase = input
for layer in self.layers:
_lowerCamelCase = layer(a__ )
return hidden_state
class __a ( nn.Module ):
def __init__( self , a__ , a__ , a__ , a__ ):
super().__init__()
_lowerCamelCase = pool_scales
_lowerCamelCase = align_corners
_lowerCamelCase = in_channels
_lowerCamelCase = channels
_lowerCamelCase = []
for i, pool_scale in enumerate(a__ ):
_lowerCamelCase = UperNetPyramidPoolingBlock(pool_scale=a__ , in_channels=a__ , channels=a__ )
self.blocks.append(a__ )
self.add_module(str(a__ ) , a__ )
def snake_case_ ( self , a__ ):
_lowerCamelCase = []
for ppm in self.blocks:
_lowerCamelCase = ppm(a__ )
_lowerCamelCase = nn.functional.interpolate(
a__ , size=x.size()[2:] , mode='bilinear' , align_corners=self.align_corners )
ppm_outs.append(a__ )
return ppm_outs
class __a ( nn.Module ):
def __init__( self , a__ , a__ ):
super().__init__()
_lowerCamelCase = config
_lowerCamelCase = config.pool_scales # e.g. (1, 2, 3, 6)
_lowerCamelCase = in_channels
_lowerCamelCase = config.hidden_size
_lowerCamelCase = False
_lowerCamelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
# PSP Module
_lowerCamelCase = UperNetPyramidPoolingModule(
self.pool_scales , self.in_channels[-1] , self.channels , align_corners=self.align_corners , )
_lowerCamelCase = UperNetConvModule(
self.in_channels[-1] + len(self.pool_scales ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
# FPN Module
_lowerCamelCase = nn.ModuleList()
_lowerCamelCase = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
_lowerCamelCase = UperNetConvModule(a__ , self.channels , kernel_size=1 )
_lowerCamelCase = UperNetConvModule(self.channels , self.channels , kernel_size=3 , padding=1 )
self.lateral_convs.append(a__ )
self.fpn_convs.append(a__ )
_lowerCamelCase = UperNetConvModule(
len(self.in_channels ) * self.channels , self.channels , kernel_size=3 , padding=1 , )
def snake_case_ ( self ):
self.apply(self._init_weights )
def snake_case_ ( self , a__ ):
if isinstance(a__ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def snake_case_ ( self , a__ ):
_lowerCamelCase = inputs[-1]
_lowerCamelCase = [x]
psp_outs.extend(self.psp_modules(a__ ) )
_lowerCamelCase = torch.cat(a__ , dim=1 )
_lowerCamelCase = self.bottleneck(a__ )
return output
def snake_case_ ( self , a__ ):
# build laterals
_lowerCamelCase = [lateral_conv(encoder_hidden_states[i] ) for i, lateral_conv in enumerate(self.lateral_convs )]
laterals.append(self.psp_forward(a__ ) )
# build top-down path
_lowerCamelCase = len(a__ )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_lowerCamelCase = laterals[i - 1].shape[2:]
_lowerCamelCase = laterals[i - 1] + nn.functional.interpolate(
laterals[i] , size=a__ , mode='bilinear' , align_corners=self.align_corners )
# build outputs
_lowerCamelCase = [self.fpn_convs[i](laterals[i] ) for i in range(used_backbone_levels - 1 )]
# append psp feature
fpn_outs.append(laterals[-1] )
for i in range(used_backbone_levels - 1 , 0 , -1 ):
_lowerCamelCase = nn.functional.interpolate(
fpn_outs[i] , size=fpn_outs[0].shape[2:] , mode='bilinear' , align_corners=self.align_corners )
_lowerCamelCase = torch.cat(a__ , dim=1 )
_lowerCamelCase = self.fpn_bottleneck(a__ )
_lowerCamelCase = self.classifier(a__ )
return output
class __a ( nn.Module ):
def __init__( self , a__ , a__ = 2 , a__ = 3 , a__ = 1 ):
super().__init__()
_lowerCamelCase = config
_lowerCamelCase = config.auxiliary_in_channels
_lowerCamelCase = config.auxiliary_channels
_lowerCamelCase = config.auxiliary_num_convs
_lowerCamelCase = config.auxiliary_concat_input
_lowerCamelCase = in_index
_lowerCamelCase = (kernel_size // 2) * dilation
_lowerCamelCase = []
convs.append(
UperNetConvModule(
self.in_channels , self.channels , kernel_size=a__ , padding=a__ , dilation=a__ ) )
for i in range(self.num_convs - 1 ):
convs.append(
UperNetConvModule(
self.channels , self.channels , kernel_size=a__ , padding=a__ , dilation=a__ ) )
if self.num_convs == 0:
_lowerCamelCase = nn.Identity()
else:
_lowerCamelCase = nn.Sequential(*a__ )
if self.concat_input:
_lowerCamelCase = UperNetConvModule(
self.in_channels + self.channels , self.channels , kernel_size=a__ , padding=kernel_size // 2 )
_lowerCamelCase = nn.Convad(self.channels , config.num_labels , kernel_size=1 )
def snake_case_ ( self ):
self.apply(self._init_weights )
def snake_case_ ( self , a__ ):
if isinstance(a__ , nn.Convad ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
def snake_case_ ( self , a__ ):
# just take the relevant feature maps
_lowerCamelCase = encoder_hidden_states[self.in_index]
_lowerCamelCase = self.convs(a__ )
if self.concat_input:
_lowerCamelCase = self.conv_cat(torch.cat([hidden_states, output] , dim=1 ) )
_lowerCamelCase = self.classifier(a__ )
return output
class __a ( lowerCAmelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple = UperNetConfig
SCREAMING_SNAKE_CASE__ : Optional[Any] = "pixel_values"
SCREAMING_SNAKE_CASE__ : str = True
def snake_case_ ( self , a__ ):
if isinstance(a__ , a__ ):
module.backbone.init_weights()
module.decode_head.init_weights()
module.auxiliary_head.init_weights()
def snake_case_ ( self ):
self.backbone.init_weights()
self.decode_head.init_weights()
self.auxiliary_head.init_weights()
def snake_case_ ( self , a__ , a__=False ):
if isinstance(a__ , a__ ):
_lowerCamelCase = value
A_ : Union[str, Any] =R"""
Parameters:
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
A_ : int =R"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See
`attentions` under returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under
returned tensors for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes." , lowerCAmelCase__ , )
class __a ( lowerCAmelCase__ ):
def __init__( self , a__ ):
super().__init__(a__ )
_lowerCamelCase = AutoBackbone.from_config(config.backbone_config )
# Semantic segmentation head(s)
_lowerCamelCase = UperNetHead(a__ , in_channels=self.backbone.channels )
_lowerCamelCase = UperNetFCNHead(a__ ) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('batch_size, sequence_length' ) )
@replace_return_docstrings(output_type=a__ , config_class=_CONFIG_FOR_DOC )
def snake_case_ ( self , a__ = None , a__ = None , a__ = None , a__ = None , a__ = None , ):
_lowerCamelCase = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCamelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_lowerCamelCase = output_attentions if output_attentions is not None else self.config.output_attentions
_lowerCamelCase = self.backbone.forward_with_filtered_kwargs(
a__ , output_hidden_states=a__ , output_attentions=a__ )
_lowerCamelCase = outputs.feature_maps
_lowerCamelCase = self.decode_head(a__ )
_lowerCamelCase = nn.functional.interpolate(a__ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=a__ )
_lowerCamelCase = None
if self.auxiliary_head is not None:
_lowerCamelCase = self.auxiliary_head(a__ )
_lowerCamelCase = nn.functional.interpolate(
a__ , size=pixel_values.shape[2:] , mode='bilinear' , align_corners=a__ )
_lowerCamelCase = None
if labels is not None:
if self.config.num_labels == 1:
raise ValueError('The number of labels should be greater than one' )
else:
# compute weighted loss
_lowerCamelCase = CrossEntropyLoss(ignore_index=self.config.loss_ignore_index )
_lowerCamelCase = loss_fct(a__ , a__ )
_lowerCamelCase = loss_fct(a__ , a__ )
_lowerCamelCase = main_loss + self.config.auxiliary_loss_weight * auxiliary_loss
if not return_dict:
if output_hidden_states:
_lowerCamelCase = (logits,) + outputs[1:]
else:
_lowerCamelCase = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=a__ , logits=a__ , hidden_states=outputs.hidden_states , attentions=outputs.attentions , )
| 650
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Tuple = logging.get_logger(__name__)
UpperCAmelCase__ : str = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class __lowercase ( lowerCamelCase__ ):
__UpperCAmelCase = '''openai-gpt'''
__UpperCAmelCase = {
'''max_position_embeddings''': '''n_positions''',
'''hidden_size''': '''n_embd''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self , lowercase_=4_0_4_7_8 , lowercase_=5_1_2 , lowercase_=7_6_8 , lowercase_=1_2 , lowercase_=1_2 , lowercase_="gelu" , lowercase_=0.1 , lowercase_=0.1 , lowercase_=0.1 , lowercase_=1e-5 , lowercase_=0.02 , lowercase_="cls_index" , lowercase_=True , lowercase_=None , lowercase_=True , lowercase_=0.1 , **lowercase_ , ) -> Optional[int]:
__snake_case = vocab_size
__snake_case = n_positions
__snake_case = n_embd
__snake_case = n_layer
__snake_case = n_head
__snake_case = afn
__snake_case = resid_pdrop
__snake_case = embd_pdrop
__snake_case = attn_pdrop
__snake_case = layer_norm_epsilon
__snake_case = initializer_range
__snake_case = summary_type
__snake_case = summary_use_proj
__snake_case = summary_activation
__snake_case = summary_first_dropout
__snake_case = summary_proj_to_labels
super().__init__(**lowercase_)
| 676
|
from __future__ import annotations
class __lowercase :
def __init__( self , lowercase_) -> None:
__snake_case = data
__snake_case = None
__snake_case = None
def A ( snake_case__ : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def A ( snake_case__ : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def A ( snake_case__ : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def A ( ) -> None: # Main function for testing.
'''simple docstring'''
__snake_case = Node(1 )
__snake_case = Node(2 )
__snake_case = Node(3 )
__snake_case = Node(4 )
__snake_case = Node(5 )
__snake_case = Node(6 )
__snake_case = Node(7 )
__snake_case = Node(8 )
__snake_case = Node(9 )
print(is_full_binary_tree(snake_case__ ) )
print(depth_of_tree(snake_case__ ) )
print('Tree is: ' )
display(snake_case__ )
if __name__ == "__main__":
main()
| 676
| 1
|
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class __UpperCAmelCase ( __A , __A ):
"""simple docstring"""
_lowerCamelCase = 1
@register_to_config
def __init__( self , __A=2000 , __A=0.1 , __A=20 , __A=1E-3 ):
__a = None
__a = None
__a = None
def snake_case_ ( self , __A , __A = None ):
__a = torch.linspace(1 , self.config.sampling_eps , __A , device=__A )
def snake_case_ ( self , __A , __A , __A , __A=None ):
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# TODO(Patrick) better comments + non-PyTorch
# postprocess model score
__a = (
-0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
)
__a = torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff ) )
__a = std.flatten()
while len(std.shape ) < len(score.shape ):
__a = std.unsqueeze(-1 )
__a = -score / std
# compute
__a = -1.0 / len(self.timesteps )
__a = self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
__a = beta_t.flatten()
while len(beta_t.shape ) < len(x.shape ):
__a = beta_t.unsqueeze(-1 )
__a = -0.5 * beta_t * x
__a = torch.sqrt(__A )
__a = drift - diffusion**2 * score
__a = x + drift * dt
# add noise
__a = randn_tensor(x.shape , layout=x.layout , generator=__A , device=x.device , dtype=x.dtype )
__a = x_mean + diffusion * math.sqrt(-dt ) * noise
return x, x_mean
def __len__( self ):
return self.config.num_train_timesteps
| 99
|
def lowerCamelCase_ ( __UpperCamelCase ):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
A_ = grid[0]
for row_n in range(1 , len(__UpperCamelCase ) ):
A_ = grid[row_n]
A_ = fill_row(__UpperCamelCase , __UpperCamelCase )
A_ = grid[row_n]
return grid[-1][-1]
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
current_row[0] += row_above[0]
for cell_n in range(1 , len(__UpperCamelCase ) ):
current_row[cell_n] += min(current_row[cell_n - 1] , row_above[cell_n] )
return current_row
if __name__ == "__main__":
import doctest
doctest.testmod()
| 141
| 0
|
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _UpperCAmelCase ( UpperCamelCase: Tuple ):
"""simple docstring"""
__lowerCAmelCase = []
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
F"stage{idx}.patch_embed.proj.weight",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias",
F"stage{idx}.patch_embed.proj.bias",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight",
F"stage{idx}.patch_embed.norm.weight",
) )
embed.append(
(
F"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias",
F"stage{idx}.patch_embed.norm.bias",
) )
return embed
def _UpperCAmelCase ( UpperCamelCase: Optional[Any] , UpperCamelCase: Dict ):
"""simple docstring"""
__lowerCAmelCase = []
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked",
F"stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_q.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_q.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_k.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_k.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight",
F"stage{idx}.blocks.{cnt}.attn.proj_v.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias",
F"stage{idx}.blocks.{cnt}.attn.proj_v.bias",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight",
F"stage{idx}.blocks.{cnt}.attn.proj.weight",
) )
attention_weights.append(
(
F"cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias",
F"stage{idx}.blocks.{cnt}.attn.proj.bias",
) )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc1.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc1.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight", F"stage{idx}.blocks.{cnt}.mlp.fc2.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias", F"stage{idx}.blocks.{cnt}.mlp.fc2.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight", F"stage{idx}.blocks.{cnt}.norm1.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias", F"stage{idx}.blocks.{cnt}.norm1.bias") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight", F"stage{idx}.blocks.{cnt}.norm2.weight") )
attention_weights.append(
(F"cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias", F"stage{idx}.blocks.{cnt}.norm2.bias") )
return attention_weights
def _UpperCAmelCase ( UpperCamelCase: Any ):
"""simple docstring"""
__lowerCAmelCase = []
token.append((F"cvt.encoder.stages.{idx}.cls_token", "stage2.cls_token") )
return token
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCAmelCase = []
head.append(("layernorm.weight", "norm.weight") )
head.append(("layernorm.bias", "norm.bias") )
head.append(("classifier.weight", "head.weight") )
head.append(("classifier.bias", "head.bias") )
return head
def _UpperCAmelCase ( UpperCamelCase: List[Any] , UpperCamelCase: Tuple , UpperCamelCase: Optional[int] , UpperCamelCase: Any ):
"""simple docstring"""
__lowerCAmelCase = "imagenet-1k-id2label.json"
__lowerCAmelCase = 1_0_0_0
__lowerCAmelCase = "huggingface/label-files"
__lowerCAmelCase = num_labels
__lowerCAmelCase = json.load(open(cached_download(hf_hub_url(UpperCamelCase , UpperCamelCase , repo_type="dataset" ) ) , "r" ) )
__lowerCAmelCase = {int(UpperCamelCase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
__lowerCAmelCase = __lowerCAmelCase = CvtConfig(num_labels=UpperCamelCase , idalabel=UpperCamelCase , labelaid=UpperCamelCase )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1 )[-1][4:6] == "13":
__lowerCAmelCase = [1, 2, 1_0]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
__lowerCAmelCase = [1, 4, 1_6]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
__lowerCAmelCase = [2, 2, 2_0]
__lowerCAmelCase = [3, 1_2, 1_6]
__lowerCAmelCase = [1_9_2, 7_6_8, 1_0_2_4]
__lowerCAmelCase = CvtForImageClassification(UpperCamelCase )
__lowerCAmelCase = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
__lowerCAmelCase = image_size
__lowerCAmelCase = torch.load(UpperCamelCase , map_location=torch.device("cpu" ) )
__lowerCAmelCase = OrderedDict()
__lowerCAmelCase = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
__lowerCAmelCase = list_of_state_dict + cls_token(UpperCamelCase )
__lowerCAmelCase = list_of_state_dict + embeddings(UpperCamelCase )
for cnt in range(config.depth[idx] ):
__lowerCAmelCase = list_of_state_dict + attention(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = list_of_state_dict + final()
for gg in list_of_state_dict:
print(UpperCamelCase )
for i in range(len(UpperCamelCase ) ):
__lowerCAmelCase = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(UpperCamelCase )
model.save_pretrained(UpperCamelCase )
image_processor.save_pretrained(UpperCamelCase )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
UpperCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
"--cvt_model",
default="cvt-w24",
type=str,
help="Name of the cvt model you'd like to convert.",
)
parser.add_argument(
"--image_size",
default=3_8_4,
type=int,
help="Input Image Size",
)
parser.add_argument(
"--cvt_file_name",
default=R"cvtmodels\CvT-w24-384x384-IN-22k.pth",
type=str,
help="Input Image Size",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
UpperCamelCase_ = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 376
|
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class a ( __UpperCAmelCase ):
lowercase_ : BigBirdConfig
lowercase_ : jnp.dtype = jnp.floataa
lowercase_ : bool = True
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
super().setup()
__lowerCAmelCase = nn.Dense(5 , dtype=self.dtype )
def __call__( self : Optional[Any] , *snake_case__ : List[str] , **snake_case__ : str ):
"""simple docstring"""
__lowerCAmelCase = super().__call__(*snake_case__ , **snake_case__ )
__lowerCAmelCase = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class a ( __UpperCAmelCase ):
lowercase_ : List[str] = FlaxBigBirdForNaturalQuestionsModule
def _UpperCAmelCase ( UpperCamelCase: Optional[Any] , UpperCamelCase: List[str] , UpperCamelCase: Optional[Any] , UpperCamelCase: List[Any] , UpperCamelCase: Optional[Any] , UpperCamelCase: int ):
"""simple docstring"""
def cross_entropy(UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any] , UpperCamelCase: Optional[int]=None ):
__lowerCAmelCase = logits.shape[-1]
__lowerCAmelCase = (labels[..., None] == jnp.arange(UpperCamelCase )[None]).astype("f4" )
__lowerCAmelCase = jax.nn.log_softmax(UpperCamelCase , axis=-1 )
__lowerCAmelCase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
__lowerCAmelCase = reduction(UpperCamelCase )
return loss
__lowerCAmelCase = partial(UpperCamelCase , reduction=jnp.mean )
__lowerCAmelCase = cross_entropy(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = cross_entropy(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = cross_entropy(UpperCamelCase , UpperCamelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class a :
lowercase_ : str = "google/bigbird-roberta-base"
lowercase_ : int = 3_000
lowercase_ : int = 10_500
lowercase_ : int = 128
lowercase_ : int = 3
lowercase_ : int = 1
lowercase_ : int = 5
# tx_args
lowercase_ : float = 3e-5
lowercase_ : float = 0.0
lowercase_ : int = 20_000
lowercase_ : float = 0.0095
lowercase_ : str = "bigbird-roberta-natural-questions"
lowercase_ : str = "training-expt"
lowercase_ : str = "data/nq-training.jsonl"
lowercase_ : str = "data/nq-validation.jsonl"
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=snake_case__ )
__lowerCAmelCase = os.path.join(self.base_dir , self.save_dir )
__lowerCAmelCase = self.batch_size_per_device * jax.device_count()
@dataclass
class a :
lowercase_ : int
lowercase_ : int = 4_096 # no dynamic padding on TPUs
def __call__( self : List[Any] , snake_case__ : Union[str, Any] ):
"""simple docstring"""
__lowerCAmelCase = self.collate_fn(snake_case__ )
__lowerCAmelCase = jax.tree_util.tree_map(snake_case__ , snake_case__ )
return batch
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : Dict ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.fetch_inputs(features["input_ids"] )
__lowerCAmelCase = {
"input_ids": jnp.array(snake_case__ , dtype=jnp.intaa ),
"attention_mask": jnp.array(snake_case__ , dtype=jnp.intaa ),
"start_labels": jnp.array(features["start_token"] , dtype=jnp.intaa ),
"end_labels": jnp.array(features["end_token"] , dtype=jnp.intaa ),
"pooled_labels": jnp.array(features["category"] , dtype=jnp.intaa ),
}
return batch
def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : list ):
"""simple docstring"""
__lowerCAmelCase = [self._fetch_inputs(snake_case__ ) for ids in input_ids]
return zip(*snake_case__ )
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : list ):
"""simple docstring"""
__lowerCAmelCase = [1 for _ in range(len(snake_case__ ) )]
while len(snake_case__ ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def _UpperCAmelCase ( UpperCamelCase: Tuple , UpperCamelCase: Dict , UpperCamelCase: Optional[Any]=None ):
"""simple docstring"""
if seed is not None:
__lowerCAmelCase = dataset.shuffle(seed=UpperCamelCase )
for i in range(len(UpperCamelCase ) // batch_size ):
__lowerCAmelCase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(UpperCamelCase )
@partial(jax.pmap , axis_name="batch" )
def _UpperCAmelCase ( UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] , **UpperCamelCase: List[str] ):
"""simple docstring"""
def loss_fn(UpperCamelCase: Dict ):
__lowerCAmelCase = model_inputs.pop("start_labels" )
__lowerCAmelCase = model_inputs.pop("end_labels" )
__lowerCAmelCase = model_inputs.pop("pooled_labels" )
__lowerCAmelCase = state.apply_fn(**UpperCamelCase , params=UpperCamelCase , dropout_rng=UpperCamelCase , train=UpperCamelCase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = outputs
return state.loss_fn(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , )
__lowerCAmelCase , __lowerCAmelCase = jax.random.split(UpperCamelCase )
__lowerCAmelCase = jax.value_and_grad(UpperCamelCase )
__lowerCAmelCase , __lowerCAmelCase = grad_fn(state.params )
__lowerCAmelCase = jax.lax.pmean({"loss": loss} , axis_name="batch" )
__lowerCAmelCase = jax.lax.pmean(UpperCamelCase , "batch" )
__lowerCAmelCase = state.apply_gradients(grads=UpperCamelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="batch" )
def _UpperCAmelCase ( UpperCamelCase: Optional[Any] , **UpperCamelCase: List[str] ):
"""simple docstring"""
__lowerCAmelCase = model_inputs.pop("start_labels" )
__lowerCAmelCase = model_inputs.pop("end_labels" )
__lowerCAmelCase = model_inputs.pop("pooled_labels" )
__lowerCAmelCase = state.apply_fn(**UpperCamelCase , params=state.params , train=UpperCamelCase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = outputs
__lowerCAmelCase = state.loss_fn(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = jax.lax.pmean({"loss": loss} , axis_name="batch" )
return metrics
class a ( train_state.TrainState ):
lowercase_ : Callable = struct.field(pytree_node=__UpperCAmelCase )
@dataclass
class a :
lowercase_ : Args
lowercase_ : Callable
lowercase_ : Callable
lowercase_ : Callable
lowercase_ : Callable
lowercase_ : wandb
lowercase_ : Callable = None
def UpperCAmelCase__ ( self : Tuple , snake_case__ : int , snake_case__ : List[Any] , snake_case__ : Any , snake_case__ : str=None ):
"""simple docstring"""
__lowerCAmelCase = model.params
__lowerCAmelCase = TrainState.create(
apply_fn=model.__call__ , params=snake_case__ , tx=snake_case__ , loss_fn=snake_case__ , )
if ckpt_dir is not None:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = restore_checkpoint(snake_case__ , snake_case__ )
__lowerCAmelCase = {
"lr": args.lr,
"init_lr": args.init_lr,
"warmup_steps": args.warmup_steps,
"num_train_steps": num_train_steps,
"weight_decay": args.weight_decay,
}
__lowerCAmelCase , __lowerCAmelCase = build_tx(**snake_case__ )
__lowerCAmelCase = train_state.TrainState(
step=snake_case__ , apply_fn=model.__call__ , params=snake_case__ , tx=snake_case__ , opt_state=snake_case__ , )
__lowerCAmelCase = args
__lowerCAmelCase = data_collator
__lowerCAmelCase = lr
__lowerCAmelCase = params
__lowerCAmelCase = jax_utils.replicate(snake_case__ )
return state
def UpperCAmelCase__ ( self : str , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : Any ):
"""simple docstring"""
__lowerCAmelCase = self.args
__lowerCAmelCase = len(snake_case__ ) // args.batch_size
__lowerCAmelCase = jax.random.PRNGKey(0 )
__lowerCAmelCase = jax.random.split(snake_case__ , jax.device_count() )
for epoch in range(args.max_epochs ):
__lowerCAmelCase = jnp.array(0 , dtype=jnp.floataa )
__lowerCAmelCase = get_batched_dataset(snake_case__ , args.batch_size , seed=snake_case__ )
__lowerCAmelCase = 0
for batch in tqdm(snake_case__ , total=snake_case__ , desc=F"Running EPOCH-{epoch}" ):
__lowerCAmelCase = self.data_collator(snake_case__ )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.train_step_fn(snake_case__ , snake_case__ , **snake_case__ )
running_loss += jax_utils.unreplicate(metrics["loss"] )
i += 1
if i % args.logging_steps == 0:
__lowerCAmelCase = jax_utils.unreplicate(state.step )
__lowerCAmelCase = running_loss.item() / i
__lowerCAmelCase = self.scheduler_fn(state_step - 1 )
__lowerCAmelCase = self.evaluate(snake_case__ , snake_case__ )
__lowerCAmelCase = {
"step": state_step.item(),
"eval_loss": eval_loss.item(),
"tr_loss": tr_loss,
"lr": lr.item(),
}
tqdm.write(str(snake_case__ ) )
self.logger.log(snake_case__ , commit=snake_case__ )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}" , state=snake_case__ )
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : Any , snake_case__ : Optional[Any] ):
"""simple docstring"""
__lowerCAmelCase = get_batched_dataset(snake_case__ , self.args.batch_size )
__lowerCAmelCase = len(snake_case__ ) // self.args.batch_size
__lowerCAmelCase = jnp.array(0 , dtype=jnp.floataa )
__lowerCAmelCase = 0
for batch in tqdm(snake_case__ , total=snake_case__ , desc="Evaluating ... " ):
__lowerCAmelCase = self.data_collator(snake_case__ )
__lowerCAmelCase = self.val_step_fn(snake_case__ , **snake_case__ )
running_loss += jax_utils.unreplicate(metrics["loss"] )
i += 1
return running_loss / i
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : Any ):
"""simple docstring"""
__lowerCAmelCase = jax_utils.unreplicate(snake_case__ )
print(F"SAVING CHECKPOINT IN {save_dir}" , end=" ... " )
self.model_save_fn(snake_case__ , params=state.params )
with open(os.path.join(snake_case__ , "opt_state.msgpack" ) , "wb" ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(snake_case__ , "args.joblib" ) )
joblib.dump(self.data_collator , os.path.join(snake_case__ , "data_collator.joblib" ) )
with open(os.path.join(snake_case__ , "training_state.json" ) , "w" ) as f:
json.dump({"step": state.step.item()} , snake_case__ )
print("DONE" )
def _UpperCAmelCase ( UpperCamelCase: str , UpperCamelCase: List[Any] ):
"""simple docstring"""
print(F"RESTORING CHECKPOINT FROM {save_dir}" , end=" ... " )
with open(os.path.join(UpperCamelCase , "flax_model.msgpack" ) , "rb" ) as f:
__lowerCAmelCase = from_bytes(state.params , f.read() )
with open(os.path.join(UpperCamelCase , "opt_state.msgpack" ) , "rb" ) as f:
__lowerCAmelCase = from_bytes(state.opt_state , f.read() )
__lowerCAmelCase = joblib.load(os.path.join(UpperCamelCase , "args.joblib" ) )
__lowerCAmelCase = joblib.load(os.path.join(UpperCamelCase , "data_collator.joblib" ) )
with open(os.path.join(UpperCamelCase , "training_state.json" ) , "r" ) as f:
__lowerCAmelCase = json.load(UpperCamelCase )
__lowerCAmelCase = training_state["step"]
print("DONE" )
return params, opt_state, step, args, data_collator
def _UpperCAmelCase ( UpperCamelCase: Any , UpperCamelCase: Dict , UpperCamelCase: Tuple , UpperCamelCase: Dict ):
"""simple docstring"""
__lowerCAmelCase = num_train_steps - warmup_steps
__lowerCAmelCase = optax.linear_schedule(init_value=UpperCamelCase , end_value=UpperCamelCase , transition_steps=UpperCamelCase )
__lowerCAmelCase = optax.linear_schedule(init_value=UpperCamelCase , end_value=1e-7 , transition_steps=UpperCamelCase )
__lowerCAmelCase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _UpperCAmelCase ( UpperCamelCase: Union[str, Any] , UpperCamelCase: str , UpperCamelCase: List[Any] , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
def weight_decay_mask(UpperCamelCase: int ):
__lowerCAmelCase = traverse_util.flatten_dict(UpperCamelCase )
__lowerCAmelCase = {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
return traverse_util.unflatten_dict(UpperCamelCase )
__lowerCAmelCase = scheduler_fn(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = optax.adamw(learning_rate=UpperCamelCase , weight_decay=UpperCamelCase , mask=UpperCamelCase )
return tx, lr
| 376
| 1
|
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =logging.get_logger()
# the current default level is logging.WARNING
__A =logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(lowercase__ )
def __UpperCamelCase ( self ):
'''simple docstring'''
__A =logging.get_verbosity()
__A =logging.get_logger('''transformers.models.bart.tokenization_bart''' )
__A ='''Testing 1, 2, 3'''
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(lowercase__ ) as cl:
logger.warning(lowercase__ )
self.assertEqual(cl.out , msg + '''\n''' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(lowercase__ ) as cl:
logger.warning(lowercase__ )
self.assertEqual(cl.out , '''''' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(lowercase__ ) as cl:
logger.warning(lowercase__ )
self.assertEqual(cl.out , msg + '''\n''' )
# restore to the original level
logging.set_verbosity(lowercase__ )
@mockenv(TRANSFORMERS_VERBOSITY='''error''' )
def __UpperCamelCase ( self ):
'''simple docstring'''
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
__A =logging.get_logger('''transformers.models.bart.tokenization_bart''' )
__A =os.getenv('''TRANSFORMERS_VERBOSITY''' , lowercase__ )
__A =logging.log_levels[env_level_str]
__A =logging.get_verbosity()
self.assertEqual(
lowercase__ , lowercase__ , f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}''' , )
# restore to the original level
__A =''''''
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='''super-error''' )
def __UpperCamelCase ( self ):
'''simple docstring'''
transformers.utils.logging._reset_library_root_logger()
__A =logging.logging.getLogger()
with CaptureLogger(lowercase__ ) as cl:
# this action activates the env var
logging.get_logger('''transformers.models.bart.tokenization_bart''' )
self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error''' , cl.out )
# no need to restore as nothing was changed
def __UpperCamelCase ( self ):
'''simple docstring'''
transformers.utils.logging._reset_library_root_logger()
__A =logging.get_logger('''transformers.models.bart.tokenization_bart''' )
__A ='''Testing 1, 2, 3'''
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1''' ):
# nothing should be logged as env var disables this method
with CaptureLogger(lowercase__ ) as cl:
logger.warning_advice(lowercase__ )
self.assertEqual(cl.out , '''''' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''''' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(lowercase__ ) as cl:
logger.warning_advice(lowercase__ )
self.assertEqual(cl.out , msg + '''\n''' )
def A__ ( ) ->Tuple:
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 184
|
from __future__ import annotations
from statistics import mean
def a ( A__ : list[int] , A__ : list[int] , A__ : int ) -> list[int]:
"""simple docstring"""
_lowercase =[0] * no_of_processes
_lowercase =[0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(A__ ):
_lowercase =burst_time[i]
_lowercase =[]
_lowercase =0
_lowercase =0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
_lowercase =[]
_lowercase =-1
for i in range(A__ ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(A__ )
if len(A__ ) > 0:
_lowercase =ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
_lowercase =i
total_time += burst_time[target_process]
completed += 1
_lowercase =0
_lowercase =(
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def a ( A__ : list[int] , A__ : int , A__ : list[int] ) -> list[int]:
"""simple docstring"""
_lowercase =[0] * no_of_processes
for i in range(A__ ):
_lowercase =burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('[TEST CASE 01]')
lowercase_ = 4
lowercase_ = [2, 5, 3, 7]
lowercase_ = [0, 0, 0, 0]
lowercase_ = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
lowercase_ = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time')
for i, process_id in enumerate(list(range(1, 5))):
print(
f"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
f"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
)
print(f"\nAverage waiting time = {mean(waiting_time):.5f}")
print(f"Average turnaround time = {mean(turn_around_time):.5f}")
| 291
| 0
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
lowercase_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = ['''pixel_values''']
def __init__( self : Dict , _A : bool = True , _A : Dict[str, int] = None , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : bool = True , _A : Dict[str, int] = None , _A : bool = True , _A : Union[int, float] = 1 / 255 , _A : bool = True , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : bool = True , **_A : Any , ):
"""simple docstring"""
super().__init__(**_A )
__SCREAMING_SNAKE_CASE : Optional[int] = size if size is not None else {'''shortest_edge''': 224}
__SCREAMING_SNAKE_CASE : List[Any] = get_size_dict(_A , default_to_square=_A )
__SCREAMING_SNAKE_CASE : List[str] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__SCREAMING_SNAKE_CASE : List[str] = get_size_dict(_A , default_to_square=_A , param_name='''crop_size''' )
__SCREAMING_SNAKE_CASE : int = do_resize
__SCREAMING_SNAKE_CASE : Union[str, Any] = size
__SCREAMING_SNAKE_CASE : Any = resample
__SCREAMING_SNAKE_CASE : Optional[Any] = do_center_crop
__SCREAMING_SNAKE_CASE : Any = crop_size
__SCREAMING_SNAKE_CASE : List[str] = do_rescale
__SCREAMING_SNAKE_CASE : str = rescale_factor
__SCREAMING_SNAKE_CASE : Tuple = do_normalize
__SCREAMING_SNAKE_CASE : Optional[int] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__SCREAMING_SNAKE_CASE : Optional[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
__SCREAMING_SNAKE_CASE : Dict = do_convert_rgb
def UpperCAmelCase__ ( self : Optional[Any] , _A : np.ndarray , _A : Dict[str, int] , _A : PILImageResampling = PILImageResampling.BICUBIC , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = get_size_dict(_A , default_to_square=_A )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
__SCREAMING_SNAKE_CASE : int = get_resize_output_image_size(_A , size=size['''shortest_edge'''] , default_to_square=_A )
return resize(_A , size=_A , resample=_A , data_format=_A , **_A )
def UpperCAmelCase__ ( self : Optional[Any] , _A : np.ndarray , _A : Dict[str, int] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Any , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = get_size_dict(_A )
if "height" not in size or "width" not in size:
raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(_A , size=(size['''height'''], size['''width''']) , data_format=_A , **_A )
def UpperCAmelCase__ ( self : Optional[Any] , _A : np.ndarray , _A : Union[int, float] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : int , ):
"""simple docstring"""
return rescale(_A , scale=_A , data_format=_A , **_A )
def UpperCAmelCase__ ( self : str , _A : np.ndarray , _A : Union[float, List[float]] , _A : Union[float, List[float]] , _A : Optional[Union[str, ChannelDimension]] = None , **_A : Optional[int] , ):
"""simple docstring"""
return normalize(_A , mean=_A , std=_A , data_format=_A , **_A )
def UpperCAmelCase__ ( self : int , _A : ImageInput , _A : bool = None , _A : Dict[str, int] = None , _A : PILImageResampling = None , _A : bool = None , _A : int = None , _A : bool = None , _A : float = None , _A : bool = None , _A : Optional[Union[float, List[float]]] = None , _A : Optional[Union[float, List[float]]] = None , _A : bool = None , _A : Optional[Union[str, TensorType]] = None , _A : Optional[ChannelDimension] = ChannelDimension.FIRST , **_A : Any , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = do_resize if do_resize is not None else self.do_resize
__SCREAMING_SNAKE_CASE : Dict = size if size is not None else self.size
__SCREAMING_SNAKE_CASE : Optional[Any] = get_size_dict(_A , param_name='''size''' , default_to_square=_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = resample if resample is not None else self.resample
__SCREAMING_SNAKE_CASE : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
__SCREAMING_SNAKE_CASE : Optional[Any] = crop_size if crop_size is not None else self.crop_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(_A , param_name='''crop_size''' , default_to_square=_A )
__SCREAMING_SNAKE_CASE : Optional[Any] = do_rescale if do_rescale is not None else self.do_rescale
__SCREAMING_SNAKE_CASE : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
__SCREAMING_SNAKE_CASE : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
__SCREAMING_SNAKE_CASE : Dict = image_mean if image_mean is not None else self.image_mean
__SCREAMING_SNAKE_CASE : Union[str, Any] = image_std if image_std is not None else self.image_std
__SCREAMING_SNAKE_CASE : Dict = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__SCREAMING_SNAKE_CASE : Union[str, Any] = make_list_of_images(_A )
if not valid_images(_A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__SCREAMING_SNAKE_CASE : Optional[int] = [convert_to_rgb(_A ) for image in images]
# All transformations expect numpy arrays.
__SCREAMING_SNAKE_CASE : Tuple = [to_numpy_array(_A ) for image in images]
if do_resize:
__SCREAMING_SNAKE_CASE : Optional[Any] = [self.resize(image=_A , size=_A , resample=_A ) for image in images]
if do_center_crop:
__SCREAMING_SNAKE_CASE : Tuple = [self.center_crop(image=_A , size=_A ) for image in images]
if do_rescale:
__SCREAMING_SNAKE_CASE : Any = [self.rescale(image=_A , scale=_A ) for image in images]
if do_normalize:
__SCREAMING_SNAKE_CASE : Any = [self.normalize(image=_A , mean=_A , std=_A ) for image in images]
__SCREAMING_SNAKE_CASE : Tuple = [to_channel_dimension_format(_A , _A ) for image in images]
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''pixel_values''': images}
return BatchFeature(data=_A , tensor_type=_A )
| 706
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}
class __UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
lowerCAmelCase_ = '''switch_transformers'''
lowerCAmelCase_ = ['''past_key_values''']
lowerCAmelCase_ = {'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}
def __init__( self : int , _A : Dict=3_2128 , _A : List[Any]=768 , _A : int=64 , _A : List[Any]=2048 , _A : Any=64 , _A : Dict=12 , _A : Dict=3 , _A : Optional[int]=12 , _A : str=3 , _A : int=12 , _A : List[str]=8 , _A : str=False , _A : Optional[Any]=0.01 , _A : Union[str, Any]="float32" , _A : Union[str, Any]=False , _A : str=32 , _A : Any=128 , _A : List[str]=0.1 , _A : List[Any]=1e-6 , _A : Optional[int]=0.0_01 , _A : Optional[Any]=0.0_01 , _A : List[Any]=1.0 , _A : int="relu" , _A : Union[str, Any]=True , _A : str=False , _A : Optional[int]=True , _A : List[str]=0 , _A : Optional[Any]=1 , **_A : int , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
__SCREAMING_SNAKE_CASE : Union[str, Any] = d_model
__SCREAMING_SNAKE_CASE : Optional[Any] = d_kv
__SCREAMING_SNAKE_CASE : Optional[Any] = d_ff
__SCREAMING_SNAKE_CASE : Any = num_sparse_encoder_layers
__SCREAMING_SNAKE_CASE : Dict = num_layers
__SCREAMING_SNAKE_CASE : Union[str, Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
__SCREAMING_SNAKE_CASE : Optional[int] = num_sparse_decoder_layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_encoder_layers > 0:
__SCREAMING_SNAKE_CASE : Dict = self.num_layers // self.num_sparse_encoder_layers
else:
__SCREAMING_SNAKE_CASE : List[str] = self.num_layers # HACK: this will create 0 sparse layers
# This tells us, each how many encoder layer we'll have to set a sparse layer.
if self.num_sparse_decoder_layers > 0:
__SCREAMING_SNAKE_CASE : List[str] = self.num_decoder_layers // self.num_sparse_decoder_layers
else:
__SCREAMING_SNAKE_CASE : Optional[int] = self.num_decoder_layers # HACK: this will create 0 sparse layers
__SCREAMING_SNAKE_CASE : Dict = num_heads
__SCREAMING_SNAKE_CASE : List[str] = num_experts
__SCREAMING_SNAKE_CASE : Optional[int] = expert_capacity
__SCREAMING_SNAKE_CASE : Optional[Any] = router_bias
__SCREAMING_SNAKE_CASE : Any = router_jitter_noise
if router_dtype not in ["float32", "float16", "bfloat16"]:
raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}''' )
__SCREAMING_SNAKE_CASE : Dict = router_dtype
__SCREAMING_SNAKE_CASE : Tuple = router_ignore_padding_tokens
__SCREAMING_SNAKE_CASE : List[str] = relative_attention_num_buckets
__SCREAMING_SNAKE_CASE : int = relative_attention_max_distance
__SCREAMING_SNAKE_CASE : str = dropout_rate
__SCREAMING_SNAKE_CASE : List[Any] = layer_norm_epsilon
__SCREAMING_SNAKE_CASE : Optional[Any] = initializer_factor
__SCREAMING_SNAKE_CASE : Optional[Any] = feed_forward_proj
__SCREAMING_SNAKE_CASE : Union[str, Any] = use_cache
__SCREAMING_SNAKE_CASE : Tuple = add_router_probs
__SCREAMING_SNAKE_CASE : Tuple = router_z_loss_coef
__SCREAMING_SNAKE_CASE : int = router_aux_loss_coef
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.feed_forward_proj.split('''-''' )
__SCREAMING_SNAKE_CASE : int = act_info[-1]
__SCREAMING_SNAKE_CASE : Union[str, Any] = act_info[0] == '''gated'''
if len(_A ) > 1 and act_info[0] != "gated" or len(_A ) > 2:
raise ValueError(
F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
# for backwards compatibility
if feed_forward_proj == "gated-gelu":
__SCREAMING_SNAKE_CASE : Optional[int] = '''gelu_new'''
super().__init__(
pad_token_id=_A , eos_token_id=_A , is_encoder_decoder=_A , **_A , )
| 131
| 0
|
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_from_repo,
has_file,
)
SCREAMING_SNAKE_CASE_ = "hf-internal-testing/tiny-random-bert"
SCREAMING_SNAKE_CASE_ = os.path.join(TRANSFORMERS_CACHE, '''models--hf-internal-testing--tiny-random-bert''')
SCREAMING_SNAKE_CASE_ = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A__ ( self ) -> int:
__lowerCAmelCase = cached_file(A_ , A_ )
# Should have downloaded the file in here
self.assertTrue(os.path.isdir(A_ ) )
# Cache should contain at least those three subfolders:
for subfolder in ["blobs", "refs", "snapshots"]:
self.assertTrue(os.path.isdir(os.path.join(A_ , A_ ) ) )
with open(os.path.join(A_ , """refs""" , """main""" ) ) as f:
__lowerCAmelCase = f.read()
self.assertEqual(A_ , os.path.join(A_ , """snapshots""" , A_ , A_ ) )
self.assertTrue(os.path.isfile(A_ ) )
# File is cached at the same place the second time.
__lowerCAmelCase = cached_file(A_ , A_ )
self.assertEqual(A_ , A_ )
# Using a specific revision to test the full commit hash.
__lowerCAmelCase = cached_file(A_ , A_ , revision="""9b8c223""" )
self.assertEqual(A_ , os.path.join(A_ , """snapshots""" , A_ , A_ ) )
def A__ ( self ) -> Optional[int]:
with self.assertRaisesRegex(A_ , """is not a valid model identifier""" ):
__lowerCAmelCase = cached_file("""tiny-random-bert""" , A_ )
with self.assertRaisesRegex(A_ , """is not a valid git identifier""" ):
__lowerCAmelCase = cached_file(A_ , A_ , revision="""aaaa""" )
with self.assertRaisesRegex(A_ , """does not appear to have a file named""" ):
__lowerCAmelCase = cached_file(A_ , """conf""" )
def A__ ( self ) -> Optional[Any]:
with self.assertRaisesRegex(A_ , """does not appear to have a file named""" ):
__lowerCAmelCase = cached_file(A_ , """conf""" )
with open(os.path.join(A_ , """refs""" , """main""" ) ) as f:
__lowerCAmelCase = f.read()
self.assertTrue(os.path.isfile(os.path.join(A_ , """.no_exist""" , A_ , """conf""" ) ) )
__lowerCAmelCase = cached_file(A_ , """conf""" , _raise_exceptions_for_missing_entries=A_ )
self.assertIsNone(A_ )
__lowerCAmelCase = cached_file(A_ , """conf""" , local_files_only=A_ , _raise_exceptions_for_missing_entries=A_ )
self.assertIsNone(A_ )
__lowerCAmelCase = mock.Mock()
__lowerCAmelCase = 500
__lowerCAmelCase = {}
__lowerCAmelCase = HTTPError
__lowerCAmelCase = {}
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("""requests.Session.request""" , return_value=A_ ) as mock_head:
__lowerCAmelCase = cached_file(A_ , """conf""" , _raise_exceptions_for_connection_errors=A_ )
self.assertIsNone(A_ )
# This check we did call the fake head request
mock_head.assert_called()
def A__ ( self ) -> str:
self.assertTrue(has_file("""hf-internal-testing/tiny-bert-pt-only""" , A_ ) )
self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only""" , A_ ) )
self.assertFalse(has_file("""hf-internal-testing/tiny-bert-pt-only""" , A_ ) )
def A__ ( self ) -> str:
# `get_file_from_repo` returns None if the file does not exist
self.assertIsNone(get_file_from_repo("""bert-base-cased""" , """ahah.txt""" ) )
# The function raises if the repository does not exist.
with self.assertRaisesRegex(A_ , """is not a valid model identifier""" ):
get_file_from_repo("""bert-base-case""" , A_ )
# The function raises if the revision does not exist.
with self.assertRaisesRegex(A_ , """is not a valid git identifier""" ):
get_file_from_repo("""bert-base-cased""" , A_ , revision="""ahaha""" )
__lowerCAmelCase = get_file_from_repo("""bert-base-cased""" , A_ )
# The name is the cached name which is not very easy to test, so instead we load the content.
__lowerCAmelCase = json.loads(open(A_ , """r""" ).read() )
self.assertEqual(config["""hidden_size"""] , 768 )
def A__ ( self ) -> List[str]:
with tempfile.TemporaryDirectory() as tmp_dir:
__lowerCAmelCase = Path(A_ ) / """a.txt"""
filename.touch()
self.assertEqual(get_file_from_repo(A_ , """a.txt""" ) , str(A_ ) )
self.assertIsNone(get_file_from_repo(A_ , """b.txt""" ) )
| 465
|
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
__lowercase : Tuple = logging.get_logger(__name__)
class _A ( _UpperCAmelCase ):
"""simple docstring"""
UpperCamelCase_ : List[str] = ['''pixel_values''']
def __init__( self : Optional[int] , A_ : bool = True , A_ : Optional[Dict[str, int]] = None , A_ : PILImageResampling = PILImageResampling.BILINEAR , A_ : bool = True , A_ : Dict[str, int] = None , A_ : bool = True , A_ : Union[int, float] = 1 / 255 , A_ : bool = True , A_ : Optional[Union[float, List[float]]] = None , A_ : Optional[Union[float, List[float]]] = None , **A_ : str , ) -> None:
super().__init__(**A_ )
__snake_case = size if size is not None else {'''shortest_edge''': 256}
__snake_case = get_size_dict(A_ , default_to_square=A_ )
__snake_case = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__snake_case = get_size_dict(A_ , param_name='''crop_size''' )
__snake_case = do_resize
__snake_case = size
__snake_case = resample
__snake_case = do_center_crop
__snake_case = crop_size
__snake_case = do_rescale
__snake_case = rescale_factor
__snake_case = do_normalize
__snake_case = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__snake_case = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase ( self : List[str] , A_ : np.ndarray , A_ : Dict[str, int] , A_ : PILImageResampling = PILImageResampling.BICUBIC , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : List[Any] , ) -> np.ndarray:
__snake_case = get_size_dict(A_ , default_to_square=A_ )
if "shortest_edge" not in size:
raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
__snake_case = get_resize_output_image_size(A_ , size=size['''shortest_edge'''] , default_to_square=A_ )
return resize(A_ , size=A_ , resample=A_ , data_format=A_ , **A_ )
def lowercase ( self : Tuple , A_ : np.ndarray , A_ : Dict[str, int] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Union[str, Any] , ) -> np.ndarray:
__snake_case = get_size_dict(A_ )
if "height" not in size or "width" not in size:
raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}" )
return center_crop(A_ , size=(size['''height'''], size['''width''']) , data_format=A_ , **A_ )
def lowercase ( self : Optional[int] , A_ : np.ndarray , A_ : float , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : int ) -> np.ndarray:
return rescale(A_ , scale=A_ , data_format=A_ , **A_ )
def lowercase ( self : Tuple , A_ : np.ndarray , A_ : Union[float, List[float]] , A_ : Union[float, List[float]] , A_ : Optional[Union[str, ChannelDimension]] = None , **A_ : Tuple , ) -> np.ndarray:
return normalize(A_ , mean=A_ , std=A_ , data_format=A_ , **A_ )
def lowercase ( self : List[Any] , A_ : ImageInput , A_ : Optional[bool] = None , A_ : Dict[str, int] = None , A_ : PILImageResampling = None , A_ : bool = None , A_ : Dict[str, int] = None , A_ : Optional[bool] = None , A_ : Optional[float] = None , A_ : Optional[bool] = None , A_ : Optional[Union[float, List[float]]] = None , A_ : Optional[Union[float, List[float]]] = None , A_ : Optional[Union[str, TensorType]] = None , A_ : Union[str, ChannelDimension] = ChannelDimension.FIRST , **A_ : Dict , ) -> Optional[Any]:
__snake_case = do_resize if do_resize is not None else self.do_resize
__snake_case = size if size is not None else self.size
__snake_case = get_size_dict(A_ , default_to_square=A_ )
__snake_case = resample if resample is not None else self.resample
__snake_case = do_center_crop if do_center_crop is not None else self.do_center_crop
__snake_case = crop_size if crop_size is not None else self.crop_size
__snake_case = get_size_dict(A_ , param_name='''crop_size''' )
__snake_case = do_rescale if do_rescale is not None else self.do_rescale
__snake_case = rescale_factor if rescale_factor is not None else self.rescale_factor
__snake_case = do_normalize if do_normalize is not None else self.do_normalize
__snake_case = image_mean if image_mean is not None else self.image_mean
__snake_case = image_std if image_std is not None else self.image_std
__snake_case = make_list_of_images(A_ )
if not valid_images(A_ ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# All transformations expect numpy arrays.
__snake_case = [to_numpy_array(A_ ) for image in images]
if do_resize:
__snake_case = [self.resize(image=A_ , size=A_ , resample=A_ ) for image in images]
if do_center_crop:
__snake_case = [self.center_crop(image=A_ , size=A_ ) for image in images]
if do_rescale:
__snake_case = [self.rescale(image=A_ , scale=A_ ) for image in images]
if do_normalize:
__snake_case = [self.normalize(image=A_ , mean=A_ , std=A_ ) for image in images]
__snake_case = [to_channel_dimension_format(A_ , A_ ) for image in images]
__snake_case = {'''pixel_values''': images}
return BatchFeature(data=A_ , tensor_type=A_ )
def lowercase ( self : List[str] , A_ : Optional[Any] , A_ : List[Tuple] = None ) -> List[Any]:
__snake_case = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A_ ) != len(A_ ):
raise ValueError(
'''Make sure that you pass in as many target sizes as the batch dimension of the logits''' )
if is_torch_tensor(A_ ):
__snake_case = target_sizes.numpy()
__snake_case = []
for idx in range(len(A_ ) ):
__snake_case = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode='''bilinear''' , align_corners=A_ )
__snake_case = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A_ )
else:
__snake_case = logits.argmax(dim=1 )
__snake_case = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 564
| 0
|
"""simple docstring"""
import re
from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : Dict = '''naver-clova-ix/donut-base-finetuned-docvqa'''
A__ : List[str] = (
'''This is a tool that answers a question about an document (pdf). It takes an input named `document` which '''
'''should be the document containing the information, as well as a `question` that is the question about the '''
'''document. It returns a text that contains the answer to the question.'''
)
A__ : Any = '''document_qa'''
A__ : Dict = AutoProcessor
A__ : Tuple = VisionEncoderDecoderModel
A__ : Optional[int] = ['''image''', '''text''']
A__ : List[str] = ['''text''']
def __init__( self : List[str] , *__lowerCamelCase : Tuple , **__lowerCamelCase : int ):
"""simple docstring"""
if not is_vision_available():
raise ValueError('''Pillow must be installed to use the DocumentQuestionAnsweringTool.''' )
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Tuple ):
"""simple docstring"""
_snake_case = '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
_snake_case = task_prompt.replace('''{user_input}''' , __lowerCamelCase )
_snake_case = self.pre_processor.tokenizer(
__lowerCamelCase , add_special_tokens=__lowerCamelCase , return_tensors='''pt''' ).input_ids
_snake_case = self.pre_processor(__lowerCamelCase , return_tensors='''pt''' ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def __UpperCAmelCase ( self : Dict , __lowerCamelCase : List[Any] ):
"""simple docstring"""
return self.model.generate(
inputs['''pixel_values'''].to(self.device ) , decoder_input_ids=inputs['''decoder_input_ids'''].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=__lowerCamelCase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=__lowerCamelCase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=__lowerCamelCase , ).sequences
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
_snake_case = self.pre_processor.batch_decode(__lowerCamelCase )[0]
_snake_case = sequence.replace(self.pre_processor.tokenizer.eos_token , '''''' )
_snake_case = sequence.replace(self.pre_processor.tokenizer.pad_token , '''''' )
_snake_case = re.sub(R'''<.*?>''' , '''''' , __lowerCamelCase , count=1 ).strip() # remove first task start token
_snake_case = self.pre_processor.tokenajson(__lowerCamelCase )
return sequence["answer"]
| 708
|
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
snake_case = logging.get_logger(__name__)
snake_case = ['''model.decoder.embed_positions.weights''']
def snake_case ( lowerCAmelCase_ ) -> List[str]:
if "emb" in name:
_snake_case = name.replace('''emb''' , '''model.decoder.embed_tokens''' )
if "transformer" in name:
_snake_case = name.replace('''transformer''' , '''model.decoder''' )
if "cross_attention" in name:
_snake_case = name.replace('''cross_attention''' , '''encoder_attn''' )
if "linear1" in name:
_snake_case = name.replace('''linear1''' , '''fc1''' )
if "linear2" in name:
_snake_case = name.replace('''linear2''' , '''fc2''' )
if "norm1" in name:
_snake_case = name.replace('''norm1''' , '''self_attn_layer_norm''' )
if "norm_cross" in name:
_snake_case = name.replace('''norm_cross''' , '''encoder_attn_layer_norm''' )
if "norm2" in name:
_snake_case = name.replace('''norm2''' , '''final_layer_norm''' )
if "out_norm" in name:
_snake_case = name.replace('''out_norm''' , '''model.decoder.layer_norm''' )
if "linears" in name:
_snake_case = name.replace('''linears''' , '''lm_heads''' )
if "condition_provider.conditioners.description.output_proj" in name:
_snake_case = name.replace('''condition_provider.conditioners.description.output_proj''' , '''enc_to_dec_proj''' )
return name
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Tuple[Dict, Dict]:
_snake_case = list(state_dict.keys() )
_snake_case = {}
for key in keys:
_snake_case = state_dict.pop(lowerCAmelCase_ )
_snake_case = rename_keys(lowerCAmelCase_ )
if "in_proj_weight" in key:
# split fused qkv proj
_snake_case = val[:hidden_size, :]
_snake_case = val[hidden_size : 2 * hidden_size, :]
_snake_case = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
_snake_case = val
else:
_snake_case = val
return state_dict, enc_dec_proj_state_dict
def snake_case ( lowerCAmelCase_ ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
_snake_case = 1024
_snake_case = 24
_snake_case = 16
elif checkpoint == "medium":
_snake_case = 1536
_snake_case = 48
_snake_case = 24
elif checkpoint == "large":
_snake_case = 2048
_snake_case = 48
_snake_case = 32
else:
raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
_snake_case = MusicgenDecoderConfig(
hidden_size=lowerCAmelCase_ , ffn_dim=hidden_size * 4 , num_hidden_layers=lowerCAmelCase_ , num_attention_heads=lowerCAmelCase_ , )
return config
@torch.no_grad()
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_="cpu" ) -> List[Any]:
_snake_case = MusicGen.get_pretrained(lowerCAmelCase_ , device=lowerCAmelCase_ )
_snake_case = decoder_config_from_checkpoint(lowerCAmelCase_ )
_snake_case = fairseq_model.lm.state_dict()
_snake_case , _snake_case = rename_state_dict(
lowerCAmelCase_ , hidden_size=decoder_config.hidden_size )
_snake_case = TaEncoderModel.from_pretrained('''t5-base''' )
_snake_case = EncodecModel.from_pretrained('''facebook/encodec_32khz''' )
_snake_case = MusicgenForCausalLM(lowerCAmelCase_ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
_snake_case , _snake_case = decoder.load_state_dict(lowerCAmelCase_ , strict=lowerCAmelCase_ )
for key in missing_keys.copy():
if key.startswith(('''text_encoder''', '''audio_encoder''') ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowerCAmelCase_ )
if len(lowerCAmelCase_ ) > 0:
raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}""" )
if len(lowerCAmelCase_ ) > 0:
raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
_snake_case = MusicgenForConditionalGeneration(text_encoder=lowerCAmelCase_ , audio_encoder=lowerCAmelCase_ , decoder=lowerCAmelCase_ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowerCAmelCase_ )
# check we can do a forward pass
_snake_case = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
_snake_case = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
_snake_case = model(input_ids=lowerCAmelCase_ , decoder_input_ids=lowerCAmelCase_ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError('''Incorrect shape for logits''' )
# now construct the processor
_snake_case = AutoTokenizer.from_pretrained('''t5-base''' )
_snake_case = AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' , padding_side='''left''' )
_snake_case = MusicgenProcessor(feature_extractor=lowerCAmelCase_ , tokenizer=lowerCAmelCase_ )
# set the appropriate bos/pad token ids
_snake_case = 2048
_snake_case = 2048
# set other default generation config params
_snake_case = int(30 * audio_encoder.config.frame_rate )
_snake_case = True
_snake_case = 3.0
if pytorch_dump_folder is not None:
Path(lowerCAmelCase_ ).mkdir(exist_ok=lowerCAmelCase_ )
logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(lowerCAmelCase_ )
processor.save_pretrained(lowerCAmelCase_ )
if repo_id:
logger.info(f"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(lowerCAmelCase_ )
processor.push_to_hub(lowerCAmelCase_ )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint''',
default='''small''',
type=str,
help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
)
parser.add_argument(
'''--pytorch_dump_folder''',
required=True,
default=None,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
parser.add_argument(
'''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
)
snake_case = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 404
| 0
|
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=1024 ) -> List[Any]:
"""simple docstring"""
snake_case__ , snake_case__ : int = [], []
snake_case__ : Tuple = list(zip(__lowerCAmelCase , __lowerCAmelCase ) )
snake_case__ , snake_case__ : List[str] = sorted_examples[0]
def is_too_big(__lowerCAmelCase ):
return tok(__lowerCAmelCase , return_tensors='''pt''' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
snake_case__ : str = new_src + ''' ''' + src
snake_case__ : Optional[Any] = new_tgt + ''' ''' + tgt
if is_too_big(__lowerCAmelCase ) or is_too_big(__lowerCAmelCase ): # cant fit, finalize example
finished_src.append(__lowerCAmelCase )
finished_tgt.append(__lowerCAmelCase )
snake_case__ , snake_case__ : Union[str, Any] = src, tgt
else: # can fit, keep adding
snake_case__ , snake_case__ : Tuple = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(__lowerCAmelCase )
finished_tgt.append(__lowerCAmelCase )
return finished_src, finished_tgt
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
"""simple docstring"""
snake_case__ : Optional[int] = Path(__lowerCAmelCase )
save_path.mkdir(exist_ok=__lowerCAmelCase )
for split in ["train"]:
snake_case__ , snake_case__ : str = data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
snake_case__ : Any = [x.rstrip() for x in Path(__lowerCAmelCase ).open().readlines()]
snake_case__ : str = [x.rstrip() for x in Path(__lowerCAmelCase ).open().readlines()]
snake_case__ , snake_case__ : Dict = pack_examples(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
print(f"""packed {split} split from {len(__lowerCAmelCase )} examples -> {len(__lowerCAmelCase )}.""" )
Path(save_path / f"""{split}.source""" ).open('''w''' ).write('''\n'''.join(__lowerCAmelCase ) )
Path(save_path / f"""{split}.target""" ).open('''w''' ).write('''\n'''.join(__lowerCAmelCase ) )
for split in ["val", "test"]:
snake_case__ , snake_case__ : List[Any] = data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
shutil.copyfile(__lowerCAmelCase , save_path / f"""{split}.source""" )
shutil.copyfile(__lowerCAmelCase , save_path / f"""{split}.target""" )
def _lowerCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : str = argparse.ArgumentParser()
parser.add_argument('''--tok_name''' , type=__lowerCAmelCase , help='''like facebook/bart-large-cnn,t5-base, etc.''' )
parser.add_argument('''--max_seq_len''' , type=__lowerCAmelCase , default=128 )
parser.add_argument('''--data_dir''' , type=__lowerCAmelCase )
parser.add_argument('''--save_path''' , type=__lowerCAmelCase )
snake_case__ : List[Any] = parser.parse_args()
snake_case__ : str = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(__lowerCAmelCase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 252
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
A__ = logging.get_logger(__name__)
A__ = {'''vocab_file''': '''vocab.txt'''}
A__ = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
A__ = {
'''YituTech/conv-bert-base''': 512,
'''YituTech/conv-bert-medium-small''': 512,
'''YituTech/conv-bert-small''': 512,
}
A__ = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class a ( __lowerCamelCase ):
__lowerCAmelCase : List[str] = VOCAB_FILES_NAMES
__lowerCAmelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__lowerCAmelCase : List[str] = PRETRAINED_INIT_CONFIGURATION
__lowerCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCAmelCase : Optional[Any] = ConvBertTokenizer
def __init__( self :Any ,__lowercase :Optional[int]=None ,__lowercase :str=None ,__lowercase :Union[str, Any]=True ,__lowercase :Dict="[UNK]" ,__lowercase :List[Any]="[SEP]" ,__lowercase :int="[PAD]" ,__lowercase :Union[str, Any]="[CLS]" ,__lowercase :List[str]="[MASK]" ,__lowercase :List[Any]=True ,__lowercase :List[str]=None ,**__lowercase :List[str] ,):
super().__init__(
__lowercase ,tokenizer_file=__lowercase ,do_lower_case=__lowercase ,unk_token=__lowercase ,sep_token=__lowercase ,pad_token=__lowercase ,cls_token=__lowercase ,mask_token=__lowercase ,tokenize_chinese_chars=__lowercase ,strip_accents=__lowercase ,**__lowercase ,)
snake_case__ : Tuple = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' ,__lowercase ) != do_lower_case
or normalizer_state.get('''strip_accents''' ,__lowercase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' ,__lowercase ) != tokenize_chinese_chars
):
snake_case__ : Union[str, Any] = getattr(__lowercase ,normalizer_state.pop('''type''' ) )
snake_case__ : int = do_lower_case
snake_case__ : Union[str, Any] = strip_accents
snake_case__ : List[str] = tokenize_chinese_chars
snake_case__ : Tuple = normalizer_class(**__lowercase )
snake_case__ : Any = do_lower_case
def __lowerCamelCase ( self :int ,__lowercase :Union[str, Any] ,__lowercase :List[Any]=None ):
snake_case__ : Any = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __lowerCamelCase ( self :List[Any] ,__lowercase :List[int] ,__lowercase :Optional[List[int]] = None ):
snake_case__ : str = [self.sep_token_id]
snake_case__ : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __lowerCamelCase ( self :Optional[Any] ,__lowercase :str ,__lowercase :Optional[str] = None ):
snake_case__ : Optional[int] = self._tokenizer.model.save(__lowercase ,name=__lowercase )
return tuple(__lowercase )
| 252
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_bert''': ['''BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BertConfig''', '''BertOnnxConfig'''],
'''tokenization_bert''': ['''BasicTokenizer''', '''BertTokenizer''', '''WordpieceTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''BertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BertForMaskedLM''',
'''BertForMultipleChoice''',
'''BertForNextSentencePrediction''',
'''BertForPreTraining''',
'''BertForQuestionAnswering''',
'''BertForSequenceClassification''',
'''BertForTokenClassification''',
'''BertLayer''',
'''BertLMHeadModel''',
'''BertModel''',
'''BertPreTrainedModel''',
'''load_tf_weights_in_bert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFBertEmbeddings''',
'''TFBertForMaskedLM''',
'''TFBertForMultipleChoice''',
'''TFBertForNextSentencePrediction''',
'''TFBertForPreTraining''',
'''TFBertForQuestionAnswering''',
'''TFBertForSequenceClassification''',
'''TFBertForTokenClassification''',
'''TFBertLMHeadModel''',
'''TFBertMainLayer''',
'''TFBertModel''',
'''TFBertPreTrainedModel''',
]
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ['''TFBertTokenizer''']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''FlaxBertForCausalLM''',
'''FlaxBertForMaskedLM''',
'''FlaxBertForMultipleChoice''',
'''FlaxBertForNextSentencePrediction''',
'''FlaxBertForPreTraining''',
'''FlaxBertForQuestionAnswering''',
'''FlaxBertForSequenceClassification''',
'''FlaxBertForTokenClassification''',
'''FlaxBertModel''',
'''FlaxBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_fast import BertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bert import (
BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
BertForMaskedLM,
BertForMultipleChoice,
BertForNextSentencePrediction,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertForTokenClassification,
BertLayer,
BertLMHeadModel,
BertModel,
BertPreTrainedModel,
load_tf_weights_in_bert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_bert import (
TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBertEmbeddings,
TFBertForMaskedLM,
TFBertForMultipleChoice,
TFBertForNextSentencePrediction,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertForTokenClassification,
TFBertLMHeadModel,
TFBertMainLayer,
TFBertModel,
TFBertPreTrainedModel,
)
try:
if not is_tensorflow_text_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bert_tf import TFBertTokenizer
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_bert import (
FlaxBertForCausalLM,
FlaxBertForMaskedLM,
FlaxBertForMultipleChoice,
FlaxBertForNextSentencePrediction,
FlaxBertForPreTraining,
FlaxBertForQuestionAnswering,
FlaxBertForSequenceClassification,
FlaxBertForTokenClassification,
FlaxBertModel,
FlaxBertPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 719
|
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFViTMAEForPreTraining, TFViTMAEModel
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class snake_case__:
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : int=13 , SCREAMING_SNAKE_CASE : Union[str, Any]=30 , SCREAMING_SNAKE_CASE : Any=2 , SCREAMING_SNAKE_CASE : Optional[Any]=3 , SCREAMING_SNAKE_CASE : Dict=True , SCREAMING_SNAKE_CASE : Optional[Any]=True , SCREAMING_SNAKE_CASE : List[str]=32 , SCREAMING_SNAKE_CASE : Optional[int]=2 , SCREAMING_SNAKE_CASE : str=4 , SCREAMING_SNAKE_CASE : List[Any]=37 , SCREAMING_SNAKE_CASE : Tuple="gelu" , SCREAMING_SNAKE_CASE : List[str]=0.1 , SCREAMING_SNAKE_CASE : List[Any]=0.1 , SCREAMING_SNAKE_CASE : int=10 , SCREAMING_SNAKE_CASE : List[str]=0.02 , SCREAMING_SNAKE_CASE : Tuple=3 , SCREAMING_SNAKE_CASE : str=0.6 , SCREAMING_SNAKE_CASE : Optional[Any]=None , ):
lowercase__ : Union[str, Any] = parent
lowercase__ : Optional[int] = batch_size
lowercase__ : Union[str, Any] = image_size
lowercase__ : List[Any] = patch_size
lowercase__ : Any = num_channels
lowercase__ : Optional[int] = is_training
lowercase__ : Dict = use_labels
lowercase__ : Any = hidden_size
lowercase__ : List[Any] = num_hidden_layers
lowercase__ : Union[str, Any] = num_attention_heads
lowercase__ : Dict = intermediate_size
lowercase__ : Optional[int] = hidden_act
lowercase__ : Union[str, Any] = hidden_dropout_prob
lowercase__ : Union[str, Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = type_sequence_label_size
lowercase__ : Any = initializer_range
lowercase__ : Optional[int] = mask_ratio
lowercase__ : Union[str, Any] = scope
# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
# (we add 1 for the [CLS] token)
lowercase__ : List[Any] = (image_size // patch_size) ** 2
lowercase__ : str = int(math.ceil((1 - mask_ratio) * (num_patches + 1) ) )
def snake_case ( self : int ):
lowercase__ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : str = None
if self.use_labels:
lowercase__ : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def snake_case ( self : Tuple ):
return ViTMAEConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , decoder_hidden_size=self.hidden_size , decoder_num_hidden_layers=self.num_hidden_layers , decoder_num_attention_heads=self.num_attention_heads , decoder_intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , mask_ratio=self.mask_ratio , )
def snake_case ( self : str , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Tuple ):
lowercase__ : Tuple = TFViTMAEModel(config=SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = model(SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self : List[str] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : Union[str, Any] = TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = model(SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
# expected sequence length = num_patches
lowercase__ : List[str] = (self.image_size // self.patch_size) ** 2
lowercase__ : List[Any] = self.patch_size**2 * self.num_channels
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
# test greyscale images
lowercase__ : Dict = 1
lowercase__ : List[Any] = TFViTMAEForPreTraining(SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : Optional[Any] = model(SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = self.patch_size**2
self.parent.assertEqual(result.logits.shape , (self.batch_size, num_patches, expected_num_channels) )
def snake_case ( self : Optional[int] ):
lowercase__ : int = self.prepare_config_and_inputs()
((lowercase__) , (lowercase__) , (lowercase__)) : Dict = config_and_inputs
lowercase__ : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class snake_case__(_UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowercase_ = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
lowercase_ = {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}
lowercase_ = False
lowercase_ = False
lowercase_ = False
lowercase_ = False
def snake_case ( self : List[str] ):
lowercase__ : List[Any] = TFViTMAEModelTester(self )
lowercase__ : List[Any] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , has_text_modality=SCREAMING_SNAKE_CASE , hidden_size=37 )
def snake_case ( self : Tuple ):
self.config_tester.run_common_tests()
@unittest.skip(reason="ViTMAE does not use inputs_embeds" )
def snake_case ( self : Union[str, Any] ):
pass
def snake_case ( self : Optional[int] ):
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[Any] = model_class(SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowercase__ : List[Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE , tf.keras.layers.Layer ) )
def snake_case ( self : Optional[Any] ):
lowercase__ , lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Union[str, Any] = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : Dict = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Union[str, Any] = [*signature.parameters.keys()]
lowercase__ : List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[Any] ):
lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[int] ):
lowercase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[Any] ):
# make the mask reproducible
np.random.seed(2 )
lowercase__ , lowercase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[Any] = int((config.image_size // config.patch_size) ** 2 )
lowercase__ : List[str] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
lowercase__ : Optional[Any] = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : int = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = model(SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
lowercase__ : Any = copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
lowercase__ : Tuple = model(**SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
lowercase__ : Union[str, Any] = outputs_dict[0].numpy()
lowercase__ : Optional[int] = outputs_keywords[0].numpy()
self.assertLess(np.sum(np.abs(output_dict - output_keywords ) ) , 1E-6 )
def snake_case ( self : str ):
# make the mask reproducible
np.random.seed(2 )
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Optional[Any] = int((config.image_size // config.patch_size) ** 2 )
lowercase__ : int = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
def prepare_numpy_arrays(SCREAMING_SNAKE_CASE : Optional[int] ):
lowercase__ : Tuple = {}
for k, v in inputs_dict.items():
if tf.is_tensor(SCREAMING_SNAKE_CASE ):
lowercase__ : Any = v.numpy()
else:
lowercase__ : List[Any] = np.array(SCREAMING_SNAKE_CASE )
return inputs_np_dict
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : Any = prepare_numpy_arrays(SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = model(SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
lowercase__ : Tuple = model(**SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
self.assert_outputs_same(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def snake_case ( self : List[Any] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[Any] ):
# make masks reproducible
np.random.seed(2 )
lowercase__ : Optional[int] = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2 )
lowercase__ : int = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowercase__ : Union[str, Any] = tf.constant(SCREAMING_SNAKE_CASE )
# Add `noise` argument.
# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
lowercase__ : Optional[int] = tf_noise
super().check_pt_tf_models(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def snake_case ( self : str ):
# make mask reproducible
np.random.seed(2 )
lowercase__ , lowercase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : int = {
module_member
for model_class in self.all_model_classes
for module in (import_module(model_class.__module__ ),)
for module_member_name in dir(SCREAMING_SNAKE_CASE )
if module_member_name.endswith("MainLayer" )
# This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
and module_member_name[: -len("MainLayer" )] == model_class.__name__[: -len("Model" )]
for module_member in (getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ),)
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
and tf.keras.layers.Layer in module_member.__bases__
and getattr(SCREAMING_SNAKE_CASE , "_keras_serializable" , SCREAMING_SNAKE_CASE )
}
lowercase__ : List[str] = int((config.image_size // config.patch_size) ** 2 )
lowercase__ : Dict = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
lowercase__ : str = tf.convert_to_tensor(SCREAMING_SNAKE_CASE )
inputs_dict.update({"noise": noise} )
for main_layer_class in tf_main_layer_classes:
lowercase__ : Tuple = main_layer_class(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = {
name: tf.keras.Input(tensor.shape[1:] , dtype=tensor.dtype ) for name, tensor in inputs_dict.items()
}
lowercase__ : Tuple = tf.keras.Model(SCREAMING_SNAKE_CASE , outputs=main_layer(SCREAMING_SNAKE_CASE ) )
lowercase__ : str = model(SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
lowercase__ : str = os.path.join(SCREAMING_SNAKE_CASE , "keras_model.h5" )
model.save(SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = tf.keras.models.load_model(
SCREAMING_SNAKE_CASE , custom_objects={main_layer_class.__name__: main_layer_class} )
assert isinstance(SCREAMING_SNAKE_CASE , tf.keras.Model )
lowercase__ : Dict = model(SCREAMING_SNAKE_CASE )
self.assert_outputs_same(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : Optional[int] ):
# make mask reproducible
np.random.seed(2 )
lowercase__ , lowercase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Union[str, Any] = int((config.image_size // config.patch_size) ** 2 )
lowercase__ : Optional[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
lowercase__ : Any = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = model(SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
if model_class.__name__ == "TFViTMAEModel":
lowercase__ : str = outputs.last_hidden_state.numpy()
lowercase__ : Optional[Any] = 0
else:
lowercase__ : Optional[Any] = outputs.logits.numpy()
lowercase__ : Optional[int] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(SCREAMING_SNAKE_CASE , saved_model=SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = model_class.from_pretrained(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = model(SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
if model_class.__name__ == "TFViTMAEModel":
lowercase__ : Optional[int] = after_outputs["last_hidden_state"].numpy()
lowercase__ : Optional[int] = 0
else:
lowercase__ : str = after_outputs["logits"].numpy()
lowercase__ : Tuple = 0
lowercase__ : Optional[Any] = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(SCREAMING_SNAKE_CASE , 1E-5 )
def snake_case ( self : List[Any] ):
# make mask reproducible
np.random.seed(2 )
lowercase__ , lowercase__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : List[str] = int((config.image_size // config.patch_size) ** 2 )
lowercase__ : List[Any] = np.random.uniform(size=(self.model_tester.batch_size, num_patches) )
for model_class in self.all_model_classes:
lowercase__ : Tuple = model_class(SCREAMING_SNAKE_CASE )
lowercase__ : Dict = self._prepare_for_class(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
lowercase__ : int = model(SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
lowercase__ : str = model.get_config()
# make sure that returned config is jsonifiable, which is required by keras
json.dumps(SCREAMING_SNAKE_CASE )
lowercase__ : int = model_class.from_config(model.get_config() )
# make sure it also accepts a normal config
lowercase__ : Any = model_class.from_config(model.config )
lowercase__ : Tuple = new_model(SCREAMING_SNAKE_CASE ) # Build model
new_model.set_weights(model.get_weights() )
lowercase__ : Union[str, Any] = new_model(SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
self.assert_outputs_same(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@unittest.skip(
reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n to get deterministic results." )
def snake_case ( self : List[Any] ):
pass
@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load" )
def snake_case ( self : str ):
pass
@slow
def snake_case ( self : List[Any] ):
lowercase__ : List[Any] = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224" )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self : Any ):
return ViTImageProcessor.from_pretrained("facebook/vit-mae-base" ) if is_vision_available() else None
@slow
def snake_case ( self : Union[str, Any] ):
# make random mask reproducible across the PT and TF model
np.random.seed(2 )
lowercase__ : Optional[Any] = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base" )
lowercase__ : Optional[Any] = self.default_image_processor
lowercase__ : Union[str, Any] = prepare_img()
lowercase__ : Tuple = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="tf" )
# prepare a noise vector that will be also used for testing the TF model
# (this way we can ensure that the PT and TF models operate on the same inputs)
lowercase__ : Union[str, Any] = ViTMAEConfig()
lowercase__ : str = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2 )
lowercase__ : List[str] = np.random.uniform(size=(1, num_patches) )
# forward pass
lowercase__ : Optional[Any] = model(**SCREAMING_SNAKE_CASE , noise=SCREAMING_SNAKE_CASE )
# verify the logits
lowercase__ : List[str] = tf.convert_to_tensor([1, 196, 768] )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = tf.convert_to_tensor(
[[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]] )
tf.debugging.assert_near(outputs.logits[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 )
| 81
| 0
|
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class _snake_case ( unittest.TestCase ):
@slow
def lowercase__ ( self):
'''simple docstring'''
lowercase__ : Dict = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base""")
lowercase__ : Optional[int] = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ : Optional[Any] = model(SCREAMING_SNAKE_CASE_)["""last_hidden_state"""]
lowercase__ : Tuple = tf.TensorShape((1, 10, 7_68))
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE_)
# compare the actual values for a slice.
lowercase__ : Optional[Any] = tf.convert_to_tensor(
[[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4))
| 12
|
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _snake_case ( *__snake_case , __snake_case = None , __snake_case=True , __snake_case=2 ):
from .. import __version__
_UpperCamelCase = take_from
_UpperCamelCase = ()
if not isinstance(args[0] , __snake_case ):
_UpperCamelCase = (args,)
for attribute, version_name, message in args:
if version.parse(version.parse(__snake_case ).base_version ) >= version.parse(__snake_case ):
raise ValueError(
f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
f""" version {__version__} is >= {version_name}""" )
_UpperCamelCase = None
if isinstance(__snake_case , __snake_case ) and attribute in deprecated_kwargs:
values += (deprecated_kwargs.pop(__snake_case ),)
_UpperCamelCase = f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
elif hasattr(__snake_case , __snake_case ):
values += (getattr(__snake_case , __snake_case ),)
_UpperCamelCase = f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
elif deprecated_kwargs is None:
_UpperCamelCase = f"""`{attribute}` is deprecated and will be removed in version {version_name}."""
if warning is not None:
_UpperCamelCase = warning + ''' ''' if standard_warn else ''''''
warnings.warn(warning + message , __snake_case , stacklevel=__snake_case )
if isinstance(__snake_case , __snake_case ) and len(__snake_case ) > 0:
_UpperCamelCase = inspect.getouterframes(inspect.currentframe() )[1]
_UpperCamelCase = call_frame.filename
_UpperCamelCase = call_frame.lineno
_UpperCamelCase = call_frame.function
_UpperCamelCase , _UpperCamelCase = next(iter(deprecated_kwargs.items() ) )
raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`""" )
if len(__snake_case ) == 0:
return
elif len(__snake_case ) == 1:
return values[0]
return values
| 10
| 0
|
"""simple docstring"""
import functools
def _snake_case ( lowerCamelCase__ : list[int] , lowerCamelCase__ : list[int] ) -> int:
# Validation
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ) or not all(isinstance(lowerCamelCase__ , lowerCamelCase__ ) for day in days ):
raise ValueError("The parameter days should be a list of integers" )
if len(lowerCamelCase__ ) != 3 or not all(isinstance(lowerCamelCase__ , lowerCamelCase__ ) for cost in costs ):
raise ValueError("The parameter costs should be a list of three integers" )
if len(lowerCamelCase__ ) == 0:
return 0
if min(lowerCamelCase__ ) <= 0:
raise ValueError("All days elements should be greater than 0" )
if max(lowerCamelCase__ ) >= 366:
raise ValueError("All days elements should be less than 366" )
lowerCamelCase_ : List[Any] =set(lowerCamelCase__ )
@functools.cache
def dynamic_programming(lowerCamelCase__ : int ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 244
|
"""simple docstring"""
from datetime import datetime as dt
import os
from github import Github
A__ : int = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def _snake_case ( ) -> List[Any]:
lowerCamelCase_ : Optional[Any] =Github(os.environ["GITHUB_TOKEN"] )
lowerCamelCase_ : Any =g.get_repo("huggingface/transformers" )
lowerCamelCase_ : Tuple =repo.get_issues(state="open" )
for issue in open_issues:
lowerCamelCase_ : Optional[Any] =sorted([comment for comment in issue.get_comments()] , key=lambda lowerCamelCase__ : i.created_at , reverse=lowerCamelCase__ )
lowerCamelCase_ : Optional[Any] =comments[0] if len(lowerCamelCase__ ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="closed" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"This issue has been automatically marked as stale because it has not had "
"recent activity. If you think this still needs to be addressed "
"please comment on this thread.\n\nPlease note that issues that do not follow the "
"[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) "
"are likely to be ignored." )
if __name__ == "__main__":
main()
| 244
| 1
|
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
)
from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class A :
'''simple docstring'''
def __init__( self : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any]=2 , __lowerCAmelCase : Any=3 , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=2 , __lowerCAmelCase : List[Any]=7 , __lowerCAmelCase : Union[str, Any]=True , __lowerCAmelCase : List[str]=True , __lowerCAmelCase : int=True , __lowerCAmelCase : List[Any]=True , __lowerCAmelCase : int=99 , __lowerCAmelCase : Dict=36 , __lowerCAmelCase : Tuple=3 , __lowerCAmelCase : Dict=4 , __lowerCAmelCase : Dict=37 , __lowerCAmelCase : Optional[Any]="gelu" , __lowerCAmelCase : Dict=0.1 , __lowerCAmelCase : Union[str, Any]=0.1 , __lowerCAmelCase : Optional[int]=5_12 , __lowerCAmelCase : List[Any]=16 , __lowerCAmelCase : str=2 , __lowerCAmelCase : Union[str, Any]=0.0_2 , __lowerCAmelCase : Optional[int]=6 , __lowerCAmelCase : Union[str, Any]=6 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : Union[str, Any]=4 , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : List[Any]=10_00 , ) -> Optional[int]:
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = image_size
A__ = patch_size
A__ = text_seq_length
A__ = is_training
A__ = use_input_mask
A__ = use_token_type_ids
A__ = use_labels
A__ = vocab_size
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = type_sequence_label_size
A__ = initializer_range
A__ = coordinate_size
A__ = shape_size
A__ = num_labels
A__ = num_choices
A__ = scope
A__ = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
A__ = text_seq_length
A__ = (image_size // patch_size) ** 2 + 1
A__ = self.text_seq_length + self.image_seq_length
def a_ ( self : str ) -> Optional[int]:
"""simple docstring"""
A__ = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
A__ = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
A__ = bbox[i, j, 3]
A__ = bbox[i, j, 1]
A__ = t
if bbox[i, j, 2] < bbox[i, j, 0]:
A__ = bbox[i, j, 2]
A__ = bbox[i, j, 0]
A__ = t
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_input_mask:
A__ = random_attention_mask([self.batch_size, self.text_seq_length] )
A__ = None
if self.use_token_type_ids:
A__ = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
A__ = None
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A__ = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
A__ = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def a_ ( self : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : List[str] , __lowerCAmelCase : Any ) -> int:
"""simple docstring"""
A__ = LayoutLMvaModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
# text + image
A__ = model(lowerCamelCase_ , pixel_values=lowerCamelCase_ )
A__ = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ )
A__ = model(lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , token_type_ids=lowerCamelCase_ )
A__ = model(lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
A__ = model(lowerCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
A__ = model(pixel_values=lowerCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def a_ ( self : Any , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
A__ = self.num_labels
A__ = LayoutLMvaForSequenceClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
A__ = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a_ ( self : int , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple , __lowerCAmelCase : str , __lowerCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
A__ = self.num_labels
A__ = LayoutLMvaForTokenClassification(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
A__ = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , labels=lowerCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def a_ ( self : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = LayoutLMvaForQuestionAnswering(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
A__ = model(
lowerCamelCase_ , bbox=lowerCamelCase_ , pixel_values=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , start_positions=lowerCamelCase_ , end_positions=lowerCamelCase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a_ ( self : int ) -> str:
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
(
A__
) = config_and_inputs
A__ = {
'input_ids': input_ids,
'bbox': bbox,
'pixel_values': pixel_values,
'token_type_ids': token_type_ids,
'attention_mask': input_mask,
}
return config, inputs_dict
@require_torch
class A (_UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : Dict = False
__lowerCamelCase : List[Any] = False
__lowerCamelCase : Dict = False
__lowerCamelCase : List[Any] = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
__lowerCamelCase : List[Any] = (
{'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
if is_torch_available()
else {}
)
def a_ ( self : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : Any , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
return True
def a_ ( self : Tuple ) -> List[Any]:
"""simple docstring"""
A__ = LayoutLMvaModelTester(self )
A__ = ConfigTester(self , config_class=lowerCamelCase_ , hidden_size=37 )
def a_ ( self : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : List[Any] , __lowerCAmelCase : int=False ) -> Optional[Any]:
"""simple docstring"""
A__ = copy.deepcopy(lowerCamelCase_ )
if model_class in get_values(lowerCamelCase_ ):
A__ = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(lowerCamelCase_ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(lowerCamelCase_ ):
A__ = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
elif model_class in get_values(lowerCamelCase_ ):
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
elif model_class in [
*get_values(lowerCamelCase_ ),
]:
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase_ )
elif model_class in [
*get_values(lowerCamelCase_ ),
]:
A__ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=lowerCamelCase_ , )
return inputs_dict
def a_ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
def a_ ( self : Tuple ) -> Any:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def a_ ( self : List[str] ) -> Tuple:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
A__ = type
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def a_ ( self : str ) -> Optional[Any]:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_ )
def a_ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_ )
def a_ ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_ )
@slow
def a_ ( self : Tuple ) -> List[Any]:
"""simple docstring"""
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = LayoutLMvaModel.from_pretrained(lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
def __lowerCamelCase ( ) -> Optional[Any]:
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class A (unittest.TestCase ):
'''simple docstring'''
@cached_property
def a_ ( self : Tuple ) -> List[Any]:
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_ ) if is_vision_available() else None
@slow
def a_ ( self : Dict ) -> Any:
"""simple docstring"""
A__ = LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" ).to(lowerCamelCase_ )
A__ = self.default_image_processor
A__ = prepare_img()
A__ = image_processor(images=lowerCamelCase_ , return_tensors="""pt""" ).pixel_values.to(lowerCamelCase_ )
A__ = torch.tensor([[1, 2]] )
A__ = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
A__ = model(
input_ids=input_ids.to(lowerCamelCase_ ) , bbox=bbox.to(lowerCamelCase_ ) , pixel_values=pixel_values.to(lowerCamelCase_ ) , )
# verify the logits
A__ = torch.Size((1, 1_99, 7_68) )
self.assertEqual(outputs.last_hidden_state.shape , lowerCamelCase_ )
A__ = torch.tensor(
[[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]] ).to(lowerCamelCase_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , lowerCamelCase_ , atol=1e-4 ) )
| 176
|
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin
enable_full_determinism()
class __lowerCAmelCase ( _UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
_A = PriorTransformer
_A = "hidden_states"
@property
def snake_case__( self: Union[str, Any] ):
lowercase__ : List[Any] = 4
lowercase__ : Optional[int] = 8
lowercase__ : List[str] = 7
lowercase__ : Optional[Any] = floats_tensor((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : Union[str, Any] = floats_tensor((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : Any = floats_tensor((batch_size, num_embeddings, embedding_dim) ).to(lowerCamelCase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def snake_case__( self: int, lowerCamelCase_: Dict=0 ):
torch.manual_seed(lowerCamelCase_ )
lowercase__ : Tuple = 4
lowercase__ : List[Any] = 8
lowercase__ : List[str] = 7
lowercase__ : Union[str, Any] = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : Optional[int] = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : List[str] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCamelCase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
@property
def snake_case__( self: str ):
return (4, 8)
@property
def snake_case__( self: List[str] ):
return (4, 8)
def snake_case__( self: Dict ):
lowercase__ : int = {
'num_attention_heads': 2,
'attention_head_dim': 4,
'num_layers': 2,
'embedding_dim': 8,
'num_embeddings': 7,
'additional_embeddings': 4,
}
lowercase__ : Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def snake_case__( self: int ):
lowercase__ , lowercase__ : Dict = PriorTransformer.from_pretrained(
'hf-internal-testing/prior-dummy', output_loading_info=lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
self.assertEqual(len(loading_info['missing_keys'] ), 0 )
model.to(lowerCamelCase_ )
lowercase__ : Tuple = model(**self.dummy_input )[0]
assert hidden_states is not None, "Make sure output is not None"
def snake_case__( self: str ):
lowercase__ , lowercase__ : List[Any] = self.prepare_init_args_and_inputs_for_common()
lowercase__ : Optional[int] = self.model_class(**lowerCamelCase_ )
lowercase__ : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : Dict = [*signature.parameters.keys()]
lowercase__ : List[str] = ['hidden_states', 'timestep']
self.assertListEqual(arg_names[:2], lowerCamelCase_ )
def snake_case__( self: Union[str, Any] ):
lowercase__ : str = PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy' )
lowercase__ : Optional[int] = model.to(lowerCamelCase_ )
if hasattr(lowerCamelCase_, 'set_default_attn_processor' ):
model.set_default_attn_processor()
lowercase__ : List[str] = self.get_dummy_seed_input()
with torch.no_grad():
lowercase__ : str = model(**lowerCamelCase_ )[0]
lowercase__ : int = output[0, :5].flatten().cpu()
print(lowerCamelCase_ )
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
lowercase__ : List[str] = torch.tensor([-1.3_4_3_6, -0.2_8_7_0, 0.7_5_3_8, 0.4_3_6_8, -0.0_2_3_9] )
self.assertTrue(torch_all_close(lowerCamelCase_, lowerCamelCase_, rtol=1E-2 ) )
@slow
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self: Tuple, lowerCamelCase_: Union[str, Any]=1, lowerCamelCase_: Tuple=768, lowerCamelCase_: Dict=77, lowerCamelCase_: Union[str, Any]=0 ):
torch.manual_seed(lowerCamelCase_ )
lowercase__ : Dict = batch_size
lowercase__ : Dict = embedding_dim
lowercase__ : Dict = num_embeddings
lowercase__ : int = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : str = torch.randn((batch_size, embedding_dim) ).to(lowerCamelCase_ )
lowercase__ : List[str] = torch.randn((batch_size, num_embeddings, embedding_dim) ).to(lowerCamelCase_ )
return {
"hidden_states": hidden_states,
"timestep": 2,
"proj_embedding": proj_embedding,
"encoder_hidden_states": encoder_hidden_states,
}
def snake_case__( self: str ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@parameterized.expand(
[
# fmt: off
[13, [-0.5_8_6_1, 0.1_2_8_3, -0.0_9_3_1, 0.0_8_8_2, 0.4_4_7_6, 0.1_3_2_9, -0.0_4_9_8, 0.0_6_4_0]],
[37, [-0.4_9_1_3, 0.0_1_1_0, -0.0_4_8_3, 0.0_5_4_1, 0.4_9_5_4, -0.0_1_7_0, 0.0_3_5_4, 0.1_6_5_1]],
# fmt: on
] )
def snake_case__( self: Optional[Any], lowerCamelCase_: List[str], lowerCamelCase_: str ):
lowercase__ : List[Any] = PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior', subfolder='prior' )
model.to(lowerCamelCase_ )
lowercase__ : Optional[int] = self.get_dummy_seed_input(seed=lowerCamelCase_ )
with torch.no_grad():
lowercase__ : List[str] = model(**lowerCamelCase_ )[0]
assert list(sample.shape ) == [1, 768]
lowercase__ : Union[str, Any] = sample[0, :8].flatten().cpu()
print(lowerCamelCase_ )
lowercase__ : Optional[Any] = torch.tensor(lowerCamelCase_ )
assert torch_all_close(lowerCamelCase_, lowerCamelCase_, atol=1E-3 )
| 266
| 0
|
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('0.12.2'):
raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
raise Exception('requires fairseq < v2')
logging.set_verbosity_info()
lowerCAmelCase : str =logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] ='Hello, World!'
lowerCAmelCase : Tuple ='en_XX'
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
lowerCAmelCase : List[str] = Path("""data_bin""" )
lowerCAmelCase : Optional[Any] = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(SCREAMING_SNAKE_CASE__ ).parent ) ,checkpoint_file=Path(SCREAMING_SNAKE_CASE__ ).name ,_name="""xmod_base""" ,arch="""xmod_base""" ,task="""multilingual_masked_lm""" ,data_name_or_path=str(SCREAMING_SNAKE_CASE__ ) ,bpe="""sentencepiece""" ,sentencepiece_model=str(Path(SCREAMING_SNAKE_CASE__ ).parent / """sentencepiece.bpe.model""" ) ,src_dict=str(data_dir / """dict.txt""" ) ,)
xmod.eval() # disable dropout
print(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase : Optional[Any] = xmod.model.encoder.sentence_encoder
lowerCAmelCase : int = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,hidden_size=xmod.cfg.model.encoder_embed_dim ,num_hidden_layers=xmod.cfg.model.encoder_layers ,num_attention_heads=xmod.cfg.model.encoder_attention_heads ,intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,max_position_embeddings=5_1_4 ,type_vocab_size=1 ,layer_norm_eps=1e-5 ,pre_norm=xmod.cfg.model.encoder_normalize_before ,adapter_reduction_factor=getattr(xmod.cfg.model ,"""bottleneck""" ,2 ) ,adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,ln_before_adapter=xmod.cfg.model.ln_before_adapter ,languages=xmod.cfg.model.languages ,)
if classification_head:
lowerCAmelCase : Any = xmod.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our X-MOD config:""" ,SCREAMING_SNAKE_CASE__ )
lowerCAmelCase : str = XmodForSequenceClassification(SCREAMING_SNAKE_CASE__ ) if classification_head else XmodForMaskedLM(SCREAMING_SNAKE_CASE__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowerCAmelCase : str = xmod_sent_encoder.embed_tokens.weight
lowerCAmelCase : Any = xmod_sent_encoder.embed_positions.weight
lowerCAmelCase : Union[str, Any] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
lowerCAmelCase : List[Any] = xmod_sent_encoder.layernorm_embedding.weight
lowerCAmelCase : int = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowerCAmelCase : List[Any] = model.roberta.encoder.layer[i]
lowerCAmelCase : Dict = xmod_sent_encoder.layers[i]
# self attention
lowerCAmelCase : Tuple = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("""Dimensions of self-attention weights do not match.""" )
lowerCAmelCase : str = xmod_layer.self_attn.q_proj.weight
lowerCAmelCase : Any = xmod_layer.self_attn.q_proj.bias
lowerCAmelCase : Tuple = xmod_layer.self_attn.k_proj.weight
lowerCAmelCase : Dict = xmod_layer.self_attn.k_proj.bias
lowerCAmelCase : Dict = xmod_layer.self_attn.v_proj.weight
lowerCAmelCase : int = xmod_layer.self_attn.v_proj.bias
# self-attention output
lowerCAmelCase : List[str] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("""Dimensions of self-attention output weights do not match.""" )
lowerCAmelCase : Union[str, Any] = xmod_layer.self_attn.out_proj.weight
lowerCAmelCase : str = xmod_layer.self_attn.out_proj.bias
lowerCAmelCase : Optional[int] = xmod_layer.self_attn_layer_norm.weight
lowerCAmelCase : int = xmod_layer.self_attn_layer_norm.bias
# intermediate
lowerCAmelCase : int = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of intermediate weights do not match.""" )
lowerCAmelCase : Tuple = xmod_layer.fca.weight
lowerCAmelCase : str = xmod_layer.fca.bias
# output
lowerCAmelCase : Any = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("""Dimensions of feed-forward weights do not match.""" )
lowerCAmelCase : Any = xmod_layer.fca.weight
lowerCAmelCase : Optional[int] = xmod_layer.fca.bias
lowerCAmelCase : Tuple = xmod_layer.final_layer_norm.weight
lowerCAmelCase : Tuple = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
lowerCAmelCase : Union[str, Any] = xmod_layer.adapter_layer_norm.weight
lowerCAmelCase : int = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("""Lists of language adapters do not match.""" )
for lang_code, adapter in xmod_layer.adapter_modules.items():
lowerCAmelCase : Any = bert_output.adapter_modules[lang_code]
lowerCAmelCase : str = xmod_layer.adapter_modules[lang_code]
lowerCAmelCase : Tuple = from_adapter.fca.weight
lowerCAmelCase : Optional[int] = from_adapter.fca.bias
lowerCAmelCase : List[Any] = from_adapter.fca.weight
lowerCAmelCase : Any = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
lowerCAmelCase : Union[str, Any] = xmod_sent_encoder.layer_norm.weight
lowerCAmelCase : Tuple = xmod_sent_encoder.layer_norm.bias
if classification_head:
lowerCAmelCase : List[str] = xmod.model.classification_heads["""mnli"""].dense.weight
lowerCAmelCase : Any = xmod.model.classification_heads["""mnli"""].dense.bias
lowerCAmelCase : List[Any] = xmod.model.classification_heads["""mnli"""].out_proj.weight
lowerCAmelCase : int = xmod.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
lowerCAmelCase : Any = xmod.model.encoder.lm_head.dense.weight
lowerCAmelCase : Union[str, Any] = xmod.model.encoder.lm_head.dense.bias
lowerCAmelCase : int = xmod.model.encoder.lm_head.layer_norm.weight
lowerCAmelCase : List[Any] = xmod.model.encoder.lm_head.layer_norm.bias
lowerCAmelCase : Optional[int] = xmod.model.encoder.lm_head.weight
lowerCAmelCase : Any = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowerCAmelCase : Any = xmod.encode(SCREAMING_SNAKE_CASE__ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase : Tuple = model(SCREAMING_SNAKE_CASE__ )[0]
if classification_head:
lowerCAmelCase : Tuple = xmod.model.classification_heads["""mnli"""](xmod.extract_features(SCREAMING_SNAKE_CASE__ ) )
else:
lowerCAmelCase : Optional[Any] = xmod.model(SCREAMING_SNAKE_CASE__ ,lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape ,their_output.shape )
lowerCAmelCase : List[Any] = torch.max(torch.abs(our_output - their_output ) ).item()
print(F"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
lowerCAmelCase : Tuple = torch.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-3 )
print("""Do both models output the same tensors?""" ,"""🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
Path(SCREAMING_SNAKE_CASE__ ).mkdir(parents=SCREAMING_SNAKE_CASE__ ,exist_ok=SCREAMING_SNAKE_CASE__ )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
lowerCAmelCase : Dict =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
lowerCAmelCase : int =parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 711
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Optional[int] =logging.get_logger(__name__)
lowerCAmelCase : Optional[int] ={
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class _a ( snake_case_ ):
_UpperCamelCase: Tuple = "transfo-xl"
_UpperCamelCase: str = ["mems"]
_UpperCamelCase: Dict = {
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , lowercase_=267735 , lowercase_=[20000, 40000, 200000] , lowercase_=1024 , lowercase_=1024 , lowercase_=16 , lowercase_=64 , lowercase_=4096 , lowercase_=4 , lowercase_=False , lowercase_=18 , lowercase_=1600 , lowercase_=1000 , lowercase_=True , lowercase_=True , lowercase_=0 , lowercase_=-1 , lowercase_=True , lowercase_=0.1 , lowercase_=0.0 , lowercase_=True , lowercase_="normal" , lowercase_=0.0_1 , lowercase_=0.0_1 , lowercase_=0.0_2 , lowercase_=1e-5 , lowercase_=0 , **lowercase_ , ) -> Optional[int]:
lowerCAmelCase : List[str] = vocab_size
lowerCAmelCase : Union[str, Any] = []
self.cutoffs.extend(lowercase_ )
if proj_share_all_but_first:
lowerCAmelCase : Optional[int] = [False] + [True] * len(self.cutoffs )
else:
lowerCAmelCase : List[str] = [False] + [False] * len(self.cutoffs )
lowerCAmelCase : Optional[int] = d_model
lowerCAmelCase : List[Any] = d_embed
lowerCAmelCase : Union[str, Any] = d_head
lowerCAmelCase : List[Any] = d_inner
lowerCAmelCase : Optional[int] = div_val
lowerCAmelCase : List[Any] = pre_lnorm
lowerCAmelCase : Dict = n_layer
lowerCAmelCase : Tuple = n_head
lowerCAmelCase : Any = mem_len
lowerCAmelCase : Union[str, Any] = same_length
lowerCAmelCase : List[Any] = attn_type
lowerCAmelCase : int = clamp_len
lowerCAmelCase : List[str] = sample_softmax
lowerCAmelCase : Optional[int] = adaptive
lowerCAmelCase : Dict = dropout
lowerCAmelCase : Optional[Any] = dropatt
lowerCAmelCase : List[str] = untie_r
lowerCAmelCase : List[str] = init
lowerCAmelCase : Tuple = init_range
lowerCAmelCase : str = proj_init_std
lowerCAmelCase : str = init_std
lowerCAmelCase : Optional[int] = layer_norm_epsilon
super().__init__(eos_token_id=lowercase_ , **lowercase_ )
@property
def _snake_case ( self ) -> Optional[Any]:
# Message copied from Transformer-XL documentation
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def _snake_case ( self , lowercase_ ) -> Dict:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 693
| 0
|
"""simple docstring"""
import os
import sys
import unittest
UpperCAmelCase__ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
UpperCAmelCase__ : str = os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
UpperCAmelCase__ : Optional[Any] = os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Any = get_test_to_tester_mapping(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = {"""BertModelTest""": """BertModelTester"""}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"""BlipModelTest""": """BlipModelTester""",
"""BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
"""BlipTextModelTest""": """BlipTextModelTester""",
"""BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
"""BlipVQAModelTest""": """BlipVQAModelTester""",
"""BlipVisionModelTest""": """BlipVisionModelTester""",
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = get_model_to_test_mapping(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_model_to_test_mapping(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : str = {
"""BertForMaskedLM""": ["""BertModelTest"""],
"""BertForMultipleChoice""": ["""BertModelTest"""],
"""BertForNextSentencePrediction""": ["""BertModelTest"""],
"""BertForPreTraining""": ["""BertModelTest"""],
"""BertForQuestionAnswering""": ["""BertModelTest"""],
"""BertForSequenceClassification""": ["""BertModelTest"""],
"""BertForTokenClassification""": ["""BertModelTest"""],
"""BertLMHeadModel""": ["""BertModelTest"""],
"""BertModel""": ["""BertModelTest"""],
}
SCREAMING_SNAKE_CASE__ : int = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
"""BlipModel""": ["""BlipModelTest"""],
"""BlipTextModel""": ["""BlipTextModelTest"""],
"""BlipVisionModel""": ["""BlipVisionModelTest"""],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = get_model_to_tester_mapping(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = {
"""BertForMaskedLM""": ["""BertModelTester"""],
"""BertForMultipleChoice""": ["""BertModelTester"""],
"""BertForNextSentencePrediction""": ["""BertModelTester"""],
"""BertForPreTraining""": ["""BertModelTester"""],
"""BertForQuestionAnswering""": ["""BertModelTester"""],
"""BertForSequenceClassification""": ["""BertModelTester"""],
"""BertForTokenClassification""": ["""BertModelTester"""],
"""BertLMHeadModel""": ["""BertModelTester"""],
"""BertModel""": ["""BertModelTester"""],
}
SCREAMING_SNAKE_CASE__ : Optional[int] = {
"""BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
"""BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
"""BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
"""BlipModel""": ["""BlipModelTester"""],
"""BlipTextModel""": ["""BlipTextModelTester"""],
"""BlipVisionModel""": ["""BlipVisionModelTester"""],
}
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
| 223
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
UpperCAmelCase__ : List[str] = logging.get_logger(__name__)
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
def __init__(self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> None:
"""simple docstring"""
warnings.warn(
"""The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use PoolFormerImageProcessor instead.""" , SCREAMING_SNAKE_CASE__ , )
super().__init__(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
| 223
| 1
|
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaProcessor,
is_apex_available,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
__a : Optional[Any] = True
from torch.cuda.amp import autocast
__a : Any = logging.getLogger(__name__)
def _SCREAMING_SNAKE_CASE ( __lowercase : List[str]=None , __lowercase : Tuple=None ) -> Dict:
"""simple docstring"""
return field(default_factory=lambda: default , metadata=__lowercase )
@dataclass
class __lowercase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={"help": "Whether to freeze the feature extractor layers of the model."} )
SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={"help": "The dropout ratio for the attention probabilities."} )
SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={"help": "The dropout ratio for activations inside the fully connected layer."} )
SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={
"help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
} , )
SCREAMING_SNAKE_CASE = field(
default=0.1 , metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."} , )
SCREAMING_SNAKE_CASE = field(
default=0.05 , metadata={
"help": (
"Propability of each feature vector along the time axis to be chosen as the start of the vector"
"span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
"vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
)
} , )
SCREAMING_SNAKE_CASE = field(default=0.0 , metadata={"help": "The LayerDrop probability."} )
@dataclass
class __lowercase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} )
SCREAMING_SNAKE_CASE = field(
default="train+validation" , metadata={
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
} , )
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={"help": "Overwrite the cached preprocessed datasets or not."} )
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={"help": "The number of processes to use for the preprocessing."} , )
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE = field(
default=lowercase_ , metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of validation examples to this "
"value if set."
)
} , )
SCREAMING_SNAKE_CASE = list_field(
default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"] , metadata={"help": "A list of characters to remove from the transcripts."} , )
@dataclass
class __lowercase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
def __call__( self : Optional[Any] , UpperCamelCase_ : List[Dict[str, Union[List[int], torch.Tensor]]] ):
"""simple docstring"""
__A = [{"""input_values""": feature["""input_values"""]} for feature in features]
__A = [{"""input_ids""": feature["""labels"""]} for feature in features]
__A = self.processor.pad(
UpperCamelCase_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
__A = self.processor.pad(
labels=UpperCamelCase_ , padding=self.padding , max_length=self.max_length_labels , pad_to_multiple_of=self.pad_to_multiple_of_labels , return_tensors="""pt""" , )
# replace padding with -100 to ignore loss correctly
__A = labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
__A = labels
return batch
class __lowercase ( lowercase_ ):
'''simple docstring'''
def lowerCAmelCase_ ( self : List[str] , UpperCamelCase_ : nn.Module , UpperCamelCase_ : Dict[str, Union[torch.Tensor, Any]] ):
"""simple docstring"""
model.train()
__A = self._prepare_inputs(UpperCamelCase_ )
if self.use_amp:
with autocast():
__A = self.compute_loss(UpperCamelCase_ , UpperCamelCase_ )
else:
__A = self.compute_loss(UpperCamelCase_ , UpperCamelCase_ )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
__A = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
__A = loss.sum() / (inputs["""labels"""] >= 0).sum()
else:
raise ValueError(F"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']" )
if self.args.gradient_accumulation_steps > 1:
__A = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(UpperCamelCase_ ).backward()
elif self.use_apex:
with amp.scale_loss(UpperCamelCase_ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(UpperCamelCase_ )
else:
loss.backward()
return loss.detach()
def _SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
__A = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__A , __A , __A = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__A , __A , __A = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__A = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__A = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , __lowercase )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
__A = datasets.load_dataset(
"""common_voice""" , data_args.dataset_config_name , split=data_args.train_split_name )
__A = datasets.load_dataset("""common_voice""" , data_args.dataset_config_name , split="""test""" )
# Create and save tokenizer
__A = f"[{''.join(data_args.chars_to_ignore )}]"
def remove_special_characters(__lowercase : Any ):
__A = re.sub(__lowercase , """""" , batch["""sentence"""] ).lower() + """ """
return batch
__A = train_dataset.map(__lowercase , remove_columns=["""sentence"""] )
__A = eval_dataset.map(__lowercase , remove_columns=["""sentence"""] )
def extract_all_chars(__lowercase : Tuple ):
__A = """ """.join(batch["""text"""] )
__A = list(set(__lowercase ) )
return {"vocab": [vocab], "all_text": [all_text]}
__A = train_dataset.map(
__lowercase , batched=__lowercase , batch_size=-1 , keep_in_memory=__lowercase , remove_columns=train_dataset.column_names , )
__A = train_dataset.map(
__lowercase , batched=__lowercase , batch_size=-1 , keep_in_memory=__lowercase , remove_columns=eval_dataset.column_names , )
__A = list(set(vocab_train["""vocab"""][0] ) | set(vocab_test["""vocab"""][0] ) )
__A = {v: k for k, v in enumerate(__lowercase )}
__A = vocab_dict[""" """]
del vocab_dict[" "]
__A = len(__lowercase )
__A = len(__lowercase )
with open("""vocab.json""" , """w""" ) as vocab_file:
json.dump(__lowercase , __lowercase )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__A = WavaVecaCTCTokenizer(
"""vocab.json""" , unk_token="""[UNK]""" , pad_token="""[PAD]""" , word_delimiter_token="""|""" , )
__A = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6_0_0_0 , padding_value=0.0 , do_normalize=__lowercase , return_attention_mask=__lowercase )
__A = WavaVecaProcessor(feature_extractor=__lowercase , tokenizer=__lowercase )
__A = WavaVecaForCTC.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , activation_dropout=model_args.activation_dropout , attention_dropout=model_args.attention_dropout , hidden_dropout=model_args.hidden_dropout , feat_proj_dropout=model_args.feat_proj_dropout , mask_time_prob=model_args.mask_time_prob , gradient_checkpointing=training_args.gradient_checkpointing , layerdrop=model_args.layerdrop , ctc_loss_reduction="""mean""" , pad_token_id=processor.tokenizer.pad_token_id , vocab_size=len(processor.tokenizer ) , )
if data_args.max_train_samples is not None:
__A = min(len(__lowercase ) , data_args.max_train_samples )
__A = train_dataset.select(range(__lowercase ) )
if data_args.max_val_samples is not None:
__A = eval_dataset.select(range(data_args.max_val_samples ) )
__A = torchaudio.transforms.Resample(4_8_0_0_0 , 1_6_0_0_0 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(__lowercase : Tuple ):
__A , __A = torchaudio.load(batch["""path"""] )
__A = resampler(__lowercase ).squeeze().numpy()
__A = 1_6_0_0_0
__A = batch["""text"""]
return batch
__A = train_dataset.map(
__lowercase , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
__A = eval_dataset.map(
__lowercase , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(__lowercase : List[Any] ):
# check that all files have the correct sampling rate
assert (
len(set(batch["""sampling_rate"""] ) ) == 1
), f"Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."
__A = processor(
audio=batch["""speech"""] , text=batch["""target_text"""] , sampling_rate=batch["""sampling_rate"""][0] )
batch.update(__lowercase )
return batch
__A = train_dataset.map(
__lowercase , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__lowercase , num_proc=data_args.preprocessing_num_workers , )
__A = eval_dataset.map(
__lowercase , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=__lowercase , num_proc=data_args.preprocessing_num_workers , )
# Metric
__A = datasets.load_metric("""wer""" )
def compute_metrics(__lowercase : Union[str, Any] ):
__A = pred.predictions
__A = np.argmax(__lowercase , axis=-1 )
__A = processor.tokenizer.pad_token_id
__A = processor.batch_decode(__lowercase )
# we do not want to group tokens when computing the metrics
__A = processor.batch_decode(pred.label_ids , group_tokens=__lowercase )
__A = wer_metric.compute(predictions=__lowercase , references=__lowercase )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
__A = DataCollatorCTCWithPadding(processor=__lowercase , padding=__lowercase )
# Initialize our Trainer
__A = CTCTrainer(
model=__lowercase , data_collator=__lowercase , args=__lowercase , compute_metrics=__lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=processor.feature_extractor , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
__A = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
__A = model_args.model_name_or_path
else:
__A = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
__A = trainer.train(resume_from_checkpoint=__lowercase )
trainer.save_model()
__A = train_result.metrics
__A = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowercase )
)
__A = min(__lowercase , len(__lowercase ) )
trainer.log_metrics("""train""" , __lowercase )
trainer.save_metrics("""train""" , __lowercase )
trainer.save_state()
# Evaluation
__A = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__A = trainer.evaluate()
__A = data_args.max_val_samples if data_args.max_val_samples is not None else len(__lowercase )
__A = min(__lowercase , len(__lowercase ) )
trainer.log_metrics("""eval""" , __lowercase )
trainer.save_metrics("""eval""" , __lowercase )
return results
if __name__ == "__main__":
main()
| 199
|
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _SCREAMING_SNAKE_CASE ( __lowercase : str ) -> str:
"""simple docstring"""
if "cls_token" in name:
__A = name.replace("""cls_token""" , """vit.embeddings.cls_token""" )
if "mask_token" in name:
__A = name.replace("""mask_token""" , """decoder.mask_token""" )
if "decoder_pos_embed" in name:
__A = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__A = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__A = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__A = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""" )
if "decoder_blocks" in name:
__A = name.replace("""decoder_blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__A = name.replace("""blocks""" , """vit.encoder.layer""" )
if "attn.proj" in name:
__A = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
__A = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
__A = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__A = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__A = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__A = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__A = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__A = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__A = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name:
__A = name.replace("""norm.weight""" , """vit.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name:
__A = name.replace("""norm.bias""" , """vit.layernorm.bias""" )
return name
def _SCREAMING_SNAKE_CASE ( __lowercase : Any , __lowercase : Dict ) -> str:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
__A = orig_state_dict.pop(__lowercase )
if "qkv" in key:
__A = key.split(""".""" )
__A = int(key_split[1] )
if "decoder_blocks" in key:
__A = config.decoder_hidden_size
__A = """decoder.decoder_layers."""
if "weight" in key:
__A = val[:dim, :]
__A = val[dim : dim * 2, :]
__A = val[-dim:, :]
elif "bias" in key:
__A = val[:dim]
__A = val[dim : dim * 2]
__A = val[-dim:]
else:
__A = config.hidden_size
__A = """vit.encoder.layer."""
if "weight" in key:
__A = val[:dim, :]
__A = val[dim : dim * 2, :]
__A = val[-dim:, :]
elif "bias" in key:
__A = val[:dim]
__A = val[dim : dim * 2]
__A = val[-dim:]
else:
__A = val
return orig_state_dict
def _SCREAMING_SNAKE_CASE ( __lowercase : Tuple , __lowercase : str ) -> Optional[Any]:
"""simple docstring"""
__A = ViTMAEConfig()
if "large" in checkpoint_url:
__A = 1_0_2_4
__A = 4_0_9_6
__A = 2_4
__A = 1_6
elif "huge" in checkpoint_url:
__A = 1_4
__A = 1_2_8_0
__A = 5_1_2_0
__A = 3_2
__A = 1_6
__A = ViTMAEForPreTraining(__lowercase )
__A = torch.hub.load_state_dict_from_url(__lowercase , map_location="""cpu""" )["""model"""]
__A = ViTMAEImageProcessor(size=config.image_size )
__A = convert_state_dict(__lowercase , __lowercase )
model.load_state_dict(__lowercase )
model.eval()
__A = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
__A = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
__A = ViTMAEImageProcessor(size=config.image_size )
__A = image_processor(images=__lowercase , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
__A = model(**__lowercase )
__A = outputs.logits
if "large" in checkpoint_url:
__A = torch.tensor(
[[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]] )
elif "huge" in checkpoint_url:
__A = torch.tensor(
[[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]] )
else:
__A = torch.tensor(
[[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , __lowercase , atol=1E-4 )
print(f"Saving model to {pytorch_dump_folder_path}" )
model.save_pretrained(__lowercase )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__lowercase )
if __name__ == "__main__":
__a : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__a : List[str] = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 199
| 1
|
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCamelCase = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
"cache_dir",
"data_dir",
"all_configs",
"save_infos",
"ignore_verifications",
"force_redownload",
"clear_cache",
],
defaults=[None, None, None, False, False, False, False, False],
)
def _a ( _lowerCamelCase , _lowerCamelCase ) -> List[Any]:
"""simple docstring"""
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def _a ( _lowerCamelCase ) -> int:
"""simple docstring"""
__snake_case : Optional[int] = _TestCommandArgs(dataset=_lowerCamelCase , all_configs=_lowerCamelCase , save_infos=_lowerCamelCase )
__snake_case : Any = TestCommand(*_lowerCamelCase )
test_command.run()
__snake_case : Tuple = os.path.join(_lowerCamelCase , """README.md""" )
assert os.path.exists(_lowerCamelCase )
__snake_case : Optional[int] = DatasetInfosDict.from_directory(_lowerCamelCase )
__snake_case : Dict = DatasetInfosDict(
{
"""default""": DatasetInfo(
features=Features(
{
"""tokens""": Sequence(Value("""string""" ) ),
"""ner_tags""": Sequence(
ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""] ) ),
"""langs""": Sequence(Value("""string""" ) ),
"""spans""": Sequence(Value("""string""" ) ),
} ) , splits=[
{
"""name""": """train""",
"""num_bytes""": 235_1563,
"""num_examples""": 1_0000,
},
{
"""name""": """validation""",
"""num_bytes""": 23_8418,
"""num_examples""": 1000,
},
] , download_size=394_0680 , dataset_size=258_9981 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
__snake_case , __snake_case : Dict = getattr(dataset_infos["""default"""] , _lowerCamelCase ), getattr(expected_dataset_infos["""default"""] , _lowerCamelCase )
if key == "num_bytes":
assert is_apercent_close(_lowerCamelCase , _lowerCamelCase )
elif key == "splits":
assert list(_lowerCamelCase ) == list(_lowerCamelCase )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 26
|
import requests
def lowercase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> None:
_snake_case : Union[str, Any] = {"""Content-Type""": """application/json"""}
_snake_case : Tuple = requests.post(SCREAMING_SNAKE_CASE__ , json={"""text""": message_body} , headers=SCREAMING_SNAKE_CASE__ )
if response.status_code != 200:
_snake_case : Any = (
"""Request to slack returned an error """
F'''{response.status_code}, the response is:\n{response.text}'''
)
raise ValueError(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")
| 477
| 0
|
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class a__ ( __SCREAMING_SNAKE_CASE ):
_A = (DEISMultistepScheduler,)
_A = (("num_inference_steps", 25),)
def lowerCAmelCase ( self : int , **A_ : Any ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_: Tuple = {
"""num_train_timesteps""": 10_00,
"""beta_start""": 0.0001,
"""beta_end""": 0.02,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
}
config.update(**A_ )
return config
def lowerCAmelCase ( self : Dict , A_ : int=0 , **A_ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_: List[str] = dict(self.forward_default_kwargs )
lowerCamelCase_: Union[str, Any] = kwargs.pop("""num_inference_steps""" , A_ )
lowerCamelCase_: List[Any] = self.dummy_sample
lowerCamelCase_: Union[str, Any] = 0.1 * sample
lowerCamelCase_: Optional[int] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase_: Dict = self.get_scheduler_config(**A_ )
lowerCamelCase_: List[Any] = scheduler_class(**A_ )
scheduler.set_timesteps(A_ )
# copy over dummy past residuals
lowerCamelCase_: Optional[int] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A_ )
lowerCamelCase_: str = scheduler_class.from_pretrained(A_ )
new_scheduler.set_timesteps(A_ )
# copy over dummy past residuals
lowerCamelCase_: Tuple = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase_ , lowerCamelCase_: Optional[Any] = sample, sample
for t in range(A_ , time_step + scheduler.config.solver_order + 1 ):
lowerCamelCase_: str = scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
lowerCamelCase_: Any = new_scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCAmelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
pass
def lowerCAmelCase ( self : Optional[int] , A_ : Dict=0 , **A_ : List[Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_: Any = dict(self.forward_default_kwargs )
lowerCamelCase_: Optional[Any] = kwargs.pop("""num_inference_steps""" , A_ )
lowerCamelCase_: Tuple = self.dummy_sample
lowerCamelCase_: Optional[int] = 0.1 * sample
lowerCamelCase_: List[str] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase_: List[Any] = self.get_scheduler_config()
lowerCamelCase_: Dict = scheduler_class(**A_ )
scheduler.set_timesteps(A_ )
# copy over dummy past residuals (must be after setting timesteps)
lowerCamelCase_: Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A_ )
lowerCamelCase_: Union[str, Any] = scheduler_class.from_pretrained(A_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(A_ )
# copy over dummy past residual (must be after setting timesteps)
lowerCamelCase_: Dict = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase_: Any = scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
lowerCamelCase_: List[Any] = new_scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def lowerCAmelCase ( self : Union[str, Any] , A_ : Optional[Any]=None , **A_ : Dict ) -> Dict:
"""simple docstring"""
if scheduler is None:
lowerCamelCase_: str = self.scheduler_classes[0]
lowerCamelCase_: str = self.get_scheduler_config(**A_ )
lowerCamelCase_: Dict = scheduler_class(**A_ )
lowerCamelCase_: int = self.scheduler_classes[0]
lowerCamelCase_: int = self.get_scheduler_config(**A_ )
lowerCamelCase_: Tuple = scheduler_class(**A_ )
lowerCamelCase_: List[str] = 10
lowerCamelCase_: Optional[int] = self.dummy_model()
lowerCamelCase_: List[Any] = self.dummy_sample_deter
scheduler.set_timesteps(A_ )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase_: Optional[Any] = model(A_ , A_ )
lowerCamelCase_: Tuple = scheduler.step(A_ , A_ , A_ ).prev_sample
return sample
def lowerCAmelCase ( self : str ) -> Any:
"""simple docstring"""
lowerCamelCase_: Union[str, Any] = dict(self.forward_default_kwargs )
lowerCamelCase_: Optional[Any] = kwargs.pop("""num_inference_steps""" , A_ )
for scheduler_class in self.scheduler_classes:
lowerCamelCase_: Optional[int] = self.get_scheduler_config()
lowerCamelCase_: List[str] = scheduler_class(**A_ )
lowerCamelCase_: Optional[int] = self.dummy_sample
lowerCamelCase_: Optional[Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(A_ , """set_timesteps""" ):
scheduler.set_timesteps(A_ )
elif num_inference_steps is not None and not hasattr(A_ , """set_timesteps""" ):
lowerCamelCase_: Optional[int] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
lowerCamelCase_: Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
lowerCamelCase_: List[str] = dummy_past_residuals[: scheduler.config.solver_order]
lowerCamelCase_: Optional[int] = scheduler.timesteps[5]
lowerCamelCase_: int = scheduler.timesteps[6]
lowerCamelCase_: Optional[int] = scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
lowerCamelCase_: str = scheduler.step(A_ , A_ , A_ , **A_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCAmelCase ( self : List[str] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_: Union[str, Any] = DEISMultistepScheduler(**self.get_scheduler_config() )
lowerCamelCase_: Dict = self.full_loop(scheduler=A_ )
lowerCamelCase_: int = torch.mean(torch.abs(A_ ) )
assert abs(result_mean.item() - 0.23916 ) < 1e-3
lowerCamelCase_: Optional[Any] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowerCamelCase_: str = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowerCamelCase_: Tuple = UniPCMultistepScheduler.from_config(scheduler.config )
lowerCamelCase_: Union[str, Any] = DEISMultistepScheduler.from_config(scheduler.config )
lowerCamelCase_: str = self.full_loop(scheduler=A_ )
lowerCamelCase_: Optional[int] = torch.mean(torch.abs(A_ ) )
assert abs(result_mean.item() - 0.23916 ) < 1e-3
def lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
for timesteps in [25, 50, 1_00, 9_99, 10_00]:
self.check_over_configs(num_train_timesteps=A_ )
def lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
self.check_over_configs(thresholding=A_ )
for order in [1, 2, 3]:
for solver_type in ["logrho"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=A_ , prediction_type=A_ , sample_max_value=A_ , algorithm_type="""deis""" , solver_order=A_ , solver_type=A_ , )
def lowerCAmelCase ( self : Tuple ) -> int:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A_ )
def lowerCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
for algorithm_type in ["deis"]:
for solver_type in ["logrho"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=A_ , solver_type=A_ , prediction_type=A_ , algorithm_type=A_ , )
lowerCamelCase_: List[str] = self.full_loop(
solver_order=A_ , solver_type=A_ , prediction_type=A_ , algorithm_type=A_ , )
assert not torch.isnan(A_ ).any(), "Samples have nan numbers"
def lowerCAmelCase ( self : int ) -> Tuple:
"""simple docstring"""
self.check_over_configs(lower_order_final=A_ )
self.check_over_configs(lower_order_final=A_ )
def lowerCAmelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
self.check_over_forward(num_inference_steps=A_ , time_step=0 )
def lowerCAmelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
lowerCamelCase_: Optional[int] = self.full_loop()
lowerCamelCase_: Any = torch.mean(torch.abs(A_ ) )
assert abs(result_mean.item() - 0.23916 ) < 1e-3
def lowerCAmelCase ( self : List[str] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_: List[str] = self.full_loop(prediction_type="""v_prediction""" )
lowerCamelCase_: List[str] = torch.mean(torch.abs(A_ ) )
assert abs(result_mean.item() - 0.091 ) < 1e-3
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_: Optional[int] = self.scheduler_classes[0]
lowerCamelCase_: Optional[Any] = self.get_scheduler_config(thresholding=A_ , dynamic_thresholding_ratio=0 )
lowerCamelCase_: Tuple = scheduler_class(**A_ )
lowerCamelCase_: Union[str, Any] = 10
lowerCamelCase_: List[Any] = self.dummy_model()
lowerCamelCase_: Optional[Any] = self.dummy_sample_deter.half()
scheduler.set_timesteps(A_ )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase_: Any = model(A_ , A_ )
lowerCamelCase_: List[Any] = scheduler.step(A_ , A_ , A_ ).prev_sample
assert sample.dtype == torch.floataa
| 706
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : Optional[Any] = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class a__ ( __SCREAMING_SNAKE_CASE ):
_A = "canine"
def __init__( self : List[str] , A_ : str=7_68 , A_ : List[str]=12 , A_ : Optional[int]=12 , A_ : Any=30_72 , A_ : Dict="gelu" , A_ : Optional[Any]=0.1 , A_ : Dict=0.1 , A_ : str=1_63_84 , A_ : Dict=16 , A_ : Tuple=0.02 , A_ : int=1e-12 , A_ : Any=0 , A_ : Optional[int]=0XE000 , A_ : str=0XE001 , A_ : Any=4 , A_ : List[Any]=4 , A_ : Optional[Any]=8 , A_ : Dict=1_63_84 , A_ : Optional[int]=1_28 , **A_ : List[Any] , ) -> Any:
"""simple docstring"""
super().__init__(pad_token_id=A_ , bos_token_id=A_ , eos_token_id=A_ , **A_ )
lowerCamelCase_: Dict = max_position_embeddings
lowerCamelCase_: List[str] = hidden_size
lowerCamelCase_: Tuple = num_hidden_layers
lowerCamelCase_: List[Any] = num_attention_heads
lowerCamelCase_: List[Any] = intermediate_size
lowerCamelCase_: Tuple = hidden_act
lowerCamelCase_: List[Any] = hidden_dropout_prob
lowerCamelCase_: List[Any] = attention_probs_dropout_prob
lowerCamelCase_: Union[str, Any] = initializer_range
lowerCamelCase_: str = type_vocab_size
lowerCamelCase_: List[Any] = layer_norm_eps
# Character config:
lowerCamelCase_: List[str] = downsampling_rate
lowerCamelCase_: List[str] = upsampling_kernel_size
lowerCamelCase_: Tuple = num_hash_functions
lowerCamelCase_: Dict = num_hash_buckets
lowerCamelCase_: Optional[Any] = local_transformer_stride
| 584
| 0
|
from functools import lru_cache
@lru_cache
def lowerCamelCase_(lowerCamelCase_ ) -> Optional[Any]:
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 323
|
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - 1 , 2 ): # iterating over all even indices
if input_list[i] > input_list[i + 1]:
__lowerCAmelCase , __lowerCAmelCase = input_list[i + 1], input_list[i]
# swapping if elements not in order
__lowerCAmelCase = False
for i in range(1 , len(_UpperCamelCase ) - 1 , 2 ): # iterating over all odd indices
if input_list[i] > input_list[i + 1]:
__lowerCAmelCase , __lowerCAmelCase = input_list[i + 1], input_list[i]
# swapping if elements not in order
__lowerCAmelCase = False
return input_list
if __name__ == "__main__":
print("Enter list to be sorted")
A : Tuple = [int(x) for x in input().split()]
# inputing elements of the list in one line
A : Dict = odd_even_sort(input_list)
print("The sorted list is")
print(sorted_list)
| 636
| 0
|
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCamelCase_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class a ( datasets.BuilderConfig ):
lowercase_ : Optional[datasets.Features] = None
lowercase_ : str = "utf-8"
lowercase_ : Optional[str] = None
lowercase_ : Optional[str] = None
lowercase_ : bool = True # deprecated
lowercase_ : Optional[int] = None # deprecated
lowercase_ : int = 10 << 20 # 10MB
lowercase_ : Optional[bool] = None
class a ( datasets.ArrowBasedBuilder ):
lowercase_ : Union[str, Any] = JsonConfig
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
if self.config.block_size is not None:
logger.warning("The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead" )
__lowerCAmelCase = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
"The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore." )
if self.config.newlines_in_values is not None:
raise ValueError("The JSON loader parameter `newlines_in_values` is no longer supported" )
return datasets.DatasetInfo(features=self.config.features )
def UpperCAmelCase__ ( self : List[str] , snake_case__ : List[Any] ):
"""simple docstring"""
if not self.config.data_files:
raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}" )
__lowerCAmelCase = dl_manager.download_and_extract(self.config.data_files )
if isinstance(snake_case__ , (str, list, tuple) ):
__lowerCAmelCase = data_files
if isinstance(snake_case__ , snake_case__ ):
__lowerCAmelCase = [files]
__lowerCAmelCase = [dl_manager.iter_files(snake_case__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"files": files} )]
__lowerCAmelCase = []
for split_name, files in data_files.items():
if isinstance(snake_case__ , snake_case__ ):
__lowerCAmelCase = [files]
__lowerCAmelCase = [dl_manager.iter_files(snake_case__ ) for file in files]
splits.append(datasets.SplitGenerator(name=snake_case__ , gen_kwargs={"files": files} ) )
return splits
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : pa.Table ):
"""simple docstring"""
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
__lowerCAmelCase = self.config.features.arrow_schema.field(snake_case__ ).type
__lowerCAmelCase = pa_table.append_column(snake_case__ , pa.array([None] * len(snake_case__ ) , type=snake_case__ ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__lowerCAmelCase = table_cast(snake_case__ , self.config.features.arrow_schema )
return pa_table
def UpperCAmelCase__ ( self : int , snake_case__ : Tuple ):
"""simple docstring"""
for file_idx, file in enumerate(itertools.chain.from_iterable(snake_case__ ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(snake_case__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__lowerCAmelCase = json.load(snake_case__ )
# We keep only the field we are interested in
__lowerCAmelCase = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(snake_case__ , (list, tuple) ):
__lowerCAmelCase = set().union(*[row.keys() for row in dataset] )
__lowerCAmelCase = {col: [row.get(snake_case__ ) for row in dataset] for col in keys}
else:
__lowerCAmelCase = dataset
__lowerCAmelCase = pa.Table.from_pydict(snake_case__ )
yield file_idx, self._cast_table(snake_case__ )
# If the file has one json object per line
else:
with open(snake_case__ , "rb" ) as f:
__lowerCAmelCase = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
__lowerCAmelCase = max(self.config.chunksize // 32 , 16 << 10 )
__lowerCAmelCase = (
self.config.encoding_errors if self.config.encoding_errors is not None else "strict"
)
while True:
__lowerCAmelCase = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(snake_case__ )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__lowerCAmelCase = batch.decode(self.config.encoding , errors=snake_case__ ).encode("utf-8" )
try:
while True:
try:
__lowerCAmelCase = paj.read_json(
io.BytesIO(snake_case__ ) , read_options=paj.ReadOptions(block_size=snake_case__ ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(snake_case__ , pa.ArrowInvalid )
and "straddling" not in str(snake_case__ )
or block_size > len(snake_case__ )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
F"Batch of {len(snake_case__ )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}." )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
snake_case__ , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__lowerCAmelCase = json.load(snake_case__ )
except json.JSONDecodeError:
logger.error(F"Failed to read file '{file}' with error {type(snake_case__ )}: {e}" )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(snake_case__ , snake_case__ ): # list is the only sequence type supported in JSON
try:
__lowerCAmelCase = set().union(*[row.keys() for row in dataset] )
__lowerCAmelCase = {col: [row.get(snake_case__ ) for row in dataset] for col in keys}
__lowerCAmelCase = pa.Table.from_pydict(snake_case__ )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(F"Failed to read file '{file}' with error {type(snake_case__ )}: {e}" )
raise ValueError(F"Not able to read records in the JSON file at {file}." ) from None
yield file_idx, self._cast_table(snake_case__ )
break
else:
logger.error(F"Failed to read file '{file}' with error {type(snake_case__ )}: {e}" )
raise ValueError(
F"Not able to read records in the JSON file at {file}. "
F"You should probably indicate the field of the JSON file containing your records. "
F"This JSON file contain the following fields: {str(list(dataset.keys() ) )}. "
F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. " ) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(snake_case__ )
batch_idx += 1
| 376
|
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class a ( __UpperCAmelCase ):
lowercase_ : BigBirdConfig
lowercase_ : jnp.dtype = jnp.floataa
lowercase_ : bool = True
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
super().setup()
__lowerCAmelCase = nn.Dense(5 , dtype=self.dtype )
def __call__( self : Optional[Any] , *snake_case__ : List[str] , **snake_case__ : str ):
"""simple docstring"""
__lowerCAmelCase = super().__call__(*snake_case__ , **snake_case__ )
__lowerCAmelCase = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class a ( __UpperCAmelCase ):
lowercase_ : List[str] = FlaxBigBirdForNaturalQuestionsModule
def _UpperCAmelCase ( UpperCamelCase: Optional[Any] , UpperCamelCase: List[str] , UpperCamelCase: Optional[Any] , UpperCamelCase: List[Any] , UpperCamelCase: Optional[Any] , UpperCamelCase: int ):
"""simple docstring"""
def cross_entropy(UpperCamelCase: Union[str, Any] , UpperCamelCase: List[Any] , UpperCamelCase: Optional[int]=None ):
__lowerCAmelCase = logits.shape[-1]
__lowerCAmelCase = (labels[..., None] == jnp.arange(UpperCamelCase )[None]).astype("f4" )
__lowerCAmelCase = jax.nn.log_softmax(UpperCamelCase , axis=-1 )
__lowerCAmelCase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
__lowerCAmelCase = reduction(UpperCamelCase )
return loss
__lowerCAmelCase = partial(UpperCamelCase , reduction=jnp.mean )
__lowerCAmelCase = cross_entropy(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = cross_entropy(UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = cross_entropy(UpperCamelCase , UpperCamelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class a :
lowercase_ : str = "google/bigbird-roberta-base"
lowercase_ : int = 3_000
lowercase_ : int = 10_500
lowercase_ : int = 128
lowercase_ : int = 3
lowercase_ : int = 1
lowercase_ : int = 5
# tx_args
lowercase_ : float = 3e-5
lowercase_ : float = 0.0
lowercase_ : int = 20_000
lowercase_ : float = 0.0095
lowercase_ : str = "bigbird-roberta-natural-questions"
lowercase_ : str = "training-expt"
lowercase_ : str = "data/nq-training.jsonl"
lowercase_ : str = "data/nq-validation.jsonl"
def UpperCAmelCase__ ( self : Optional[int] ):
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=snake_case__ )
__lowerCAmelCase = os.path.join(self.base_dir , self.save_dir )
__lowerCAmelCase = self.batch_size_per_device * jax.device_count()
@dataclass
class a :
lowercase_ : int
lowercase_ : int = 4_096 # no dynamic padding on TPUs
def __call__( self : List[Any] , snake_case__ : Union[str, Any] ):
"""simple docstring"""
__lowerCAmelCase = self.collate_fn(snake_case__ )
__lowerCAmelCase = jax.tree_util.tree_map(snake_case__ , snake_case__ )
return batch
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : Dict ):
"""simple docstring"""
__lowerCAmelCase , __lowerCAmelCase = self.fetch_inputs(features["input_ids"] )
__lowerCAmelCase = {
"input_ids": jnp.array(snake_case__ , dtype=jnp.intaa ),
"attention_mask": jnp.array(snake_case__ , dtype=jnp.intaa ),
"start_labels": jnp.array(features["start_token"] , dtype=jnp.intaa ),
"end_labels": jnp.array(features["end_token"] , dtype=jnp.intaa ),
"pooled_labels": jnp.array(features["category"] , dtype=jnp.intaa ),
}
return batch
def UpperCAmelCase__ ( self : Optional[int] , snake_case__ : list ):
"""simple docstring"""
__lowerCAmelCase = [self._fetch_inputs(snake_case__ ) for ids in input_ids]
return zip(*snake_case__ )
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : list ):
"""simple docstring"""
__lowerCAmelCase = [1 for _ in range(len(snake_case__ ) )]
while len(snake_case__ ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def _UpperCAmelCase ( UpperCamelCase: Tuple , UpperCamelCase: Dict , UpperCamelCase: Optional[Any]=None ):
"""simple docstring"""
if seed is not None:
__lowerCAmelCase = dataset.shuffle(seed=UpperCamelCase )
for i in range(len(UpperCamelCase ) // batch_size ):
__lowerCAmelCase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(UpperCamelCase )
@partial(jax.pmap , axis_name="batch" )
def _UpperCAmelCase ( UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] , **UpperCamelCase: List[str] ):
"""simple docstring"""
def loss_fn(UpperCamelCase: Dict ):
__lowerCAmelCase = model_inputs.pop("start_labels" )
__lowerCAmelCase = model_inputs.pop("end_labels" )
__lowerCAmelCase = model_inputs.pop("pooled_labels" )
__lowerCAmelCase = state.apply_fn(**UpperCamelCase , params=UpperCamelCase , dropout_rng=UpperCamelCase , train=UpperCamelCase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = outputs
return state.loss_fn(
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , )
__lowerCAmelCase , __lowerCAmelCase = jax.random.split(UpperCamelCase )
__lowerCAmelCase = jax.value_and_grad(UpperCamelCase )
__lowerCAmelCase , __lowerCAmelCase = grad_fn(state.params )
__lowerCAmelCase = jax.lax.pmean({"loss": loss} , axis_name="batch" )
__lowerCAmelCase = jax.lax.pmean(UpperCamelCase , "batch" )
__lowerCAmelCase = state.apply_gradients(grads=UpperCamelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="batch" )
def _UpperCAmelCase ( UpperCamelCase: Optional[Any] , **UpperCamelCase: List[str] ):
"""simple docstring"""
__lowerCAmelCase = model_inputs.pop("start_labels" )
__lowerCAmelCase = model_inputs.pop("end_labels" )
__lowerCAmelCase = model_inputs.pop("pooled_labels" )
__lowerCAmelCase = state.apply_fn(**UpperCamelCase , params=state.params , train=UpperCamelCase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = outputs
__lowerCAmelCase = state.loss_fn(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = jax.lax.pmean({"loss": loss} , axis_name="batch" )
return metrics
class a ( train_state.TrainState ):
lowercase_ : Callable = struct.field(pytree_node=__UpperCAmelCase )
@dataclass
class a :
lowercase_ : Args
lowercase_ : Callable
lowercase_ : Callable
lowercase_ : Callable
lowercase_ : Callable
lowercase_ : wandb
lowercase_ : Callable = None
def UpperCAmelCase__ ( self : Tuple , snake_case__ : int , snake_case__ : List[Any] , snake_case__ : Any , snake_case__ : str=None ):
"""simple docstring"""
__lowerCAmelCase = model.params
__lowerCAmelCase = TrainState.create(
apply_fn=model.__call__ , params=snake_case__ , tx=snake_case__ , loss_fn=snake_case__ , )
if ckpt_dir is not None:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = restore_checkpoint(snake_case__ , snake_case__ )
__lowerCAmelCase = {
"lr": args.lr,
"init_lr": args.init_lr,
"warmup_steps": args.warmup_steps,
"num_train_steps": num_train_steps,
"weight_decay": args.weight_decay,
}
__lowerCAmelCase , __lowerCAmelCase = build_tx(**snake_case__ )
__lowerCAmelCase = train_state.TrainState(
step=snake_case__ , apply_fn=model.__call__ , params=snake_case__ , tx=snake_case__ , opt_state=snake_case__ , )
__lowerCAmelCase = args
__lowerCAmelCase = data_collator
__lowerCAmelCase = lr
__lowerCAmelCase = params
__lowerCAmelCase = jax_utils.replicate(snake_case__ )
return state
def UpperCAmelCase__ ( self : str , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : Any ):
"""simple docstring"""
__lowerCAmelCase = self.args
__lowerCAmelCase = len(snake_case__ ) // args.batch_size
__lowerCAmelCase = jax.random.PRNGKey(0 )
__lowerCAmelCase = jax.random.split(snake_case__ , jax.device_count() )
for epoch in range(args.max_epochs ):
__lowerCAmelCase = jnp.array(0 , dtype=jnp.floataa )
__lowerCAmelCase = get_batched_dataset(snake_case__ , args.batch_size , seed=snake_case__ )
__lowerCAmelCase = 0
for batch in tqdm(snake_case__ , total=snake_case__ , desc=F"Running EPOCH-{epoch}" ):
__lowerCAmelCase = self.data_collator(snake_case__ )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self.train_step_fn(snake_case__ , snake_case__ , **snake_case__ )
running_loss += jax_utils.unreplicate(metrics["loss"] )
i += 1
if i % args.logging_steps == 0:
__lowerCAmelCase = jax_utils.unreplicate(state.step )
__lowerCAmelCase = running_loss.item() / i
__lowerCAmelCase = self.scheduler_fn(state_step - 1 )
__lowerCAmelCase = self.evaluate(snake_case__ , snake_case__ )
__lowerCAmelCase = {
"step": state_step.item(),
"eval_loss": eval_loss.item(),
"tr_loss": tr_loss,
"lr": lr.item(),
}
tqdm.write(str(snake_case__ ) )
self.logger.log(snake_case__ , commit=snake_case__ )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}" , state=snake_case__ )
def UpperCAmelCase__ ( self : List[Any] , snake_case__ : Any , snake_case__ : Optional[Any] ):
"""simple docstring"""
__lowerCAmelCase = get_batched_dataset(snake_case__ , self.args.batch_size )
__lowerCAmelCase = len(snake_case__ ) // self.args.batch_size
__lowerCAmelCase = jnp.array(0 , dtype=jnp.floataa )
__lowerCAmelCase = 0
for batch in tqdm(snake_case__ , total=snake_case__ , desc="Evaluating ... " ):
__lowerCAmelCase = self.data_collator(snake_case__ )
__lowerCAmelCase = self.val_step_fn(snake_case__ , **snake_case__ )
running_loss += jax_utils.unreplicate(metrics["loss"] )
i += 1
return running_loss / i
def UpperCAmelCase__ ( self : Union[str, Any] , snake_case__ : Optional[int] , snake_case__ : Any ):
"""simple docstring"""
__lowerCAmelCase = jax_utils.unreplicate(snake_case__ )
print(F"SAVING CHECKPOINT IN {save_dir}" , end=" ... " )
self.model_save_fn(snake_case__ , params=state.params )
with open(os.path.join(snake_case__ , "opt_state.msgpack" ) , "wb" ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(snake_case__ , "args.joblib" ) )
joblib.dump(self.data_collator , os.path.join(snake_case__ , "data_collator.joblib" ) )
with open(os.path.join(snake_case__ , "training_state.json" ) , "w" ) as f:
json.dump({"step": state.step.item()} , snake_case__ )
print("DONE" )
def _UpperCAmelCase ( UpperCamelCase: str , UpperCamelCase: List[Any] ):
"""simple docstring"""
print(F"RESTORING CHECKPOINT FROM {save_dir}" , end=" ... " )
with open(os.path.join(UpperCamelCase , "flax_model.msgpack" ) , "rb" ) as f:
__lowerCAmelCase = from_bytes(state.params , f.read() )
with open(os.path.join(UpperCamelCase , "opt_state.msgpack" ) , "rb" ) as f:
__lowerCAmelCase = from_bytes(state.opt_state , f.read() )
__lowerCAmelCase = joblib.load(os.path.join(UpperCamelCase , "args.joblib" ) )
__lowerCAmelCase = joblib.load(os.path.join(UpperCamelCase , "data_collator.joblib" ) )
with open(os.path.join(UpperCamelCase , "training_state.json" ) , "r" ) as f:
__lowerCAmelCase = json.load(UpperCamelCase )
__lowerCAmelCase = training_state["step"]
print("DONE" )
return params, opt_state, step, args, data_collator
def _UpperCAmelCase ( UpperCamelCase: Any , UpperCamelCase: Dict , UpperCamelCase: Tuple , UpperCamelCase: Dict ):
"""simple docstring"""
__lowerCAmelCase = num_train_steps - warmup_steps
__lowerCAmelCase = optax.linear_schedule(init_value=UpperCamelCase , end_value=UpperCamelCase , transition_steps=UpperCamelCase )
__lowerCAmelCase = optax.linear_schedule(init_value=UpperCamelCase , end_value=1e-7 , transition_steps=UpperCamelCase )
__lowerCAmelCase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def _UpperCAmelCase ( UpperCamelCase: Union[str, Any] , UpperCamelCase: str , UpperCamelCase: List[Any] , UpperCamelCase: Optional[int] , UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
def weight_decay_mask(UpperCamelCase: int ):
__lowerCAmelCase = traverse_util.flatten_dict(UpperCamelCase )
__lowerCAmelCase = {k: (v[-1] != "bias" and v[-2:] != ("LayerNorm", "scale")) for k, v in params.items()}
return traverse_util.unflatten_dict(UpperCamelCase )
__lowerCAmelCase = scheduler_fn(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase )
__lowerCAmelCase = optax.adamw(learning_rate=UpperCamelCase , weight_decay=UpperCamelCase , mask=UpperCamelCase )
return tx, lr
| 376
| 1
|
"""simple docstring"""
def lowerCAmelCase_ ( snake_case_ : int = 1_0 ) ->str:
if not isinstance(snake_case_ , snake_case_ ) or n < 0:
raise ValueError('Invalid input' )
lowerCamelCase__ : List[str] =1_0**n
lowerCamelCase__ : List[Any] =2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , snake_case_ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"""{solution(10) = }""")
| 174
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
@skip_mps
class A_ ( A__ , A__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = StableDiffusionPanoramaPipeline
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_BATCH_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_IMAGE_PARAMS
SCREAMING_SNAKE_CASE_ = TEXT_TO_IMAGE_IMAGE_PARAMS
def UpperCAmelCase__ ( self :int ):
"""simple docstring"""
torch.manual_seed(0 )
lowerCamelCase__ : int =UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
lowerCamelCase__ : Optional[int] =DDIMScheduler()
torch.manual_seed(0 )
lowerCamelCase__ : Any =AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase__ : Union[str, Any] =CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
lowerCamelCase__ : Union[str, Any] =CLIPTextModel(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCamelCase__ : Optional[int] ={
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def UpperCAmelCase__ ( self :Dict , lowerCamelCase_ :List[str] , lowerCamelCase_ :List[str]=0 ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =torch.manual_seed(lowerCamelCase_ )
lowerCamelCase__ : int ={
'prompt': 'a photo of the dolomites',
'generator': generator,
# Setting height and width to None to prevent OOMs on CPU.
'height': None,
'width': None,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def UpperCAmelCase__ ( self :int ):
"""simple docstring"""
lowerCamelCase__ : List[Any] ='cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : Optional[Any] =self.get_dummy_components()
lowerCamelCase__ : int =StableDiffusionPanoramaPipeline(**lowerCamelCase_ )
lowerCamelCase__ : List[str] =sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Optional[int] =self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Tuple =sd_pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : Optional[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : List[str] =np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self :Union[str, Any] ):
"""simple docstring"""
super().test_inference_batch_consistent(batch_sizes=[1, 2] )
def UpperCAmelCase__ ( self :str ):
"""simple docstring"""
super().test_inference_batch_single_identical(batch_size=2 , expected_max_diff=3.25e-3 )
def UpperCAmelCase__ ( self :Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] ='cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : str =self.get_dummy_components()
lowerCamelCase__ : int =StableDiffusionPanoramaPipeline(**lowerCamelCase_ )
lowerCamelCase__ : int =sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] =self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] ='french fries'
lowerCamelCase__ : Optional[Any] =sd_pipe(**lowerCamelCase_ , negative_prompt=lowerCamelCase_ )
lowerCamelCase__ : Any =output.images
lowerCamelCase__ : Optional[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : List[str] =np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self :Optional[Any] ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] ='cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : int =self.get_dummy_components()
lowerCamelCase__ : Any =StableDiffusionPanoramaPipeline(**lowerCamelCase_ )
lowerCamelCase__ : int =sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Any =self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : int =sd_pipe(**lowerCamelCase_ , view_batch_size=2 )
lowerCamelCase__ : Dict =output.images
lowerCamelCase__ : List[str] =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : List[str] =np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self :Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] ='cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : List[Any] =self.get_dummy_components()
lowerCamelCase__ : Any =EulerAncestralDiscreteScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' )
lowerCamelCase__ : Optional[Any] =StableDiffusionPanoramaPipeline(**lowerCamelCase_ )
lowerCamelCase__ : Tuple =sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Dict =self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Optional[int] =sd_pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : Any =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : Optional[int] =np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase__ ( self :List[Any] ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] ='cpu' # ensure determinism for the device-dependent torch.Generator
lowerCamelCase__ : List[Any] =self.get_dummy_components()
lowerCamelCase__ : Union[str, Any] =PNDMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule='scaled_linear' , skip_prk_steps=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] =StableDiffusionPanoramaPipeline(**lowerCamelCase_ )
lowerCamelCase__ : int =sd_pipe.to(lowerCamelCase_ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase_ )
lowerCamelCase__ : Dict =self.get_dummy_inputs(lowerCamelCase_ )
lowerCamelCase__ : Any =sd_pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : str =np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase__ ( self :Tuple ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase__ ( self :List[Any] , lowerCamelCase_ :Tuple=0 ):
"""simple docstring"""
lowerCamelCase__ : Any =torch.manual_seed(lowerCamelCase_ )
lowerCamelCase__ : Dict ={
'prompt': 'a photo of the dolomites',
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'output_type': 'numpy',
}
return inputs
def UpperCAmelCase__ ( self :str ):
"""simple docstring"""
lowerCamelCase__ : Optional[Any] ='stabilityai/stable-diffusion-2-base'
lowerCamelCase__ : Tuple =DDIMScheduler.from_pretrained(lowerCamelCase_ , subfolder='scheduler' )
lowerCamelCase__ : Dict =StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase_ , scheduler=lowerCamelCase_ , safety_checker=lowerCamelCase_ )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
lowerCamelCase__ : List[Any] =self.get_inputs()
lowerCamelCase__ : List[Any] =pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : int =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2_048, 3)
lowerCamelCase__ : Dict =np.array(
[
0.36_96_83_92,
0.27_02_53_72,
0.32_44_67_66,
0.28_37_93_87,
0.36_36_32_74,
0.30_73_33_47,
0.27_10_00_27,
0.27_05_41_25,
0.25_53_60_96,
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-2
def UpperCAmelCase__ ( self :Dict ):
"""simple docstring"""
lowerCamelCase__ : Tuple =StableDiffusionPanoramaPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-base' , safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Dict =LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
lowerCamelCase__ : Optional[int] =self.get_inputs()
lowerCamelCase__ : Optional[Any] =pipe(**lowerCamelCase_ ).images
lowerCamelCase__ : Optional[Any] =image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 2_048, 3)
lowerCamelCase__ : int =np.array(
[
[
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def UpperCAmelCase__ ( self :List[str] ):
"""simple docstring"""
lowerCamelCase__ : str =0
def callback_fn(lowerCamelCase_ :int , lowerCamelCase_ :int , lowerCamelCase_ :torch.FloatTensor ) -> None:
lowerCamelCase__ : int =True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
lowerCamelCase__ : Any =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowerCamelCase__ : Optional[Any] =latents[0, -3:, -3:, -1]
lowerCamelCase__ : Union[str, Any] =np.array(
[
0.18_68_18_69,
0.33_90_78_16,
0.5_36_12_76,
0.14_43_28_65,
-0.02_85_66_11,
-0.73_94_11_23,
0.23_39_79_87,
0.47_32_26_82,
-0.37_82_31_64,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
lowerCamelCase__ : Union[str, Any] =latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 256)
lowerCamelCase__ : int =latents[0, -3:, -3:, -1]
lowerCamelCase__ : List[Any] =np.array(
[
0.18_53_96_45,
0.33_98_72_48,
0.5_37_85_59,
0.14_43_71_42,
-0.02_45_52_61,
-0.7_33_83_17,
0.23_99_07_55,
0.47_35_62_72,
-0.3_78_65_05,
] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
lowerCamelCase__ : Optional[int] =False
lowerCamelCase__ : Optional[int] ='stabilityai/stable-diffusion-2-base'
lowerCamelCase__ : Union[str, Any] =DDIMScheduler.from_pretrained(lowerCamelCase_ , subfolder='scheduler' )
lowerCamelCase__ : int =StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase_ , scheduler=lowerCamelCase_ , safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Any =pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing()
lowerCamelCase__ : Optional[int] =self.get_inputs()
pipe(**lowerCamelCase_ , callback=lowerCamelCase_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def UpperCAmelCase__ ( self :Dict ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase__ : Dict ='stabilityai/stable-diffusion-2-base'
lowerCamelCase__ : Optional[int] =DDIMScheduler.from_pretrained(lowerCamelCase_ , subfolder='scheduler' )
lowerCamelCase__ : str =StableDiffusionPanoramaPipeline.from_pretrained(lowerCamelCase_ , scheduler=lowerCamelCase_ , safety_checker=lowerCamelCase_ )
lowerCamelCase__ : Dict =pipe.to(lowerCamelCase_ )
pipe.set_progress_bar_config(disable=lowerCamelCase_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
lowerCamelCase__ : Any =self.get_inputs()
lowerCamelCase__ : Optional[Any] =pipe(**lowerCamelCase_ )
lowerCamelCase__ : int =torch.cuda.max_memory_allocated()
# make sure that less than 5.2 GB is allocated
assert mem_bytes < 5.5 * 10**9
| 174
| 1
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : Any = {"vocab_file": "sentencepiece.bpe.model"}
__lowerCamelCase : Dict = {
"vocab_file": {
"moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
"moussaKam/barthez-orangesum-title": (
"https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
),
},
}
__lowerCamelCase : List[str] = {
"moussaKam/mbarthez": 1024,
"moussaKam/barthez": 1024,
"moussaKam/barthez-orangesum-title": 1024,
}
__lowerCamelCase : int = "▁"
class UpperCAmelCase ( _UpperCAmelCase ):
UpperCAmelCase : Union[str, Any] = VOCAB_FILES_NAMES
UpperCAmelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Any = ['''input_ids''', '''attention_mask''']
def __init__(self : str , A__ : Any , A__ : Optional[int]="<s>" , A__ : Any="</s>" , A__ : Optional[int]="</s>" , A__ : Tuple="<s>" , A__ : str="<unk>" , A__ : int="<pad>" , A__ : Any="<mask>" , A__ : Optional[Dict[str, Any]] = None , **A__ : str , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
lowercase = AddedToken(A_ , lstrip=A_ , rstrip=A_ ) if isinstance(A_ , A_ ) else mask_token
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A_ , eos_token=A_ , unk_token=A_ , sep_token=A_ , cls_token=A_ , pad_token=A_ , mask_token=A_ , sp_model_kwargs=self.sp_model_kwargs , **A_ , )
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(A_ ) )
lowercase = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
lowercase = len(self.sp_model ) - 1
lowercase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def UpperCAmelCase__ (self : List[Any] , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase = [self.cls_token_id]
lowercase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase__ (self : Optional[Any] , A__ : List[int] , A__ : Optional[List[int]] = None , A__ : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A_ , token_ids_a=A_ , already_has_special_tokens=A_ )
if token_ids_a is None:
return [1] + ([0] * len(A_ )) + [1]
return [1] + ([0] * len(A_ )) + [1, 1] + ([0] * len(A_ )) + [1]
def UpperCAmelCase__ (self : Union[str, Any] , A__ : List[int] , A__ : Optional[List[int]] = None ) -> List[int]:
lowercase = [self.sep_token_id]
lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def UpperCAmelCase__ (self : int ) -> Union[str, Any]:
return len(self.sp_model )
def UpperCAmelCase__ (self : List[str] ) -> List[str]:
lowercase = {self.convert_ids_to_tokens(A_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def UpperCAmelCase__ (self : Any , A__ : str ) -> List[str]:
return self.sp_model.encode(A_ , out_type=A_ )
def UpperCAmelCase__ (self : str , A__ : Union[str, Any] ) -> List[Any]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
lowercase = self.sp_model.PieceToId(A_ )
return spm_id if spm_id else self.unk_token_id
def UpperCAmelCase__ (self : Any , A__ : str ) -> Dict:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(A_ )
def UpperCAmelCase__ (self : str , A__ : int ) -> Union[str, Any]:
lowercase = []
lowercase = ""
lowercase = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(A_ ) + token
lowercase = True
lowercase = []
else:
current_sub_tokens.append(A_ )
lowercase = False
out_string += self.sp_model.decode(A_ )
return out_string.strip()
def __getstate__(self : Dict ) -> int:
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__(self : Union[str, Any] , A__ : List[str] ) -> List[Any]:
lowercase = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase__ (self : Tuple , A__ : str , A__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(A_ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase = os.path.join(
A_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A_ )
elif not os.path.isfile(self.vocab_file ):
with open(A_ , "wb" ) as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(A_ )
return (out_vocab_file,)
| 705
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}
class UpperCAmelCase ( _lowercase ):
UpperCAmelCase : List[str] = '''data2vec-text'''
def __init__(self : List[str] , A__ : str=3_0_5_2_2 , A__ : Tuple=7_6_8 , A__ : Any=1_2 , A__ : Optional[int]=1_2 , A__ : str=3_0_7_2 , A__ : List[str]="gelu" , A__ : List[Any]=0.1 , A__ : Optional[int]=0.1 , A__ : Union[str, Any]=5_1_2 , A__ : Any=2 , A__ : str=0.0_2 , A__ : int=1e-12 , A__ : Union[str, Any]=1 , A__ : Optional[int]=0 , A__ : Union[str, Any]=2 , A__ : Optional[int]="absolute" , A__ : Tuple=True , A__ : int=None , **A__ : Any , ) -> Any:
super().__init__(pad_token_id=A__ , bos_token_id=A__ , eos_token_id=A__ , **A__ )
lowercase = vocab_size
lowercase = hidden_size
lowercase = num_hidden_layers
lowercase = num_attention_heads
lowercase = hidden_act
lowercase = intermediate_size
lowercase = hidden_dropout_prob
lowercase = attention_probs_dropout_prob
lowercase = max_position_embeddings
lowercase = type_vocab_size
lowercase = initializer_range
lowercase = layer_norm_eps
lowercase = position_embedding_type
lowercase = use_cache
lowercase = classifier_dropout
class UpperCAmelCase ( _lowercase ):
@property
def UpperCAmelCase__ (self : List[str] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowercase = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowercase = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 459
| 0
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
UpperCAmelCase = None
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
UpperCAmelCase = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
),
},
}
UpperCAmelCase = {
"""moussaKam/mbarthez""": 1_0_2_4,
"""moussaKam/barthez""": 1_0_2_4,
"""moussaKam/barthez-orangesum-title""": 1_0_2_4,
}
UpperCAmelCase = """▁"""
class lowercase ( lowercase__ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = ['''input_ids''', '''attention_mask''']
lowercase = BarthezTokenizer
def __init__(self : int ,SCREAMING_SNAKE_CASE_ : List[Any]=None ,SCREAMING_SNAKE_CASE_ : List[str]=None ,SCREAMING_SNAKE_CASE_ : List[str]="<s>" ,SCREAMING_SNAKE_CASE_ : List[str]="</s>" ,SCREAMING_SNAKE_CASE_ : Optional[Any]="</s>" ,SCREAMING_SNAKE_CASE_ : List[str]="<s>" ,SCREAMING_SNAKE_CASE_ : List[Any]="<unk>" ,SCREAMING_SNAKE_CASE_ : Union[str, Any]="<pad>" ,SCREAMING_SNAKE_CASE_ : Dict="<mask>" ,**SCREAMING_SNAKE_CASE_ : Optional[Any] ,) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase = AddedToken(SCREAMING_SNAKE_CASE_ ,lstrip=SCREAMING_SNAKE_CASE_ ,rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else mask_token
super().__init__(
SCREAMING_SNAKE_CASE_ ,tokenizer_file=SCREAMING_SNAKE_CASE_ ,bos_token=SCREAMING_SNAKE_CASE_ ,eos_token=SCREAMING_SNAKE_CASE_ ,unk_token=SCREAMING_SNAKE_CASE_ ,sep_token=SCREAMING_SNAKE_CASE_ ,cls_token=SCREAMING_SNAKE_CASE_ ,pad_token=SCREAMING_SNAKE_CASE_ ,mask_token=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
lowerCAmelCase = vocab_file
lowerCAmelCase = False if not self.vocab_file else True
def UpperCAmelCase (self : Union[str, Any] ,SCREAMING_SNAKE_CASE_ : List[int] ,SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCAmelCase (self : Optional[int] ,SCREAMING_SNAKE_CASE_ : List[int] ,SCREAMING_SNAKE_CASE_ : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
lowerCAmelCase = [self.sep_token_id]
lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCAmelCase (self : str ,SCREAMING_SNAKE_CASE_ : str ,SCREAMING_SNAKE_CASE_ : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCAmelCase = os.path.join(
SCREAMING_SNAKE_CASE_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
copyfile(self.vocab_file ,SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 535
|
"""simple docstring"""
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class lowercase ( lowercase__ ):
def __get__(self : str ,SCREAMING_SNAKE_CASE_ : List[Any] ,SCREAMING_SNAKE_CASE_ : Optional[Any]=None ) -> int:
"""simple docstring"""
if obj is None:
return self
if self.fget is None:
raise AttributeError('''unreadable attribute''' )
lowerCAmelCase = '''__cached_''' + self.fget.__name__
lowerCAmelCase = getattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
if cached is None:
lowerCAmelCase = self.fget(SCREAMING_SNAKE_CASE_ )
setattr(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
return cached
def __magic_name__ ( _lowerCamelCase: Union[str, Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"""invalid truth value {val!r}""" )
def __magic_name__ ( _lowerCamelCase: int ) -> Union[str, Any]:
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase, torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase, tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase, (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase, np.ndarray )
def __magic_name__ ( _lowerCamelCase: Optional[Any] ) -> Tuple:
'''simple docstring'''
return isinstance(_lowerCamelCase, np.ndarray )
def __magic_name__ ( _lowerCamelCase: str ) -> List[Any]:
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: Dict ) -> Optional[int]:
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase, torch.Tensor )
def __magic_name__ ( _lowerCamelCase: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: Optional[Any] ) -> List[Any]:
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase, torch.device )
def __magic_name__ ( _lowerCamelCase: List[Any] ) -> int:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: str ) -> Dict:
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase, _lowerCamelCase ):
if hasattr(_lowerCamelCase, _lowerCamelCase ):
lowerCAmelCase = getattr(_lowerCamelCase, _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase, torch.dtype )
def __magic_name__ ( _lowerCamelCase: Optional[Any] ) -> Tuple:
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: Tuple ) -> Tuple:
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase, tf.Tensor )
def __magic_name__ ( _lowerCamelCase: int ) -> Tuple:
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: List[Any] ) -> Union[str, Any]:
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase, '''is_symbolic_tensor''' ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def __magic_name__ ( _lowerCamelCase: Union[str, Any] ) -> str:
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: List[Any] ) -> List[Any]:
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase, jnp.ndarray )
def __magic_name__ ( _lowerCamelCase: Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def __magic_name__ ( _lowerCamelCase: List[str] ) -> Any:
'''simple docstring'''
if isinstance(_lowerCamelCase, (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase, (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase, (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def __magic_name__ ( _lowerCamelCase: Optional[int] ) -> Any:
'''simple docstring'''
if isinstance(_lowerCamelCase, (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase, (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class lowercase ( lowercase__ ):
def UpperCAmelCase (self : Any ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase = fields(self )
# Safety and consistency checks
if not len(SCREAMING_SNAKE_CASE_ ):
raise ValueError(F"""{self.__class__.__name__} has no fields.""" )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(F"""{self.__class__.__name__} should not have more than one required field.""" )
lowerCAmelCase = getattr(self ,class_fields[0].name )
lowerCAmelCase = all(getattr(self ,field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(SCREAMING_SNAKE_CASE_ ):
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase = first_field.items()
lowerCAmelCase = True
else:
try:
lowerCAmelCase = iter(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase = True
except TypeError:
lowerCAmelCase = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(SCREAMING_SNAKE_CASE_ ):
if (
not isinstance(SCREAMING_SNAKE_CASE_ ,(list, tuple) )
or not len(SCREAMING_SNAKE_CASE_ ) == 2
or not isinstance(element[0] ,SCREAMING_SNAKE_CASE_ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
lowerCAmelCase = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
F"""Cannot set key/value for {element}. It needs to be a tuple (key, value).""" )
break
setattr(self ,element[0] ,element[1] )
if element[1] is not None:
lowerCAmelCase = element[1]
elif first_field is not None:
lowerCAmelCase = first_field
else:
for field in class_fields:
lowerCAmelCase = getattr(self ,field.name )
if v is not None:
lowerCAmelCase = v
def __delitem__(self : Optional[Any] ,*SCREAMING_SNAKE_CASE_ : Tuple ,**SCREAMING_SNAKE_CASE_ : Dict ) -> Tuple:
"""simple docstring"""
raise Exception(F"""You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.""" )
def UpperCAmelCase (self : List[Any] ,*SCREAMING_SNAKE_CASE_ : Any ,**SCREAMING_SNAKE_CASE_ : int ) -> List[Any]:
"""simple docstring"""
raise Exception(F"""You cannot use ``setdefault`` on a {self.__class__.__name__} instance.""" )
def UpperCAmelCase (self : Union[str, Any] ,*SCREAMING_SNAKE_CASE_ : List[Any] ,**SCREAMING_SNAKE_CASE_ : Dict ) -> Union[str, Any]:
"""simple docstring"""
raise Exception(F"""You cannot use ``pop`` on a {self.__class__.__name__} instance.""" )
def UpperCAmelCase (self : Any ,*SCREAMING_SNAKE_CASE_ : Optional[Any] ,**SCREAMING_SNAKE_CASE_ : Dict ) -> Any:
"""simple docstring"""
raise Exception(F"""You cannot use ``update`` on a {self.__class__.__name__} instance.""" )
def __getitem__(self : int ,SCREAMING_SNAKE_CASE_ : Tuple ) -> Optional[int]:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
lowerCAmelCase = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__(self : int ,SCREAMING_SNAKE_CASE_ : Any ,SCREAMING_SNAKE_CASE_ : int ) -> List[str]:
"""simple docstring"""
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
super().__setattr__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def __setitem__(self : List[str] ,SCREAMING_SNAKE_CASE_ : str ,SCREAMING_SNAKE_CASE_ : Tuple ) -> List[Any]:
"""simple docstring"""
super().__setitem__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )
def UpperCAmelCase (self : List[str] ) -> Tuple[Any]:
"""simple docstring"""
return tuple(self[k] for k in self.keys() )
class lowercase ( lowercase__ ,lowercase__ ):
@classmethod
def UpperCAmelCase (cls : int ,SCREAMING_SNAKE_CASE_ : Any ) -> Dict:
"""simple docstring"""
raise ValueError(
F"""{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}""" )
class lowercase ( lowercase__ ):
lowercase = '''longest'''
lowercase = '''max_length'''
lowercase = '''do_not_pad'''
class lowercase ( lowercase__ ):
lowercase = '''pt'''
lowercase = '''tf'''
lowercase = '''np'''
lowercase = '''jax'''
class lowercase :
def __init__(self : Optional[Any] ,SCREAMING_SNAKE_CASE_ : List[ContextManager] ) -> Optional[Any]:
"""simple docstring"""
lowerCAmelCase = context_managers
lowerCAmelCase = ExitStack()
def __enter__(self : int ) -> Dict:
"""simple docstring"""
for context_manager in self.context_managers:
self.stack.enter_context(SCREAMING_SNAKE_CASE_ )
def __exit__(self : Tuple ,*SCREAMING_SNAKE_CASE_ : Tuple ,**SCREAMING_SNAKE_CASE_ : int ) -> str:
"""simple docstring"""
self.stack.__exit__(*SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ )
def __magic_name__ ( _lowerCamelCase: str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase = infer_framework(_lowerCamelCase )
if framework == "tf":
lowerCAmelCase = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCAmelCase = inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCAmelCase = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def __magic_name__ ( _lowerCamelCase: Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase = model_class.__name__
lowerCAmelCase = infer_framework(_lowerCamelCase )
if framework == "tf":
lowerCAmelCase = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
lowerCAmelCase = inspect.signature(model_class.forward ) # PyTorch models
else:
lowerCAmelCase = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def __magic_name__ ( _lowerCamelCase: MutableMapping, _lowerCamelCase: str = "", _lowerCamelCase: str = "." ) -> Optional[int]:
'''simple docstring'''
def _flatten_dict(_lowerCamelCase: Any, _lowerCamelCase: Optional[int]="", _lowerCamelCase: Union[str, Any]="." ):
for k, v in d.items():
lowerCAmelCase = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase, _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase, _lowerCamelCase, delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase, _lowerCamelCase, _lowerCamelCase ) )
@contextmanager
def __magic_name__ ( _lowerCamelCase: Union[str, Any], _lowerCamelCase: bool = False ) -> Tuple:
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def __magic_name__ ( _lowerCamelCase: Optional[int], _lowerCamelCase: int=None ) -> List[Any]:
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase, axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase, perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase, axes=_lowerCamelCase )
else:
raise ValueError(F"""Type not supported for transpose: {type(_lowerCamelCase )}.""" )
def __magic_name__ ( _lowerCamelCase: List[Any], _lowerCamelCase: Any ) -> List[str]:
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase, _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase, _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase, _lowerCamelCase )
else:
raise ValueError(F"""Type not supported for reshape: {type(_lowerCamelCase )}.""" )
def __magic_name__ ( _lowerCamelCase: int, _lowerCamelCase: Tuple=None ) -> List[str]:
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase, axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase, axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase, axis=_lowerCamelCase )
else:
raise ValueError(F"""Type not supported for squeeze: {type(_lowerCamelCase )}.""" )
def __magic_name__ ( _lowerCamelCase: List[str], _lowerCamelCase: int ) -> List[Any]:
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase, _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase, axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase, axis=_lowerCamelCase )
else:
raise ValueError(F"""Type not supported for expand_dims: {type(_lowerCamelCase )}.""" )
def __magic_name__ ( _lowerCamelCase: Any ) -> Dict:
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"""Type not supported for expand_dims: {type(_lowerCamelCase )}.""" )
def __magic_name__ ( _lowerCamelCase: Optional[Any], _lowerCamelCase: Optional[int] ) -> Optional[int]:
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase, (tuple, list) ):
lowerCAmelCase = [F"""{repo_id}--{v}""" if (v is not None and '''--''' not in v) else v for v in value]
elif value is not None and "--" not in value:
lowerCAmelCase = F"""{repo_id}--{value}"""
return auto_map
def __magic_name__ ( _lowerCamelCase: str ) -> Any:
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
lowerCAmelCase = base_class.__module__
lowerCAmelCase = base_class.__name__
if module.startswith('''tensorflow''' ) or module.startswith('''keras''' ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith('''torch''' ) or name == "PreTrainedModel":
return "pt"
elif module.startswith('''flax''' ) or module.startswith('''jax''' ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"""Could not infer framework from class {model_class}.""" )
| 535
| 1
|
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
lowercase : Optional[Any] = TypeVar("_T")
class __UpperCAmelCase ( Generic[_T] ):
def __init__( self , lowerCAmelCase_ = None ):
"""simple docstring"""
_snake_case = list(iterable or [] )
_snake_case = []
def __len__( self ):
"""simple docstring"""
return len(self._stacka ) + len(self._stacka )
def __repr__( self ):
"""simple docstring"""
return F'Queue({tuple(self._stacka[::-1] + self._stacka )})'
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
self._stacka.append(lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self._stacka.pop
_snake_case = self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('Queue is empty' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 713
|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_=13 , lowerCAmelCase_=7 , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=True , lowerCAmelCase_=99 , lowerCAmelCase_=32 , lowerCAmelCase_=5 , lowerCAmelCase_=4 , lowerCAmelCase_=37 , lowerCAmelCase_="gelu" , lowerCAmelCase_=0.1 , lowerCAmelCase_=0.1 , lowerCAmelCase_=5_12 , lowerCAmelCase_=16 , lowerCAmelCase_=2 , lowerCAmelCase_=0.02 , lowerCAmelCase_=3 , lowerCAmelCase_=4 , lowerCAmelCase_=None , ):
"""simple docstring"""
_snake_case = parent
_snake_case = batch_size
_snake_case = seq_length
_snake_case = is_training
_snake_case = use_token_type_ids
_snake_case = use_labels
_snake_case = vocab_size
_snake_case = hidden_size
_snake_case = num_hidden_layers
_snake_case = num_attention_heads
_snake_case = intermediate_size
_snake_case = hidden_act
_snake_case = hidden_dropout_prob
_snake_case = attention_probs_dropout_prob
_snake_case = max_position_embeddings
_snake_case = type_vocab_size
_snake_case = type_sequence_label_size
_snake_case = initializer_range
_snake_case = num_labels
_snake_case = num_choices
_snake_case = scope
_snake_case = self.vocab_size - 1
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_snake_case = None
if self.use_token_type_ids:
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_snake_case = None
_snake_case = None
_snake_case = None
if self.use_labels:
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_snake_case = ids_tensor([self.batch_size] , self.num_choices )
_snake_case = OpenAIGPTConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , pad_token_id=self.pad_token_id , )
_snake_case = ids_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = OpenAIGPTModel(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , head_mask=lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ )
_snake_case = model(lowerCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = OpenAIGPTLMHeadModel(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = OpenAIGPTDoubleHeadsModel(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = self.num_labels
_snake_case = OpenAIGPTForSequenceClassification(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
_snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_snake_case = model(lowerCAmelCase_ , token_type_ids=lowerCAmelCase_ , labels=lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.prepare_config_and_inputs()
(
(
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) , (
_snake_case
) ,
) = config_and_inputs
_snake_case = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_torch
class __UpperCAmelCase ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
__lowercase = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
__lowercase = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
__lowercase = (
{
"""feature-extraction""": OpenAIGPTModel,
"""text-classification""": OpenAIGPTForSequenceClassification,
"""text-generation""": OpenAIGPTLMHeadModel,
"""zero-shot""": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def lowerCamelCase ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_=False ):
"""simple docstring"""
_snake_case = super()._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ , return_labels=lowerCAmelCase_ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
_snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) , dtype=torch.long , device=lowerCAmelCase_ , )
_snake_case = inputs_dict['labels']
_snake_case = inputs_dict['labels']
_snake_case = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) , dtype=torch.long , device=lowerCAmelCase_ , )
_snake_case = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase_ )
return inputs_dict
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OpenAIGPTModelTester(self )
_snake_case = ConfigTester(self , config_class=lowerCAmelCase_ , n_embd=37 )
def lowerCamelCase ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowerCAmelCase_ )
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCAmelCase_ )
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_snake_case = OpenAIGPTModel.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
@require_torch
class __UpperCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase ( self ):
"""simple docstring"""
_snake_case = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt' )
model.to(lowerCAmelCase_ )
_snake_case = torch.tensor([[4_81, 47_35, 5_44]] , dtype=torch.long , device=lowerCAmelCase_ ) # the president is
_snake_case = [
4_81,
47_35,
5_44,
2_46,
9_63,
8_70,
7_62,
2_39,
2_44,
4_04_77,
2_44,
2_49,
7_19,
8_81,
4_87,
5_44,
2_40,
2_44,
6_03,
4_81,
] # the president is a very good man. " \n " i\'m sure he is, " said the
_snake_case = model.generate(lowerCAmelCase_ , do_sample=lowerCAmelCase_ )
self.assertListEqual(output_ids[0].tolist() , lowerCAmelCase_ )
| 542
| 0
|
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
lowercase_ = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
["memory_attention", "encoder_attn"],
["attention", "attn"],
["/", "."],
[".LayerNorm.gamma", "_layer_norm.weight"],
[".LayerNorm.beta", "_layer_norm.bias"],
["r.layer_", "r.layers."],
["output_proj", "out_proj"],
["ffn.dense_1.", "fc2."],
["ffn.dense.", "fc1."],
["ffn_layer_norm", "final_layer_norm"],
["kernel", "weight"],
["encoder_layer_norm.", "encoder.layer_norm."],
["decoder_layer_norm.", "decoder.layer_norm."],
["embeddings.weights", "shared.weight"],
]
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
for pegasus_name, hf_name in PATTERNS:
__lowerCamelCase : Any = k.replace(lowerCamelCase__ , lowerCamelCase__ )
return k
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Any = DEFAULTS.copy()
cfg_kwargs.update(lowerCamelCase__ )
__lowerCamelCase : str = PegasusConfig(**lowerCamelCase__ )
__lowerCamelCase : List[str] = PegasusForConditionalGeneration(lowerCamelCase__ )
__lowerCamelCase : List[Any] = torch_model.model.state_dict()
__lowerCamelCase : List[str] = {}
for k, v in tf_weights.items():
__lowerCamelCase : Optional[int] = rename_state_dict_key(lowerCamelCase__ )
if new_k not in sd:
raise ValueError(f'could not find new key {new_k} in state dict. (converted from {k})' )
if "dense" in k or "proj" in new_k:
__lowerCamelCase : Union[str, Any] = v.T
__lowerCamelCase : List[str] = torch.tensor(lowerCamelCase__ , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, f'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
# make sure embedding.padding_idx is respected
__lowerCamelCase : Any = torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1] )
__lowerCamelCase : Optional[Any] = mapping['shared.weight']
__lowerCamelCase : int = mapping['shared.weight']
__lowerCamelCase : Optional[int] = {k: torch.zeros_like(lowerCamelCase__ ) for k, v in sd.items() if k.endswith('bias' ) and k not in mapping}
mapping.update(**lowerCamelCase__ )
__lowerCamelCase , __lowerCamelCase : Any = torch_model.model.load_state_dict(lowerCamelCase__ , strict=lowerCamelCase__ )
__lowerCamelCase : List[str] = [
k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
]
assert unexpected_missing == [], f'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], f'no matches found for the following tf keys {extra}'
return torch_model
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__="./ckpt/aeslc/model.ckpt-32000" ):
__lowerCamelCase : Optional[Any] = tf.train.list_variables(lowerCamelCase__ )
__lowerCamelCase : List[Any] = {}
__lowerCamelCase : Union[str, Any] = ['Adafactor', 'global_step']
for name, shape in tqdm(lowerCamelCase__ , desc='converting tf checkpoint to dict' ):
__lowerCamelCase : Optional[int] = any(pat in name for pat in ignore_name )
if skip_key:
continue
__lowerCamelCase : Tuple = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Any = array
return tf_weights
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# save tokenizer first
__lowerCamelCase : List[str] = Path(lowerCamelCase__ ).parent.name
__lowerCamelCase : Union[str, Any] = task_specific_params[f'summarization_{dataset}']['max_position_embeddings']
__lowerCamelCase : Dict = PegasusTokenizer.from_pretrained('sshleifer/pegasus' , model_max_length=lowerCamelCase__ )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(lowerCamelCase__ )
# convert model
__lowerCamelCase : Union[str, Any] = get_tf_weights_as_numpy(lowerCamelCase__ )
__lowerCamelCase : Union[str, Any] = task_specific_params[f'summarization_{dataset}']
if dataset == "large":
__lowerCamelCase : Optional[Any] = task_specific_params
__lowerCamelCase : Optional[int] = convert_pegasus(lowerCamelCase__ , lowerCamelCase__ )
torch_model.save_pretrained(lowerCamelCase__ )
__lowerCamelCase : str = torch_model.state_dict()
sd.pop('model.decoder.embed_positions.weight' )
sd.pop('model.encoder.embed_positions.weight' )
torch.save(lowerCamelCase__ , Path(lowerCamelCase__ ) / 'pytorch_model.bin' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
lowercase_ = parser.parse_args()
if args.save_dir is None:
lowercase_ = Path(args.tf_ckpt_path).parent.name
lowercase_ = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 669
|
'''simple docstring'''
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class A :
def __init__( self , snake_case_ , snake_case_=1_3 , snake_case_=7 , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False , snake_case_=2 , snake_case_=9_9 , snake_case_=0 , snake_case_=3_2 , snake_case_=5 , snake_case_=4 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=5_1_2 , snake_case_=2 , snake_case_=0.02 , snake_case_=2 , snake_case_=4 , snake_case_="last" , snake_case_=True , snake_case_=None , snake_case_=0 , ) -> Any:
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_input_lengths
_a = use_token_type_ids
_a = use_labels
_a = gelu_activation
_a = sinusoidal_embeddings
_a = causal
_a = asm
_a = n_langs
_a = vocab_size
_a = n_special
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_sequence_label_size
_a = initializer_range
_a = num_labels
_a = num_choices
_a = summary_type
_a = use_proj
_a = scope
_a = bos_token_id
def __lowerCAmelCase ( self ) -> Tuple:
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_input_lengths:
_a = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
_a = None
_a = None
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_a = ids_tensor([self.batch_size] , 2 ).float()
_a = ids_tensor([self.batch_size] , self.num_choices )
_a = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def __lowerCAmelCase ( self ) -> str:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> Optional[int]:
_a = XLMModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ , lengths=snake_case_ , langs=snake_case_ )
_a = model(snake_case_ , langs=snake_case_ )
_a = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> Union[str, Any]:
_a = XLMWithLMHeadModel(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> str:
_a = XLMForQuestionAnsweringSimple(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ )
_a = model(snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ )
_a = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> Optional[int]:
_a = XLMForQuestionAnswering(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ )
_a = model(
snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , cls_index=snake_case_ , is_impossible=snake_case_ , p_mask=snake_case_ , )
_a = model(
snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ , cls_index=snake_case_ , is_impossible=snake_case_ , )
((_a) , ) = result_with_labels.to_tuple()
_a = model(snake_case_ , start_positions=snake_case_ , end_positions=snake_case_ )
((_a) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> Tuple:
_a = XLMForSequenceClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ )
_a = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> Union[str, Any]:
_a = self.num_labels
_a = XLMForTokenClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ , attention_mask=snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ) -> str:
_a = self.num_choices
_a = XLMForMultipleChoice(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_a = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_a = model(
snake_case_ , attention_mask=snake_case_ , token_type_ids=snake_case_ , labels=snake_case_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCAmelCase ( self ) -> Optional[int]:
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {"input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths}
return config, inputs_dict
@require_torch
class A ( a , a , a , unittest.TestCase ):
__UpperCAmelCase : str = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
__UpperCAmelCase : int = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
__UpperCAmelCase : List[Any] = (
{
"""feature-extraction""": XLMModel,
"""fill-mask""": XLMWithLMHeadModel,
"""question-answering""": XLMForQuestionAnsweringSimple,
"""text-classification""": XLMForSequenceClassification,
"""text-generation""": XLMWithLMHeadModel,
"""token-classification""": XLMForTokenClassification,
"""zero-shot""": XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ) -> Tuple:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_=False ) -> List[Any]:
_a = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case_ )
_a = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case_ )
return inputs_dict
def __lowerCAmelCase ( self ) -> Dict:
_a = XLMModelTester(self )
_a = ConfigTester(self , config_class=snake_case_ , emb_dim=3_7 )
def __lowerCAmelCase ( self ) -> Dict:
self.config_tester.run_common_tests()
def __lowerCAmelCase ( self ) -> Optional[Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*snake_case_ )
def __lowerCAmelCase ( self ) -> List[str]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*snake_case_ )
def __lowerCAmelCase ( self ) -> Dict:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*snake_case_ )
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*snake_case_ )
def __lowerCAmelCase ( self ) -> List[str]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_ )
def __lowerCAmelCase ( self ) -> List[Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*snake_case_ )
def __lowerCAmelCase ( self ) -> List[str]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_ )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=False , snake_case_=1 ) -> Dict:
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertListEqual(
[isinstance(snake_case_ , snake_case_ ) for iter_attentions in attentions] , [True] * len(snake_case_ ) )
self.assertEqual(len(snake_case_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(snake_case_ ):
# adds PAD dummy token
_a = min_length + idx + 1
_a = min_length + idx + 1
_a = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(snake_case_ ) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=False , snake_case_=1 ) -> Dict:
self.assertIsInstance(snake_case_ , snake_case_ )
self.assertListEqual(
[isinstance(snake_case_ , snake_case_ ) for iter_hidden_states in hidden_states] , [True] * len(snake_case_ ) , )
self.assertEqual(len(snake_case_ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(snake_case_ ):
# adds PAD dummy token
_a = min_length + idx + 1
_a = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(snake_case_ ) , )
pass
@slow
def __lowerCAmelCase ( self ) -> Optional[Any]:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = XLMModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
@require_torch
class A ( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self ) -> Union[str, Any]:
_a = XLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048" )
model.to(snake_case_ )
_a = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=snake_case_ ) # the president
_a = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
_a = model.generate(snake_case_ , do_sample=snake_case_ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , snake_case_ )
| 131
| 0
|
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __snake_case (unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self : int ) -> List[Any]:
'''simple docstring'''
_lowerCAmelCase : Dict = 10
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[Any]:
'''simple docstring'''
_lowerCAmelCase : Dict = [1, 2, 3, 4]
_lowerCAmelCase : Optional[int] = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_UpperCAmelCase , self.block_size , 0 ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int ) -> Tuple:
'''simple docstring'''
_lowerCAmelCase : int = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
_lowerCAmelCase : Any = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_UpperCAmelCase , self.block_size , 0 ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ) -> Tuple:
'''simple docstring'''
_lowerCAmelCase : List[Any] = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
_lowerCAmelCase : Dict = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_UpperCAmelCase , self.block_size , 0 ) , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : str ) -> Optional[Any]:
'''simple docstring'''
_lowerCAmelCase : Optional[int] = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
_lowerCAmelCase : int = process_story(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , [] )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Any:
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = """"""
_lowerCAmelCase : Union[str, Any] = process_story(_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , [] )
self.assertEqual(_UpperCAmelCase , [] )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
_lowerCAmelCase : List[str] = (
"""It was the year of Our Lord one thousand seven hundred and """
"""seventy-five\n\nSpiritual revelations were conceded to England """
"""at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
)
_lowerCAmelCase : Any = process_story(_UpperCAmelCase )
_lowerCAmelCase : int = [
"""It was the year of Our Lord one thousand seven hundred and seventy-five.""",
"""Spiritual revelations were conceded to England at that favoured period, as at this.""",
]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
_lowerCAmelCase : Optional[int] = ["""It was the best of times."""]
self.assertEqual(_UpperCAmelCase , _UpperCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
_lowerCAmelCase : List[str] = torch.tensor([1, 2, 3, 4] )
_lowerCAmelCase : int = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_UpperCAmelCase , 0 ).numpy() , expected.numpy() )
def SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
_lowerCAmelCase : List[str] = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_UpperCAmelCase , 23 ).numpy() , expected.numpy() )
def SCREAMING_SNAKE_CASE ( self : int ) -> Union[str, Any]:
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
_lowerCAmelCase : int = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_UpperCAmelCase , 1 ).numpy() , expected.numpy() )
def SCREAMING_SNAKE_CASE ( self : Dict ) -> Tuple:
'''simple docstring'''
_lowerCAmelCase : Dict = 101
_lowerCAmelCase : Union[str, Any] = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
_lowerCAmelCase : Union[str, Any] = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
_lowerCAmelCase : str = compute_token_type_ids(_UpperCAmelCase , _UpperCAmelCase )
np.testing.assert_array_equal(_UpperCAmelCase , _UpperCAmelCase )
| 720
|
from math import loga
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
if a < 0:
raise ValueError("""Input value must be a positive integer""" )
elif isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""Input value must be a 'int' type""" )
return 0 if (a == 0) else int(loga(a & -a ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196
| 0
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Optional[Any] = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Union[str, Any] = ['PLBartTokenizer']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase : Optional[Any] = [
'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
'PLBartForCausalLM',
'PLBartForConditionalGeneration',
'PLBartForSequenceClassification',
'PLBartModel',
'PLBartPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__lowercase : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 476
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__lowercase : List[str] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = ["pixel_values"]
def __init__( self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = None , __a = True , __a = 1 / 255 , __a = True , __a = None , __a = None , __a = True , **__a , ):
'''simple docstring'''
super().__init__(**__a )
__a : Dict = size if size is not None else {'shortest_edge': 224}
__a : Optional[Any] = get_size_dict(__a , default_to_square=__a )
__a : str = crop_size if crop_size is not None else {'height': 224, 'width': 224}
__a : Dict = get_size_dict(__a , default_to_square=__a , param_name='crop_size' )
__a : int = do_resize
__a : Tuple = size
__a : Optional[int] = resample
__a : List[str] = do_center_crop
__a : int = crop_size
__a : int = do_rescale
__a : List[Any] = rescale_factor
__a : Dict = do_normalize
__a : Tuple = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
__a : Union[str, Any] = image_std if image_std is not None else OPENAI_CLIP_STD
__a : Tuple = do_convert_rgb
def __UpperCAmelCase ( self , __a , __a , __a = PILImageResampling.BICUBIC , __a = None , **__a , ):
'''simple docstring'''
__a : Optional[Any] = get_size_dict(__a , default_to_square=__a )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
__a : Tuple = get_resize_output_image_size(__a , size=size['shortest_edge'] , default_to_square=__a )
return resize(__a , size=__a , resample=__a , data_format=__a , **__a )
def __UpperCAmelCase ( self , __a , __a , __a = None , **__a , ):
'''simple docstring'''
__a : Tuple = get_size_dict(__a )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__a , size=(size['height'], size['width']) , data_format=__a , **__a )
def __UpperCAmelCase ( self , __a , __a , __a = None , **__a , ):
'''simple docstring'''
return rescale(__a , scale=__a , data_format=__a , **__a )
def __UpperCAmelCase ( self , __a , __a , __a , __a = None , **__a , ):
'''simple docstring'''
return normalize(__a , mean=__a , std=__a , data_format=__a , **__a )
def __UpperCAmelCase ( self , __a , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = None , __a = ChannelDimension.FIRST , **__a , ):
'''simple docstring'''
__a : str = do_resize if do_resize is not None else self.do_resize
__a : Dict = size if size is not None else self.size
__a : Dict = get_size_dict(__a , param_name='size' , default_to_square=__a )
__a : Optional[Any] = resample if resample is not None else self.resample
__a : str = do_center_crop if do_center_crop is not None else self.do_center_crop
__a : Optional[Any] = crop_size if crop_size is not None else self.crop_size
__a : int = get_size_dict(__a , param_name='crop_size' , default_to_square=__a )
__a : Optional[int] = do_rescale if do_rescale is not None else self.do_rescale
__a : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
__a : Optional[int] = do_normalize if do_normalize is not None else self.do_normalize
__a : List[str] = image_mean if image_mean is not None else self.image_mean
__a : List[str] = image_std if image_std is not None else self.image_std
__a : Tuple = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
__a : int = make_list_of_images(__a )
if not valid_images(__a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None:
raise ValueError('Size must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
__a : Dict = [convert_to_rgb(__a ) for image in images]
# All transformations expect numpy arrays.
__a : int = [to_numpy_array(__a ) for image in images]
if do_resize:
__a : Tuple = [self.resize(image=__a , size=__a , resample=__a ) for image in images]
if do_center_crop:
__a : List[Any] = [self.center_crop(image=__a , size=__a ) for image in images]
if do_rescale:
__a : Tuple = [self.rescale(image=__a , scale=__a ) for image in images]
if do_normalize:
__a : Optional[Any] = [self.normalize(image=__a , mean=__a , std=__a ) for image in images]
__a : Optional[Any] = [to_channel_dimension_format(__a , __a ) for image in images]
__a : int = {'pixel_values': images}
return BatchFeature(data=__a , tensor_type=__a )
| 476
| 1
|
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def lowerCamelCase__ ( a , a , a ):
__snake_case = namedtuple('result' , 'name value' )
if (voltage, current, power).count(0 ) != 1:
raise ValueError('Only one argument must be 0' )
elif power < 0:
raise ValueError(
'Power cannot be negative in any electrical/electronics system' )
elif voltage == 0:
return result('voltage' , power / current )
elif current == 0:
return result('current' , power / voltage )
elif power == 0:
return result('power' , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721
|
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( a , a , a ):
# Construct model
if openai_config_file == "":
__snake_case = OpenAIGPTConfig()
else:
__snake_case = OpenAIGPTConfig.from_json_file(a )
__snake_case = OpenAIGPTModel(a )
# Load weights from numpy
load_tf_weights_in_openai_gpt(a , a , a )
# Save pytorch-model
__snake_case = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
__snake_case = pytorch_dump_folder_path + '/' + CONFIG_NAME
print(f'Save PyTorch model to {pytorch_weights_dump_path}' )
torch.save(model.state_dict() , a )
print(f'Save configuration file to {pytorch_config_dump_path}' )
with open(a , 'w' , encoding='utf-8' ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
_lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--openai_checkpoint_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the TensorFlow checkpoint path.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--openai_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
_lowercase = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 427
| 0
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __init__( self : List[str] , UpperCAmelCase__ : str , UpperCAmelCase__ : Tuple=7 , UpperCAmelCase__ : str=3 , UpperCAmelCase__ : Union[str, Any]=10 , UpperCAmelCase__ : int=18 , UpperCAmelCase__ : Optional[int]=30 , UpperCAmelCase__ : Dict=400 , UpperCAmelCase__ : Union[str, Any]=True , UpperCAmelCase__ : int=None , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : str=[0.5, 0.5, 0.5] , UpperCAmelCase__ : Optional[Any]=[0.5, 0.5, 0.5] , UpperCAmelCase__ : List[Any]=None , ):
'''simple docstring'''
lowercase : Optional[Any] =size if size is not None else {'''shortest_edge''': 18}
lowercase : Union[str, Any] =crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
lowercase : List[Any] =parent
lowercase : Optional[Any] =batch_size
lowercase : str =num_channels
lowercase : Any =num_frames
lowercase : Any =image_size
lowercase : Optional[Any] =min_resolution
lowercase : Tuple =max_resolution
lowercase : Any =do_resize
lowercase : Optional[int] =size
lowercase : int =do_normalize
lowercase : List[str] =image_mean
lowercase : Union[str, Any] =image_std
lowercase : int =crop_size
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE ( lowercase__ , unittest.TestCase ):
lowerCamelCase_ = VivitImageProcessor if is_vision_available() else None
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
lowercase : Optional[int] =VivitImageProcessingTester(self )
@property
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase : int =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''image_std''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''do_center_crop''' ) )
self.assertTrue(hasattr(UpperCAmelCase__ , '''size''' ) )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
lowercase : Dict =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''shortest_edge''': 18} )
self.assertEqual(image_processor.crop_size , {'''height''': 18, '''width''': 18} )
lowercase : Dict =self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {'''shortest_edge''': 42} )
self.assertEqual(image_processor.crop_size , {'''height''': 84, '''width''': 84} )
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
# Initialize image_processing
lowercase : Optional[int] =self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
lowercase : Tuple =prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
lowercase : Union[str, Any] =image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase : Optional[int] =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
# Initialize image_processing
lowercase : List[str] =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase : List[Any] =prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , numpify=UpperCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
lowercase : Tuple =image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase : str =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
# Initialize image_processing
lowercase : Optional[Any] =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase : Optional[int] =prepare_video_inputs(self.image_processor_tester , equal_resolution=UpperCAmelCase__ , torchify=UpperCAmelCase__ )
for video in video_inputs:
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
lowercase : str =image_processing(video_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
# Test batched
lowercase : Any =image_processing(UpperCAmelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['''height'''],
self.image_processor_tester.crop_size['''width'''],
) , )
| 92
|
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available
if is_sentencepiece_available():
from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase = get_tests_dir('fixtures/test_sentencepiece.model')
__UpperCAmelCase = {'target_lang': 'fi', 'source_lang': 'en'}
__UpperCAmelCase = '>>zh<<'
__UpperCAmelCase = 'Helsinki-NLP/'
if is_torch_available():
__UpperCAmelCase = 'pt'
elif is_tf_available():
__UpperCAmelCase = 'tf'
else:
__UpperCAmelCase = 'jax'
@require_sentencepiece
class __lowercase ( __lowerCamelCase , unittest.TestCase ):
snake_case_ = MarianTokenizer
snake_case_ = False
snake_case_ = True
def __lowercase ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
UpperCAmelCase__ : Optional[Any] = ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
UpperCAmelCase__ : int = dict(zip(A ,range(len(A ) ) ) )
UpperCAmelCase__ : Optional[int] = Path(self.tmpdirname )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""vocab"""] )
save_json(A ,save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""] )
if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""source_spm"""] )
copyfile(A ,save_dir / VOCAB_FILES_NAMES["""target_spm"""] )
UpperCAmelCase__ : Dict = MarianTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : List[Any] ,**A : List[Any] ):
'''simple docstring'''
return MarianTokenizer.from_pretrained(self.tmpdirname ,**A )
def __lowercase ( self : Union[str, Any] ,A : Tuple ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = """</s>"""
UpperCAmelCase__ : int = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A ) ,A )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A ) ,A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Dict = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"""</s>""" )
self.assertEqual(vocab_keys[1] ,"""<unk>""" )
self.assertEqual(vocab_keys[-1] ,"""<pad>""" )
self.assertEqual(len(A ) ,9 )
def __lowercase ( self : Dict ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size ,9 )
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = MarianTokenizer.from_pretrained(f"{ORG_NAME}opus-mt-en-de" )
UpperCAmelCase__ : List[str] = en_de_tokenizer(["""I am a small frog"""] ,return_tensors=A )
self.assertIsInstance(A ,A )
UpperCAmelCase__ : str = [38, 121, 14, 697, 38_848, 0]
self.assertListEqual(A ,batch.input_ids[0] )
UpperCAmelCase__ : Optional[Any] = tempfile.mkdtemp()
en_de_tokenizer.save_pretrained(A )
UpperCAmelCase__ : Tuple = [x.name for x in Path(A ).glob("""*""" )]
self.assertIn("""source.spm""" ,A )
MarianTokenizer.from_pretrained(A )
def __lowercase ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = self.get_tokenizer()
UpperCAmelCase__ : Any = tok(
["""I am a small frog""" * 1_000, """I am a small frog"""] ,padding=A ,truncation=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch.input_ids.shape ,(2, 512) )
def __lowercase ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : int = self.get_tokenizer()
UpperCAmelCase__ : Tuple = tok(["""I am a tiny frog""", """I am a small frog"""] ,padding=A ,return_tensors=A )
self.assertIsInstance(A ,A )
self.assertEqual(batch_smaller.input_ids.shape ,(2, 10) )
@slow
def __lowercase ( self : Dict ):
'''simple docstring'''
# fmt: off
UpperCAmelCase__ : Optional[int] = {"""input_ids""": [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=A ,model_name="""Helsinki-NLP/opus-mt-en-de""" ,revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,decode_kwargs={"""use_source_tokenizer""": True} ,)
def __lowercase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs""" )
UpperCAmelCase__ : Any = """Tämä on testi"""
UpperCAmelCase__ : int = """This is a test"""
UpperCAmelCase__ : List[str] = [76, 7, 2_047, 2]
UpperCAmelCase__ : Optional[Any] = [69, 12, 11, 940, 2]
UpperCAmelCase__ : List[str] = tokenizer(A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : Optional[int] = tokenizer(text_target=A ).input_ids
self.assertListEqual(A ,A )
UpperCAmelCase__ : int = tokenizer.decode(A ,skip_special_tokens=A )
self.assertEqual(A ,A )
| 65
| 0
|
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _SCREAMING_SNAKE_CASE ( snake_case_ : int ):
__magic_name__ = prime_factors(snake_case_ )
if is_square_free(snake_case_ ):
return -1 if len(snake_case_ ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 678
|
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
a_ : str = True
except ImportError:
a_ : Optional[int] = False
try:
from torch.hub import _get_torch_home
a_ : Optional[Any] = _get_torch_home()
except ImportError:
a_ : List[Any] = os.path.expanduser(
os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
)
a_ : Any = os.path.join(torch_cache_home, 'transformers')
a_ : Any = 'https://cdn.huggingface.co'
a_ : Any = 'https://s3.amazonaws.com/models.huggingface.co/bert'
a_ : int = '/'.join(str(Path(__file__).resolve()).split('/')[:-1])
a_ : Any = os.path.join(PATH, 'config.yaml')
a_ : Any = os.path.join(PATH, 'attributes.txt')
a_ : Any = os.path.join(PATH, 'objects.txt')
a_ : List[Any] = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', default_cache_path)
a_ : Any = os.getenv('PYTORCH_TRANSFORMERS_CACHE', PYTORCH_PRETRAINED_BERT_CACHE)
a_ : Optional[int] = os.getenv('TRANSFORMERS_CACHE', PYTORCH_TRANSFORMERS_CACHE)
a_ : int = 'pytorch_model.bin'
a_ : Union[str, Any] = 'config.yaml'
def _SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any]=OBJECTS , snake_case_ : str=ATTRIBUTES ):
__magic_name__ = []
with open(snake_case_ ) as f:
for object in f.readlines():
vg_classes.append(object.split(''',''' )[0].lower().strip() )
__magic_name__ = []
with open(snake_case_ ) as f:
for object in f.readlines():
vg_attrs.append(object.split(''',''' )[0].lower().strip() )
return vg_classes, vg_attrs
def _SCREAMING_SNAKE_CASE ( snake_case_ : int ):
__magic_name__ = OrderedDict()
with open(snake_case_ , '''rb''' ) as f:
__magic_name__ = pkl.load(snake_case_ )['''model''']
for k in copy.deepcopy(list(ckp.keys() ) ):
__magic_name__ = ckp.pop(snake_case_ )
if isinstance(snake_case_ , np.ndarray ):
__magic_name__ = torch.tensor(snake_case_ )
else:
assert isinstance(snake_case_ , torch.tensor ), type(snake_case_ )
__magic_name__ = v
return r
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
_a = {}
def __init__( self , A , A = "root" , A=0 ) -> List[str]:
'''simple docstring'''
__magic_name__ = name
__magic_name__ = level
__magic_name__ = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
__magic_name__ = copy.deepcopy(A )
__magic_name__ = copy.deepcopy(A )
if isinstance(A , A ):
__magic_name__ = Config(A , name=A , level=level + 1 )
__magic_name__ = v
setattr(self , A , A )
__magic_name__ = d
def __repr__( self ) -> Union[str, Any]:
'''simple docstring'''
return str(list((self._pointer.keys()) ) )
def __setattr__( self , A , A ) -> Tuple:
'''simple docstring'''
__magic_name__ = val
__magic_name__ = val
__magic_name__ = key.split('''.''' )
__magic_name__ = len(A ) - 1
__magic_name__ = self._pointer
if len(A ) > 1:
for i, l in enumerate(A ):
if hasattr(self , A ) and isinstance(getattr(self , A ) , A ):
setattr(getattr(self , A ) , '''.'''.join(levels[i:] ) , A )
if l == last_level:
__magic_name__ = val
else:
__magic_name__ = pointer[l]
def __A ( self ) -> List[Any]:
'''simple docstring'''
return self._pointer
def __A ( self , A , A ) -> Any:
'''simple docstring'''
with open(F'{file_name}' , '''w''' ) as stream:
dump(A , A )
def __A ( self , A , A ) -> List[Any]:
'''simple docstring'''
with open(F'{file_name}' , '''w''' ) as stream:
json.dump(A , A )
@staticmethod
def __A ( A ) -> Optional[Any]:
'''simple docstring'''
with open(A ) as stream:
__magic_name__ = load(A , Loader=A )
return data
def __str__( self ) -> List[Any]:
'''simple docstring'''
__magic_name__ = ''' '''
if self._name != "root":
__magic_name__ = F'{t * (self._level-1)}{self._name}:\n'
else:
__magic_name__ = ''''''
__magic_name__ = self._level
for i, (k, v) in enumerate(self._pointer.items() ):
if isinstance(A , A ):
r += F'{t * (self._level)}{v}\n'
self._level += 1
else:
r += F'{t * (self._level)}{k}: {v} ({type(A ).__name__})\n'
__magic_name__ = level
return r[:-1]
@classmethod
def __A ( cls , A , **A ) -> int:
'''simple docstring'''
__magic_name__ , __magic_name__ = cls.get_config_dict(A , **A )
return cls(A )
@classmethod
def __A ( cls , A , **A ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ = kwargs.pop('''cache_dir''' , A )
__magic_name__ = kwargs.pop('''force_download''' , A )
__magic_name__ = kwargs.pop('''resume_download''' , A )
__magic_name__ = kwargs.pop('''proxies''' , A )
__magic_name__ = kwargs.pop('''local_files_only''' , A )
if os.path.isdir(A ):
__magic_name__ = os.path.join(A , A )
elif os.path.isfile(A ) or is_remote_url(A ):
__magic_name__ = pretrained_model_name_or_path
else:
__magic_name__ = hf_bucket_url(A , filename=A , use_cdn=A )
try:
# Load from URL or cache if already cached
__magic_name__ = cached_path(
A , cache_dir=A , force_download=A , proxies=A , resume_download=A , local_files_only=A , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
__magic_name__ = Config.load_yaml(A )
except EnvironmentError:
__magic_name__ = '''Can\'t load config for'''
raise EnvironmentError(A )
if resolved_config_file == config_file:
print('''loading configuration file from path''' )
else:
print('''loading configuration file cache''' )
return Config.load_yaml(A ), kwargs
def _SCREAMING_SNAKE_CASE ( snake_case_ : Tuple ):
__magic_name__ = torch.load('''dump.pt''' , map_location=in_tensor.device )
__magic_name__ = in_tensor.numpy()
__magic_name__ = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(snake_case_ , snake_case_ , rtol=0.01 , atol=0.1 ), (
f'{sum([1 for x in np.isclose(snake_case_ , snake_case_ , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %'
" element-wise mismatch"
)
raise Exception('''tensors are all good''' )
# Hugging face functions below
def _SCREAMING_SNAKE_CASE ( snake_case_ : List[Any] ):
__magic_name__ = urlparse(snake_case_ )
return parsed.scheme in ("http", "https")
def _SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str , snake_case_ : Optional[Any]=True ):
__magic_name__ = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
__magic_name__ = '''/''' not in model_id
if legacy_format:
return f'{endpoint}/{model_id}-{filename}'
else:
return f'{endpoint}/{model_id}/{filename}'
def _SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : Tuple , snake_case_ : List[str]=None , snake_case_ : Dict=0 , snake_case_ : Tuple=None , ):
__magic_name__ = '''python/{}'''.format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(snake_case_ , snake_case_ ):
ua += "; " + "; ".join('''{}/{}'''.format(snake_case_ , snake_case_ ) for k, v in user_agent.items() )
elif isinstance(snake_case_ , snake_case_ ):
ua += "; " + user_agent
__magic_name__ = {'''user-agent''': ua}
if resume_size > 0:
__magic_name__ = '''bytes=%d-''' % (resume_size,)
__magic_name__ = requests.get(snake_case_ , stream=snake_case_ , proxies=snake_case_ , headers=snake_case_ )
if response.status_code == 416: # Range not satisfiable
return
__magic_name__ = response.headers.get('''Content-Length''' )
__magic_name__ = resume_size + int(snake_case_ ) if content_length is not None else None
__magic_name__ = tqdm(
unit='''B''' , unit_scale=snake_case_ , total=snake_case_ , initial=snake_case_ , desc='''Downloading''' , )
for chunk in response.iter_content(chunk_size=1024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(snake_case_ ) )
temp_file.write(snake_case_ )
progress.close()
def _SCREAMING_SNAKE_CASE ( snake_case_ : Any , snake_case_ : Dict=None , snake_case_ : int=False , snake_case_ : List[Any]=None , snake_case_ : Tuple=10 , snake_case_ : int=False , snake_case_ : Any=None , snake_case_ : Tuple=False , ):
if cache_dir is None:
__magic_name__ = TRANSFORMERS_CACHE
if isinstance(snake_case_ , snake_case_ ):
__magic_name__ = str(snake_case_ )
os.makedirs(snake_case_ , exist_ok=snake_case_ )
__magic_name__ = None
if not local_files_only:
try:
__magic_name__ = requests.head(snake_case_ , allow_redirects=snake_case_ , proxies=snake_case_ , timeout=snake_case_ )
if response.status_code == 200:
__magic_name__ = response.headers.get('''ETag''' )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
__magic_name__ = url_to_filename(snake_case_ , snake_case_ )
# get cache path to put the file
__magic_name__ = os.path.join(snake_case_ , snake_case_ )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(snake_case_ ):
return cache_path
else:
__magic_name__ = [
file
for file in fnmatch.filter(os.listdir(snake_case_ ) , filename + '''.*''' )
if not file.endswith('''.json''' ) and not file.endswith('''.lock''' )
]
if len(snake_case_ ) > 0:
return os.path.join(snake_case_ , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
'''Cannot find the requested files in the cached path and outgoing traffic has been'''
''' disabled. To enable model look-ups and downloads online, set \'local_files_only\''''
''' to False.''' )
return None
# From now on, etag is not None.
if os.path.exists(snake_case_ ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
__magic_name__ = cache_path + '''.lock'''
with FileLock(snake_case_ ):
# If the download just completed while the lock was activated.
if os.path.exists(snake_case_ ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
__magic_name__ = cache_path + '''.incomplete'''
@contextmanager
def _resumable_file_manager():
with open(snake_case_ , '''a+b''' ) as f:
yield f
__magic_name__ = _resumable_file_manager
if os.path.exists(snake_case_ ):
__magic_name__ = os.stat(snake_case_ ).st_size
else:
__magic_name__ = 0
else:
__magic_name__ = partial(tempfile.NamedTemporaryFile , dir=snake_case_ , delete=snake_case_ )
__magic_name__ = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
'''%s not found in cache or force_download set to True, downloading to %s''' , snake_case_ , temp_file.name , )
http_get(
snake_case_ , snake_case_ , proxies=snake_case_ , resume_size=snake_case_ , user_agent=snake_case_ , )
os.replace(temp_file.name , snake_case_ )
__magic_name__ = {'''url''': url, '''etag''': etag}
__magic_name__ = cache_path + '''.json'''
with open(snake_case_ , '''w''' ) as meta_file:
json.dump(snake_case_ , snake_case_ )
return cache_path
def _SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] , snake_case_ : List[Any]=None ):
__magic_name__ = url.encode('''utf-8''' )
__magic_name__ = shaaaa(snake_case_ )
__magic_name__ = url_hash.hexdigest()
if etag:
__magic_name__ = etag.encode('''utf-8''' )
__magic_name__ = shaaaa(snake_case_ )
filename += "." + etag_hash.hexdigest()
if url.endswith('''.h5''' ):
filename += ".h5"
return filename
def _SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : str=None , snake_case_ : Tuple=False , snake_case_ : Union[str, Any]=None , snake_case_ : List[Any]=False , snake_case_ : Union[str, Any]=None , snake_case_ : List[str]=False , snake_case_ : Optional[int]=False , snake_case_ : Optional[int]=False , ):
if cache_dir is None:
__magic_name__ = TRANSFORMERS_CACHE
if isinstance(snake_case_ , snake_case_ ):
__magic_name__ = str(snake_case_ )
if isinstance(snake_case_ , snake_case_ ):
__magic_name__ = str(snake_case_ )
if is_remote_url(snake_case_ ):
# URL, so get it from the cache (downloading if necessary)
__magic_name__ = get_from_cache(
snake_case_ , cache_dir=snake_case_ , force_download=snake_case_ , proxies=snake_case_ , resume_download=snake_case_ , user_agent=snake_case_ , local_files_only=snake_case_ , )
elif os.path.exists(snake_case_ ):
# File, and it exists.
__magic_name__ = url_or_filename
elif urlparse(snake_case_ ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError('''file {} not found'''.format(snake_case_ ) )
else:
# Something unknown
raise ValueError('''unable to parse {} as a URL or as a local path'''.format(snake_case_ ) )
if extract_compressed_file:
if not is_zipfile(snake_case_ ) and not tarfile.is_tarfile(snake_case_ ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
__magic_name__ , __magic_name__ = os.path.split(snake_case_ )
__magic_name__ = output_file.replace('''.''' , '''-''' ) + '''-extracted'''
__magic_name__ = os.path.join(snake_case_ , snake_case_ )
if os.path.isdir(snake_case_ ) and os.listdir(snake_case_ ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
__magic_name__ = output_path + '''.lock'''
with FileLock(snake_case_ ):
shutil.rmtree(snake_case_ , ignore_errors=snake_case_ )
os.makedirs(snake_case_ )
if is_zipfile(snake_case_ ):
with ZipFile(snake_case_ , '''r''' ) as zip_file:
zip_file.extractall(snake_case_ )
zip_file.close()
elif tarfile.is_tarfile(snake_case_ ):
__magic_name__ = tarfile.open(snake_case_ )
tar_file.extractall(snake_case_ )
tar_file.close()
else:
raise EnvironmentError('''Archive format of {} could not be identified'''.format(snake_case_ ) )
return output_path_extracted
return output_path
def _SCREAMING_SNAKE_CASE ( snake_case_ : Dict , snake_case_ : int="," ):
assert isinstance(snake_case_ , snake_case_ )
if os.path.isfile(snake_case_ ):
with open(snake_case_ ) as f:
__magic_name__ = eval(f.read() )
else:
__magic_name__ = requests.get(snake_case_ )
try:
__magic_name__ = requests.json()
except Exception:
__magic_name__ = req.content.decode()
assert data is not None, "could not connect"
try:
__magic_name__ = eval(snake_case_ )
except Exception:
__magic_name__ = data.split('''\n''' )
req.close()
return data
def _SCREAMING_SNAKE_CASE ( snake_case_ : Optional[int] ):
__magic_name__ = requests.get(snake_case_ )
__magic_name__ = np.array(Image.open(BytesIO(response.content ) ) )
return img
def _SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any] ):
__magic_name__ = url.split('''/''' )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(snake_case_ )
with open(snake_case_ , '''rb''' ) as stream:
__magic_name__ = pkl.load(snake_case_ )
__magic_name__ = weights.pop('''model''' )
__magic_name__ = {}
for k, v in model.items():
__magic_name__ = torch.from_numpy(snake_case_ )
if "running_var" in k:
__magic_name__ = torch.tensor([0] )
__magic_name__ = k.replace('''running_var''' , '''num_batches_tracked''' )
__magic_name__ = zero
return new
def _SCREAMING_SNAKE_CASE ( ):
print(f'{os.path.abspath(os.path.join(snake_case_ , os.pardir ) )}/demo.ipynb' )
def _SCREAMING_SNAKE_CASE ( snake_case_ : str , snake_case_ : Tuple="RGB" ):
assert isinstance(snake_case_ , snake_case_ )
if os.path.isfile(snake_case_ ):
__magic_name__ = cva.imread(snake_case_ )
else:
__magic_name__ = get_image_from_url(snake_case_ )
assert img is not None, f'could not connect to: {im}'
__magic_name__ = cva.cvtColor(snake_case_ , cva.COLOR_BGR2RGB )
if input_format == "RGB":
__magic_name__ = img[:, :, ::-1]
return img
def _SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any] , snake_case_ : Dict=1 ):
return (images[i : i + batch] for i in range(0 , len(snake_case_ ) , snake_case_ ))
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