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'''simple docstring'''
import torch
from torch import nn
class lowercase__ ( nn.Module ):
def __init__( self : Any ,lowerCamelCase__ : Any ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple=1 ,lowerCamelCase__ : List[str]=False ):
'''simple docstring'''
super().__init__()
_UpperCamelCase : Tuple = n_token
_UpperCamelCase : str = d_embed
_UpperCamelCase : List[Any] = d_proj
_UpperCamelCase : Dict = cutoffs + [n_token]
_UpperCamelCase : str = [0] + self.cutoffs
_UpperCamelCase : List[Any] = div_val
_UpperCamelCase : List[Any] = self.cutoffs[0]
_UpperCamelCase : List[Any] = len(self.cutoffs ) - 1
_UpperCamelCase : Tuple = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
_UpperCamelCase : Any = nn.Parameter(torch.zeros(self.n_clusters ,self.d_embed ) )
_UpperCamelCase : Union[str, Any] = nn.Parameter(torch.zeros(self.n_clusters ) )
_UpperCamelCase : str = nn.ModuleList()
_UpperCamelCase : Tuple = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase__ ,lowerCamelCase__ ) ) )
else:
self.out_projs.append(lowerCamelCase__ )
self.out_layers.append(nn.Linear(lowerCamelCase__ ,lowerCamelCase__ ) )
else:
for i in range(len(self.cutoffs ) ):
_UpperCamelCase , _UpperCamelCase : int = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCamelCase : Union[str, Any] = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase__ ,lowerCamelCase__ ) ) )
self.out_layers.append(nn.Linear(lowerCamelCase__ ,r_idx - l_idx ) )
_UpperCamelCase : Optional[int] = keep_order
def UpperCamelCase_ ( self : str ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Union[str, Any] ):
'''simple docstring'''
if proj is None:
_UpperCamelCase : Optional[Any] = nn.functional.linear(lowerCamelCase__ ,lowerCamelCase__ ,bias=lowerCamelCase__ )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
_UpperCamelCase : Dict = nn.functional.linear(lowerCamelCase__ ,proj.t().contiguous() )
_UpperCamelCase : Dict = nn.functional.linear(lowerCamelCase__ ,lowerCamelCase__ ,bias=lowerCamelCase__ )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def UpperCamelCase_ ( self : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : List[Any]=False ):
'''simple docstring'''
if labels is not None:
# Shift so that tokens < n predict n
_UpperCamelCase : Tuple = hidden[..., :-1, :].contiguous()
_UpperCamelCase : int = labels[..., 1:].contiguous()
_UpperCamelCase : Dict = hidden.view(-1 ,hidden.size(-1 ) )
_UpperCamelCase : Optional[Any] = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError('Input and labels should have the same size in the batch dimension.' )
else:
_UpperCamelCase : List[Any] = hidden.view(-1 ,hidden.size(-1 ) )
if self.n_clusters == 0:
_UpperCamelCase : int = self._compute_logit(lowerCamelCase__ ,self.out_layers[0].weight ,self.out_layers[0].bias ,self.out_projs[0] )
if labels is not None:
_UpperCamelCase : Dict = labels != -100
_UpperCamelCase : Optional[Any] = torch.zeros_like(lowerCamelCase__ ,dtype=hidden.dtype ,device=hidden.device )
_UpperCamelCase : int = (
-nn.functional.log_softmax(lowerCamelCase__ ,dim=-1 )[mask].gather(1 ,labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
_UpperCamelCase : Any = nn.functional.log_softmax(lowerCamelCase__ ,dim=-1 )
else:
# construct weights and biases
_UpperCamelCase , _UpperCamelCase : int = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_UpperCamelCase , _UpperCamelCase : Any = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCamelCase : Optional[int] = self.out_layers[0].weight[l_idx:r_idx]
_UpperCamelCase : Optional[int] = self.out_layers[0].bias[l_idx:r_idx]
else:
_UpperCamelCase : Tuple = self.out_layers[i].weight
_UpperCamelCase : int = self.out_layers[i].bias
if i == 0:
_UpperCamelCase : Optional[int] = torch.cat([weight_i, self.cluster_weight] ,dim=0 )
_UpperCamelCase : Any = torch.cat([bias_i, self.cluster_bias] ,dim=0 )
weights.append(lowerCamelCase__ )
biases.append(lowerCamelCase__ )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Optional[Any] = weights[0], biases[0], self.out_projs[0]
_UpperCamelCase : List[Any] = self._compute_logit(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : int = nn.functional.log_softmax(lowerCamelCase__ ,dim=1 )
if labels is None:
_UpperCamelCase : Optional[int] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
_UpperCamelCase : Any = torch.zeros_like(lowerCamelCase__ ,dtype=hidden.dtype ,device=hidden.device )
_UpperCamelCase : Tuple = 0
_UpperCamelCase : Optional[Any] = [0] + self.cutoffs
for i in range(len(lowerCamelCase__ ) - 1 ):
_UpperCamelCase , _UpperCamelCase : List[str] = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
_UpperCamelCase : Any = (labels >= l_idx) & (labels < r_idx)
_UpperCamelCase : Union[str, Any] = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
_UpperCamelCase : Tuple = labels.index_select(0 ,lowerCamelCase__ ) - l_idx
_UpperCamelCase : List[str] = head_logprob.index_select(0 ,lowerCamelCase__ )
_UpperCamelCase : Dict = hidden.index_select(0 ,lowerCamelCase__ )
else:
_UpperCamelCase : List[Any] = hidden
if i == 0:
if labels is not None:
_UpperCamelCase : str = head_logprob_i.gather(1 ,target_i[:, None] ).squeeze(1 )
else:
_UpperCamelCase : Optional[int] = head_logprob[:, : self.cutoffs[0]]
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Tuple = weights[i], biases[i], self.out_projs[i]
_UpperCamelCase : Optional[Any] = self._compute_logit(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Tuple = nn.functional.log_softmax(lowerCamelCase__ ,dim=1 )
_UpperCamelCase : List[str] = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
_UpperCamelCase : Optional[Any] = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 ,target_i[:, None] ).squeeze(1 )
else:
_UpperCamelCase : Optional[int] = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
_UpperCamelCase : Optional[int] = logprob_i
if labels is not None:
if (hasattr(self ,'keep_order' ) and self.keep_order) or keep_order:
out.index_copy_(0 ,lowerCamelCase__ ,-logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : str ):
'''simple docstring'''
if self.n_clusters == 0:
_UpperCamelCase : List[str] = self._compute_logit(lowerCamelCase__ ,self.out_layers[0].weight ,self.out_layers[0].bias ,self.out_projs[0] )
return nn.functional.log_softmax(lowerCamelCase__ ,dim=-1 )
else:
# construct weights and biases
_UpperCamelCase , _UpperCamelCase : Optional[Any] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_UpperCamelCase , _UpperCamelCase : Dict = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCamelCase : str = self.out_layers[0].weight[l_idx:r_idx]
_UpperCamelCase : Dict = self.out_layers[0].bias[l_idx:r_idx]
else:
_UpperCamelCase : Tuple = self.out_layers[i].weight
_UpperCamelCase : Optional[Any] = self.out_layers[i].bias
if i == 0:
_UpperCamelCase : Dict = torch.cat([weight_i, self.cluster_weight] ,dim=0 )
_UpperCamelCase : List[Any] = torch.cat([bias_i, self.cluster_bias] ,dim=0 )
weights.append(lowerCamelCase__ )
biases.append(lowerCamelCase__ )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Union[str, Any] = weights[0], biases[0], self.out_projs[0]
_UpperCamelCase : List[str] = self._compute_logit(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Optional[int] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
_UpperCamelCase : Dict = nn.functional.log_softmax(lowerCamelCase__ ,dim=1 )
_UpperCamelCase : List[Any] = [0] + self.cutoffs
for i in range(len(lowerCamelCase__ ) - 1 ):
_UpperCamelCase , _UpperCamelCase : List[Any] = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
_UpperCamelCase : Optional[Any] = head_logprob[:, : self.cutoffs[0]]
else:
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase : Any = weights[i], biases[i], self.out_projs[i]
_UpperCamelCase : str = self._compute_logit(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
_UpperCamelCase : Optional[int] = nn.functional.log_softmax(lowerCamelCase__ ,dim=1 )
_UpperCamelCase : List[Any] = head_logprob[:, -i] + tail_logprob_i
_UpperCamelCase : Tuple = logprob_i
return out
| 195
|
'''simple docstring'''
from math import ceil
def A__ ( UpperCAmelCase_ = 1_0_0_1 ):
_UpperCamelCase : int = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
_UpperCamelCase : Dict = 2 * i + 1
_UpperCamelCase : Tuple = 2 * i
_UpperCamelCase : Any = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
snake_case_ : Tuple = int(sys.argv[1])
print(solution(n))
except ValueError:
print('Invalid entry - please enter a number')
| 195
| 1
|
"""simple docstring"""
from typing import Any
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
snake_case : List[Any] = data
snake_case : str = None
class _lowerCAmelCase :
def __init__( self ) -> Optional[int]:
'''simple docstring'''
snake_case : str = None
def lowerCamelCase ( self ) -> List[Any]:
'''simple docstring'''
snake_case : Tuple = self.head
while temp is not None:
print(temp.data , end=" " )
snake_case : Optional[Any] = temp.next
print()
def lowerCamelCase ( self , UpperCamelCase__ ) -> Any:
'''simple docstring'''
snake_case : Any = Node(UpperCamelCase__ )
snake_case : List[str] = self.head
snake_case : Tuple = new_node
def lowerCamelCase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
if node_data_a == node_data_a:
return
else:
snake_case : Any = self.head
while node_a is not None and node_a.data != node_data_a:
snake_case : Optional[Any] = node_a.next
snake_case : Optional[Any] = self.head
while node_a is not None and node_a.data != node_data_a:
snake_case : int = node_a.next
if node_a is None or node_a is None:
return
snake_case ,snake_case : Dict = node_a.data, node_a.data
if __name__ == "__main__":
__snake_case = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print("""After swapping""")
ll.print_list()
| 117
|
"""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 = logging.get_logger(__name__)
__snake_case = {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
"""google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
"""google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _lowerCAmelCase ( snake_case_ ):
__UpperCAmelCase : Optional[Any] = '''big_bird'''
def __init__( self , UpperCamelCase__=5_0358 , UpperCamelCase__=768 , UpperCamelCase__=12 , UpperCamelCase__=12 , UpperCamelCase__=3072 , UpperCamelCase__="gelu_new" , UpperCamelCase__=0.1 , UpperCamelCase__=0.1 , UpperCamelCase__=4096 , UpperCamelCase__=2 , UpperCamelCase__=0.02 , UpperCamelCase__=1e-12 , UpperCamelCase__=True , UpperCamelCase__=0 , UpperCamelCase__=1 , UpperCamelCase__=2 , UpperCamelCase__=66 , UpperCamelCase__="block_sparse" , UpperCamelCase__=True , UpperCamelCase__=False , UpperCamelCase__=64 , UpperCamelCase__=3 , UpperCamelCase__=None , **UpperCamelCase__ , ) -> Tuple:
'''simple docstring'''
super().__init__(
pad_token_id=UpperCamelCase__ , bos_token_id=UpperCamelCase__ , eos_token_id=UpperCamelCase__ , sep_token_id=UpperCamelCase__ , **UpperCamelCase__ , )
snake_case : Union[str, Any] = vocab_size
snake_case : List[Any] = max_position_embeddings
snake_case : int = hidden_size
snake_case : str = num_hidden_layers
snake_case : Any = num_attention_heads
snake_case : int = intermediate_size
snake_case : Union[str, Any] = hidden_act
snake_case : Optional[Any] = hidden_dropout_prob
snake_case : List[Any] = attention_probs_dropout_prob
snake_case : int = initializer_range
snake_case : List[str] = type_vocab_size
snake_case : Optional[Any] = layer_norm_eps
snake_case : Optional[Any] = use_cache
snake_case : List[Any] = rescale_embeddings
snake_case : Any = attention_type
snake_case : List[Any] = use_bias
snake_case : int = block_size
snake_case : int = num_random_blocks
snake_case : Optional[int] = classifier_dropout
class _lowerCAmelCase ( snake_case_ ):
@property
def lowerCamelCase ( self ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : int = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case : Optional[Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 117
| 1
|
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowerCAmelCase__ ( yaml.SafeLoader ):
'''simple docstring'''
def _lowerCAmelCase ( self : List[Any] , _SCREAMING_SNAKE_CASE : Dict ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [self.constructed_objects[key_node] for key_node, _ in node.value]
SCREAMING_SNAKE_CASE : Optional[Any] = [tuple(_SCREAMING_SNAKE_CASE ) if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else key for key in keys]
SCREAMING_SNAKE_CASE : Any = Counter(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : List[Any] = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}""" )
def _lowerCAmelCase ( self : Dict , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Dict=False ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = super().construct_mapping(_SCREAMING_SNAKE_CASE , deep=_SCREAMING_SNAKE_CASE )
self._check_no_duplicates_on_constructed_node(_SCREAMING_SNAKE_CASE )
return mapping
def __snake_case ( __A : str ) -> Tuple[Optional[str], str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
SCREAMING_SNAKE_CASE : Any = full_content[1:].index('---' ) + 1
SCREAMING_SNAKE_CASE : List[str] = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(__A )
class lowerCAmelCase__ ( _lowerCamelCase ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : str = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def _lowerCAmelCase ( cls : Dict , _SCREAMING_SNAKE_CASE : Path ) -> "DatasetMetadata":
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as readme_file:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(_SCREAMING_SNAKE_CASE )
else:
return cls()
def _lowerCAmelCase ( self : Optional[Any] , _SCREAMING_SNAKE_CASE : Path ) -> Optional[Any]:
"""simple docstring"""
if path.exists():
with open(_SCREAMING_SNAKE_CASE , encoding='utf-8' ) as readme_file:
SCREAMING_SNAKE_CASE : Dict = readme_file.read()
else:
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : Tuple = self._to_readme(_SCREAMING_SNAKE_CASE )
with open(_SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : Optional[int] , _SCREAMING_SNAKE_CASE : Optional[str] = None ) -> str:
"""simple docstring"""
if readme_content is not None:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = _split_yaml_from_readme(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE : str = '---\n' + self.to_yaml_string() + '---\n' + content
else:
SCREAMING_SNAKE_CASE : Optional[int] = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def _lowerCAmelCase ( cls : int , _SCREAMING_SNAKE_CASE : str ) -> "DatasetMetadata":
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = yaml.load(_SCREAMING_SNAKE_CASE , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
SCREAMING_SNAKE_CASE : Union[str, Any] = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self : List[Any] ) -> str:
"""simple docstring"""
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=_SCREAMING_SNAKE_CASE , allow_unicode=_SCREAMING_SNAKE_CASE , encoding='utf-8' , ).decode('utf-8' )
A_ : List[Any] = {
'image-classification': [],
'translation': [],
'image-segmentation': [],
'fill-mask': [],
'automatic-speech-recognition': [],
'token-classification': [],
'sentence-similarity': [],
'audio-classification': [],
'question-answering': [],
'summarization': [],
'zero-shot-classification': [],
'table-to-text': [],
'feature-extraction': [],
'other': [],
'multiple-choice': [],
'text-classification': [],
'text-to-image': [],
'text2text-generation': [],
'zero-shot-image-classification': [],
'tabular-classification': [],
'tabular-regression': [],
'image-to-image': [],
'tabular-to-text': [],
'unconditional-image-generation': [],
'text-retrieval': [],
'text-to-speech': [],
'object-detection': [],
'audio-to-audio': [],
'text-generation': [],
'conversational': [],
'table-question-answering': [],
'visual-question-answering': [],
'image-to-text': [],
'reinforcement-learning': [],
'voice-activity-detection': [],
'time-series-forecasting': [],
'document-question-answering': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
A_ : Dict = ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
ap.add_argument('readme_filepath')
A_ : Optional[Any] = ap.parse_args()
A_ : int = Path(args.readme_filepath)
A_ : List[str] = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 265
|
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
'stable diffusion controlnet',
'0.22.0',
'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
standard_warn=False,
stacklevel=3,
)
| 265
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
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
enable_full_determinism()
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def _SCREAMING_SNAKE_CASE ( self: int) -> Optional[Any]:
"""simple docstring"""
__lowerCAmelCase : int = 1
__lowerCAmelCase : Union[str, Any] = 3
__lowerCAmelCase : int = (32, 32)
__lowerCAmelCase : Any = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0)).to(_SCREAMING_SNAKE_CASE)
return image
@property
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Any:
"""simple docstring"""
torch.manual_seed(0)
__lowerCAmelCase : Any = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
return model
@property
def _SCREAMING_SNAKE_CASE ( self: List[str]) -> Optional[int]:
"""simple docstring"""
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 , )
return model
@property
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> int:
"""simple docstring"""
torch.manual_seed(0)
__lowerCAmelCase : Optional[Any] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5006 , )
return RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE)
@property
def _SCREAMING_SNAKE_CASE ( self: int) -> Dict:
"""simple docstring"""
def extract(*_SCREAMING_SNAKE_CASE: List[Any] , **_SCREAMING_SNAKE_CASE: str):
class A__ :
'''simple docstring'''
def __init__( self: Dict) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Any = torch.ones([0])
def _SCREAMING_SNAKE_CASE ( self: Dict , _SCREAMING_SNAKE_CASE: List[Any]) -> Optional[int]:
"""simple docstring"""
self.pixel_values.to(_SCREAMING_SNAKE_CASE)
return self
return Out()
return extract
def _SCREAMING_SNAKE_CASE ( self: str) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : Tuple = "cpu" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase : Tuple = self.dummy_cond_unet
__lowerCAmelCase : List[Any] = PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = self.dummy_vae
__lowerCAmelCase : Dict = self.dummy_text_encoder
__lowerCAmelCase : Tuple = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
__lowerCAmelCase : List[str] = 77
__lowerCAmelCase : List[Any] = self.dummy_image.to(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Dict = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
__lowerCAmelCase : Optional[Any] = AltDiffusionImgaImgPipeline(
unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , vae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__lowerCAmelCase : List[str] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : List[str] = alt_pipe.to(_SCREAMING_SNAKE_CASE)
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Any = "A painting of a squirrel eating a burger"
__lowerCAmelCase : Optional[int] = torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
__lowerCAmelCase : str = alt_pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , image=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : int = output.images
__lowerCAmelCase : Optional[Any] = torch.Generator(device=_SCREAMING_SNAKE_CASE).manual_seed(0)
__lowerCAmelCase : int = alt_pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=2 , output_type="np" , image=_SCREAMING_SNAKE_CASE , return_dict=_SCREAMING_SNAKE_CASE , )[0]
__lowerCAmelCase : Any = image[0, -3:, -3:, -1]
__lowerCAmelCase : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowerCAmelCase : Optional[int] = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-3
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU")
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Union[str, Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = self.dummy_cond_unet
__lowerCAmelCase : Optional[Any] = PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : int = self.dummy_vae
__lowerCAmelCase : List[Any] = self.dummy_text_encoder
__lowerCAmelCase : List[str] = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta")
__lowerCAmelCase : int = 77
__lowerCAmelCase : List[Any] = self.dummy_image.to(_SCREAMING_SNAKE_CASE)
# put models in fp16
__lowerCAmelCase : Union[str, Any] = unet.half()
__lowerCAmelCase : List[Any] = vae.half()
__lowerCAmelCase : Optional[int] = bert.half()
# make sure here that pndm scheduler skips prk
__lowerCAmelCase : List[str] = AltDiffusionImgaImgPipeline(
unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE , vae=_SCREAMING_SNAKE_CASE , text_encoder=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , feature_extractor=self.dummy_extractor , )
__lowerCAmelCase : Any = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor , do_normalize=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Tuple = alt_pipe.to(_SCREAMING_SNAKE_CASE)
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : str = "A painting of a squirrel eating a burger"
__lowerCAmelCase : Tuple = torch.manual_seed(0)
__lowerCAmelCase : Any = alt_pipe(
[prompt] , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="np" , image=_SCREAMING_SNAKE_CASE , ).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != "cuda" , "This test requires a GPU")
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> List[str]:
"""simple docstring"""
__lowerCAmelCase : List[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg")
# resize to resolution that is divisible by 8 but not 16 or 32
__lowerCAmelCase : Tuple = init_image.resize((760, 504))
__lowerCAmelCase : Union[str, Any] = "BAAI/AltDiffusion"
__lowerCAmelCase : List[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , )
pipe.to(_SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
__lowerCAmelCase : Dict = "A fantasy landscape, trending on artstation"
__lowerCAmelCase : List[str] = torch.manual_seed(0)
__lowerCAmelCase : int = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , strength=0.75 , guidance_scale=7.5 , generator=_SCREAMING_SNAKE_CASE , output_type="np" , )
__lowerCAmelCase : Optional[Any] = output.images[0]
__lowerCAmelCase : Tuple = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
__lowerCAmelCase : int = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _SCREAMING_SNAKE_CASE ( self: Optional[int]) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg")
__lowerCAmelCase : List[str] = init_image.resize((768, 512))
__lowerCAmelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy")
__lowerCAmelCase : List[Any] = "BAAI/AltDiffusion"
__lowerCAmelCase : Optional[int] = AltDiffusionImgaImgPipeline.from_pretrained(
_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE , )
pipe.to(_SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE)
pipe.enable_attention_slicing()
__lowerCAmelCase : Optional[int] = "A fantasy landscape, trending on artstation"
__lowerCAmelCase : int = torch.manual_seed(0)
__lowerCAmelCase : Optional[Any] = pipe(
prompt=_SCREAMING_SNAKE_CASE , image=_SCREAMING_SNAKE_CASE , strength=0.75 , guidance_scale=7.5 , generator=_SCREAMING_SNAKE_CASE , output_type="np" , )
__lowerCAmelCase : List[Any] = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image).max() < 1e-2
| 615
|
"""simple docstring"""
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def _lowercase ( __snake_case ) -> int:
if hor == 128:
__lowerCAmelCase : str = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase : int = (32, 128, 256)
__lowerCAmelCase : Optional[Any] = ("UpResnetBlock1D", "UpResnetBlock1D")
elif hor == 32:
__lowerCAmelCase : List[str] = ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D")
__lowerCAmelCase : Optional[Any] = (32, 64, 128, 256)
__lowerCAmelCase : Any = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D")
__lowerCAmelCase : Union[str, Any] = torch.load(F"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
__lowerCAmelCase : List[Any] = model.state_dict()
__lowerCAmelCase : Optional[Any] = {
"down_block_types": down_block_types,
"block_out_channels": block_out_channels,
"up_block_types": up_block_types,
"layers_per_block": 1,
"use_timestep_embedding": True,
"out_block_type": "OutConv1DBlock",
"norm_num_groups": 8,
"downsample_each_block": False,
"in_channels": 14,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"flip_sin_to_cos": False,
"freq_shift": 1,
"sample_size": 65_536,
"mid_block_type": "MidResTemporalBlock1D",
"act_fn": "mish",
}
__lowerCAmelCase : Dict = UNetaDModel(**__snake_case )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase : Dict = dict(zip(model.state_dict().keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase : int = state_dict.pop(__snake_case )
hf_value_function.load_state_dict(__snake_case )
torch.save(hf_value_function.state_dict() ,F"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(F"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" ,"w" ) as f:
json.dump(__snake_case ,__snake_case )
def _lowercase ( ) -> List[str]:
__lowerCAmelCase : Union[str, Any] = {
"in_channels": 14,
"down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"),
"up_block_types": (),
"out_block_type": "ValueFunction",
"mid_block_type": "ValueFunctionMidBlock1D",
"block_out_channels": (32, 64, 128, 256),
"layers_per_block": 1,
"downsample_each_block": True,
"sample_size": 65_536,
"out_channels": 14,
"extra_in_channels": 0,
"time_embedding_type": "positional",
"use_timestep_embedding": True,
"flip_sin_to_cos": False,
"freq_shift": 1,
"norm_num_groups": 8,
"act_fn": "mish",
}
__lowerCAmelCase : int = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch" )
__lowerCAmelCase : Any = model
__lowerCAmelCase : Optional[int] = UNetaDModel(**__snake_case )
print(F"""length of state dict: {len(state_dict.keys() )}""" )
print(F"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
__lowerCAmelCase : Any = dict(zip(state_dict.keys() ,hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
__lowerCAmelCase : Union[str, Any] = state_dict.pop(__snake_case )
hf_value_function.load_state_dict(__snake_case )
torch.save(hf_value_function.state_dict() ,"hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin" )
with open("hub/hopper-medium-v2/value_function/config.json" ,"w" ) as f:
json.dump(__snake_case ,__snake_case )
if __name__ == "__main__":
unet(32)
# unet(128)
value_function()
| 615
| 1
|
import os
def lowerCamelCase ( ) -> str:
with open(os.path.dirname(a_ ) + '/grid.txt' ) as f:
lowerCAmelCase_ = [] # noqa: E741
for _ in range(20 ):
l.append([int(a_ ) for x in f.readline().split()] )
lowerCAmelCase_ = 0
# right
for i in range(20 ):
for j in range(17 ):
lowerCAmelCase_ = l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
if temp > maximum:
lowerCAmelCase_ = temp
# down
for i in range(17 ):
for j in range(20 ):
lowerCAmelCase_ = l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
if temp > maximum:
lowerCAmelCase_ = temp
# diagonal 1
for i in range(17 ):
for j in range(17 ):
lowerCAmelCase_ = l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
if temp > maximum:
lowerCAmelCase_ = temp
# diagonal 2
for i in range(17 ):
for j in range(3 , 20 ):
lowerCAmelCase_ = l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
if temp > maximum:
lowerCAmelCase_ = temp
return maximum
if __name__ == "__main__":
print(solution())
| 318
|
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
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
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, 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)
#
# 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 lowerCamelCase ( a_ , a_ = 16 ) -> Tuple:
lowerCAmelCase_ = AutoTokenizer.from_pretrained('bert-base-cased' )
lowerCAmelCase_ = load_dataset('glue' , 'mrpc' )
def tokenize_function(a_ ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase_ = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=a_ , max_length=a_ )
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():
lowerCAmelCase_ = datasets.map(
a_ , batched=a_ , 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
lowerCAmelCase_ = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(a_ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase_ = 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":
lowerCAmelCase_ = 16
elif accelerator.mixed_precision != "no":
lowerCAmelCase_ = 8
else:
lowerCAmelCase_ = None
return tokenizer.pad(
a_ , padding='longest' , max_length=a_ , pad_to_multiple_of=a_ , return_tensors='pt' , )
# Instantiate dataloaders.
lowerCAmelCase_ = DataLoader(
tokenized_datasets['train'] , shuffle=a_ , collate_fn=a_ , batch_size=a_ )
lowerCAmelCase_ = DataLoader(
tokenized_datasets['validation'] , shuffle=a_ , collate_fn=a_ , batch_size=a_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
lowerCamelCase_ = mocked_dataloaders # noqa: F811
def lowerCamelCase ( a_ , a_ ) -> Dict:
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS' , a_ ) == "1":
lowerCAmelCase_ = 2
# Initialize accelerator
lowerCAmelCase_ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase_ = config['lr']
lowerCAmelCase_ = int(config['num_epochs'] )
lowerCAmelCase_ = int(config['seed'] )
lowerCAmelCase_ = int(config['batch_size'] )
lowerCAmelCase_ = evaluate.load('glue' , 'mrpc' )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=a_ )
def inner_training_loop(a_ ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(a_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase_ = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=a_ )
# 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).
lowerCAmelCase_ = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase_ = AdamW(params=model.parameters() , lr=a_ )
lowerCAmelCase_ , lowerCAmelCase_ = get_dataloaders(a_ , a_ )
# Instantiate scheduler
lowerCAmelCase_ = get_linear_schedule_with_warmup(
optimizer=a_ , num_warmup_steps=100 , num_training_steps=(len(a_ ) * num_epochs) , )
# 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.
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = accelerator.prepare(
a_ , a_ , a_ , a_ , a_ )
# Now we train the model
for epoch in range(a_ ):
model.train()
for step, batch in enumerate(a_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
lowerCAmelCase_ = model(**a_ )
lowerCAmelCase_ = outputs.loss
accelerator.backward(a_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(a_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowerCAmelCase_ = model(**a_ )
lowerCAmelCase_ = outputs.logits.argmax(dim=-1 )
lowerCAmelCase_ , lowerCAmelCase_ = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=a_ , references=a_ , )
lowerCAmelCase_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , a_ )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def lowerCamelCase ( ) -> Tuple:
lowerCAmelCase_ = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=a_ , default=a_ , 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.' )
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(a_ , a_ )
if __name__ == "__main__":
main()
| 318
| 1
|
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_UpperCAmelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 0.1 ) -> int:
lowerCamelCase__ : Tuple = 3
lowerCamelCase__ : Optional[int] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 , (j + 2) * (j + 2) , j + 1 ):
primes += is_prime(_UpperCAmelCase )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Union[str, Any] = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class lowerCAmelCase ( __UpperCamelCase ):
UpperCAmelCase__ = """donut-swin"""
UpperCAmelCase__ = {
"""num_attention_heads""": """num_heads""",
"""num_hidden_layers""": """num_layers""",
}
def __init__( self : Tuple , UpperCAmelCase : List[str]=224 , UpperCAmelCase : Optional[Any]=4 , UpperCAmelCase : Union[str, Any]=3 , UpperCAmelCase : Union[str, Any]=96 , UpperCAmelCase : int=[2, 2, 6, 2] , UpperCAmelCase : Dict=[3, 6, 12, 24] , UpperCAmelCase : Any=7 , UpperCAmelCase : Optional[int]=4.0 , UpperCAmelCase : List[Any]=True , UpperCAmelCase : Any=0.0 , UpperCAmelCase : Optional[Any]=0.0 , UpperCAmelCase : int=0.1 , UpperCAmelCase : List[str]="gelu" , UpperCAmelCase : List[str]=False , UpperCAmelCase : str=0.0_2 , UpperCAmelCase : str=1e-5 , **UpperCAmelCase : Tuple , ) -> int:
super().__init__(**UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = image_size
lowerCamelCase__ : Optional[int] = patch_size
lowerCamelCase__ : int = num_channels
lowerCamelCase__ : int = embed_dim
lowerCamelCase__ : Optional[Any] = depths
lowerCamelCase__ : Optional[int] = len(UpperCAmelCase )
lowerCamelCase__ : Optional[int] = num_heads
lowerCamelCase__ : Tuple = window_size
lowerCamelCase__ : Dict = mlp_ratio
lowerCamelCase__ : str = qkv_bias
lowerCamelCase__ : Tuple = hidden_dropout_prob
lowerCamelCase__ : str = attention_probs_dropout_prob
lowerCamelCase__ : str = drop_path_rate
lowerCamelCase__ : int = hidden_act
lowerCamelCase__ : str = use_absolute_embeddings
lowerCamelCase__ : List[Any] = layer_norm_eps
lowerCamelCase__ : Optional[Any] = initializer_range
# 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
lowerCamelCase__ : Optional[int] = int(embed_dim * 2 ** (len(UpperCAmelCase ) - 1) )
| 188
| 0
|
from .data_collator import (
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForSeqaSeq,
DataCollatorForSOP,
DataCollatorForTokenClassification,
DataCollatorForWholeWordMask,
DataCollatorWithPadding,
DefaultDataCollator,
default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
DataProcessor,
InputExample,
InputFeatures,
SingleSentenceClassificationProcessor,
SquadExample,
SquadFeatures,
SquadVaProcessor,
SquadVaProcessor,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
squad_convert_examples_to_features,
xnli_output_modes,
xnli_processors,
xnli_tasks_num_labels,
)
| 605
|
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
snake_case : Tuple = (7_20, 12_80) # Height, Width
snake_case : List[Any] = (0.4, 0.6) # if height or width lower than this scale, drop it.
snake_case : str = 1 / 1_00
snake_case : List[Any] = ''
snake_case : Union[str, Any] = ''
snake_case : Tuple = ''
snake_case : List[str] = 2_50
def SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = get_dataset(UpperCAmelCase__ ,UpperCAmelCase__ )
for index in range(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = random.sample(range(len(UpperCAmelCase__ ) ) ,4 )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = update_image_and_anno(
UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,filter_scale=UpperCAmelCase__ ,)
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_SCREAMING_SNAKE_CASE = random_chars(32 )
_SCREAMING_SNAKE_CASE = path.split(os.sep )[-1].rsplit('.' ,1 )[0]
_SCREAMING_SNAKE_CASE = f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
cva.imwrite(f'''{file_root}.jpg''' ,UpperCAmelCase__ ,[cva.IMWRITE_JPEG_QUALITY, 85] )
print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}''' )
_SCREAMING_SNAKE_CASE = []
for anno in new_annos:
_SCREAMING_SNAKE_CASE = anno[3] - anno[1]
_SCREAMING_SNAKE_CASE = anno[4] - anno[2]
_SCREAMING_SNAKE_CASE = anno[1] + width / 2
_SCREAMING_SNAKE_CASE = anno[2] + height / 2
_SCREAMING_SNAKE_CASE = f'''{anno[0]} {x_center} {y_center} {width} {height}'''
annos_list.append(UpperCAmelCase__ )
with open(f'''{file_root}.txt''' ,'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for label_file in glob.glob(os.path.join(UpperCAmelCase__ ,'*.txt' ) ):
_SCREAMING_SNAKE_CASE = label_file.split(os.sep )[-1].rsplit('.' ,1 )[0]
with open(UpperCAmelCase__ ) as in_file:
_SCREAMING_SNAKE_CASE = in_file.readlines()
_SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ ,f'''{label_name}.jpg''' )
_SCREAMING_SNAKE_CASE = []
for obj_list in obj_lists:
_SCREAMING_SNAKE_CASE = obj_list.rstrip('\n' ).split(' ' )
_SCREAMING_SNAKE_CASE = float(obj[1] ) - float(obj[3] ) / 2
_SCREAMING_SNAKE_CASE = float(obj[2] ) - float(obj[4] ) / 2
_SCREAMING_SNAKE_CASE = float(obj[1] ) + float(obj[3] ) / 2
_SCREAMING_SNAKE_CASE = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(UpperCAmelCase__ )
labels.append(UpperCAmelCase__ )
return img_paths, labels
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ = 0.0 ,):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = np.zeros([output_size[0], output_size[1], 3] ,dtype=np.uinta )
_SCREAMING_SNAKE_CASE = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_SCREAMING_SNAKE_CASE = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_SCREAMING_SNAKE_CASE = int(scale_x * output_size[1] )
_SCREAMING_SNAKE_CASE = int(scale_y * output_size[0] )
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for i, index in enumerate(UpperCAmelCase__ ):
_SCREAMING_SNAKE_CASE = all_img_list[index]
path_list.append(UpperCAmelCase__ )
_SCREAMING_SNAKE_CASE = all_annos[index]
_SCREAMING_SNAKE_CASE = cva.imread(UpperCAmelCase__ )
if i == 0: # top-left
_SCREAMING_SNAKE_CASE = cva.resize(UpperCAmelCase__ ,(divid_point_x, divid_point_y) )
_SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
_SCREAMING_SNAKE_CASE = bbox[1] * scale_x
_SCREAMING_SNAKE_CASE = bbox[2] * scale_y
_SCREAMING_SNAKE_CASE = bbox[3] * scale_x
_SCREAMING_SNAKE_CASE = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_SCREAMING_SNAKE_CASE = cva.resize(UpperCAmelCase__ ,(output_size[1] - divid_point_x, divid_point_y) )
_SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
_SCREAMING_SNAKE_CASE = scale_x + bbox[1] * (1 - scale_x)
_SCREAMING_SNAKE_CASE = bbox[2] * scale_y
_SCREAMING_SNAKE_CASE = scale_x + bbox[3] * (1 - scale_x)
_SCREAMING_SNAKE_CASE = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_SCREAMING_SNAKE_CASE = cva.resize(UpperCAmelCase__ ,(divid_point_x, output_size[0] - divid_point_y) )
_SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
_SCREAMING_SNAKE_CASE = bbox[1] * scale_x
_SCREAMING_SNAKE_CASE = scale_y + bbox[2] * (1 - scale_y)
_SCREAMING_SNAKE_CASE = bbox[3] * scale_x
_SCREAMING_SNAKE_CASE = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_SCREAMING_SNAKE_CASE = cva.resize(
UpperCAmelCase__ ,(output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_SCREAMING_SNAKE_CASE = img
for bbox in img_annos:
_SCREAMING_SNAKE_CASE = scale_x + bbox[1] * (1 - scale_x)
_SCREAMING_SNAKE_CASE = scale_y + bbox[2] * (1 - scale_y)
_SCREAMING_SNAKE_CASE = scale_x + bbox[3] * (1 - scale_x)
_SCREAMING_SNAKE_CASE = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_SCREAMING_SNAKE_CASE = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
_SCREAMING_SNAKE_CASE = ascii_lowercase + digits
return "".join(random.choice(UpperCAmelCase__ ) for _ in range(UpperCAmelCase__ ) )
if __name__ == "__main__":
main()
print('DONE ✅')
| 605
| 1
|
'''simple docstring'''
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : int = Dict[str, Any]
_lowerCamelCase : int = List[Prediction]
@add_end_docstrings(__snake_case )
class lowerCamelCase__ ( __snake_case ):
def __init__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Tuple:
"""simple docstring"""
super().__init__(*lowerCAmelCase__ , **lowerCAmelCase__ )
if self.framework == "tf":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
requires_backends(self , """vision""" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def _UpperCamelCase ( self , **lowerCAmelCase__ ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :Optional[int] ={}
if "threshold" in kwargs:
_UpperCamelCase :Optional[Any] =kwargs["""threshold"""]
return {}, {}, postprocess_kwargs
def __call__( self , *lowerCAmelCase__ , **lowerCAmelCase__ ) -> Union[Predictions, List[Prediction]]:
"""simple docstring"""
return super().__call__(*lowerCAmelCase__ , **lowerCAmelCase__ )
def _UpperCamelCase ( self , lowerCAmelCase__ ) -> List[str]:
"""simple docstring"""
_UpperCamelCase :List[Any] =load_image(lowerCAmelCase__ )
_UpperCamelCase :Any =torch.IntTensor([[image.height, image.width]] )
_UpperCamelCase :int =self.image_processor(images=[image] , return_tensors="""pt""" )
if self.tokenizer is not None:
_UpperCamelCase :Optional[Any] =self.tokenizer(text=inputs["""words"""] , boxes=inputs["""boxes"""] , return_tensors="""pt""" )
_UpperCamelCase :int =target_size
return inputs
def _UpperCamelCase ( self , lowerCAmelCase__ ) -> str:
"""simple docstring"""
_UpperCamelCase :int =model_inputs.pop("""target_size""" )
_UpperCamelCase :Tuple =self.model(**lowerCAmelCase__ )
_UpperCamelCase :int =outputs.__class__({"""target_size""": target_size, **outputs} )
if self.tokenizer is not None:
_UpperCamelCase :List[str] =model_inputs["""bbox"""]
return model_outputs
def _UpperCamelCase ( self , lowerCAmelCase__ , lowerCAmelCase__=0.9 ) -> Any:
"""simple docstring"""
_UpperCamelCase :Optional[int] =model_outputs["""target_size"""]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
_UpperCamelCase , _UpperCamelCase :Optional[Any] =target_size[0].tolist()
def unnormalize(lowerCAmelCase__ ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1_000),
(height * bbox[1] / 1_000),
(width * bbox[2] / 1_000),
(height * bbox[3] / 1_000),
] ) )
_UpperCamelCase , _UpperCamelCase :List[Any] =model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
_UpperCamelCase :Dict =[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
_UpperCamelCase :List[Any] =[unnormalize(lowerCAmelCase__ ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
_UpperCamelCase :List[Any] =["""score""", """label""", """box"""]
_UpperCamelCase :str =[dict(zip(lowerCAmelCase__ , lowerCAmelCase__ ) ) for vals in zip(scores.tolist() , lowerCAmelCase__ , lowerCAmelCase__ ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
_UpperCamelCase :Optional[int] =self.image_processor.post_process_object_detection(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCamelCase :Tuple =raw_annotations[0]
_UpperCamelCase :int =raw_annotation["""scores"""]
_UpperCamelCase :Optional[Any] =raw_annotation["""labels"""]
_UpperCamelCase :Any =raw_annotation["""boxes"""]
_UpperCamelCase :Dict =scores.tolist()
_UpperCamelCase :Tuple =[self.model.config.idalabel[label.item()] for label in labels]
_UpperCamelCase :Tuple =[self._get_bounding_box(lowerCAmelCase__ ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
_UpperCamelCase :int =["""score""", """label""", """box"""]
_UpperCamelCase :Any =[
dict(zip(lowerCAmelCase__ , lowerCAmelCase__ ) )
for vals in zip(raw_annotation["""scores"""] , raw_annotation["""labels"""] , raw_annotation["""boxes"""] )
]
return annotation
def _UpperCamelCase ( self , lowerCAmelCase__ ) -> Dict[str, int]:
"""simple docstring"""
if self.framework != "pt":
raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase :Optional[Any] =box.int().tolist()
_UpperCamelCase :List[str] ={
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 512
|
'''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_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import PoolFormerImageProcessor
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=30 , lowerCAmelCase__=400 , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=0.9 , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=[0.5, 0.5, 0.5] , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :List[str] =size if size is not None else {"""shortest_edge""": 30}
_UpperCamelCase :str =crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
_UpperCamelCase :Tuple =parent
_UpperCamelCase :Optional[int] =batch_size
_UpperCamelCase :Tuple =num_channels
_UpperCamelCase :int =min_resolution
_UpperCamelCase :Union[str, Any] =max_resolution
_UpperCamelCase :Tuple =do_resize_and_center_crop
_UpperCamelCase :Union[str, Any] =size
_UpperCamelCase :Union[str, Any] =crop_pct
_UpperCamelCase :Tuple =crop_size
_UpperCamelCase :List[str] =do_normalize
_UpperCamelCase :Any =image_mean
_UpperCamelCase :Optional[Any] =image_std
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
return {
"size": self.size,
"do_resize_and_center_crop": self.do_resize_and_center_crop,
"crop_pct": self.crop_pct,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class lowerCamelCase__ ( __snake_case , unittest.TestCase ):
__UpperCAmelCase = PoolFormerImageProcessor if is_vision_available() else None
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
_UpperCamelCase :Dict =PoolFormerImageProcessingTester(self )
@property
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] =self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_resize_and_center_crop""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """size""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """crop_pct""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """do_normalize""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """image_mean""" ) )
self.assertTrue(hasattr(lowerCAmelCase__ , """image_std""" ) )
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
_UpperCamelCase :Dict =self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 30} )
self.assertEqual(image_processor.crop_size , {"""height""": 30, """width""": 30} )
_UpperCamelCase :Union[str, Any] =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 _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
pass
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :Any =self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCamelCase :List[str] =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
_UpperCamelCase :int =image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_UpperCamelCase :Optional[Any] =image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def _UpperCamelCase ( self ) -> str:
"""simple docstring"""
_UpperCamelCase :Optional[Any] =self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCamelCase :int =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
_UpperCamelCase :List[Any] =image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_UpperCamelCase :Tuple =image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def _UpperCamelCase ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase :Union[str, Any] =self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCamelCase :Optional[int] =prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
_UpperCamelCase :Dict =image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_UpperCamelCase :Tuple =image_processing(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 512
| 1
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A_ ( __lowerCamelCase , unittest.TestCase ):
lowerCAmelCase__ = DiTPipeline
lowerCAmelCase__ = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
lowerCAmelCase__ = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
lowerCAmelCase__ = False
def _lowercase ( self: int ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCamelCase : Any = TransformeraDModel(
sample_size=16 ,num_layers=2 ,patch_size=4 ,attention_head_dim=8 ,num_attention_heads=2 ,in_channels=4 ,out_channels=8 ,attention_bias=__lowerCAmelCase ,activation_fn="gelu-approximate" ,num_embeds_ada_norm=1_000 ,norm_type="ada_norm_zero" ,norm_elementwise_affine=__lowerCAmelCase ,)
_lowerCamelCase : List[str] = AutoencoderKL()
_lowerCamelCase : Dict = DDIMScheduler()
_lowerCamelCase : Optional[int] = {'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
return components
def _lowercase ( self: int ,__lowerCAmelCase: int ,__lowerCAmelCase: Any=0 ):
'''simple docstring'''
if str(__lowerCAmelCase ).startswith("mps" ):
_lowerCamelCase : Optional[int] = torch.manual_seed(__lowerCAmelCase )
else:
_lowerCamelCase : Union[str, Any] = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
_lowerCamelCase : Tuple = {
'''class_labels''': [1],
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : Tuple = '''cpu'''
_lowerCamelCase : List[Any] = self.get_dummy_components()
_lowerCamelCase : List[Any] = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
_lowerCamelCase : Tuple = self.get_dummy_inputs(__lowerCAmelCase )
_lowerCamelCase : str = pipe(**__lowerCAmelCase ).images
_lowerCamelCase : List[Any] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape ,(1, 16, 16, 3) )
_lowerCamelCase : List[str] = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
_lowerCamelCase : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCAmelCase ,1e-3 )
def _lowercase ( self: Any ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=__lowerCAmelCase ,expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() ,reason="XFormers attention is only available with CUDA and `xformers` installed" ,)
def _lowercase ( self: List[Any] ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class A_ ( unittest.TestCase ):
def _lowercase ( self: str ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowercase ( self: Any ):
'''simple docstring'''
_lowerCamelCase : Optional[Any] = torch.manual_seed(0 )
_lowerCamelCase : List[Any] = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
_lowerCamelCase : str = ['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
_lowerCamelCase : Dict = pipe.get_label_ids(__lowerCAmelCase )
_lowerCamelCase : Optional[int] = pipe(__lowerCAmelCase ,generator=__lowerCAmelCase ,num_inference_steps=40 ,output_type="np" ).images
for word, image in zip(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : Tuple = load_numpy(
F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def _lowercase ( self: Union[str, Any] ):
'''simple docstring'''
_lowerCamelCase : int = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
_lowerCamelCase : List[str] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
_lowerCamelCase : int = ['''vase''', '''umbrella''']
_lowerCamelCase : List[Any] = pipe.get_label_ids(__lowerCAmelCase )
_lowerCamelCase : Union[str, Any] = torch.manual_seed(0 )
_lowerCamelCase : str = pipe(__lowerCAmelCase ,generator=__lowerCAmelCase ,num_inference_steps=25 ,output_type="np" ).images
for word, image in zip(__lowerCAmelCase ,__lowerCAmelCase ):
_lowerCamelCase : Dict = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
F"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 46
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
def _A ( snake_case__ : List[str] ):
snake_case__ : str = DPTConfig()
if "large" in checkpoint_url:
snake_case__ : Any = 10_24
snake_case__ : Union[str, Any] = 40_96
snake_case__ : Optional[int] = 24
snake_case__ : int = 16
snake_case__ : Optional[int] = [5, 11, 17, 23]
snake_case__ : Tuple = [2_56, 5_12, 10_24, 10_24]
snake_case__ : List[Any] = (1, 3_84, 3_84)
if "ade" in checkpoint_url:
snake_case__ : Dict = True
snake_case__ : Optional[int] = 1_50
snake_case__ : Dict = '''huggingface/label-files'''
snake_case__ : Optional[Any] = '''ade20k-id2label.json'''
snake_case__ : List[Any] = json.load(open(cached_download(hf_hub_url(snake_case__ , snake_case__ , repo_type='''dataset''' ) ) , '''r''' ) )
snake_case__ : List[Any] = {int(snake_case__ ): v for k, v in idalabel.items()}
snake_case__ : Optional[int] = idalabel
snake_case__ : List[str] = {v: k for k, v in idalabel.items()}
snake_case__ : List[str] = [1, 1_50, 4_80, 4_80]
return config, expected_shape
def _A ( snake_case__ : int ):
snake_case__ : Optional[Any] = ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
for k in ignore_keys:
state_dict.pop(snake_case__ , snake_case__ )
def _A ( snake_case__ : Union[str, Any] ):
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
snake_case__ : str = name.replace('''pretrained.model''' , '''dpt.encoder''' )
if "pretrained.model" in name:
snake_case__ : List[Any] = name.replace('''pretrained.model''' , '''dpt.embeddings''' )
if "patch_embed" in name:
snake_case__ : Union[str, Any] = name.replace('''patch_embed''' , '''patch_embeddings''' )
if "pos_embed" in name:
snake_case__ : Tuple = name.replace('''pos_embed''' , '''position_embeddings''' )
if "attn.proj" in name:
snake_case__ : Dict = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "proj" in name and "project" not in name:
snake_case__ : Any = name.replace('''proj''' , '''projection''' )
if "blocks" in name:
snake_case__ : int = name.replace('''blocks''' , '''layer''' )
if "mlp.fc1" in name:
snake_case__ : str = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
snake_case__ : int = name.replace('''mlp.fc2''' , '''output.dense''' )
if "norm1" in name:
snake_case__ : Dict = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
snake_case__ : Optional[int] = name.replace('''norm2''' , '''layernorm_after''' )
if "scratch.output_conv" in name:
snake_case__ : List[str] = name.replace('''scratch.output_conv''' , '''head''' )
if "scratch" in name:
snake_case__ : Optional[Any] = name.replace('''scratch''' , '''neck''' )
if "layer1_rn" in name:
snake_case__ : str = name.replace('''layer1_rn''' , '''convs.0''' )
if "layer2_rn" in name:
snake_case__ : Dict = name.replace('''layer2_rn''' , '''convs.1''' )
if "layer3_rn" in name:
snake_case__ : Optional[Any] = name.replace('''layer3_rn''' , '''convs.2''' )
if "layer4_rn" in name:
snake_case__ : Optional[int] = name.replace('''layer4_rn''' , '''convs.3''' )
if "refinenet" in name:
snake_case__ : Any = int(name[len('''neck.refinenet''' ) : len('''neck.refinenet''' ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
snake_case__ : List[Any] = name.replace(f'''refinenet{layer_idx}''' , f'''fusion_stage.layers.{abs(layer_idx-4 )}''' )
if "out_conv" in name:
snake_case__ : str = name.replace('''out_conv''' , '''projection''' )
if "resConfUnit1" in name:
snake_case__ : str = name.replace('''resConfUnit1''' , '''residual_layer1''' )
if "resConfUnit2" in name:
snake_case__ : List[Any] = name.replace('''resConfUnit2''' , '''residual_layer2''' )
if "conv1" in name:
snake_case__ : Union[str, Any] = name.replace('''conv1''' , '''convolution1''' )
if "conv2" in name:
snake_case__ : Optional[int] = name.replace('''conv2''' , '''convolution2''' )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
snake_case__ : Optional[int] = name.replace('''pretrained.act_postprocess1.0.project.0''' , '''neck.reassemble_stage.readout_projects.0.0''' )
if "pretrained.act_postprocess2.0.project.0" in name:
snake_case__ : Tuple = name.replace('''pretrained.act_postprocess2.0.project.0''' , '''neck.reassemble_stage.readout_projects.1.0''' )
if "pretrained.act_postprocess3.0.project.0" in name:
snake_case__ : List[str] = name.replace('''pretrained.act_postprocess3.0.project.0''' , '''neck.reassemble_stage.readout_projects.2.0''' )
if "pretrained.act_postprocess4.0.project.0" in name:
snake_case__ : Tuple = name.replace('''pretrained.act_postprocess4.0.project.0''' , '''neck.reassemble_stage.readout_projects.3.0''' )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
snake_case__ : str = name.replace('''pretrained.act_postprocess1.3''' , '''neck.reassemble_stage.layers.0.projection''' )
if "pretrained.act_postprocess1.4" in name:
snake_case__ : Tuple = name.replace('''pretrained.act_postprocess1.4''' , '''neck.reassemble_stage.layers.0.resize''' )
if "pretrained.act_postprocess2.3" in name:
snake_case__ : Union[str, Any] = name.replace('''pretrained.act_postprocess2.3''' , '''neck.reassemble_stage.layers.1.projection''' )
if "pretrained.act_postprocess2.4" in name:
snake_case__ : Optional[Any] = name.replace('''pretrained.act_postprocess2.4''' , '''neck.reassemble_stage.layers.1.resize''' )
if "pretrained.act_postprocess3.3" in name:
snake_case__ : List[Any] = name.replace('''pretrained.act_postprocess3.3''' , '''neck.reassemble_stage.layers.2.projection''' )
if "pretrained.act_postprocess4.3" in name:
snake_case__ : int = name.replace('''pretrained.act_postprocess4.3''' , '''neck.reassemble_stage.layers.3.projection''' )
if "pretrained.act_postprocess4.4" in name:
snake_case__ : Any = name.replace('''pretrained.act_postprocess4.4''' , '''neck.reassemble_stage.layers.3.resize''' )
if "pretrained" in name:
snake_case__ : List[Any] = name.replace('''pretrained''' , '''dpt''' )
if "bn" in name:
snake_case__ : Union[str, Any] = name.replace('''bn''' , '''batch_norm''' )
if "head" in name:
snake_case__ : Optional[Any] = name.replace('''head''' , '''head.head''' )
if "encoder.norm" in name:
snake_case__ : int = name.replace('''encoder.norm''' , '''layernorm''' )
if "auxlayer" in name:
snake_case__ : Any = name.replace('''auxlayer''' , '''auxiliary_head.head''' )
return name
def _A ( snake_case__ : Dict , snake_case__ : List[Any] ):
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
snake_case__ : Any = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight''' )
snake_case__ : Any = state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Union[str, Any] = in_proj_weight[: config.hidden_size, :]
snake_case__ : Dict = in_proj_bias[: config.hidden_size]
snake_case__ : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
snake_case__ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
snake_case__ : Tuple = in_proj_weight[
-config.hidden_size :, :
]
snake_case__ : Optional[int] = in_proj_bias[-config.hidden_size :]
def _A ( ):
snake_case__ : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case__ : Dict = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
return im
@torch.no_grad()
def _A ( snake_case__ : Dict , snake_case__ : Any , snake_case__ : str , snake_case__ : Any ):
snake_case__ ,snake_case__ : Optional[Any] = get_dpt_config(snake_case__ )
# load original state_dict from URL
snake_case__ : Any = torch.hub.load_state_dict_from_url(snake_case__ , map_location='''cpu''' )
# remove certain keys
remove_ignore_keys_(snake_case__ )
# rename keys
for key in state_dict.copy().keys():
snake_case__ : Optional[Any] = state_dict.pop(snake_case__ )
snake_case__ : Union[str, Any] = val
# read in qkv matrices
read_in_q_k_v(snake_case__ , snake_case__ )
# load HuggingFace model
snake_case__ : Tuple = DPTForSemanticSegmentation(snake_case__ ) if '''ade''' in checkpoint_url else DPTForDepthEstimation(snake_case__ )
model.load_state_dict(snake_case__ )
model.eval()
# Check outputs on an image
snake_case__ : List[Any] = 4_80 if '''ade''' in checkpoint_url else 3_84
snake_case__ : int = DPTImageProcessor(size=snake_case__ )
snake_case__ : List[Any] = prepare_img()
snake_case__ : str = image_processor(snake_case__ , return_tensors='''pt''' )
# forward pass
snake_case__ : Optional[int] = model(**snake_case__ ).logits if '''ade''' in checkpoint_url else model(**snake_case__ ).predicted_depth
# Assert logits
snake_case__ : Dict = torch.tensor([[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]] )
if "ade" in checkpoint_url:
snake_case__ : List[Any] = torch.tensor([[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]] )
assert outputs.shape == torch.Size(snake_case__ )
assert (
torch.allclose(outputs[0, 0, :3, :3] , snake_case__ , atol=1E-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , snake_case__ )
)
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(snake_case__ )
if push_to_hub:
print('''Pushing model to hub...''' )
model.push_to_hub(
repo_path_or_name=Path(snake_case__ , snake_case__ ) , organization='''nielsr''' , commit_message='''Add model''' , use_temp_dir=snake_case__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case__ , snake_case__ ) , organization='''nielsr''' , commit_message='''Add image processor''' , use_temp_dir=snake_case__ , )
if __name__ == "__main__":
_lowerCAmelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
type=str,
help="URL of the original DPT checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
parser.add_argument(
"--model_name",
default="dpt-large",
type=str,
help="Name of the model, in case you're pushing to the hub.",
)
_lowerCAmelCase : Tuple = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 261
| 0
|
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class lowercase ( unittest.TestCase , _UpperCAmelCase ):
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = load_tool("""text-to-speech""" )
self.tool.setup()
def _snake_case ( self ) -> str:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
lowerCAmelCase = self.tool("""hey""" )
lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485] ) , ) )
def _snake_case ( self ) -> List[Any]:
# SpeechT5 isn't deterministic
torch.manual_seed(0 )
lowerCAmelCase = self.tool("""hey""" )
lowerCAmelCase = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] , torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485] ) , ) )
| 393
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
"kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
"kssteven/ibert-roberta-large-mnli": (
"https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
),
}
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = 'ibert'
def __init__( self , lowercase=30_522 , lowercase=768 , lowercase=12 , lowercase=12 , lowercase=3_072 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=512 , lowercase=2 , lowercase=0.02 , lowercase=1e-12 , lowercase=1 , lowercase=0 , lowercase=2 , lowercase="absolute" , lowercase=False , lowercase="none" , **lowercase , ) -> str:
super().__init__(pad_token_id=lowercase , bos_token_id=lowercase , eos_token_id=lowercase , **lowercase )
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = hidden_act
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = type_vocab_size
lowerCAmelCase = initializer_range
lowerCAmelCase = layer_norm_eps
lowerCAmelCase = position_embedding_type
lowerCAmelCase = quant_mode
lowerCAmelCase = force_dequant
class lowercase ( _UpperCAmelCase ):
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 393
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__a : List[Any] = ['''BartphoTokenizer''']
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__a : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 397
|
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
__a : List[str] = random.Random()
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_=1.0 ,SCREAMING_SNAKE_CASE_=None ,SCREAMING_SNAKE_CASE_=None ) -> Optional[int]:
if rng is None:
lowercase__ : Optional[Any] = global_rng
lowercase__ : Union[str, Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=400 , lowerCamelCase=2000 , lowerCamelCase=1 , lowerCamelCase=0.0 , lowerCamelCase=16000 , lowerCamelCase=True , lowerCamelCase=80 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase="hann_window" , lowerCamelCase=80 , lowerCamelCase=7600 , lowerCamelCase=1E-10 , lowerCamelCase=True , ) -> int:
"""simple docstring"""
lowercase__ : Optional[int] = parent
lowercase__ : Optional[Any] = batch_size
lowercase__ : Dict = min_seq_length
lowercase__ : Optional[int] = max_seq_length
lowercase__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowercase__ : List[Any] = feature_size
lowercase__ : Union[str, Any] = padding_value
lowercase__ : Dict = sampling_rate
lowercase__ : int = do_normalize
lowercase__ : Union[str, Any] = num_mel_bins
lowercase__ : Optional[Any] = hop_length
lowercase__ : Tuple = win_length
lowercase__ : Any = win_function
lowercase__ : Optional[Any] = fmin
lowercase__ : str = fmax
lowercase__ : Union[str, Any] = mel_floor
lowercase__ : str = return_attention_mask
def __a ( self ) -> Any:
"""simple docstring"""
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def __a ( self , lowerCamelCase=False , lowerCamelCase=False ) -> List[str]:
"""simple docstring"""
def _flatten(lowerCamelCase ):
return list(itertools.chain(*lowerCamelCase ) )
if equal_length:
lowercase__ : Optional[int] = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
lowercase__ : List[str] = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowercase__ : Dict = [np.asarray(lowerCamelCase ) for x in speech_inputs]
return speech_inputs
def __a ( self , lowerCamelCase=False , lowerCamelCase=False ) -> Optional[int]:
"""simple docstring"""
if equal_length:
lowercase__ : Union[str, Any] = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowercase__ : Tuple = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowercase__ : List[str] = [np.asarray(lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
class UpperCAmelCase( snake_case_ , unittest.TestCase ):
"""simple docstring"""
a : List[Any] = SpeechTaFeatureExtractor
def __a ( self ) -> Tuple:
"""simple docstring"""
lowercase__ : Union[str, Any] = SpeechTaFeatureExtractionTester(self )
def __a ( self , lowerCamelCase ) -> List[Any]:
"""simple docstring"""
self.assertTrue(np.all(np.mean(lowerCamelCase , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCamelCase , axis=0 ) - 1 ) < 1E-3 ) )
def __a ( self ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowercase__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowercase__ : str = [np.asarray(lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
lowercase__ : int = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
lowercase__ : Union[str, Any] = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
# Test batched
lowercase__ : Optional[int] = feat_extract(lowerCamelCase , return_tensors="np" ).input_values
lowercase__ : Union[str, Any] = feat_extract(lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
def __a ( self ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : List[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowercase__ : Any = ["longest", "max_length", "do_not_pad"]
lowercase__ : List[Any] = [None, 1600, None]
for max_length, padding in zip(lowerCamelCase , lowerCamelCase ):
lowercase__ : Optional[int] = feat_extract(lowerCamelCase , padding=lowerCamelCase , max_length=lowerCamelCase , return_tensors="np" )
lowercase__ : List[str] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self.assertTrue(input_values[0][800:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self.assertTrue(input_values[0][1000:].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __a ( self ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : Dict = range(800 , 1400 , 200 )
lowercase__ : List[str] = [floats_list((1, x) )[0] for x in lengths]
lowercase__ : Tuple = ["longest", "max_length", "do_not_pad"]
lowercase__ : str = [None, 1600, None]
for max_length, padding in zip(lowerCamelCase , lowerCamelCase ):
lowercase__ : List[str] = feat_extract(lowerCamelCase , max_length=lowerCamelCase , padding=lowerCamelCase )
lowercase__ : Any = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:800] )
self._check_zero_mean_unit_variance(input_values[1][:1000] )
self._check_zero_mean_unit_variance(input_values[2][:1200] )
def __a ( self ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : List[str] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowercase__ : Tuple = feat_extract(
lowerCamelCase , truncation=lowerCamelCase , max_length=1000 , padding="max_length" , return_tensors="np" )
lowercase__ : Optional[Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def __a ( self ) -> Any:
"""simple docstring"""
lowercase__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowercase__ : Tuple = feat_extract(
lowerCamelCase , truncation=lowerCamelCase , max_length=1000 , padding="longest" , return_tensors="np" )
lowercase__ : Dict = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1000) )
lowercase__ : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowercase__ : Union[str, Any] = feat_extract(
lowerCamelCase , truncation=lowerCamelCase , max_length=2000 , padding="longest" , return_tensors="np" )
lowercase__ : Union[str, Any] = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :800] )
self._check_zero_mean_unit_variance(input_values[1, :1000] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1200) )
def __a ( self ) -> Any:
"""simple docstring"""
lowercase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowercase__ : Tuple = np.random.rand(100 ).astype(np.floataa )
lowercase__ : int = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowercase__ : Tuple = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
lowercase__ : Dict = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def __a ( self ) -> str:
"""simple docstring"""
lowercase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowercase__ : List[str] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
lowercase__ : List[str] = [np.asarray(lowerCamelCase ) for speech_input in speech_inputs]
# Test feature size
lowercase__ : str = feature_extractor(audio_target=lowerCamelCase , padding=lowerCamelCase , return_tensors="np" ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
lowercase__ : Union[str, Any] = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_values
lowercase__ : Optional[Any] = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
# Test batched
lowercase__ : Dict = feature_extractor(lowerCamelCase , return_tensors="np" ).input_values
lowercase__ : List[str] = feature_extractor(lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
lowercase__ : str = [floats_list((1, x) )[0] for x in (800, 800, 800)]
lowercase__ : Optional[Any] = np.asarray(lowerCamelCase )
lowercase__ : List[Any] = feature_extractor(lowerCamelCase , return_tensors="np" ).input_values
lowercase__ : List[str] = feature_extractor(lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
def __a ( self ) -> str:
"""simple docstring"""
lowercase__ : Dict = self.feat_extract_tester.prepare_inputs_for_target()
lowercase__ : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_dict )
lowercase__ : Dict = feat_extract.model_input_names[0]
lowercase__ : int = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(lowerCamelCase ) == len(lowerCamelCase ) for x, y in zip(lowerCamelCase , processed_features[input_name] ) ) )
lowercase__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase )
lowercase__ : List[Any] = BatchFeature({input_name: speech_inputs} , tensor_type="np" )
lowercase__ : Optional[int] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowercase__ : Tuple = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __a ( self ) -> Tuple:
"""simple docstring"""
lowercase__ : Dict = self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase )
lowercase__ : List[str] = self.feature_extraction_class(**self.feat_extract_dict )
lowercase__ : Optional[Any] = feat_extract.model_input_names[0]
lowercase__ : Dict = BatchFeature({input_name: speech_inputs} , tensor_type="pt" )
lowercase__ : List[str] = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
lowercase__ : int = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def __a ( self ) -> Tuple:
"""simple docstring"""
lowercase__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
lowercase__ : Optional[Any] = self.feat_extract_tester.prepare_inputs_for_target()
lowercase__ : Optional[Any] = feat_extract.model_input_names[0]
lowercase__ : Optional[Any] = BatchFeature({input_name: speech_inputs} )
lowercase__ : Optional[int] = feat_extract.num_mel_bins # hack!
lowercase__ : Optional[int] = feat_extract.pad(lowerCamelCase , padding="longest" , return_tensors="np" )[input_name]
lowercase__ : Optional[int] = feat_extract.pad(lowerCamelCase , padding="longest" , return_tensors="pt" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1E-2 )
def __a ( self ) -> Tuple:
"""simple docstring"""
lowercase__ : Tuple = self.feat_extract_dict
lowercase__ : int = True
lowercase__ : Optional[Any] = self.feature_extraction_class(**lowerCamelCase )
lowercase__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
lowercase__ : Union[str, Any] = [len(lowerCamelCase ) for x in speech_inputs]
lowercase__ : Any = feat_extract.model_input_names[0]
lowercase__ : Optional[int] = BatchFeature({input_name: speech_inputs} )
lowercase__ : int = feat_extract.num_mel_bins # hack!
lowercase__ : int = feat_extract.pad(lowerCamelCase , padding="longest" , return_tensors="np" )
self.assertIn("attention_mask" , lowerCamelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , lowerCamelCase )
def __a ( self ) -> Dict:
"""simple docstring"""
lowercase__ : List[Any] = self.feat_extract_dict
lowercase__ : Optional[int] = True
lowercase__ : List[Any] = self.feature_extraction_class(**lowerCamelCase )
lowercase__ : Optional[int] = self.feat_extract_tester.prepare_inputs_for_target()
lowercase__ : List[str] = [len(lowerCamelCase ) for x in speech_inputs]
lowercase__ : Any = feat_extract.model_input_names[0]
lowercase__ : Dict = BatchFeature({input_name: speech_inputs} )
lowercase__ : int = min(lowerCamelCase )
lowercase__ : List[str] = feat_extract.num_mel_bins # hack!
lowercase__ : Dict = feat_extract.pad(
lowerCamelCase , padding="max_length" , max_length=lowerCamelCase , truncation=lowerCamelCase , return_tensors="np" )
self.assertIn("attention_mask" , lowerCamelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def __a ( self , lowerCamelCase ) -> List[Any]:
"""simple docstring"""
from datasets import load_dataset
lowercase__ : Any = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
lowercase__ : int = ds.sort("id" ).select(range(lowerCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def __a ( self ) -> List[str]:
"""simple docstring"""
lowercase__ : List[str] = torch.tensor(
[2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03] )
# fmt: on
lowercase__ : List[Any] = self._load_datasamples(1 )
lowercase__ : int = SpeechTaFeatureExtractor()
lowercase__ : Tuple = feature_extractor(lowerCamelCase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 93680) )
self.assertTrue(torch.allclose(input_values[0, :30] , lowerCamelCase , atol=1E-6 ) )
def __a ( self ) -> int:
"""simple docstring"""
lowercase__ : Optional[int] = torch.tensor(
[-2.68_70, -3.01_04, -3.13_56, -3.53_52, -3.00_44, -3.03_53, -3.47_19, -3.67_77,
-3.15_20, -2.94_35, -2.65_53, -2.87_95, -2.99_44, -2.59_21, -3.02_79, -3.03_86,
-3.08_64, -3.12_91, -3.23_53, -2.74_44, -2.68_31, -2.72_87, -3.17_61, -3.15_71,
-3.27_26, -3.05_82, -3.10_07, -3.45_33, -3.46_95, -3.09_98] )
# fmt: on
lowercase__ : Any = self._load_datasamples(1 )
lowercase__ : List[Any] = SpeechTaFeatureExtractor()
lowercase__ : int = feature_extractor(audio_target=lowerCamelCase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 366, 80) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , lowerCamelCase , atol=1E-4 ) )
| 397
| 1
|
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def A ( _UpperCAmelCase : NDArray[floataa] ,_UpperCAmelCase : NDArray[floataa] ,_UpperCAmelCase : list[int] ,_UpperCAmelCase : int ,) -> Tuple:
'''simple docstring'''
__lowerCAmelCase : Any = coefficient_matrix.shape
__lowerCAmelCase : Dict = constant_matrix.shape
if rowsa != colsa:
__lowerCAmelCase : List[str] = F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(_UpperCAmelCase )
if colsa != 1:
__lowerCAmelCase : Tuple = F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(_UpperCAmelCase )
if rowsa != rowsa:
__lowerCAmelCase : Any = (
'''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(_UpperCAmelCase )
if len(_UpperCAmelCase ) != rowsa:
__lowerCAmelCase : Optional[Any] = (
'''Number of initial values must be equal to number of rows in coefficient '''
F"""matrix but received {len(_UpperCAmelCase )} and {rowsa}"""
)
raise ValueError(_UpperCAmelCase )
if iterations <= 0:
raise ValueError('Iterations must be at least 1' )
__lowerCAmelCase : NDArray[floataa] = np.concatenate(
(coefficient_matrix, constant_matrix) ,axis=1 )
__lowerCAmelCase : Union[str, Any] = table.shape
strictly_diagonally_dominant(_UpperCAmelCase )
# Iterates the whole matrix for given number of times
for _ in range(_UpperCAmelCase ):
__lowerCAmelCase : Union[str, Any] = []
for row in range(_UpperCAmelCase ):
__lowerCAmelCase : Optional[int] = 0
for col in range(_UpperCAmelCase ):
if col == row:
__lowerCAmelCase : List[Any] = table[row][col]
elif col == cols - 1:
__lowerCAmelCase : List[Any] = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
__lowerCAmelCase : Optional[Any] = (temp + val) / denom
new_val.append(_UpperCAmelCase )
__lowerCAmelCase : Dict = new_val
return [float(_UpperCAmelCase ) for i in new_val]
def A ( _UpperCAmelCase : NDArray[floataa] ) -> List[str]:
'''simple docstring'''
__lowerCAmelCase : Optional[int] = table.shape
__lowerCAmelCase : Any = True
for i in range(0 ,_UpperCAmelCase ):
__lowerCAmelCase : Dict = 0
for j in range(0 ,cols - 1 ):
if i == j:
continue
else:
total += table[i][j]
if table[i][i] <= total:
raise ValueError('Coefficient matrix is not strictly diagonally dominant' )
return is_diagonally_dominant
# Test Cases
if __name__ == "__main__":
import doctest
doctest.testmod()
| 718
|
'''simple docstring'''
def A ( _UpperCAmelCase : int = 1_0 ,_UpperCAmelCase : int = 1_0_0_0 ,_UpperCAmelCase : bool = True ) -> int:
'''simple docstring'''
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def A ( _UpperCAmelCase : int ,_UpperCAmelCase : int ) -> int:
'''simple docstring'''
return int((number_a + number_a) / 2 )
def A ( _UpperCAmelCase : int ,_UpperCAmelCase : int ,_UpperCAmelCase : int ) -> None:
'''simple docstring'''
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and isinstance(_UpperCAmelCase ,_UpperCAmelCase ) and isinstance(_UpperCAmelCase ,_UpperCAmelCase )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(_UpperCAmelCase : int ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
__lowerCAmelCase : Union[str, Any] = lower
__lowerCAmelCase : List[Any] = higher
__lowerCAmelCase : List[str] = []
while True:
__lowerCAmelCase : Union[str, Any] = get_avg(_UpperCAmelCase ,_UpperCAmelCase )
last_numbers.append(_UpperCAmelCase )
if answer(_UpperCAmelCase ) == "low":
__lowerCAmelCase : List[Any] = number
elif answer(_UpperCAmelCase ) == "high":
__lowerCAmelCase : Optional[Any] = number
else:
break
print(F"""guess the number : {last_numbers[-1]}""" )
print(F"""details : {last_numbers!s}""" )
def A ( ) -> None:
'''simple docstring'''
__lowerCAmelCase : int = int(input('Enter lower value : ' ).strip() )
__lowerCAmelCase : Optional[Any] = int(input('Enter high value : ' ).strip() )
__lowerCAmelCase : int = int(input('Enter value to guess : ' ).strip() )
guess_the_number(_UpperCAmelCase ,_UpperCAmelCase ,_UpperCAmelCase )
if __name__ == "__main__":
main()
| 123
| 0
|
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> float:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
__lowercase : str = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(__lowerCAmelCase ) )
return round(__lowerCAmelCase , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 509
|
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__lowerCAmelCase : Any = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
__lowerCAmelCase : List[Any] = json.load(f)
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self : List[str] , _snake_case : List[Any] ):
return FSMTTokenizer.from_pretrained(_snake_case )
def snake_case_ ( self : Any , _snake_case : List[str] ):
__lowercase : str = FSMTForConditionalGeneration.from_pretrained(_snake_case ).to(_snake_case )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['''en-ru''', 26.0],
['''ru-en''', 22.0],
['''en-de''', 22.0],
['''de-en''', 29.0],
] )
@slow
def snake_case_ ( self : Tuple , _snake_case : int , _snake_case : Union[str, Any] ):
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
__lowercase : Tuple = F'facebook/wmt19-{pair}'
__lowercase : Tuple = self.get_tokenizer(_snake_case )
__lowercase : Dict = self.get_model(_snake_case )
__lowercase : Dict = bleu_data[pair]['''src''']
__lowercase : Any = bleu_data[pair]['''tgt''']
__lowercase : Any = tokenizer(_snake_case , return_tensors='''pt''' , truncation=_snake_case , padding='''longest''' ).to(_snake_case )
__lowercase : Optional[int] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
__lowercase : Any = tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case )
__lowercase : Tuple = calculate_bleu(_snake_case , _snake_case )
print(_snake_case )
self.assertGreaterEqual(scores['''bleu'''] , _snake_case )
| 509
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class __snake_case :
def __init__( self, A, A=13, A=7, A=True, A=True, A=True, A=True, A=99, A=32, A=2, A=4, A=37, A="gelu", A=0.1, A=0.1, A=512, A=16, A=2, A=0.02, A=3, A=4, A=None, A=0, ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = parent
lowerCamelCase : Any = batch_size
lowerCamelCase : Optional[Any] = seq_length
lowerCamelCase : List[str] = is_training
lowerCamelCase : Optional[int] = use_input_mask
lowerCamelCase : int = use_token_type_ids
lowerCamelCase : List[Any] = use_labels
lowerCamelCase : List[str] = vocab_size
lowerCamelCase : List[Any] = hidden_size
lowerCamelCase : Dict = num_hidden_layers
lowerCamelCase : List[str] = num_attention_heads
lowerCamelCase : Union[str, Any] = intermediate_size
lowerCamelCase : int = hidden_act
lowerCamelCase : str = hidden_dropout_prob
lowerCamelCase : Union[str, Any] = attention_probs_dropout_prob
lowerCamelCase : str = max_position_embeddings
lowerCamelCase : int = type_vocab_size
lowerCamelCase : List[Any] = type_sequence_label_size
lowerCamelCase : int = initializer_range
lowerCamelCase : Any = num_labels
lowerCamelCase : Tuple = num_choices
lowerCamelCase : Dict = scope
lowerCamelCase : List[Any] = projection_dim
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase : Tuple = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
lowerCamelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase : Union[str, Any] = None
if self.use_token_type_ids:
lowerCamelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase : Dict = None
lowerCamelCase : Any = None
lowerCamelCase : Optional[Any] = None
if self.use_labels:
lowerCamelCase : Any = ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase : Dict = ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase : int = BertConfig(
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, is_decoder=A, initializer_range=self.initializer_range, )
lowerCamelCase : Dict = DPRConfig(projection_dim=self.projection_dim, **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Dict = TFDPRContextEncoder(config=A )
lowerCamelCase : Optional[Any] = model(A, attention_mask=A, token_type_ids=A )
lowerCamelCase : int = model(A, token_type_ids=A )
lowerCamelCase : int = model(A )
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = TFDPRQuestionEncoder(config=A )
lowerCamelCase : Optional[int] = model(A, attention_mask=A, token_type_ids=A )
lowerCamelCase : Union[str, Any] = model(A, token_type_ids=A )
lowerCamelCase : List[Any] = model(A )
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.projection_dim or self.hidden_size) )
def UpperCAmelCase_ ( self, A, A, A, A, A, A, A ):
"""simple docstring"""
lowerCamelCase : Dict = TFDPRReader(config=A )
lowerCamelCase : Dict = model(A, attention_mask=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) )
self.parent.assertEqual(result.relevance_logits.shape, (self.batch_size,) )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : int = self.prepare_config_and_inputs()
(
lowerCamelCase
) : Dict = config_and_inputs
lowerCamelCase : Optional[int] = {'input_ids': input_ids}
return config, inputs_dict
@require_tf
class __snake_case ( a__ , a__ , unittest.TestCase):
_lowerCAmelCase = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
_lowerCAmelCase = {'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
_lowerCAmelCase = False
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = TFDPRModelTester(self )
lowerCamelCase : Union[str, Any] = ConfigTester(self, config_class=A, hidden_size=37 )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*A )
@slow
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : Any = TFDPRContextEncoder.from_pretrained(A )
self.assertIsNotNone(A )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : Union[str, Any] = TFDPRContextEncoder.from_pretrained(A )
self.assertIsNotNone(A )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : str = TFDPRQuestionEncoder.from_pretrained(A )
self.assertIsNotNone(A )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase : Union[str, Any] = TFDPRReader.from_pretrained(A )
self.assertIsNotNone(A )
@require_tf
class __snake_case ( unittest.TestCase):
@slow
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Tuple = TFDPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base' )
lowerCamelCase : Optional[int] = tf.constant(
[[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]] ) # [CLS] hello, is my dog cute? [SEP]
lowerCamelCase : Optional[Any] = model(A )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
lowerCamelCase : List[Any] = tf.constant(
[
[
0.0323_6253,
0.1275_3335,
0.1681_8509,
0.0027_9786,
0.389_6933,
0.2426_4945,
0.217_8971,
-0.0233_5227,
-0.0848_1959,
-0.1432_4117,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy(), expected_slice.numpy(), atol=1e-4 ) )
| 701
|
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a__):
_lowerCAmelCase = (DPMSolverSinglestepScheduler,)
_lowerCAmelCase = (('''num_inference_steps''', 25),)
def UpperCAmelCase_ ( self, **A ):
"""simple docstring"""
lowerCamelCase : List[Any] = {
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**A )
return config
def UpperCAmelCase_ ( self, A=0, **A ):
"""simple docstring"""
lowerCamelCase : List[str] = dict(self.forward_default_kwargs )
lowerCamelCase : Optional[Any] = kwargs.pop('num_inference_steps', A )
lowerCamelCase : Union[str, Any] = self.dummy_sample
lowerCamelCase : Dict = 0.1 * sample
lowerCamelCase : Dict = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase : Optional[Any] = self.get_scheduler_config(**A )
lowerCamelCase : Dict = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals
lowerCamelCase : str = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
lowerCamelCase : List[Any] = scheduler_class.from_pretrained(A )
new_scheduler.set_timesteps(A )
# copy over dummy past residuals
lowerCamelCase : Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase , lowerCamelCase : Optional[int] = sample, sample
for t in range(A, time_step + scheduler.config.solver_order + 1 ):
lowerCamelCase : Dict = scheduler.step(A, A, A, **A ).prev_sample
lowerCamelCase : Optional[int] = 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 UpperCAmelCase_ ( self ):
"""simple docstring"""
pass
def UpperCAmelCase_ ( self, A=0, **A ):
"""simple docstring"""
lowerCamelCase : List[str] = dict(self.forward_default_kwargs )
lowerCamelCase : str = kwargs.pop('num_inference_steps', A )
lowerCamelCase : Union[str, Any] = self.dummy_sample
lowerCamelCase : List[str] = 0.1 * sample
lowerCamelCase : List[str] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase : Tuple = self.get_scheduler_config()
lowerCamelCase : Optional[Any] = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals (must be after setting timesteps)
lowerCamelCase : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
lowerCamelCase : Tuple = 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 : List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase : int = scheduler.step(A, A, A, **A ).prev_sample
lowerCamelCase : Dict = 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 UpperCAmelCase_ ( self, A=None, **A ):
"""simple docstring"""
if scheduler is None:
lowerCamelCase : Any = self.scheduler_classes[0]
lowerCamelCase : Optional[Any] = self.get_scheduler_config(**A )
lowerCamelCase : Optional[int] = scheduler_class(**A )
lowerCamelCase : List[Any] = self.scheduler_classes[0]
lowerCamelCase : Optional[Any] = self.get_scheduler_config(**A )
lowerCamelCase : Optional[int] = scheduler_class(**A )
lowerCamelCase : Any = 10
lowerCamelCase : Dict = self.dummy_model()
lowerCamelCase : Any = self.dummy_sample_deter
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase : Dict = model(A, A )
lowerCamelCase : List[str] = scheduler.step(A, A, A ).prev_sample
return sample
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
lowerCamelCase : Dict = 50
lowerCamelCase : Tuple = self.dummy_model()
lowerCamelCase : Optional[int] = self.dummy_sample_deter
scheduler.set_timesteps(A )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
lowerCamelCase : Any = model(A, A )
lowerCamelCase : Optional[int] = scheduler.step(A, A, A ).prev_sample
lowerCamelCase : Any = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2574 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
lowerCamelCase : str = self.full_loop(scheduler=A )
lowerCamelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2791 ) < 1e-3
lowerCamelCase : Dict = DEISMultistepScheduler.from_config(scheduler.config )
lowerCamelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowerCamelCase : Any = UniPCMultistepScheduler.from_config(scheduler.config )
lowerCamelCase : Optional[Any] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowerCamelCase : str = self.full_loop(scheduler=A )
lowerCamelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2791 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=A )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
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='dpmsolver++', solver_order=A, solver_type=A, )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
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 : Optional[Any] = 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 UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(lower_order_final=A )
self.check_over_configs(lower_order_final=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(variance_type=A )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=A, time_step=0 )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.full_loop()
lowerCamelCase : str = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2791 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.full_loop(use_karras_sigmas=A )
lowerCamelCase : Tuple = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2248 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.full_loop(prediction_type='v_prediction' )
lowerCamelCase : Dict = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.1453 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.full_loop(prediction_type='v_prediction', use_karras_sigmas=A )
lowerCamelCase : Optional[Any] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.0649 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = self.scheduler_classes[0]
lowerCamelCase : Dict = self.get_scheduler_config(thresholding=A, dynamic_thresholding_ratio=0 )
lowerCamelCase : str = scheduler_class(**A )
lowerCamelCase : List[Any] = 10
lowerCamelCase : List[str] = self.dummy_model()
lowerCamelCase : int = self.dummy_sample_deter.half()
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase : str = model(A, A )
lowerCamelCase : Tuple = scheduler.step(A, A, A ).prev_sample
assert sample.dtype == torch.floataa
| 449
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''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 __lowerCAmelCase ( _a ):
lowerCamelCase_ : Dict = '''cvt'''
def __init__(self , __magic_name__=3 , __magic_name__=[7, 3, 3] , __magic_name__=[4, 2, 2] , __magic_name__=[2, 1, 1] , __magic_name__=[64, 192, 384] , __magic_name__=[1, 3, 6] , __magic_name__=[1, 2, 10] , __magic_name__=[4.0, 4.0, 4.0] , __magic_name__=[0.0, 0.0, 0.0] , __magic_name__=[0.0, 0.0, 0.0] , __magic_name__=[0.0, 0.0, 0.1] , __magic_name__=[True, True, True] , __magic_name__=[False, False, True] , __magic_name__=["dw_bn", "dw_bn", "dw_bn"] , __magic_name__=[3, 3, 3] , __magic_name__=[1, 1, 1] , __magic_name__=[2, 2, 2] , __magic_name__=[1, 1, 1] , __magic_name__=[1, 1, 1] , __magic_name__=0.02 , __magic_name__=1e-12 , **__magic_name__ , ) -> List[str]:
'''simple docstring'''
super().__init__(**__magic_name__ )
snake_case_ : int = num_channels
snake_case_ : str = patch_sizes
snake_case_ : Dict = patch_stride
snake_case_ : str = patch_padding
snake_case_ : List[str] = embed_dim
snake_case_ : int = num_heads
snake_case_ : Union[str, Any] = depth
snake_case_ : Union[str, Any] = mlp_ratio
snake_case_ : List[str] = attention_drop_rate
snake_case_ : Tuple = drop_rate
snake_case_ : Any = drop_path_rate
snake_case_ : Optional[int] = qkv_bias
snake_case_ : Tuple = cls_token
snake_case_ : Dict = qkv_projection_method
snake_case_ : Dict = kernel_qkv
snake_case_ : List[Any] = padding_kv
snake_case_ : Dict = stride_kv
snake_case_ : List[str] = padding_q
snake_case_ : List[Any] = stride_q
snake_case_ : Dict = initializer_range
snake_case_ : Dict = layer_norm_eps
| 60
|
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class __lowerCAmelCase :
lowerCamelCase_ : Any = None
def lowerCamelCase (self ) -> Optional[int]:
'''simple docstring'''
snake_case_ : List[Any] = self.feature_extraction_class(**self.feat_extract_dict )
snake_case_ : List[Any] = json.loads(feat_extract.to_json_string() )
for key, value in self.feat_extract_dict.items():
self.assertEqual(obj[key] , __magic_name__ )
def lowerCamelCase (self ) -> int:
'''simple docstring'''
snake_case_ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : Optional[int] = os.path.join(__magic_name__ , '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
snake_case_ : str = self.feature_extraction_class.from_json_file(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> Optional[Any]:
'''simple docstring'''
snake_case_ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case_ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
snake_case_ : Dict = self.feature_extraction_class.from_pretrained(__magic_name__ )
self.assertEqual(feat_extract_second.to_dict() , feat_extract_first.to_dict() )
def lowerCamelCase (self ) -> str:
'''simple docstring'''
snake_case_ : Tuple = self.feature_extraction_class()
self.assertIsNotNone(__magic_name__ )
| 60
| 1
|
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
__A ={
'vocab_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
),
},
'merges_file': {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
'allenai/longformer-large-4096': (
'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
),
},
}
__A ={
'allenai/longformer-base-4096': 40_96,
'allenai/longformer-large-4096': 40_96,
'allenai/longformer-large-4096-finetuned-triviaqa': 40_96,
'allenai/longformer-base-4096-extra.pos.embd.only': 40_96,
'allenai/longformer-large-4096-extra.pos.embd.only': 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def _UpperCamelCase ( ):
UpperCAmelCase__ : Any = (
list(range(ord("""!""" ) , ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" ) , ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" ) , ord("""ÿ""" ) + 1 ) )
)
UpperCAmelCase__ : Optional[Any] = bs[:]
UpperCAmelCase__ : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(UpperCamelCase__ )
cs.append(2**8 + n )
n += 1
UpperCAmelCase__ : List[Any] = [chr(UpperCamelCase__ ) for n in cs]
return dict(zip(UpperCamelCase__ , UpperCamelCase__ ) )
def _UpperCamelCase ( UpperCamelCase__ ):
UpperCAmelCase__ : Optional[int] = set()
UpperCAmelCase__ : Optional[int] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
UpperCAmelCase__ : List[Any] = char
return pairs
class _snake_case ( a__ ):
lowerCAmelCase :int = VOCAB_FILES_NAMES
lowerCAmelCase :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase :int = ['''input_ids''', '''attention_mask''']
def __init__( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase="replace" , _lowerCamelCase="<s>" , _lowerCamelCase="</s>" , _lowerCamelCase="</s>" , _lowerCamelCase="<s>" , _lowerCamelCase="<unk>" , _lowerCamelCase="<pad>" , _lowerCamelCase="<mask>" , _lowerCamelCase=False , **_lowerCamelCase , ):
UpperCAmelCase__ : Dict = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else bos_token
UpperCAmelCase__ : int = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else eos_token
UpperCAmelCase__ : str = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else sep_token
UpperCAmelCase__ : Union[str, Any] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else cls_token
UpperCAmelCase__ : Union[str, Any] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else unk_token
UpperCAmelCase__ : Any = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
UpperCAmelCase__ : List[str] = AddedToken(_lowerCamelCase , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase , _lowerCamelCase) else mask_token
super().__init__(
errors=_lowerCamelCase , bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , add_prefix_space=_lowerCamelCase , **_lowerCamelCase , )
with open(_lowerCamelCase , encoding="""utf-8""") as vocab_handle:
UpperCAmelCase__ : Optional[Any] = json.load(_lowerCamelCase)
UpperCAmelCase__ : Any = {v: k for k, v in self.encoder.items()}
UpperCAmelCase__ : List[str] = errors # how to handle errors in decoding
UpperCAmelCase__ : Optional[Any] = bytes_to_unicode()
UpperCAmelCase__ : Dict = {v: k for k, v in self.byte_encoder.items()}
with open(_lowerCamelCase , encoding="""utf-8""") as merges_handle:
UpperCAmelCase__ : Optional[int] = merges_handle.read().split("""\n""")[1:-1]
UpperCAmelCase__ : Union[str, Any] = [tuple(merge.split()) for merge in bpe_merges]
UpperCAmelCase__ : str = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase))))
UpperCAmelCase__ : Dict = {}
UpperCAmelCase__ : Dict = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
UpperCAmelCase__ : Optional[int] = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
@property
def snake_case__ ( self):
return len(self.encoder)
def snake_case__ ( self):
return dict(self.encoder , **self.added_tokens_encoder)
def snake_case__ ( self , _lowerCamelCase):
if token in self.cache:
return self.cache[token]
UpperCAmelCase__ : List[Any] = tuple(_lowerCamelCase)
UpperCAmelCase__ : str = get_pairs(_lowerCamelCase)
if not pairs:
return token
while True:
UpperCAmelCase__ : Dict = min(_lowerCamelCase , key=lambda _lowerCamelCase: self.bpe_ranks.get(_lowerCamelCase , float("""inf""")))
if bigram not in self.bpe_ranks:
break
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = bigram
UpperCAmelCase__ : str = []
UpperCAmelCase__ : int = 0
while i < len(_lowerCamelCase):
try:
UpperCAmelCase__ : List[str] = word.index(_lowerCamelCase , _lowerCamelCase)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
UpperCAmelCase__ : Any = j
if word[i] == first and i < len(_lowerCamelCase) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
UpperCAmelCase__ : List[Any] = tuple(_lowerCamelCase)
UpperCAmelCase__ : Any = new_word
if len(_lowerCamelCase) == 1:
break
else:
UpperCAmelCase__ : str = get_pairs(_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = """ """.join(_lowerCamelCase)
UpperCAmelCase__ : List[Any] = word
return word
def snake_case__ ( self , _lowerCamelCase):
UpperCAmelCase__ : Dict = []
for token in re.findall(self.pat , _lowerCamelCase):
UpperCAmelCase__ : Dict = """""".join(
self.byte_encoder[b] for b in token.encode("""utf-8""")) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase).split(""" """))
return bpe_tokens
def snake_case__ ( self , _lowerCamelCase):
return self.encoder.get(_lowerCamelCase , self.encoder.get(self.unk_token))
def snake_case__ ( self , _lowerCamelCase):
return self.decoder.get(_lowerCamelCase)
def snake_case__ ( self , _lowerCamelCase):
UpperCAmelCase__ : Tuple = """""".join(_lowerCamelCase)
UpperCAmelCase__ : str = bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8""" , errors=self.errors)
return text
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = None):
if not os.path.isdir(_lowerCamelCase):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
return
UpperCAmelCase__ : Dict = os.path.join(
_lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
UpperCAmelCase__ : int = os.path.join(
_lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])
with open(_lowerCamelCase , """w""" , encoding="""utf-8""") as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCamelCase , ensure_ascii=_lowerCamelCase) + """\n""")
UpperCAmelCase__ : Optional[Any] = 0
with open(_lowerCamelCase , """w""" , encoding="""utf-8""") as writer:
writer.write("""#version: 0.2\n""")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCamelCase: kv[1]):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
""" Please check that the tokenizer is not corrupted!""")
UpperCAmelCase__ : Tuple = token_index
writer.write(""" """.join(_lowerCamelCase) + """\n""")
index += 1
return vocab_file, merge_file
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = None):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
UpperCAmelCase__ : Optional[int] = [self.cls_token_id]
UpperCAmelCase__ : Optional[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase)
if token_ids_a is None:
return [1] + ([0] * len(_lowerCamelCase)) + [1]
return [1] + ([0] * len(_lowerCamelCase)) + [1, 1] + ([0] * len(_lowerCamelCase)) + [1]
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase = None):
UpperCAmelCase__ : Union[str, Any] = [self.sep_token_id]
UpperCAmelCase__ : 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 snake_case__ ( self , _lowerCamelCase , _lowerCamelCase=False , **_lowerCamelCase):
UpperCAmelCase__ : Optional[Any] = kwargs.pop("""add_prefix_space""" , self.add_prefix_space)
if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase) > 0 and not text[0].isspace()):
UpperCAmelCase__ : Tuple = """ """ + text
return (text, kwargs)
| 113
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class _snake_case ( a__ , a__ , unittest.TestCase ):
lowerCAmelCase :Optional[int] = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
lowerCAmelCase :Union[str, Any] = (
{
'''feature-extraction''': TFMobileBertModel,
'''fill-mask''': TFMobileBertForMaskedLM,
'''question-answering''': TFMobileBertForQuestionAnswering,
'''text-classification''': TFMobileBertForSequenceClassification,
'''token-classification''': TFMobileBertForTokenClassification,
'''zero-shot''': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCAmelCase :Optional[Any] = False
lowerCAmelCase :Dict = False
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False):
UpperCAmelCase__ : int = super()._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase)
if return_labels:
if model_class in get_values(_lowerCamelCase):
UpperCAmelCase__ : Tuple = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa)
return inputs_dict
class _snake_case ( a__ ):
def __init__( self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=7 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=99 , _lowerCamelCase=32 , _lowerCamelCase=32 , _lowerCamelCase=2 , _lowerCamelCase=4 , _lowerCamelCase=37 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=512 , _lowerCamelCase=16 , _lowerCamelCase=2 , _lowerCamelCase=0.02 , _lowerCamelCase=3 , _lowerCamelCase=4 , _lowerCamelCase=None , ):
UpperCAmelCase__ : Optional[Any] = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : Any = seq_length
UpperCAmelCase__ : Dict = is_training
UpperCAmelCase__ : str = use_input_mask
UpperCAmelCase__ : int = use_token_type_ids
UpperCAmelCase__ : Optional[Any] = use_labels
UpperCAmelCase__ : Any = vocab_size
UpperCAmelCase__ : Optional[Any] = hidden_size
UpperCAmelCase__ : Optional[int] = num_hidden_layers
UpperCAmelCase__ : List[Any] = num_attention_heads
UpperCAmelCase__ : Dict = intermediate_size
UpperCAmelCase__ : str = hidden_act
UpperCAmelCase__ : int = hidden_dropout_prob
UpperCAmelCase__ : Union[str, Any] = attention_probs_dropout_prob
UpperCAmelCase__ : Optional[int] = max_position_embeddings
UpperCAmelCase__ : Union[str, Any] = type_vocab_size
UpperCAmelCase__ : Optional[int] = type_sequence_label_size
UpperCAmelCase__ : Optional[int] = initializer_range
UpperCAmelCase__ : Union[str, Any] = num_labels
UpperCAmelCase__ : List[str] = num_choices
UpperCAmelCase__ : str = scope
UpperCAmelCase__ : Optional[int] = embedding_size
def snake_case__ ( self):
UpperCAmelCase__ : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
UpperCAmelCase__ : Optional[Any] = None
if self.use_input_mask:
UpperCAmelCase__ : Tuple = random_attention_mask([self.batch_size, self.seq_length])
UpperCAmelCase__ : Union[str, Any] = None
if self.use_token_type_ids:
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size)
UpperCAmelCase__ : int = None
UpperCAmelCase__ : List[Any] = None
UpperCAmelCase__ : Any = None
if self.use_labels:
UpperCAmelCase__ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size)
UpperCAmelCase__ : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
UpperCAmelCase__ : Optional[int] = ids_tensor([self.batch_size] , self.num_choices)
UpperCAmelCase__ : str = 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 , 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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : str = TFMobileBertModel(config=_lowerCamelCase)
UpperCAmelCase__ : Dict = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : int = model(_lowerCamelCase)
UpperCAmelCase__ : Dict = [input_ids, input_mask]
UpperCAmelCase__ : List[Any] = model(_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = model(_lowerCamelCase)
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 snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : List[str] = TFMobileBertForMaskedLM(config=_lowerCamelCase)
UpperCAmelCase__ : List[str] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : Dict = model(_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = TFMobileBertForNextSentencePrediction(config=_lowerCamelCase)
UpperCAmelCase__ : str = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : List[str] = model(_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2))
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : List[str] = TFMobileBertForPreTraining(config=_lowerCamelCase)
UpperCAmelCase__ : Optional[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : Any = model(_lowerCamelCase)
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 snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : Any = self.num_labels
UpperCAmelCase__ : Optional[Any] = TFMobileBertForSequenceClassification(config=_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : Tuple = model(_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : Tuple = self.num_choices
UpperCAmelCase__ : Dict = TFMobileBertForMultipleChoice(config=_lowerCamelCase)
UpperCAmelCase__ : int = tf.tile(tf.expand_dims(_lowerCamelCase , 1) , (1, self.num_choices, 1))
UpperCAmelCase__ : str = tf.tile(tf.expand_dims(_lowerCamelCase , 1) , (1, self.num_choices, 1))
UpperCAmelCase__ : Optional[Any] = tf.tile(tf.expand_dims(_lowerCamelCase , 1) , (1, self.num_choices, 1))
UpperCAmelCase__ : Optional[int] = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
UpperCAmelCase__ : List[str] = model(_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices))
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : List[Any] = self.num_labels
UpperCAmelCase__ : Optional[Any] = TFMobileBertForTokenClassification(config=_lowerCamelCase)
UpperCAmelCase__ : Dict = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : List[Any] = model(_lowerCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : Any = TFMobileBertForQuestionAnswering(config=_lowerCamelCase)
UpperCAmelCase__ : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase__ : List[str] = model(_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 snake_case__ ( self):
UpperCAmelCase__ : Any = self.prepare_config_and_inputs()
(
(
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) , (
UpperCAmelCase__
) ,
) : Any = config_and_inputs
UpperCAmelCase__ : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
def snake_case__ ( self):
UpperCAmelCase__ : str = TFMobileBertModelTest.TFMobileBertModelTester(self)
UpperCAmelCase__ : List[Any] = ConfigTester(self , config_class=_lowerCamelCase , hidden_size=37)
def snake_case__ ( self):
self.config_tester.run_common_tests()
def snake_case__ ( self):
UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_lowerCamelCase)
def snake_case__ ( self):
UpperCAmelCase__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*_lowerCamelCase)
@slow
def snake_case__ ( self):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
UpperCAmelCase__ : Optional[Any] = TFMobileBertModel.from_pretrained(_lowerCamelCase)
self.assertIsNotNone(_lowerCamelCase)
@require_tf
class _snake_case ( unittest.TestCase ):
@slow
def snake_case__ ( self):
UpperCAmelCase__ : Any = TFMobileBertForPreTraining.from_pretrained("""google/mobilebert-uncased""")
UpperCAmelCase__ : str = tf.constant([[0, 1, 2, 3, 4, 5]])
UpperCAmelCase__ : Optional[int] = model(_lowerCamelCase)[0]
UpperCAmelCase__ : List[str] = [1, 6, 3_0522]
self.assertEqual(output.shape , _lowerCamelCase)
UpperCAmelCase__ : List[Any] = tf.constant(
[
[
[-4.5919547, -9.248295, -9.645256],
[-6.7306175, -6.440284, -6.6052837],
[-7.2743506, -6.7847915, -6.024673],
]
])
tf.debugging.assert_near(output[:, :3, :3] , _lowerCamelCase , atol=1e-4)
| 113
| 1
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( __snake_case , __snake_case , unittest.TestCase ):
_lowerCAmelCase = CycleDiffusionPipeline
_lowerCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
"negative_prompt",
"height",
"width",
"negative_prompt_embeds",
}
_lowerCAmelCase = PipelineTesterMixin.required_optional_params - {"latents"}
_lowerCAmelCase = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"} )
_lowerCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
_lowerCAmelCase = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase__(self ):
'''simple docstring'''
torch.manual_seed(0 )
__a : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__a : Any = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , num_train_timesteps=1000 , clip_sample=_lowercase , set_alpha_to_one=_lowercase , )
torch.manual_seed(0 )
__a : Dict = 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 )
__a : int = 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=1000 , )
__a : Tuple = CLIPTextModel(_lowercase )
__a : List[str] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__a : List[Any] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCAmelCase__(self , _lowercase , _lowercase=0 ):
'''simple docstring'''
__a : Tuple = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowercase ) ).to(_lowercase )
__a : List[str] = image / 2 + 0.5
if str(_lowercase ).startswith("""mps""" ):
__a : str = torch.manual_seed(_lowercase )
else:
__a : Optional[Any] = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
__a : Dict = {
"""prompt""": """An astronaut riding an elephant""",
"""source_prompt""": """An astronaut riding a horse""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""eta""": 0.1,
"""strength""": 0.8,
"""guidance_scale""": 3,
"""source_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Union[str, Any] = """cpu""" # ensure determinism for the device-dependent torch.Generator
__a : Tuple = self.get_dummy_components()
__a : Optional[int] = CycleDiffusionPipeline(**_lowercase )
__a : Dict = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
__a : Union[str, Any] = self.get_dummy_inputs(_lowercase )
__a : Optional[Any] = pipe(**_lowercase )
__a : Optional[Any] = output.images
__a : Any = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__a : Dict = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Dict = self.get_dummy_components()
for name, module in components.items():
if hasattr(_lowercase , """half""" ):
__a : Optional[int] = module.half()
__a : List[Any] = CycleDiffusionPipeline(**_lowercase )
__a : Tuple = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
__a : List[str] = self.get_dummy_inputs(_lowercase )
__a : Optional[Any] = pipe(**_lowercase )
__a : str = output.images
__a : int = images[0, -3:, -3:, -1]
assert images.shape == (1, 32, 32, 3)
__a : Tuple = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def lowerCAmelCase__(self ):
'''simple docstring'''
return super().test_save_load_local()
@unittest.skip("""non-deterministic pipeline""" )
def lowerCAmelCase__(self ):
'''simple docstring'''
return super().test_inference_batch_single_identical()
@skip_mps
def lowerCAmelCase__(self ):
'''simple docstring'''
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def lowerCAmelCase__(self ):
'''simple docstring'''
return super().test_save_load_optional_components()
@skip_mps
def lowerCAmelCase__(self ):
'''simple docstring'''
return super().test_attention_slicing_forward_pass()
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def lowerCAmelCase__(self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : List[str] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
__a : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy""" )
__a : str = init_image.resize((512, 512) )
__a : str = """CompVis/stable-diffusion-v1-4"""
__a : Tuple = DDIMScheduler.from_pretrained(_lowercase , subfolder="""scheduler""" )
__a : str = CycleDiffusionPipeline.from_pretrained(
_lowercase , scheduler=_lowercase , safety_checker=_lowercase , torch_dtype=torch.floataa , revision="""fp16""" )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
__a : List[str] = """A black colored car"""
__a : List[str] = """A blue colored car"""
__a : Tuple = torch.manual_seed(0 )
__a : Any = pipe(
prompt=_lowercase , source_prompt=_lowercase , image=_lowercase , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=_lowercase , output_type="""np""" , )
__a : Tuple = output.images
# the values aren't exactly equal, but the images look the same visually
assert np.abs(image - expected_image ).max() < 5e-1
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/cycle-diffusion/black_colored_car.png""" )
__a : List[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy""" )
__a : Optional[int] = init_image.resize((512, 512) )
__a : Union[str, Any] = """CompVis/stable-diffusion-v1-4"""
__a : Optional[Any] = DDIMScheduler.from_pretrained(_lowercase , subfolder="""scheduler""" )
__a : Optional[Any] = CycleDiffusionPipeline.from_pretrained(_lowercase , scheduler=_lowercase , safety_checker=_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
pipe.enable_attention_slicing()
__a : Optional[Any] = """A black colored car"""
__a : int = """A blue colored car"""
__a : Union[str, Any] = torch.manual_seed(0 )
__a : Dict = pipe(
prompt=_lowercase , source_prompt=_lowercase , image=_lowercase , num_inference_steps=100 , eta=0.1 , strength=0.85 , guidance_scale=3 , source_guidance_scale=1 , generator=_lowercase , output_type="""np""" , )
__a : Any = output.images
assert np.abs(image - expected_image ).max() < 2e-2
| 581
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase__ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 581
| 1
|
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 ):
"""simple docstring"""
@slow
def __a ( self : int ):
'''simple docstring'''
__a = XLMRobertaModel.from_pretrained("""xlm-roberta-base""" )
__a = 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
__a = torch.Size((1, 1_2, 7_6_8) ) # batch_size, sequence_length, embedding_vector_dim
__a = torch.tensor(
[[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )
# 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():
__a = model(SCREAMING_SNAKE_CASE__ )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , SCREAMING_SNAKE_CASE__ , atol=1E-3 ) )
@slow
def __a ( self : int ):
'''simple docstring'''
__a = XLMRobertaModel.from_pretrained("""xlm-roberta-large""" )
__a = 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
__a = torch.Size((1, 1_2, 1_0_2_4) ) # batch_size, sequence_length, embedding_vector_dim
__a = torch.tensor(
[[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]] )
# 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():
__a = model(SCREAMING_SNAKE_CASE__ )["""last_hidden_state"""].detach()
self.assertEqual(output.shape , SCREAMING_SNAKE_CASE__ )
# compare the actual values for a slice of last dim
self.assertTrue(torch.allclose(output[:, :, -1] , SCREAMING_SNAKE_CASE__ , atol=1E-3 ) )
| 708
|
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str]=1_3 , SCREAMING_SNAKE_CASE__ : int=3_0 , SCREAMING_SNAKE_CASE__ : List[str]=2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=3 , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : Dict=True , SCREAMING_SNAKE_CASE__ : str=3_2 , SCREAMING_SNAKE_CASE__ : Tuple=5 , SCREAMING_SNAKE_CASE__ : List[Any]=4 , SCREAMING_SNAKE_CASE__ : Dict=3_7 , SCREAMING_SNAKE_CASE__ : Any="gelu" , SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.1 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1_0 , SCREAMING_SNAKE_CASE__ : str=0.0_2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : Any=2 , ):
'''simple docstring'''
__a = parent
__a = batch_size
__a = image_size
__a = patch_size
__a = num_channels
__a = is_training
__a = use_labels
__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 = type_sequence_label_size
__a = initializer_range
__a = scope
__a = encoder_stride
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
__a = (image_size // patch_size) ** 2
__a = num_patches + 1
def __a ( self : Any ):
'''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.type_sequence_label_size )
__a = self.get_config()
return config, pixel_values, labels
def __a ( self : Optional[int] ):
'''simple docstring'''
return ViTConfig(
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=SCREAMING_SNAKE_CASE__ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __a ( self : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Optional[Any] ):
'''simple docstring'''
__a = ViTModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self : str , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
__a = ViTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__a = 1
__a = ViTForMaskedImageModeling(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__a = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __a ( self : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict ):
'''simple docstring'''
__a = self.type_sequence_label_size
__a = ViTForImageClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__a = 1
__a = ViTForImageClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
__a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__a = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __a ( self : str ):
'''simple docstring'''
__a = self.prepare_config_and_inputs()
(
(
__a
) , (
__a
) , (
__a
) ,
) = config_and_inputs
__a = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase_ ( snake_case__ , snake_case__ , unittest.TestCase ):
"""simple docstring"""
a_ :str =(
(
ViTModel,
ViTForImageClassification,
ViTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
a_ :Tuple =(
{"""feature-extraction""": ViTModel, """image-classification""": ViTForImageClassification}
if is_torch_available()
else {}
)
a_ :List[str] =True
a_ :str =False
a_ :Optional[int] =False
a_ :Tuple =False
def __a ( self : str ):
'''simple docstring'''
__a = ViTModelTester(self )
__a = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , has_text_modality=SCREAMING_SNAKE_CASE__ , hidden_size=3_7 )
def __a ( self : Dict ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""ViT does not use inputs_embeds""" )
def __a ( self : List[Any] ):
'''simple docstring'''
pass
def __a ( self : int ):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(SCREAMING_SNAKE_CASE__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__ , nn.Linear ) )
def __a ( self : int ):
'''simple docstring'''
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__a = model_class(SCREAMING_SNAKE_CASE__ )
__a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__a = [*signature.parameters.keys()]
__a = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , SCREAMING_SNAKE_CASE__ )
def __a ( self : List[str] ):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__ )
def __a ( self : Union[str, Any] ):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__ )
def __a ( self : Optional[Any] ):
'''simple docstring'''
__a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__ )
@slow
def __a ( self : List[str] ):
'''simple docstring'''
for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__a = ViTModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
def __lowercase ( ) -> int:
"""simple docstring"""
__a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def __a ( self : str ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained("""google/vit-base-patch16-224""" ) if is_vision_available() else None
@slow
def __a ( self : Dict ):
'''simple docstring'''
__a = ViTForImageClassification.from_pretrained("""google/vit-base-patch16-224""" ).to(SCREAMING_SNAKE_CASE__ )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" ).to(SCREAMING_SNAKE_CASE__ )
# forward pass
with torch.no_grad():
__a = model(**SCREAMING_SNAKE_CASE__ )
# verify the logits
__a = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , SCREAMING_SNAKE_CASE__ )
__a = torch.tensor([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6] ).to(SCREAMING_SNAKE_CASE__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
@slow
def __a ( self : int ):
'''simple docstring'''
__a = ViTModel.from_pretrained("""facebook/dino-vits8""" ).to(SCREAMING_SNAKE_CASE__ )
__a = ViTImageProcessor.from_pretrained("""facebook/dino-vits8""" , size=4_8_0 )
__a = prepare_img()
__a = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" )
__a = inputs.pixel_values.to(SCREAMING_SNAKE_CASE__ )
# forward pass
with torch.no_grad():
__a = model(SCREAMING_SNAKE_CASE__ , interpolate_pos_encoding=SCREAMING_SNAKE_CASE__ )
# verify the logits
__a = torch.Size((1, 3_6_0_1, 3_8_4) )
self.assertEqual(outputs.last_hidden_state.shape , SCREAMING_SNAKE_CASE__ )
__a = torch.tensor(
[[4.2_3_4_0, 4.3_9_0_6, -6.6_6_9_2], [4.5_4_6_3, 1.8_9_2_8, -6.7_2_5_7], [4.4_4_2_9, 0.8_4_9_6, -5.8_5_8_5]] ).to(SCREAMING_SNAKE_CASE__ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE__ , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __a ( self : Optional[Any] ):
'''simple docstring'''
__a = ViTModel.from_pretrained("""facebook/dino-vits8""" , torch_dtype=torch.floataa , device_map="""auto""" )
__a = self.default_image_processor
__a = prepare_img()
__a = image_processor(images=SCREAMING_SNAKE_CASE__ , return_tensors="""pt""" )
__a = inputs.pixel_values.to(SCREAMING_SNAKE_CASE__ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
__a = model(SCREAMING_SNAKE_CASE__ )
| 201
| 0
|
import torch
from transformers import AutoModel
class lowercase__ (torch.nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , __a : int="sayef/fsner-bert-base-uncased" ):
super(__a , self ).__init__()
snake_case__ : str = AutoModel.from_pretrained(__a , return_dict=__a )
snake_case__ : Dict = torch.nn.CosineSimilarity(3 , 1e-08 )
snake_case__ : Any = torch.nn.Softmax(dim=1 )
def lowercase ( self : str , **__a : Optional[int] ):
return self.bert(**__a ).last_hidden_state
def lowercase ( self : int , __a : Any ):
return token_embeddings.sum(2 , keepdim=__a )
def lowercase ( self : Tuple , __a : Optional[Any] , __a : str , __a : int=1 ):
return self.softmax(T * self.cos(__a , __a ) )
def lowercase ( self : List[str] , __a : List[str] , __a : List[str] ):
snake_case__ : Union[str, Any] = W_supports["""sizes"""].tolist()
snake_case__ : int = W_supports["""start_token_id"""].item()
snake_case__ : Union[str, Any] = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
snake_case__ : Tuple = self.BERT(**__a )
snake_case__ : Tuple = self.BERT(**__a )
snake_case__ : List[str] = None
snake_case__ : List[Any] = None
snake_case__ : Tuple = W_supports["""input_ids"""] == start_token_id
snake_case__ : Dict = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(__a ):
if i == 0:
snake_case__ : Any = 0
else:
snake_case__ : Tuple = support_sizes[i - 1]
snake_case__ : Optional[Any] = S[s : s + size][start_token_masks[s : s + size]]
snake_case__ : List[Any] = S[s : s + size][end_token_masks[s : s + size]]
snake_case__ : str = torch.matmul(q[i] , s_start.T ).sum(1 ).softmax(0 )
snake_case__ : Tuple = torch.matmul(q[i] , s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
snake_case__ : Optional[int] = torch.vstack((p_starts, p_start) )
snake_case__ : List[str] = torch.vstack((p_ends, p_end) )
else:
snake_case__ : Optional[int] = p_start
snake_case__ : List[Any] = p_end
return p_starts, p_ends
| 648
|
from __future__ import annotations
from math import pi
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError("""One and only one argument must be 0""")
if inductance < 0:
raise ValueError("""Inductance cannot be negative""")
if frequency < 0:
raise ValueError("""Frequency cannot be negative""")
if reactance < 0:
raise ValueError("""Inductive reactance cannot be negative""")
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("""Exactly one argument must be 0""")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 648
| 1
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Any:
__SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
__SCREAMING_SNAKE_CASE = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"的",
"价",
"格",
"是",
"15",
"便",
"alex",
"##andra",
"，",
"。",
"-",
"t",
"shirt",
]
__SCREAMING_SNAKE_CASE = 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] ) )
__SCREAMING_SNAKE_CASE = {
"do_resize": True,
"size": {"height": 2_24, "width": 2_24},
"do_center_crop": True,
"crop_size": {"height": 18, "width": 18},
"do_normalize": True,
"image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
"image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
"do_convert_rgb": True,
}
__SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname, A_ )
with open(self.image_processor_file, "w", encoding="utf-8" ) as fp:
json.dump(A_, A_ )
def __lowerCAmelCase ( self, **_a ) -> List[Any]:
return BertTokenizer.from_pretrained(self.tmpdirname, **A_ )
def __lowerCAmelCase ( self, **_a ) -> List[Any]:
return BertTokenizerFast.from_pretrained(self.tmpdirname, **A_ )
def __lowerCAmelCase ( self, **_a ) -> List[str]:
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname, **A_ )
def __lowerCAmelCase ( self ) -> str:
shutil.rmtree(self.tmpdirname )
def __lowerCAmelCase ( self ) -> int:
__SCREAMING_SNAKE_CASE = [np.random.randint(2_55, size=(3, 30, 4_00), dtype=np.uinta )]
__SCREAMING_SNAKE_CASE = [Image.fromarray(np.moveaxis(A_, 0, -1 ) ) for x in image_inputs]
return image_inputs
def __lowerCAmelCase ( self ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
processor_slow.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor.from_pretrained(self.tmpdirname, use_fast=A_ )
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
processor_fast.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor.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, A_ )
self.assertIsInstance(processor_fast.tokenizer, A_ )
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, A_ )
self.assertIsInstance(processor_fast.image_processor, A_ )
def __lowerCAmelCase ( self ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__SCREAMING_SNAKE_CASE = self.get_tokenizer(cls_token="(CLS)", sep_token="(SEP)" )
__SCREAMING_SNAKE_CASE = self.get_image_processor(do_normalize=A_ )
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor.from_pretrained(
self.tmpdirname, cls_token="(CLS)", sep_token="(SEP)", do_normalize=A_ )
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer, A_ )
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor, A_ )
def __lowerCAmelCase ( self ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = image_processor(A_, return_tensors="np" )
__SCREAMING_SNAKE_CASE = processor(images=A_, 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 __lowerCAmelCase ( self ) -> List[str]:
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
__SCREAMING_SNAKE_CASE = "Alexandra，T-shirt的价格是15便士。"
__SCREAMING_SNAKE_CASE = processor(text=A_ )
__SCREAMING_SNAKE_CASE = tokenizer(A_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key], encoded_processor[key] )
def __lowerCAmelCase ( self ) -> Dict:
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
__SCREAMING_SNAKE_CASE = "Alexandra，T-shirt的价格是15便士。"
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=A_, images=A_ )
self.assertListEqual(list(inputs.keys() ), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(A_ ):
processor()
def __lowerCAmelCase ( self ) -> Tuple:
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
__SCREAMING_SNAKE_CASE = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__SCREAMING_SNAKE_CASE = processor.batch_decode(A_ )
__SCREAMING_SNAKE_CASE = tokenizer.batch_decode(A_ )
self.assertListEqual(A_, A_ )
def __lowerCAmelCase ( self ) -> str:
__SCREAMING_SNAKE_CASE = self.get_image_processor()
__SCREAMING_SNAKE_CASE = self.get_tokenizer()
__SCREAMING_SNAKE_CASE = ChineseCLIPProcessor(tokenizer=A_, image_processor=A_ )
__SCREAMING_SNAKE_CASE = "Alexandra，T-shirt的价格是15便士。"
__SCREAMING_SNAKE_CASE = self.prepare_image_inputs()
__SCREAMING_SNAKE_CASE = processor(text=A_, images=A_ )
self.assertListEqual(list(inputs.keys() ), processor.model_input_names )
| 716
|
def _A ( __snake_case :list[int] ) -> float:
"""simple docstring"""
if not nums: # Makes sure that the list is not empty
raise ValueError("List is empty" )
__SCREAMING_SNAKE_CASE = sum(__snake_case ) / len(__snake_case ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 214
| 0
|
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def __UpperCamelCase ( snake_case__ ):
@wraps(snake_case__ )
def _inner_fn(*snake_case__ , **snake_case__ ):
warnings.warn(
(F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""") , snake_case__ , )
return fn(*snake_case__ , **snake_case__ )
return _inner_fn
| 180
|
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def __UpperCamelCase ( snake_case__ ):
return 1.0 / (1.0 + np.exp(-_outputs ))
def __UpperCamelCase ( snake_case__ ):
A_ : Union[str, Any] = np.max(_outputs , axis=-1 , keepdims=snake_case__ )
A_ : str = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=snake_case__ )
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Any = """sigmoid"""
_A : Any = """softmax"""
_A : Union[str, Any] = """none"""
@add_end_docstrings(
_SCREAMING_SNAKE_CASE , R"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""" , )
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Optional[int] = False
_A : Dict = ClassificationFunction.NONE
def __init__(self , **lowerCAmelCase_ ):
super().__init__(**lowerCAmelCase_ )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase(self , lowerCAmelCase_=None , lowerCAmelCase_=None , lowerCAmelCase_="" , **lowerCAmelCase_ ):
# Using "" as default argument because we're going to use `top_k=None` in user code to declare
# "No top_k"
A_ : Union[str, Any] = tokenizer_kwargs
A_ : List[str] = {}
if hasattr(self.model.config , """return_all_scores""" ) and return_all_scores is None:
A_ : Optional[int] = self.model.config.return_all_scores
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) or top_k is None:
A_ : Dict = top_k
A_ : Any = False
elif return_all_scores is not None:
warnings.warn(
"""`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"""
""" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.""" , lowerCAmelCase_ , )
if return_all_scores:
A_ : List[Any] = None
else:
A_ : List[str] = 1
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
A_ : str = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
A_ : List[str] = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__(self , *lowerCAmelCase_ , **lowerCAmelCase_ ):
A_ : List[str] = super().__call__(*lowerCAmelCase_ , **lowerCAmelCase_ )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
A_ : Union[str, Any] = """top_k""" not in kwargs
if isinstance(args[0] , lowerCAmelCase_ ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase(self , lowerCAmelCase_ , **lowerCAmelCase_ ):
A_ : Union[str, Any] = self.framework
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return self.tokenizer(**lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and len(lowerCAmelCase_ ) == 1 and isinstance(inputs[0] , lowerCAmelCase_ ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0] , text_pair=inputs[0][1] , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"""The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
""" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.""" )
return self.tokenizer(lowerCAmelCase_ , return_tensors=lowerCAmelCase_ , **lowerCAmelCase_ )
def lowerCamelCase(self , lowerCAmelCase_ ):
return self.model(**lowerCAmelCase_ )
def lowerCamelCase(self , lowerCAmelCase_ , lowerCAmelCase_=None , lowerCAmelCase_=1 , lowerCAmelCase_=True ):
# `_legacy` is used to determine if we're running the naked pipeline and in backward
# compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
# the more natural result containing the list.
# Default value before `set_parameters`
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
A_ : Optional[Any] = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
A_ : Tuple = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config , """function_to_apply""" ) and function_to_apply is None:
A_ : Any = self.model.config.function_to_apply
else:
A_ : Dict = ClassificationFunction.NONE
A_ : Optional[Any] = model_outputs["""logits"""][0]
A_ : Tuple = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
A_ : str = sigmoid(lowerCAmelCase_ )
elif function_to_apply == ClassificationFunction.SOFTMAX:
A_ : Dict = softmax(lowerCAmelCase_ )
elif function_to_apply == ClassificationFunction.NONE:
A_ : Optional[int] = outputs
else:
raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""" )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
A_ : Optional[Any] = [
{"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(lowerCAmelCase_ )
]
if not _legacy:
dict_scores.sort(key=lambda lowerCAmelCase_ : x["score"] , reverse=lowerCAmelCase_ )
if top_k is not None:
A_ : str = dict_scores[:top_k]
return dict_scores
| 180
| 1
|
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : float , lowercase : float ) -> float:
'''simple docstring'''
return round(float(moles / volume ) * nfactor )
def _lowerCamelCase ( lowercase : float , lowercase : float , lowercase : float ) -> float:
'''simple docstring'''
return round(float((moles * 0.08_21 * temperature) / (volume) ) )
def _lowerCamelCase ( lowercase : float , lowercase : float , lowercase : float ) -> float:
'''simple docstring'''
return round(float((moles * 0.08_21 * temperature) / (pressure) ) )
def _lowerCamelCase ( lowercase : float , lowercase : float , lowercase : float ) -> float:
'''simple docstring'''
return round(float((pressure * volume) / (0.08_21 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 710
|
'''simple docstring'''
def _lowerCamelCase ( lowercase : int , lowercase : int ) -> int:
return 1 if input_a == input_a else 0
def _lowerCamelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 521
| 0
|
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_lowerCamelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
_lowerCamelCase : Optional[Any] = '\n Examples:\n ```py\n >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n >>> pipe_prior.to("cuda")\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> zero_image_emb = out.negative_image_embeds\n >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n >>> pipe.to("cuda")\n >>> image = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=50,\n ... ).images\n >>> image[0].save("cat.png")\n ```\n'
def _lowerCAmelCase ( __magic_name__ :Tuple , __magic_name__ :Tuple , __magic_name__ :Union[str, Any]=8 ):
UpperCAmelCase_ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
UpperCAmelCase_ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class snake_case__ ( __snake_case ):
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCAmelCase_ : UNetaDConditionModel , lowerCAmelCase_ : DDPMScheduler , lowerCAmelCase_ : VQModel , ) -> Tuple:
super().__init__()
self.register_modules(
unet=lowerCAmelCase_ , scheduler=lowerCAmelCase_ , movq=lowerCAmelCase_ , )
UpperCAmelCase_ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCamelCase ( self : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : Any , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
if latents is None:
UpperCAmelCase_ = randn_tensor(lowerCAmelCase_ , generator=lowerCAmelCase_ , device=lowerCAmelCase_ , dtype=lowerCAmelCase_ )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
UpperCAmelCase_ = latents.to(lowerCAmelCase_ )
UpperCAmelCase_ = latents * scheduler.init_noise_sigma
return latents
def UpperCamelCase ( self : List[str] , lowerCAmelCase_ : Optional[Any]=0 ) -> List[str]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
UpperCAmelCase_ = torch.device(F'''cuda:{gpu_id}''' )
UpperCAmelCase_ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase_ , lowerCAmelCase_ )
def UpperCamelCase ( self : Tuple , lowerCAmelCase_ : Dict=0 ) -> int:
if is_accelerate_available() and is_accelerate_version('''>=''' , '''0.17.0.dev0''' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.''' )
UpperCAmelCase_ = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to('''cpu''' , silence_dtype_warnings=lowerCAmelCase_ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase_ = None
for cpu_offloaded_model in [self.unet, self.movq]:
UpperCAmelCase_, UpperCAmelCase_ = cpu_offload_with_hook(lowerCAmelCase_ , lowerCAmelCase_ , prev_module_hook=lowerCAmelCase_ )
# We'll offload the last model manually.
UpperCAmelCase_ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCamelCase ( self : List[Any] ) -> int:
if not hasattr(self.unet , '''_hf_hook''' ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCAmelCase_ , '''_hf_hook''' )
and hasattr(module._hf_hook , '''execution_device''' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCAmelCase_ )
def __call__( self : Tuple , lowerCAmelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCAmelCase_ : Union[torch.FloatTensor, List[torch.FloatTensor]] , lowerCAmelCase_ : int = 5_12 , lowerCAmelCase_ : int = 5_12 , lowerCAmelCase_ : int = 1_00 , lowerCAmelCase_ : float = 4.0 , lowerCAmelCase_ : int = 1 , lowerCAmelCase_ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , lowerCAmelCase_ : Optional[torch.FloatTensor] = None , lowerCAmelCase_ : Optional[str] = "pil" , lowerCAmelCase_ : bool = True , ) -> Union[str, Any]:
UpperCAmelCase_ = self._execution_device
UpperCAmelCase_ = guidance_scale > 1.0
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ = torch.cat(lowerCAmelCase_ , dim=0 )
UpperCAmelCase_ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
UpperCAmelCase_ = torch.cat(lowerCAmelCase_ , dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase_ = image_embeds.repeat_interleave(lowerCAmelCase_ , dim=0 )
UpperCAmelCase_ = negative_image_embeds.repeat_interleave(lowerCAmelCase_ , dim=0 )
UpperCAmelCase_ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCAmelCase_ )
self.scheduler.set_timesteps(lowerCAmelCase_ , device=lowerCAmelCase_ )
UpperCAmelCase_ = self.scheduler.timesteps
UpperCAmelCase_ = self.unet.config.in_channels
UpperCAmelCase_, UpperCAmelCase_ = downscale_height_and_width(lowerCAmelCase_ , lowerCAmelCase_ , self.movq_scale_factor )
# create initial latent
UpperCAmelCase_ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCAmelCase_ ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase_ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase_ = {'''image_embeds''': image_embeds}
UpperCAmelCase_ = self.unet(
sample=lowerCAmelCase_ , timestep=lowerCAmelCase_ , encoder_hidden_states=lowerCAmelCase_ , added_cond_kwargs=lowerCAmelCase_ , return_dict=lowerCAmelCase_ , )[0]
if do_classifier_free_guidance:
UpperCAmelCase_, UpperCAmelCase_ = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase_, UpperCAmelCase_ = noise_pred.chunk(2 )
UpperCAmelCase_, UpperCAmelCase_ = variance_pred.chunk(2 )
UpperCAmelCase_ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase_ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , '''variance_type''' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase_, UpperCAmelCase_ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase_ = self.scheduler.step(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , generator=lowerCAmelCase_ , )[0]
# post-processing
UpperCAmelCase_ = self.movq.decode(lowerCAmelCase_ , force_not_quantize=lowerCAmelCase_ )['''sample''']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
UpperCAmelCase_ = image * 0.5 + 0.5
UpperCAmelCase_ = image.clamp(0 , 1 )
UpperCAmelCase_ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase_ = self.numpy_to_pil(lowerCAmelCase_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase_ )
| 121
|
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _lowerCAmelCase ( __magic_name__ :Optional[Any] ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , '''parameters.json''' )
UpperCAmelCase_ = json.loads(open(__magic_name__ ).read() )
if not params:
raise ValueError(
F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith('''.pt''' ):
UpperCAmelCase_ = args.output + '''.pt'''
UpperCAmelCase_ = OrderedDict()
with tf.device('''/CPU:0''' ):
UpperCAmelCase_ = tf.train.load_checkpoint(args.tf_model_dir )
UpperCAmelCase_ = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
UpperCAmelCase_ = reader.get_tensor(__magic_name__ ).astype(np.floataa )
if key_name.endswith('''/adam_m''' ) or key_name.endswith('''/adam_v''' ):
continue
if key_name.startswith('''pasts/''' ):
if key_name.startswith('''pasts/mlp''' ):
UpperCAmelCase_ = int(key_name[9] )
elif key_name.startswith('''pasts/out''' ):
UpperCAmelCase_ = 8
UpperCAmelCase_ = '''model.sqout.%d.weight''' % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
UpperCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.startswith('''model/moe''' ):
UpperCAmelCase_ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/switch_gating/kernel''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
UpperCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/softmlp/kernel''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
UpperCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/wo/kernel''' ) or key_name.endswith('''/wi/kernel''' ):
UpperCAmelCase_ = key_name[-9:-7]
for i in range(1_6 ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
UpperCAmelCase_ = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.startswith('''model/mlp''' ):
UpperCAmelCase_ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/p1/kernel''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
UpperCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/p1/bias''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/p2/kernel''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
UpperCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/p2/bias''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.startswith('''model/ln''' ):
UpperCAmelCase_ = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.norm.bias''' % player
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/g''' ):
UpperCAmelCase_ = '''model.blocks.%d.feed_forward.norm.weight''' % player
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.startswith('''model/att''' ):
UpperCAmelCase_ = int(key_name[9:].split('''/''' )[0] )
if key_name.endswith('''/qkv/kernel''' ):
UpperCAmelCase_ = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
UpperCAmelCase_ = state[:, 0, :, :]
UpperCAmelCase_ = state[:, 1, :, :]
UpperCAmelCase_ = state[:, 2, :, :]
UpperCAmelCase_ = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
UpperCAmelCase_ = torch.tensor(__magic_name__ )
UpperCAmelCase_ = '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
UpperCAmelCase_ = torch.tensor(__magic_name__ )
UpperCAmelCase_ = '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/o/kernel''' ):
UpperCAmelCase_ = '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
UpperCAmelCase_ = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.startswith('''model/an''' ):
UpperCAmelCase_ = int(key_name[8:].split('''/''' )[0] )
if key_name.endswith('''/b''' ):
UpperCAmelCase_ = '''model.blocks.%d.self_attn.norm.bias''' % player
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.endswith('''/g''' ):
UpperCAmelCase_ = '''model.blocks.%d.self_attn.norm.weight''' % player
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif (
key_name.startswith('''model/wte''' )
or key_name.startswith('''model/wpe''' )
or key_name.startswith('''model/ete''' )
):
UpperCAmelCase_ = {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
key_name[-3:]
]
UpperCAmelCase_ = '''model.%s.weight''' % nlayer
UpperCAmelCase_ = vnp.copy() # same in embedded
UpperCAmelCase_ = torch.tensor(__magic_name__ )
if key_name.startswith('''model/wte''' ):
UpperCAmelCase_ = '''lm_head.weight'''
UpperCAmelCase_ = vnp.copy() # same in embedded
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name.startswith('''model/wob''' ):
UpperCAmelCase_ = '''final_logits_bias'''
UpperCAmelCase_ = vnp.copy() # same in embedded
UpperCAmelCase_ = state.reshape((1, -1) )
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name == "model/dense/kernel":
UpperCAmelCase_ = '''model.last_project.weight'''
UpperCAmelCase_ = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
UpperCAmelCase_ = torch.tensor(__magic_name__ )
elif key_name == "model/dense_1/bias":
UpperCAmelCase_ = '''model.last_project.bias'''
UpperCAmelCase_ = vnp.copy() # same because it is one dimensional
UpperCAmelCase_ = torch.tensor(__magic_name__ )
torch.save(__magic_name__ , args.output )
if __name__ == "__main__":
_lowerCamelCase : Dict = argparse.ArgumentParser(
description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
_lowerCamelCase : Optional[Any] = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 121
| 1
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__(self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=32 , _lowercase=5 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , ):
'''simple docstring'''
__a : Dict = parent
__a : Optional[int] = batch_size
__a : Any = seq_length
__a : Union[str, Any] = is_training
__a : Dict = use_attention_mask
__a : Union[str, Any] = use_token_type_ids
__a : str = use_labels
__a : Tuple = vocab_size
__a : Optional[Any] = hidden_size
__a : int = num_hidden_layers
__a : str = num_attention_heads
__a : Optional[int] = intermediate_size
__a : Optional[Any] = hidden_act
__a : Optional[int] = hidden_dropout_prob
__a : int = attention_probs_dropout_prob
__a : str = max_position_embeddings
__a : List[Any] = type_vocab_size
__a : Optional[int] = type_sequence_label_size
__a : Any = initializer_range
__a : Optional[Any] = num_choices
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__a : Optional[int] = None
if self.use_attention_mask:
__a : Any = random_attention_mask([self.batch_size, self.seq_length] )
__a : Optional[int] = None
if self.use_token_type_ids:
__a : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__a : Tuple = RobertaConfig(
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 , is_decoder=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Dict = self.prepare_config_and_inputs()
__a , __a , __a , __a : List[Any] = config_and_inputs
__a : str = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Optional[Any] = self.prepare_config_and_inputs()
__a , __a , __a , __a : Tuple = config_and_inputs
__a : Any = True
__a : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__a : List[str] = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class SCREAMING_SNAKE_CASE__ ( __snake_case , unittest.TestCase ):
_lowerCAmelCase = True
_lowerCAmelCase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase__(self ):
'''simple docstring'''
__a : Optional[int] = FlaxRobertaModelTester(self )
@slow
def lowerCAmelCase__(self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
__a : Optional[int] = model_class_name.from_pretrained("""roberta-base""" , from_pt=_lowercase )
__a : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
| 63
|
"""simple docstring"""
def __magic_name__ ( _lowerCamelCase : list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
__a : Any = sum(_lowerCamelCase ) / len(_lowerCamelCase ) # Calculate the average
return sum(abs(x - average ) for x in nums ) / len(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 63
| 1
|
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
A__ : List[str] = '''CompVis/stable-diffusion-v1-1'''
A__ : Any = '''CompVis/stable-diffusion-v1-2'''
A__ : Optional[int] = '''CompVis/stable-diffusion-v1-3'''
A__ : Dict = '''CompVis/stable-diffusion-v1-4'''
class snake_case__ ( _UpperCAmelCase ):
def __init__( self : Tuple , __a : List[Any] , __a : Optional[int] , __a : Optional[Any] , __a : Optional[int] , __a : int , __a : Tuple , __a : Any , __a : Any = True , ) -> Optional[Any]:
'''simple docstring'''
super()._init_()
__snake_case : Optional[Any] = StableDiffusionPipeline.from_pretrained(lowercase__ )
__snake_case : Optional[int] = StableDiffusionPipeline.from_pretrained(lowercase__ )
__snake_case : Optional[Any] = StableDiffusionPipeline.from_pretrained(lowercase__ )
__snake_case : Dict = StableDiffusionPipeline(
vae=lowercase__ , text_encoder=lowercase__ , tokenizer=lowercase__ , unet=lowercase__ , scheduler=lowercase__ , safety_checker=lowercase__ , feature_extractor=lowercase__ , requires_safety_checker=lowercase__ , )
self.register_modules(pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea , pipelinea=self.pipea )
@property
def A_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
return {k: getattr(self , lowercase__ ) for k in self.config.keys() if not k.startswith('_' )}
def A_ ( self : Union[str, Any] , __a : Dict = "auto" ) -> str:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__snake_case : int = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowercase__ )
def A_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
self.enable_attention_slicing(lowercase__ )
@torch.no_grad()
def A_ ( self : List[str] , __a : List[Any] , __a : Optional[Any] = 512 , __a : List[Any] = 512 , __a : List[str] = 50 , __a : int = 7.5 , __a : Dict = None , __a : List[str] = 1 , __a : List[Any] = 0.0 , __a : Optional[Any] = None , __a : Optional[int] = None , __a : int = "pil" , __a : Any = True , __a : List[str] = None , __a : Any = 1 , **__a : int , ) -> Tuple:
'''simple docstring'''
return self.pipea(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
@torch.no_grad()
def A_ ( self : Optional[Any] , __a : Dict , __a : int = 512 , __a : Optional[Any] = 512 , __a : List[str] = 50 , __a : Optional[Any] = 7.5 , __a : List[Any] = None , __a : Dict = 1 , __a : int = 0.0 , __a : Union[str, Any] = None , __a : Optional[int] = None , __a : Optional[int] = "pil" , __a : Dict = True , __a : int = None , __a : Union[str, Any] = 1 , **__a : List[Any] , ) -> Optional[int]:
'''simple docstring'''
return self.pipea(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
@torch.no_grad()
def A_ ( self : Optional[Any] , __a : Union[str, Any] , __a : str = 512 , __a : Dict = 512 , __a : Dict = 50 , __a : Union[str, Any] = 7.5 , __a : List[Any] = None , __a : List[str] = 1 , __a : Optional[Any] = 0.0 , __a : Any = None , __a : str = None , __a : Tuple = "pil" , __a : Optional[int] = True , __a : int = None , __a : Any = 1 , **__a : str , ) -> Dict:
'''simple docstring'''
return self.pipea(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
@torch.no_grad()
def A_ ( self : Any , __a : Any , __a : int = 512 , __a : Any = 512 , __a : Tuple = 50 , __a : Optional[Any] = 7.5 , __a : List[str] = None , __a : Optional[int] = 1 , __a : List[str] = 0.0 , __a : Optional[int] = None , __a : Tuple = None , __a : Optional[Any] = "pil" , __a : Union[str, Any] = True , __a : List[str] = None , __a : int = 1 , **__a : Union[str, Any] , ) -> str:
'''simple docstring'''
return self.pipea(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
@torch.no_grad()
def A_ ( self : int , __a : Union[str, Any] , __a : Any = 512 , __a : Any = 512 , __a : Optional[int] = 50 , __a : Dict = 7.5 , __a : Optional[int] = None , __a : str = 1 , __a : str = 0.0 , __a : Optional[Any] = None , __a : Optional[int] = None , __a : Optional[int] = "pil" , __a : Tuple = True , __a : Tuple = None , __a : List[Any] = 1 , **__a : str , ) -> Any:
'''simple docstring'''
__snake_case : Optional[int] = """cuda""" if torch.cuda.is_available() else """cpu"""
self.to(lowercase__ )
# Checks if the height and width are divisible by 8 or not
if height % 8 != 0 or width % 8 != 0:
raise ValueError(f'''`height` and `width` must be divisible by 8 but are {height} and {width}.''' )
# Get first result from Stable Diffusion Checkpoint v1.1
__snake_case : List[Any] = self.textaimg_sda_a(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
# Get first result from Stable Diffusion Checkpoint v1.2
__snake_case : int = self.textaimg_sda_a(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
# Get first result from Stable Diffusion Checkpoint v1.3
__snake_case : Any = self.textaimg_sda_a(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
# Get first result from Stable Diffusion Checkpoint v1.4
__snake_case : Tuple = self.textaimg_sda_a(
prompt=lowercase__ , height=lowercase__ , width=lowercase__ , num_inference_steps=lowercase__ , guidance_scale=lowercase__ , negative_prompt=lowercase__ , num_images_per_prompt=lowercase__ , eta=lowercase__ , generator=lowercase__ , latents=lowercase__ , output_type=lowercase__ , return_dict=lowercase__ , callback=lowercase__ , callback_steps=lowercase__ , **lowercase__ , )
# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]] )
| 286
|
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __lowercase :
"""simple docstring"""
_A : float
_A : TreeNode | None = None
_A : TreeNode | None = None
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : TreeNode | None ):
"""simple docstring"""
def is_valid_tree(SCREAMING_SNAKE_CASE__ : TreeNode | None ) -> bool:
if node is None:
return True
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
SCREAMING_SNAKE_CASE__ : TreeNode | None , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , SCREAMING_SNAKE_CASE__ , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , SCREAMING_SNAKE_CASE__ )
)
return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE__ , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 480
| 0
|
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
A = 'platform'
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def a(lowercase__ , lowercase__ , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , lowercase__=None , ):
'''simple docstring'''
if attention_mask is None:
snake_case_ = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
snake_case_ = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
snake_case_ = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case_ = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
snake_case_ = np.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": attention_mask,
}
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase=13 , __UpperCamelCase=7 , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=99 , __UpperCamelCase=16 , __UpperCamelCase=2 , __UpperCamelCase=4 , __UpperCamelCase=4 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=32 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=0.02 , ):
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = seq_length
snake_case_ = is_training
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_ = eos_token_id
snake_case_ = pad_token_id
snake_case_ = bos_token_id
snake_case_ = initializer_range
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
snake_case_ = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
snake_case_ = shift_tokens_right(_lowerCamelCase , 1 , 2 )
snake_case_ = BlenderbotConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=_lowerCamelCase , )
snake_case_ = prepare_blenderbot_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return config, inputs_dict
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.prepare_config_and_inputs()
return config, inputs_dict
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 20
snake_case_ = model_class_name(_lowerCamelCase )
snake_case_ = model.encode(inputs_dict['input_ids'] )
snake_case_ , snake_case_ = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
snake_case_ = model.init_cache(decoder_input_ids.shape[0] , _lowerCamelCase , _lowerCamelCase )
snake_case_ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype='i4' )
snake_case_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
snake_case_ = model.decode(
decoder_input_ids[:, :-1] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
snake_case_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
snake_case_ = model.decode(
decoder_input_ids[:, -1:] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_lowerCamelCase , )
snake_case_ = model.decode(_lowerCamelCase , _lowerCamelCase )
snake_case_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"""Max diff is {diff}""" )
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = 20
snake_case_ = model_class_name(_lowerCamelCase )
snake_case_ = model.encode(inputs_dict['input_ids'] )
snake_case_ , snake_case_ = (
inputs_dict['decoder_input_ids'],
inputs_dict['decoder_attention_mask'],
)
snake_case_ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
snake_case_ = model.init_cache(decoder_input_ids.shape[0] , _lowerCamelCase , _lowerCamelCase )
snake_case_ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
snake_case_ = model.decode(
decoder_input_ids[:, :-1] , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase , past_key_values=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
snake_case_ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype='i4' )
snake_case_ = model.decode(
decoder_input_ids[:, -1:] , _lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_lowerCamelCase , decoder_position_ids=_lowerCamelCase , )
snake_case_ = model.decode(_lowerCamelCase , _lowerCamelCase , decoder_attention_mask=_lowerCamelCase )
snake_case_ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
__A = 9_9
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
snake_case_ = input_ids.shape[0]
snake_case_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ , snake_case_ = self._get_config_and_data()
snake_case_ = FlaxBlenderbotForConditionalGeneration(_lowerCamelCase )
snake_case_ = lm_model(input_ids=_lowerCamelCase )
snake_case_ = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs['logits'].shape , _lowerCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
snake_case_ = FlaxBlenderbotForConditionalGeneration(_lowerCamelCase )
snake_case_ = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
snake_case_ = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
snake_case_ = lm_model(input_ids=_lowerCamelCase , decoder_input_ids=_lowerCamelCase )
snake_case_ = (*summary.shape, config.vocab_size)
self.assertEqual(outputs['logits'].shape , _lowerCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
snake_case_ = shift_tokens_right(_lowerCamelCase , 1 , 2 )
snake_case_ = np.equal(_lowerCamelCase , 1 ).astype(np.floataa ).sum()
snake_case_ = np.equal(_lowerCamelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(_lowerCamelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class SCREAMING_SNAKE_CASE ( __UpperCAmelCase , unittest.TestCase , __UpperCAmelCase ):
"""simple docstring"""
__A = True
__A = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
__A = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = FlaxBlenderbotModelTester(self )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case_ = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase )
snake_case_ = model_class(_lowerCamelCase )
@jax.jit
def encode_jitted(__UpperCamelCase , __UpperCamelCase=None , **__UpperCamelCase ):
return model.encode(input_ids=_lowerCamelCase , attention_mask=_lowerCamelCase )
with self.subTest('JIT Enabled' ):
snake_case_ = encode_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
snake_case_ = encode_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case_ = model_class(_lowerCamelCase )
snake_case_ = model.encode(inputs_dict['input_ids'] , inputs_dict['attention_mask'] )
snake_case_ = {
'decoder_input_ids': inputs_dict['decoder_input_ids'],
'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
'encoder_outputs': encoder_outputs,
}
@jax.jit
def decode_jitted(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
return model.decode(
decoder_input_ids=_lowerCamelCase , decoder_attention_mask=_lowerCamelCase , encoder_outputs=_lowerCamelCase , )
with self.subTest('JIT Enabled' ):
snake_case_ = decode_jitted(**_lowerCamelCase ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
snake_case_ = decode_jitted(**_lowerCamelCase ).to_tuple()
self.assertEqual(len(_lowerCamelCase ) , len(_lowerCamelCase ) )
for jitted_output, output in zip(_lowerCamelCase , _lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
snake_case_ = model_class_name.from_pretrained('facebook/blenderbot-400M-distill' )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
snake_case_ = np.ones((1, 1) ) * model.config.eos_token_id
snake_case_ = model(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
@unittest.skipUnless(jax_device != 'cpu' , '3B test too slow on CPU.' )
@slow
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ = {'num_beams': 1, 'early_stopping': True, 'min_length': 15, 'max_length': 25}
snake_case_ = {'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}
snake_case_ = FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' , from_pt=_lowerCamelCase )
snake_case_ = BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B' )
snake_case_ = ['Sam']
snake_case_ = tokenizer(_lowerCamelCase , return_tensors='jax' )
snake_case_ = model.generate(**_lowerCamelCase , **_lowerCamelCase )
snake_case_ = 'Sam is a great name. It means "sun" in Gaelic.'
snake_case_ = tokenizer.batch_decode(_lowerCamelCase , **_lowerCamelCase )
assert generated_txt[0].strip() == tgt_text
| 700
|
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model.
# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
# TapasConfig to False.
# initialize configuration from json file
snake_case_ = TapasConfig.from_json_file(lowercase__ )
# set absolute/relative position embeddings parameter
snake_case_ = reset_position_index_per_cell
# set remaining parameters of TapasConfig as well as the model based on the task
if task == "SQA":
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WTQ":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = True
# hparam_utils.py hparams
snake_case_ = 0.66_4694
snake_case_ = 0.20_7951
snake_case_ = 0.12_1194
snake_case_ = True
snake_case_ = True
snake_case_ = False
snake_case_ = 0.035_2513
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "WIKISQL_SUPERVISED":
# run_task_main.py hparams
snake_case_ = 4
snake_case_ = False
# hparam_utils.py hparams
snake_case_ = 36.4519
snake_case_ = 0.90_3421
snake_case_ = 222.088
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 0.76_3141
snake_case_ = TapasForQuestionAnswering(config=lowercase__ )
elif task == "TABFACT":
snake_case_ = TapasForSequenceClassification(config=lowercase__ )
elif task == "MLM":
snake_case_ = TapasForMaskedLM(config=lowercase__ )
elif task == "INTERMEDIATE_PRETRAINING":
snake_case_ = TapasModel(config=lowercase__ )
else:
raise ValueError(f"""Task {task} not supported.""" )
print(f"""Building PyTorch model from configuration: {config}""" )
# Load weights from tf checkpoint
load_tf_weights_in_tapas(lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model (weights and configuration)
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Save tokenizer files
print(f"""Save tokenizer files to {pytorch_dump_path}""" )
snake_case_ = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' , model_max_length=512 )
tokenizer.save_pretrained(lowercase__ )
print('Used relative position embeddings:' , model.config.reset_position_index_per_cell )
if __name__ == "__main__":
A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--task', default='SQA', type=str, help='Model task for which to convert a checkpoint. Defaults to SQA.'
)
parser.add_argument(
'--reset_position_index_per_cell',
default=False,
action='store_true',
help='Whether to use relative position embeddings or not. Defaults to True.',
)
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--tapas_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained TAPAS 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.'
)
A = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.task,
args.reset_position_index_per_cell,
args.tf_checkpoint_path,
args.tapas_config_file,
args.pytorch_dump_path,
)
| 46
| 0
|
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class lowerCAmelCase__ ( unittest.TestCase ):
def __init__( self : List[Any] , __UpperCamelCase : List[Any] , __UpperCamelCase : str=13 , __UpperCamelCase : Optional[int]=7 , __UpperCamelCase : Union[str, Any]=True , __UpperCamelCase : Optional[int]=True , __UpperCamelCase : Any=True , __UpperCamelCase : List[Any]=True , __UpperCamelCase : List[str]=99 , __UpperCamelCase : Optional[int]=32 , __UpperCamelCase : List[Any]=5 , __UpperCamelCase : int=4 , __UpperCamelCase : Any=37 , __UpperCamelCase : Tuple="gelu" , __UpperCamelCase : Union[str, Any]=0.1 , __UpperCamelCase : Optional[Any]=0.1 , __UpperCamelCase : int=512 , __UpperCamelCase : Union[str, Any]=16 , __UpperCamelCase : Union[str, Any]=2 , __UpperCamelCase : List[Any]=0.0_2 , __UpperCamelCase : int=4 , ) -> Union[str, Any]:
A = parent
A = batch_size
A = seq_length
A = is_training
A = use_attention_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 = num_choices
def __UpperCamelCase ( self : List[str] ) -> Optional[int]:
A = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
A = None
if self.use_attention_mask:
A = random_attention_mask([self.batch_size, self.seq_length] )
A = None
if self.use_token_type_ids:
A = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
A = RobertaPreLayerNormConfig(
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 , is_decoder=__UpperCamelCase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[Any]:
A = self.prepare_config_and_inputs()
A , A , A , A = config_and_inputs
A = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def __UpperCamelCase ( self : Any ) -> Tuple:
A = self.prepare_config_and_inputs()
A , A , A , A = config_and_inputs
A = True
A = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
A = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class lowerCAmelCase__ ( _lowerCamelCase , unittest.TestCase ):
A_ : Tuple = True
A_ : Union[str, Any] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def __UpperCamelCase ( self : List[str] ) -> List[str]:
A = FlaxRobertaPreLayerNormModelTester(self )
@slow
def __UpperCamelCase ( self : Any ) -> Optional[Any]:
for model_class_name in self.all_model_classes:
A = model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=__UpperCamelCase )
A = model(np.ones((1, 1) ) )
self.assertIsNotNone(__UpperCamelCase )
@require_flax
class lowerCAmelCase__ ( unittest.TestCase ):
@slow
def __UpperCamelCase ( self : Union[str, Any] ) -> Tuple:
A = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=__UpperCamelCase )
A = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
A = model(__UpperCamelCase )[0]
A = [1, 11, 50_265]
self.assertEqual(list(output.shape ) , __UpperCamelCase )
# compare the actual values for a slice.
A = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1e-4 ) )
@slow
def __UpperCamelCase ( self : Dict ) -> List[str]:
A = FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' , from_pt=__UpperCamelCase )
A = np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] , dtype=jnp.intaa )
A = model(__UpperCamelCase )[0]
# compare the actual values for a slice.
A = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , __UpperCamelCase , atol=1e-4 ) )
| 106
|
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
| 1
|
from __future__ import annotations
def _lowerCamelCase ( a_ : list[int] , a_ : int):
lowerCamelCase :Optional[Any] = 0
lowerCamelCase :str = len(a_) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
lowerCamelCase :Optional[Any] = i + 1
else:
lowerCamelCase :Any = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 721
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
),
}
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = 'roberta-prelayernorm'
def __init__( self : str , __snake_case : List[str]=50265 , __snake_case : Union[str, Any]=768 , __snake_case : Tuple=12 , __snake_case : int=12 , __snake_case : Any=3072 , __snake_case : Optional[int]="gelu" , __snake_case : List[Any]=0.1 , __snake_case : int=0.1 , __snake_case : Union[str, Any]=512 , __snake_case : Dict=2 , __snake_case : int=0.0_2 , __snake_case : Any=1e-1_2 , __snake_case : Optional[int]=1 , __snake_case : Dict=0 , __snake_case : Optional[int]=2 , __snake_case : Any="absolute" , __snake_case : Union[str, Any]=True , __snake_case : List[str]=None , **__snake_case : Optional[int] , ):
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
lowerCamelCase :Optional[int] = vocab_size
lowerCamelCase :Dict = hidden_size
lowerCamelCase :Tuple = num_hidden_layers
lowerCamelCase :Optional[int] = num_attention_heads
lowerCamelCase :Any = hidden_act
lowerCamelCase :List[Any] = intermediate_size
lowerCamelCase :Union[str, Any] = hidden_dropout_prob
lowerCamelCase :str = attention_probs_dropout_prob
lowerCamelCase :Tuple = max_position_embeddings
lowerCamelCase :int = type_vocab_size
lowerCamelCase :Optional[Any] = initializer_range
lowerCamelCase :Union[str, Any] = layer_norm_eps
lowerCamelCase :Dict = position_embedding_type
lowerCamelCase :List[Any] = use_cache
lowerCamelCase :Optional[int] = classifier_dropout
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
@property
def snake_case ( self : Any ):
if self.task == "multiple-choice":
lowerCamelCase :Optional[Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowerCamelCase :List[str] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 49
| 0
|
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"pipelines_utils",
"0.22.0",
"Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
standard_warn=False,
stacklevel=3,
)
| 617
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase ={
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase =[
"INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"InformerForPrediction",
"InformerModel",
"InformerPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_informer import (
INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
InformerForPrediction,
InformerModel,
InformerPreTrainedModel,
)
else:
import sys
UpperCAmelCase =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 617
| 1
|
import copy
import re
class __A :
'''simple docstring'''
lowerCAmelCase_ = """hp"""
lowerCAmelCase_ = {}
lowerCAmelCase_ = None
@classmethod
def __lowerCamelCase ( cls , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = prefix
lowerCamelCase__ = defaults
cls.build_naming_info()
@staticmethod
def __lowerCamelCase ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
if len(__lowerCAmelCase ) == 0:
return ""
lowerCamelCase__ = None
if any(char.isdigit() for char in word ):
raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number' )
if word in info["short_word"]:
return info["short_word"][word]
for prefix_len in range(1 , len(__lowerCAmelCase ) + 1 ):
lowerCamelCase__ = word[:prefix_len]
if prefix in info["reverse_short_word"]:
continue
else:
lowerCamelCase__ = prefix
break
if short_word is None:
# Paranoid fallback
def int_to_alphabetic(__lowerCAmelCase ):
lowerCamelCase__ = ''''''
while integer != 0:
lowerCamelCase__ = chr(ord('''A''' ) + integer % 1_0 ) + s
integer //= 1_0
return s
lowerCamelCase__ = 0
while True:
lowerCamelCase__ = word + '''#''' + int_to_alphabetic(__lowerCAmelCase )
if sword in info["reverse_short_word"]:
continue
else:
lowerCamelCase__ = sword
break
lowerCamelCase__ = short_word
lowerCamelCase__ = word
return short_word
@staticmethod
def __lowerCamelCase ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = param_name.split('''_''' )
lowerCamelCase__ = [TrialShortNamer.shortname_for_word(__lowerCAmelCase , __lowerCAmelCase ) for word in words]
# We try to create a separatorless short name, but if there is a collision we have to fallback
# to a separated short name
lowerCamelCase__ = ['''''', '''_''']
for separator in separators:
lowerCamelCase__ = separator.join(__lowerCAmelCase )
if shortname not in info["reverse_short_param"]:
lowerCamelCase__ = shortname
lowerCamelCase__ = param_name
return shortname
return param_name
@staticmethod
def __lowerCamelCase ( __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = TrialShortNamer.shortname_for_key(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = short_name
lowerCamelCase__ = param_name
@classmethod
def __lowerCamelCase ( cls ):
'''simple docstring'''
if cls.NAMING_INFO is not None:
return
lowerCamelCase__ = {
'''short_word''': {},
'''reverse_short_word''': {},
'''short_param''': {},
'''reverse_short_param''': {},
}
lowerCamelCase__ = list(cls.DEFAULTS.keys() )
for k in field_keys:
cls.add_new_param_name(__lowerCAmelCase , __lowerCAmelCase )
lowerCamelCase__ = info
@classmethod
def __lowerCamelCase ( cls , __lowerCAmelCase ):
'''simple docstring'''
cls.build_naming_info()
assert cls.PREFIX is not None
lowerCamelCase__ = [copy.copy(cls.PREFIX )]
for k, v in params.items():
if k not in cls.DEFAULTS:
raise Exception(F'You should provide a default value for the param name {k} with value {v}' )
if v == cls.DEFAULTS[k]:
# The default value is not added to the name
continue
lowerCamelCase__ = cls.NAMING_INFO['''short_param'''][k]
if isinstance(__lowerCAmelCase , __lowerCAmelCase ):
lowerCamelCase__ = 1 if v else 0
lowerCamelCase__ = '''''' if isinstance(__lowerCAmelCase , (int, float) ) else '''-'''
lowerCamelCase__ = F'{key}{sep}{v}'
name.append(__lowerCAmelCase )
return "_".join(__lowerCAmelCase )
@classmethod
def __lowerCamelCase ( cls , __lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = repr[len(cls.PREFIX ) + 1 :]
if repr == "":
lowerCamelCase__ = []
else:
lowerCamelCase__ = repr.split('''_''' )
lowerCamelCase__ = {}
for value in values:
if "-" in value:
lowerCamelCase__ , lowerCamelCase__ = value.split('''-''' )
else:
lowerCamelCase__ = re.sub('''[0-9.]''' , '''''' , __lowerCAmelCase )
lowerCamelCase__ = float(re.sub('''[^0-9.]''' , '''''' , __lowerCAmelCase ) )
lowerCamelCase__ = cls.NAMING_INFO['''reverse_short_param'''][p_k]
lowerCamelCase__ = p_v
for k in cls.DEFAULTS:
if k not in parameters:
lowerCamelCase__ = cls.DEFAULTS[k]
return parameters
| 29
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( lowerCAmelCase ):
'''simple docstring'''
lowerCAmelCase_ = """ClapFeatureExtractor"""
lowerCAmelCase_ = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self , __lowerCAmelCase , __lowerCAmelCase ):
'''simple docstring'''
super().__init__(__lowerCAmelCase , __lowerCAmelCase )
def __call__( self , __lowerCAmelCase=None , __lowerCAmelCase=None , __lowerCAmelCase=None , **__lowerCAmelCase ):
'''simple docstring'''
lowerCamelCase__ = kwargs.pop('''sampling_rate''' , __lowerCAmelCase )
if text is None and audios is None:
raise ValueError('''You have to specify either text or audios. Both cannot be none.''' )
if text is not None:
lowerCamelCase__ = self.tokenizer(__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
if audios is not None:
lowerCamelCase__ = self.feature_extractor(
__lowerCAmelCase , sampling_rate=__lowerCAmelCase , return_tensors=__lowerCAmelCase , **__lowerCAmelCase )
if text is not None and audios is not None:
lowerCamelCase__ = audio_features.input_features
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__lowerCAmelCase ) , tensor_type=__lowerCAmelCase )
def __lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__lowerCAmelCase , **__lowerCAmelCase )
def __lowerCamelCase ( self , *__lowerCAmelCase , **__lowerCAmelCase ):
'''simple docstring'''
return self.tokenizer.decode(*__lowerCAmelCase , **__lowerCAmelCase )
@property
def __lowerCamelCase ( self ):
'''simple docstring'''
lowerCamelCase__ = self.tokenizer.model_input_names
lowerCamelCase__ = self.feature_extractor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )
| 29
| 1
|
import math
from collections import defaultdict
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 KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def __a ( A__ : List[str] , A__ : str=0.9_9_9 , A__ : Tuple="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(A__ : Optional[int] ):
return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A__ : Tuple ):
return math.exp(t * -1_2.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
SCREAMING_SNAKE_CASE = []
for i in range(A__ ):
SCREAMING_SNAKE_CASE = i / num_diffusion_timesteps
SCREAMING_SNAKE_CASE = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A__ ) / alpha_bar_fn(A__ ) , A__ ) )
return torch.tensor(A__ , dtype=torch.floataa )
class _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ):
'''simple docstring'''
lowerCamelCase__ = [e.name for e in KarrasDiffusionSchedulers]
lowerCamelCase__ = 2
@register_to_config
def __init__( self : Tuple , __lowerCamelCase : int = 1000 , __lowerCamelCase : float = 0.00_085 , __lowerCamelCase : float = 0.012 , __lowerCamelCase : str = "linear" , __lowerCamelCase : Optional[Union[np.ndarray, List[float]]] = None , __lowerCamelCase : str = "epsilon" , __lowerCamelCase : str = "linspace" , __lowerCamelCase : int = 0 , ):
if trained_betas is not None:
SCREAMING_SNAKE_CASE = torch.tensor(__lowerCamelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
SCREAMING_SNAKE_CASE = torch.linspace(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
SCREAMING_SNAKE_CASE = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , __lowerCamelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
SCREAMING_SNAKE_CASE = betas_for_alpha_bar(__lowerCamelCase )
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )
SCREAMING_SNAKE_CASE = 1.0 - self.betas
SCREAMING_SNAKE_CASE = torch.cumprod(self.alphas , dim=0 )
# set all values
self.set_timesteps(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : str , __lowerCamelCase : List[str]=None ):
if schedule_timesteps is None:
SCREAMING_SNAKE_CASE = self.timesteps
SCREAMING_SNAKE_CASE = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
SCREAMING_SNAKE_CASE = 1 if len(__lowerCamelCase ) > 1 else 0
else:
SCREAMING_SNAKE_CASE = timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
SCREAMING_SNAKE_CASE = self._index_counter[timestep_int]
return indices[pos].item()
@property
def _snake_case ( self : str ):
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def _snake_case ( self : List[Any] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : Union[float, torch.FloatTensor] , ):
SCREAMING_SNAKE_CASE = self.index_for_timestep(__lowerCamelCase )
if self.state_in_first_order:
SCREAMING_SNAKE_CASE = self.sigmas[step_index]
else:
SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index]
SCREAMING_SNAKE_CASE = sample / ((sigma**2 + 1) ** 0.5)
return sample
def _snake_case ( self : Dict , __lowerCamelCase : int , __lowerCamelCase : Union[str, torch.device] = None , __lowerCamelCase : Optional[int] = None , ):
SCREAMING_SNAKE_CASE = num_inference_steps
SCREAMING_SNAKE_CASE = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
SCREAMING_SNAKE_CASE = np.linspace(0 , num_train_timesteps - 1 , __lowerCamelCase , dtype=__lowerCamelCase )[::-1].copy()
elif self.config.timestep_spacing == "leading":
SCREAMING_SNAKE_CASE = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
SCREAMING_SNAKE_CASE = (np.arange(0 , __lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
SCREAMING_SNAKE_CASE = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
SCREAMING_SNAKE_CASE = (np.arange(__lowerCamelCase , 0 , -step_ratio )).round().copy().astype(__lowerCamelCase )
timesteps -= 1
else:
raise ValueError(
f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )
SCREAMING_SNAKE_CASE = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
SCREAMING_SNAKE_CASE = torch.from_numpy(np.log(__lowerCamelCase ) ).to(__lowerCamelCase )
SCREAMING_SNAKE_CASE = np.interp(__lowerCamelCase , np.arange(0 , len(__lowerCamelCase ) ) , __lowerCamelCase )
SCREAMING_SNAKE_CASE = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
SCREAMING_SNAKE_CASE = torch.from_numpy(__lowerCamelCase ).to(device=__lowerCamelCase )
# interpolate sigmas
SCREAMING_SNAKE_CASE = sigmas.log().lerp(sigmas.roll(1 ).log() , 0.5 ).exp()
SCREAMING_SNAKE_CASE = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2 ), sigmas[-1:]] )
SCREAMING_SNAKE_CASE = torch.cat(
[sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2 ), sigmas_interpol[-1:]] )
if str(__lowerCamelCase ).startswith("mps" ):
# mps does not support float64
SCREAMING_SNAKE_CASE = torch.from_numpy(__lowerCamelCase ).to(__lowerCamelCase , dtype=torch.floataa )
else:
SCREAMING_SNAKE_CASE = torch.from_numpy(__lowerCamelCase ).to(__lowerCamelCase )
# interpolate timesteps
SCREAMING_SNAKE_CASE = self.sigma_to_t(__lowerCamelCase ).to(__lowerCamelCase , dtype=timesteps.dtype )
SCREAMING_SNAKE_CASE = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) , dim=-1 ).flatten()
SCREAMING_SNAKE_CASE = torch.cat([timesteps[:1], interleaved_timesteps] )
SCREAMING_SNAKE_CASE = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
SCREAMING_SNAKE_CASE = defaultdict(__lowerCamelCase )
def _snake_case ( self : Any , __lowerCamelCase : List[str] ):
# get log sigma
SCREAMING_SNAKE_CASE = sigma.log()
# get distribution
SCREAMING_SNAKE_CASE = log_sigma - self.log_sigmas[:, None]
# get sigmas range
SCREAMING_SNAKE_CASE = dists.ge(0 ).cumsum(dim=0 ).argmax(dim=0 ).clamp(max=self.log_sigmas.shape[0] - 2 )
SCREAMING_SNAKE_CASE = low_idx + 1
SCREAMING_SNAKE_CASE = self.log_sigmas[low_idx]
SCREAMING_SNAKE_CASE = self.log_sigmas[high_idx]
# interpolate sigmas
SCREAMING_SNAKE_CASE = (low - log_sigma) / (low - high)
SCREAMING_SNAKE_CASE = w.clamp(0 , 1 )
# transform interpolation to time range
SCREAMING_SNAKE_CASE = (1 - w) * low_idx + w * high_idx
SCREAMING_SNAKE_CASE = t.view(sigma.shape )
return t
@property
def _snake_case ( self : str ):
return self.sample is None
def _snake_case ( self : str , __lowerCamelCase : Union[torch.FloatTensor, np.ndarray] , __lowerCamelCase : Union[float, torch.FloatTensor] , __lowerCamelCase : Union[torch.FloatTensor, np.ndarray] , __lowerCamelCase : bool = True , ):
SCREAMING_SNAKE_CASE = self.index_for_timestep(__lowerCamelCase )
# advance index counter by 1
SCREAMING_SNAKE_CASE = timestep.cpu().item() if torch.is_tensor(__lowerCamelCase ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
SCREAMING_SNAKE_CASE = self.sigmas[step_index]
SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index + 1]
SCREAMING_SNAKE_CASE = self.sigmas[step_index + 1]
else:
# 2nd order / KDPM2's method
SCREAMING_SNAKE_CASE = self.sigmas[step_index - 1]
SCREAMING_SNAKE_CASE = self.sigmas_interpol[step_index]
SCREAMING_SNAKE_CASE = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
SCREAMING_SNAKE_CASE = sigma_hat if self.state_in_first_order else sigma_interpol
SCREAMING_SNAKE_CASE = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
SCREAMING_SNAKE_CASE = sigma_hat if self.state_in_first_order else sigma_interpol
SCREAMING_SNAKE_CASE = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
raise NotImplementedError("prediction_type not implemented yet: sample" )
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
SCREAMING_SNAKE_CASE = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
SCREAMING_SNAKE_CASE = sigma_interpol - sigma_hat
# store for 2nd order step
SCREAMING_SNAKE_CASE = sample
else:
# DPM-Solver-2
# 2. Convert to an ODE derivative for 2nd order
SCREAMING_SNAKE_CASE = (sample - pred_original_sample) / sigma_interpol
# 3. delta timestep
SCREAMING_SNAKE_CASE = sigma_next - sigma_hat
SCREAMING_SNAKE_CASE = self.sample
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=__lowerCamelCase )
def _snake_case ( self : List[str] , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : torch.FloatTensor , __lowerCamelCase : torch.FloatTensor , ):
# Make sure sigmas and timesteps have the same device and dtype as original_samples
SCREAMING_SNAKE_CASE = self.sigmas.to(device=original_samples.device , dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase ):
# mps does not support float64
SCREAMING_SNAKE_CASE = self.timesteps.to(original_samples.device , dtype=torch.floataa )
SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device , dtype=torch.floataa )
else:
SCREAMING_SNAKE_CASE = self.timesteps.to(original_samples.device )
SCREAMING_SNAKE_CASE = timesteps.to(original_samples.device )
SCREAMING_SNAKE_CASE = [self.index_for_timestep(__lowerCamelCase , __lowerCamelCase ) for t in timesteps]
SCREAMING_SNAKE_CASE = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
SCREAMING_SNAKE_CASE = sigma.unsqueeze(-1 )
SCREAMING_SNAKE_CASE = original_samples + noise * sigma
return noisy_samples
def __len__( self : Optional[Any] ):
return self.config.num_train_timesteps
| 16
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""post_extract_proj""": """feature_projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.upsample.0""": """encoder.upsample.projection""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def UpperCamelCase ( __lowercase : int ,__lowercase : List[str] ,__lowercase : str ,__lowercase : Optional[Any] ,__lowercase : Any ):
'''simple docstring'''
for attribute in key.split('.' ):
A_ : Dict = getattr(__lowercase ,__lowercase )
if weight_type is not None:
A_ : Any = getattr(__lowercase ,__lowercase ).shape
else:
A_ : Optional[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
A_ : int = value
elif weight_type == "weight_g":
A_ : Tuple = value
elif weight_type == "weight_v":
A_ : Union[str, Any] = value
elif weight_type == "bias":
A_ : Any = value
else:
A_ : str = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def UpperCamelCase ( __lowercase : str ,__lowercase : Dict ,__lowercase : Tuple ):
'''simple docstring'''
A_ : Optional[Any] = []
A_ : Tuple = fairseq_model.state_dict()
A_ : Any = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
A_ : Union[str, Any] = False
if "conv_layers" in name:
load_conv_layer(
__lowercase ,__lowercase ,__lowercase ,__lowercase ,hf_model.config.feat_extract_norm == 'group' ,)
A_ : List[str] = True
else:
for key, mapped_key in MAPPING.items():
A_ : str = 'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
A_ : int = True
if "*" in mapped_key:
A_ : str = name.split(__lowercase )[0].split('.' )[-2]
A_ : Optional[Any] = mapped_key.replace('*' ,__lowercase )
if "weight_g" in name:
A_ : Dict = 'weight_g'
elif "weight_v" in name:
A_ : Tuple = 'weight_v'
elif "weight" in name:
A_ : Union[str, Any] = 'weight'
elif "bias" in name:
A_ : Optional[Any] = 'bias'
else:
A_ : Union[str, Any] = None
set_recursively(__lowercase ,__lowercase ,__lowercase ,__lowercase ,__lowercase )
continue
if not is_used:
unused_weights.append(__lowercase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def UpperCamelCase ( __lowercase : Optional[Any] ,__lowercase : Union[str, Any] ,__lowercase : Any ,__lowercase : List[Any] ,__lowercase : Union[str, Any] ):
'''simple docstring'''
A_ : Optional[int] = full_name.split('conv_layers.' )[-1]
A_ : Any = name.split('.' )
A_ : Dict = int(items[0] )
A_ : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
A_ : Optional[int] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
A_ : Union[str, Any] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
A_ : Any = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
A_ : Tuple = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowercase )
def UpperCamelCase ( __lowercase : List[str] ,__lowercase : str ):
'''simple docstring'''
A_ : Union[str, Any] = SEWConfig()
if is_finetuned:
A_ : Any = model.wav_encoder.wav_model.cfg
else:
A_ : int = model.cfg
A_ : Any = fs_config.conv_bias
A_ : Dict = eval(fs_config.conv_feature_layers )
A_ : List[Any] = [x[0] for x in conv_layers]
A_ : Optional[Any] = [x[1] for x in conv_layers]
A_ : List[Any] = [x[2] for x in conv_layers]
A_ : Optional[int] = 'gelu'
A_ : Union[str, Any] = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
A_ : Tuple = 0.0
A_ : Dict = fs_config.activation_fn.name
A_ : List[Any] = fs_config.encoder_embed_dim
A_ : int = 0.02
A_ : List[str] = fs_config.encoder_ffn_embed_dim
A_ : Any = 1e-5
A_ : Optional[Any] = fs_config.encoder_layerdrop
A_ : Optional[int] = fs_config.encoder_attention_heads
A_ : Any = fs_config.conv_pos_groups
A_ : int = fs_config.conv_pos
A_ : Tuple = len(__lowercase )
A_ : List[Any] = fs_config.encoder_layers
A_ : Any = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
A_ : Union[str, Any] = model.cfg
A_ : str = fs_config.final_dropout
A_ : Any = fs_config.layerdrop
A_ : str = fs_config.activation_dropout
A_ : Any = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
A_ : str = fs_config.attention_dropout
A_ : Any = fs_config.dropout_input
A_ : Dict = fs_config.dropout
A_ : Optional[Any] = fs_config.mask_channel_length
A_ : List[str] = fs_config.mask_channel_prob
A_ : Tuple = fs_config.mask_length
A_ : Dict = fs_config.mask_prob
A_ : Any = 'Wav2Vec2FeatureExtractor'
A_ : Union[str, Any] = 'Wav2Vec2CTCTokenizer'
return config
@torch.no_grad()
def UpperCamelCase ( __lowercase : List[Any] ,__lowercase : int ,__lowercase : Optional[int]=None ,__lowercase : Optional[Any]=None ,__lowercase : str=True ):
'''simple docstring'''
if is_finetuned:
A_ , A_ , A_ : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
A_ , A_ , A_ : Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
A_ : Union[str, Any] = SEWConfig.from_pretrained(__lowercase )
else:
A_ : Dict = convert_config(model[0] ,__lowercase )
A_ : Union[str, Any] = model[0].eval()
A_ : Optional[int] = True if config.feat_extract_norm == 'layer' else False
A_ : List[Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowercase ,return_attention_mask=__lowercase ,)
if is_finetuned:
if dict_path:
A_ : Optional[int] = Dictionary.load(__lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
A_ : int = target_dict.pad_index
A_ : List[Any] = target_dict.bos_index
A_ : Optional[Any] = target_dict.pad_index
A_ : str = target_dict.bos_index
A_ : str = target_dict.eos_index
A_ : str = len(target_dict.symbols )
A_ : Union[str, Any] = os.path.join(__lowercase ,'vocab.json' )
if not os.path.isdir(__lowercase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__lowercase ) )
return
os.makedirs(__lowercase ,exist_ok=__lowercase )
with open(__lowercase ,'w' ,encoding='utf-8' ) as vocab_handle:
json.dump(target_dict.indices ,__lowercase )
A_ : Any = WavaVecaCTCTokenizer(
__lowercase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token='|' ,do_lower_case=__lowercase ,)
A_ : Tuple = WavaVecaProcessor(feature_extractor=__lowercase ,tokenizer=__lowercase )
processor.save_pretrained(__lowercase )
A_ : Dict = SEWForCTC(__lowercase )
else:
A_ : Tuple = SEWModel(__lowercase )
feature_extractor.save_pretrained(__lowercase )
recursively_load_weights(__lowercase ,__lowercase ,__lowercase )
hf_model.save_pretrained(__lowercase )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_UpperCAmelCase = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 558
| 0
|
def A_ ( snake_case : int = 100 ) -> int:
'''simple docstring'''
__UpperCamelCase = 0
__UpperCamelCase = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main__":
print(F"{solution() = }")
| 718
|
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"):
lowercase__ : int = True
from torch.cuda.amp import autocast
lowercase__ : Dict = logging.getLogger(__name__)
def A_ ( snake_case : List[str]=None , snake_case : int=None ) -> List[str]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=snake_case )
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
_snake_case = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'Whether to freeze the feature extractor layers of the model.'} )
_snake_case = field(
default=0.1 , metadata={'help': 'The dropout ratio for the attention probabilities.'} )
_snake_case = field(
default=0.1 , metadata={'help': 'The dropout ratio for activations inside the fully connected layer.'} )
_snake_case = field(
default=0.1 , metadata={
'help': 'The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.'
} , )
_snake_case = field(
default=0.1 , metadata={'help': 'The dropout probabilitiy for all 1D convolutional layers in feature extractor.'} , )
_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``.'
)
} , )
_snake_case = field(default=0.0 , metadata={'help': 'The LayerDrop probability.'} )
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
_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\''
} , )
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
_snake_case = field(
default=SCREAMING_SNAKE_CASE_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of validation examples to this '
'value if set.'
)
} , )
_snake_case = list_field(
default=[',', '?', '.', '!', '-', ';', ':', '""', '%', '\'', '"', '�'] , metadata={'help': 'A list of characters to remove from the transcripts.'} , )
@dataclass
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
_snake_case = 42
_snake_case = True
_snake_case = None
_snake_case = None
_snake_case = None
_snake_case = None
def __call__( self , SCREAMING_SNAKE_CASE_ )-> Dict[str, torch.Tensor]:
'''simple docstring'''
__UpperCamelCase = [{'''input_values''': feature['''input_values''']} for feature in features]
__UpperCamelCase = [{'''input_ids''': feature['''labels''']} for feature in features]
__UpperCamelCase = self.processor.pad(
SCREAMING_SNAKE_CASE_ , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='''pt''' , )
__UpperCamelCase = self.processor.pad(
labels=SCREAMING_SNAKE_CASE_ , 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
__UpperCamelCase = labels_batch['''input_ids'''].masked_fill(labels_batch.attention_mask.ne(1 ) , -100 )
__UpperCamelCase = labels
return batch
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def A__ ( self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> torch.Tensor:
'''simple docstring'''
model.train()
__UpperCamelCase = self._prepare_inputs(SCREAMING_SNAKE_CASE_ )
if self.use_amp:
with autocast():
__UpperCamelCase = self.compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
__UpperCamelCase = self.compute_loss(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if self.args.n_gpu > 1:
if model.module.config.ctc_loss_reduction == "mean":
__UpperCamelCase = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
__UpperCamelCase = 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:
__UpperCamelCase = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(SCREAMING_SNAKE_CASE_ ).backward()
elif self.use_apex:
with amp.scale_loss(SCREAMING_SNAKE_CASE_ , self.optimizer ) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(SCREAMING_SNAKE_CASE_ )
else:
loss.backward()
return loss.detach()
def A_ ( ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase = 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.
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__UpperCamelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__UpperCamelCase = 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''' , snake_case )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets:
__UpperCamelCase = datasets.load_dataset(
'''common_voice''' , data_args.dataset_config_name , split=data_args.train_split_name )
__UpperCamelCase = datasets.load_dataset('''common_voice''' , data_args.dataset_config_name , split='''test''' )
# Create and save tokenizer
__UpperCamelCase = f"[{''.join(data_args.chars_to_ignore )}]"
def remove_special_characters(snake_case : Tuple ):
__UpperCamelCase = re.sub(snake_case , '''''' , batch['''sentence'''] ).lower() + ''' '''
return batch
__UpperCamelCase = train_dataset.map(snake_case , remove_columns=['''sentence'''] )
__UpperCamelCase = eval_dataset.map(snake_case , remove_columns=['''sentence'''] )
def extract_all_chars(snake_case : Dict ):
__UpperCamelCase = ''' '''.join(batch['''text'''] )
__UpperCamelCase = list(set(snake_case ) )
return {"vocab": [vocab], "all_text": [all_text]}
__UpperCamelCase = train_dataset.map(
snake_case , batched=snake_case , batch_size=-1 , keep_in_memory=snake_case , remove_columns=train_dataset.column_names , )
__UpperCamelCase = train_dataset.map(
snake_case , batched=snake_case , batch_size=-1 , keep_in_memory=snake_case , remove_columns=eval_dataset.column_names , )
__UpperCamelCase = list(set(vocab_train['''vocab'''][0] ) | set(vocab_test['''vocab'''][0] ) )
__UpperCamelCase = {v: k for k, v in enumerate(snake_case )}
__UpperCamelCase = vocab_dict[''' ''']
del vocab_dict[" "]
__UpperCamelCase = len(snake_case )
__UpperCamelCase = len(snake_case )
with open('''vocab.json''' , '''w''' ) as vocab_file:
json.dump(snake_case , snake_case )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__UpperCamelCase = WavaVecaCTCTokenizer(
'''vocab.json''' , unk_token='''[UNK]''' , pad_token='''[PAD]''' , word_delimiter_token='''|''' , )
__UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0.0 , do_normalize=snake_case , return_attention_mask=snake_case )
__UpperCamelCase = WavaVecaProcessor(feature_extractor=snake_case , tokenizer=snake_case )
__UpperCamelCase = 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:
__UpperCamelCase = min(len(snake_case ) , data_args.max_train_samples )
__UpperCamelCase = train_dataset.select(range(snake_case ) )
if data_args.max_val_samples is not None:
__UpperCamelCase = eval_dataset.select(range(data_args.max_val_samples ) )
__UpperCamelCase = torchaudio.transforms.Resample(48000 , 16000 )
# Preprocessing the datasets.
# We need to read the aduio files as arrays and tokenize the targets.
def speech_file_to_array_fn(snake_case : str ):
__UpperCamelCase , __UpperCamelCase = torchaudio.load(batch['''path'''] )
__UpperCamelCase = resampler(snake_case ).squeeze().numpy()
__UpperCamelCase = 16000
__UpperCamelCase = batch['''text''']
return batch
__UpperCamelCase = train_dataset.map(
snake_case , remove_columns=train_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
__UpperCamelCase = eval_dataset.map(
snake_case , remove_columns=eval_dataset.column_names , num_proc=data_args.preprocessing_num_workers , )
def prepare_dataset(snake_case : Optional[int] ):
# 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}."
__UpperCamelCase = processor(
audio=batch['''speech'''] , text=batch['''target_text'''] , sampling_rate=batch['''sampling_rate'''][0] )
batch.update(snake_case )
return batch
__UpperCamelCase = train_dataset.map(
snake_case , remove_columns=train_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=snake_case , num_proc=data_args.preprocessing_num_workers , )
__UpperCamelCase = eval_dataset.map(
snake_case , remove_columns=eval_dataset.column_names , batch_size=training_args.per_device_train_batch_size , batched=snake_case , num_proc=data_args.preprocessing_num_workers , )
# Metric
__UpperCamelCase = datasets.load_metric('''wer''' )
def compute_metrics(snake_case : int ):
__UpperCamelCase = pred.predictions
__UpperCamelCase = np.argmax(snake_case , axis=-1 )
__UpperCamelCase = processor.tokenizer.pad_token_id
__UpperCamelCase = processor.batch_decode(snake_case )
# we do not want to group tokens when computing the metrics
__UpperCamelCase = processor.batch_decode(pred.label_ids , group_tokens=snake_case )
__UpperCamelCase = wer_metric.compute(predictions=snake_case , references=snake_case )
return {"wer": wer}
if model_args.freeze_feature_extractor:
model.freeze_feature_extractor()
# Data collator
__UpperCamelCase = DataCollatorCTCWithPadding(processor=snake_case , padding=snake_case )
# Initialize our Trainer
__UpperCamelCase = CTCTrainer(
model=snake_case , data_collator=snake_case , args=snake_case , compute_metrics=snake_case , 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:
__UpperCamelCase = last_checkpoint
elif os.path.isdir(model_args.model_name_or_path ):
__UpperCamelCase = model_args.model_name_or_path
else:
__UpperCamelCase = None
# Save the feature_extractor and the tokenizer
if is_main_process(training_args.local_rank ):
processor.save_pretrained(training_args.output_dir )
__UpperCamelCase = trainer.train(resume_from_checkpoint=snake_case )
trainer.save_model()
__UpperCamelCase = train_result.metrics
__UpperCamelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case )
)
__UpperCamelCase = min(snake_case , len(snake_case ) )
trainer.log_metrics('''train''' , snake_case )
trainer.save_metrics('''train''' , snake_case )
trainer.save_state()
# Evaluation
__UpperCamelCase = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
__UpperCamelCase = trainer.evaluate()
__UpperCamelCase = data_args.max_val_samples if data_args.max_val_samples is not None else len(snake_case )
__UpperCamelCase = min(snake_case , len(snake_case ) )
trainer.log_metrics('''eval''' , snake_case )
trainer.save_metrics('''eval''' , snake_case )
return results
if __name__ == "__main__":
main()
| 451
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = "timm_backbone"
def __init__(self : int , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Tuple=3 , UpperCAmelCase_ : List[Any]=True , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : List[str]=None , **UpperCAmelCase_ : Optional[int] , ) ->Union[str, Any]:
'''simple docstring'''
super().__init__(**UpperCAmelCase_)
lowerCamelCase__: int =backbone
lowerCamelCase__: Optional[int] =num_channels
lowerCamelCase__: Any =features_only
lowerCamelCase__: Union[str, Any] =use_pretrained_backbone
lowerCamelCase__: str =True
lowerCamelCase__: Dict =out_indices if out_indices is not None else (-1,)
| 59
|
from __future__ import annotations
from math import pi
def lowerCAmelCase_ ( __a , __a , __a ) -> dict[str, float]:
"""simple docstring"""
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
raise ValueError("Inductance cannot be negative" )
if frequency < 0:
raise ValueError("Frequency cannot be negative" )
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 59
| 1
|
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase = None, UpperCAmelCase = None, UpperCAmelCase = None, ) ->Tuple:
"""simple docstring"""
if config_name_or_path is None:
__magic_name__ : Dict = '''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''
if generator_tokenizer_name_or_path is None:
__magic_name__ : int = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
__magic_name__ : Union[str, Any] = question_encoder_name_or_path
__magic_name__ : Dict = RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration
# Save model.
__magic_name__ : str = RagConfig.from_pretrained(UpperCAmelCase )
__magic_name__ : Dict = AutoConfig.from_pretrained(UpperCAmelCase )
__magic_name__ : str = AutoConfig.from_pretrained(UpperCAmelCase )
__magic_name__ : Tuple = gen_config
__magic_name__ : str = question_encoder_config
__magic_name__ : str = model_class.from_pretrained_question_encoder_generator(
UpperCAmelCase, UpperCAmelCase, config=UpperCAmelCase )
rag_model.save_pretrained(UpperCAmelCase )
# Sanity check.
model_class.from_pretrained(UpperCAmelCase )
# Save tokenizers.
__magic_name__ : Any = AutoTokenizer.from_pretrained(UpperCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/''' )
__magic_name__ : List[str] = AutoTokenizer.from_pretrained(UpperCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/''' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument(
'''--model_type''',
choices=['''rag_sequence''', '''rag_token'''],
required=True,
type=str,
help='''RAG model type: rag_sequence, rag_token''',
)
parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
parser.add_argument(
'''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
)
parser.add_argument(
'''--generator_tokenizer_name_or_path''',
type=str,
help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
)
parser.add_argument(
'''--question_encoder_tokenizer_name_or_path''',
type=str,
help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
)
parser.add_argument(
'''--config_name_or_path''',
type=str,
help=(
'''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
''' ``model_type``'''
),
)
lowercase_ = parser.parse_args()
lowercase_ = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 336
|
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
lowercase_ = True
except (ImportError, AttributeError):
lowercase_ = object
def lowerCAmelCase ( *UpperCAmelCase, **UpperCAmelCase ) ->Any:
"""simple docstring"""
pass
lowercase_ = False
lowercase_ = logging.get_logger('''transformers-cli/serving''')
def lowerCAmelCase ( UpperCAmelCase ) ->Optional[Any]:
"""simple docstring"""
__magic_name__ : Optional[int] = 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 A__ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : dict
class A__ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : List[str]
lowerCamelCase__ : Optional[List[int]]
class A__ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : str
class A__ ( __SCREAMING_SNAKE_CASE ):
lowerCamelCase__ : Any
class A__ ( __SCREAMING_SNAKE_CASE ):
@staticmethod
def lowercase ( lowerCamelCase ) -> Dict:
"""simple docstring"""
__magic_name__ : int = parser.add_parser(
'''serve''' , help='''CLI tool to run inference requests through REST and GraphQL endpoints.''' )
serve_parser.add_argument(
'''--task''' , type=lowerCamelCase , choices=get_supported_tasks() , help='''The task to run the pipeline on''' , )
serve_parser.add_argument('''--host''' , type=lowerCamelCase , default='''localhost''' , help='''Interface the server will listen on.''' )
serve_parser.add_argument('''--port''' , type=lowerCamelCase , default=8888 , help='''Port the serving will listen to.''' )
serve_parser.add_argument('''--workers''' , type=lowerCamelCase , default=1 , help='''Number of http workers''' )
serve_parser.add_argument('''--model''' , type=lowerCamelCase , help='''Model\'s name or path to stored model.''' )
serve_parser.add_argument('''--config''' , type=lowerCamelCase , help='''Model\'s config name or path to stored model.''' )
serve_parser.add_argument('''--tokenizer''' , type=lowerCamelCase , help='''Tokenizer name to use.''' )
serve_parser.add_argument(
'''--device''' , type=lowerCamelCase , default=-1 , help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)''' , )
serve_parser.set_defaults(func=lowerCamelCase )
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> List[str]:
"""simple docstring"""
__magic_name__ : List[str] = pipeline
__magic_name__ : Union[str, Any] = host
__magic_name__ : int = port
__magic_name__ : Optional[int] = 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}''' )
__magic_name__ : int = FastAPI(
routes=[
APIRoute(
'''/''' , self.model_info , response_model=lowerCamelCase , response_class=lowerCamelCase , methods=['''GET'''] , ),
APIRoute(
'''/tokenize''' , self.tokenize , response_model=lowerCamelCase , response_class=lowerCamelCase , methods=['''POST'''] , ),
APIRoute(
'''/detokenize''' , self.detokenize , response_model=lowerCamelCase , response_class=lowerCamelCase , methods=['''POST'''] , ),
APIRoute(
'''/forward''' , self.forward , response_model=lowerCamelCase , response_class=lowerCamelCase , methods=['''POST'''] , ),
] , timeout=600 , )
def lowercase ( self ) -> Dict:
"""simple docstring"""
run(self._app , host=self.host , port=self.port , workers=self.workers )
def lowercase ( self ) -> str:
"""simple docstring"""
return ServeModelInfoResult(infos=vars(self._pipeline.model.config ) )
def lowercase ( self , lowerCamelCase = Body(lowerCamelCase , embed=lowerCamelCase ) , lowerCamelCase = Body(lowerCamelCase , embed=lowerCamelCase ) ) -> Any:
"""simple docstring"""
try:
__magic_name__ : List[str] = self._pipeline.tokenizer.tokenize(lowerCamelCase )
if return_ids:
__magic_name__ : int = self._pipeline.tokenizer.convert_tokens_to_ids(lowerCamelCase )
return ServeTokenizeResult(tokens=lowerCamelCase , tokens_ids=lowerCamelCase )
else:
return ServeTokenizeResult(tokens=lowerCamelCase )
except Exception as e:
raise HTTPException(status_code=500 , detail={'''model''': '''''', '''error''': str(lowerCamelCase )} )
def lowercase ( self , lowerCamelCase = Body(lowerCamelCase , embed=lowerCamelCase ) , lowerCamelCase = Body(lowerCamelCase , embed=lowerCamelCase ) , lowerCamelCase = Body(lowerCamelCase , embed=lowerCamelCase ) , ) -> Any:
"""simple docstring"""
try:
__magic_name__ : Any = self._pipeline.tokenizer.decode(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return ServeDeTokenizeResult(model='''''' , text=lowerCamelCase )
except Exception as e:
raise HTTPException(status_code=500 , detail={'''model''': '''''', '''error''': str(lowerCamelCase )} )
async def lowercase ( self , lowerCamelCase=Body(lowerCamelCase , embed=lowerCamelCase ) ) -> Optional[int]:
"""simple docstring"""
if len(lowerCamelCase ) == 0:
return ServeForwardResult(output=[] , attention=[] )
try:
# Forward through the model
__magic_name__ : Optional[int] = self._pipeline(lowerCamelCase )
return ServeForwardResult(output=lowerCamelCase )
except Exception as e:
raise HTTPException(500 , {'''error''': str(lowerCamelCase )} )
| 336
| 1
|
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
_A = """."""
if __name__ == "__main__":
_A = os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
_A = []
_A = []
with open(doctest_file_path) as fp:
for line in fp:
_A = line.strip()
_A = os.path.join(REPO_PATH, line)
if not (os.path.isfile(path) or os.path.isdir(path)):
non_existent_paths.append(line)
all_paths.append(path)
if len(non_existent_paths) > 0:
_A = """\n""".join(non_existent_paths)
raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
if all_paths != sorted(all_paths):
raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")
| 258
|
'''simple docstring'''
import math
import os
import unittest
from transformers import MegatronBertConfig, 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,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class _SCREAMING_SNAKE_CASE:
def __init__( self : Optional[int] , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple=13 , UpperCamelCase_ : Union[str, Any]=7 , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : Union[str, Any]=True , UpperCamelCase_ : Any=True , UpperCamelCase_ : int=True , UpperCamelCase_ : List[Any]=99 , UpperCamelCase_ : List[str]=64 , UpperCamelCase_ : List[str]=32 , UpperCamelCase_ : Union[str, Any]=5 , UpperCamelCase_ : Dict=4 , UpperCamelCase_ : List[str]=37 , UpperCamelCase_ : str="gelu" , UpperCamelCase_ : List[Any]=0.1 , UpperCamelCase_ : Union[str, Any]=0.1 , UpperCamelCase_ : Optional[Any]=5_12 , UpperCamelCase_ : Union[str, Any]=16 , UpperCamelCase_ : Union[str, Any]=2 , UpperCamelCase_ : Optional[int]=0.02 , UpperCamelCase_ : Optional[Any]=3 , UpperCamelCase_ : Any=4 , UpperCamelCase_ : int=None , ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ :Optional[int] = parent
SCREAMING_SNAKE_CASE__ :Any = batch_size
SCREAMING_SNAKE_CASE__ :Tuple = seq_length
SCREAMING_SNAKE_CASE__ :Any = is_training
SCREAMING_SNAKE_CASE__ :int = use_input_mask
SCREAMING_SNAKE_CASE__ :Dict = use_token_type_ids
SCREAMING_SNAKE_CASE__ :str = use_labels
SCREAMING_SNAKE_CASE__ :str = vocab_size
SCREAMING_SNAKE_CASE__ :Optional[Any] = hidden_size
SCREAMING_SNAKE_CASE__ :List[Any] = embedding_size
SCREAMING_SNAKE_CASE__ :int = num_hidden_layers
SCREAMING_SNAKE_CASE__ :int = num_attention_heads
SCREAMING_SNAKE_CASE__ :Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE__ :str = hidden_act
SCREAMING_SNAKE_CASE__ :Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ :Any = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ :Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE__ :Optional[int] = type_vocab_size
SCREAMING_SNAKE_CASE__ :Tuple = type_sequence_label_size
SCREAMING_SNAKE_CASE__ :Optional[Any] = initializer_range
SCREAMING_SNAKE_CASE__ :int = num_labels
SCREAMING_SNAKE_CASE__ :Optional[Any] = num_choices
SCREAMING_SNAKE_CASE__ :List[Any] = scope
def __lowerCamelCase ( self : Any ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ :Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ :List[Any] = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE__ :str = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE__ :Any = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE__ :List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
SCREAMING_SNAKE_CASE__ :List[str] = None
SCREAMING_SNAKE_CASE__ :int = None
SCREAMING_SNAKE_CASE__ :Tuple = None
if self.use_labels:
SCREAMING_SNAKE_CASE__ :Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
SCREAMING_SNAKE_CASE__ :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
SCREAMING_SNAKE_CASE__ :Any = ids_tensor([self.batch_size] , self.num_choices )
SCREAMING_SNAKE_CASE__ :str = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self : List[str] ) -> Any:
return MegatronBertConfig(
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=UpperCamelCase_ , initializer_range=self.initializer_range , )
def __lowerCamelCase ( self : Optional[int] , UpperCamelCase_ : List[str] , UpperCamelCase_ : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Any , UpperCamelCase_ : int ) -> int:
SCREAMING_SNAKE_CASE__ :Optional[int] = MegatronBertModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :int = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[str] = 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 __lowerCamelCase ( self : str , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : str , UpperCamelCase_ : List[str] , UpperCamelCase_ : int , UpperCamelCase_ : Union[str, Any] ) -> List[str]:
SCREAMING_SNAKE_CASE__ :str = MegatronBertForMaskedLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :Optional[int] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self : Any , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Any , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> Dict:
SCREAMING_SNAKE_CASE__ :Optional[int] = MegatronBertForCausalLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :List[str] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self : List[str] , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Dict , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[int] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ :Optional[int] = MegatronBertForNextSentencePrediction(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :List[str] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __lowerCamelCase ( self : Dict , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : str , UpperCamelCase_ : List[str] , UpperCamelCase_ : Tuple , UpperCamelCase_ : int , UpperCamelCase_ : Optional[int] ) -> Any:
SCREAMING_SNAKE_CASE__ :Tuple = MegatronBertForPreTraining(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :Union[str, Any] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ , next_sentence_label=UpperCamelCase_ , )
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 : Union[str, Any] , UpperCamelCase_ : int , UpperCamelCase_ : Any , UpperCamelCase_ : Dict , UpperCamelCase_ : Dict , UpperCamelCase_ : Tuple , UpperCamelCase_ : List[Any] , UpperCamelCase_ : List[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ :int = MegatronBertForQuestionAnswering(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :Tuple = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , start_positions=UpperCamelCase_ , end_positions=UpperCamelCase_ , )
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 : List[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : int , UpperCamelCase_ : List[str] , UpperCamelCase_ : str , UpperCamelCase_ : Any , UpperCamelCase_ : Tuple , UpperCamelCase_ : Any ) -> Dict:
SCREAMING_SNAKE_CASE__ :List[Any] = self.num_labels
SCREAMING_SNAKE_CASE__ :Optional[int] = MegatronBertForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :int = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self : Any , UpperCamelCase_ : int , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : List[str] , UpperCamelCase_ : Optional[int] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE__ :Dict = self.num_labels
SCREAMING_SNAKE_CASE__ :Any = MegatronBertForTokenClassification(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :Optional[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self : str , UpperCamelCase_ : Any , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : int , UpperCamelCase_ : str , UpperCamelCase_ : Any , UpperCamelCase_ : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ :Optional[Any] = self.num_choices
SCREAMING_SNAKE_CASE__ :Tuple = MegatronBertForMultipleChoice(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE__ :Any = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ :List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ :Dict = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
SCREAMING_SNAKE_CASE__ :Union[str, Any] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self : Any ) -> int:
SCREAMING_SNAKE_CASE__ :Dict = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) , (
SCREAMING_SNAKE_CASE__
) ,
) :Union[str, Any] = config_and_inputs
SCREAMING_SNAKE_CASE__ :Tuple = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
A_ : List[Any] = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
A_ : List[Any] = (
{
'feature-extraction': MegatronBertModel,
'fill-mask': MegatronBertForMaskedLM,
'question-answering': MegatronBertForQuestionAnswering,
'text-classification': MegatronBertForSequenceClassification,
'text-generation': MegatronBertForCausalLM,
'token-classification': MegatronBertForTokenClassification,
'zero-shot': MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
A_ : int = True
# test_resize_embeddings = False
A_ : Dict = False
def __lowerCamelCase ( self : Union[str, Any] , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : str=False ) -> List[Any]:
SCREAMING_SNAKE_CASE__ :Optional[Any] = super()._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ , return_labels=UpperCamelCase_ )
if return_labels:
if model_class in get_values(UpperCamelCase_ ):
SCREAMING_SNAKE_CASE__ :str = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[str] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCamelCase_ )
return inputs_dict
def __lowerCamelCase ( self : Union[str, Any] ) -> Any:
SCREAMING_SNAKE_CASE__ :Dict = MegatronBertModelTester(self )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase_ , hidden_size=37 )
def __lowerCamelCase ( self : List[str] ) -> Optional[int]:
self.config_tester.run_common_tests()
def __lowerCamelCase ( self : List[str] ) -> List[str]:
SCREAMING_SNAKE_CASE__ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*UpperCamelCase_ )
def __lowerCamelCase ( self : Any ) -> Tuple:
SCREAMING_SNAKE_CASE__ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*UpperCamelCase_ )
def __lowerCamelCase ( self : Union[str, Any] ) -> str:
SCREAMING_SNAKE_CASE__ :Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*UpperCamelCase_ )
def __lowerCamelCase ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE__ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*UpperCamelCase_ )
def __lowerCamelCase ( self : int ) -> Any:
SCREAMING_SNAKE_CASE__ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*UpperCamelCase_ )
def __lowerCamelCase ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*UpperCamelCase_ )
def __lowerCamelCase ( self : Any ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*UpperCamelCase_ )
def __lowerCamelCase ( self : List[Any] ) -> Dict:
SCREAMING_SNAKE_CASE__ :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*UpperCamelCase_ )
def lowerCamelCase ( UpperCAmelCase__ : Dict ) -> Any:
'''simple docstring'''
return torch.tensor(
UpperCAmelCase__ , dtype=torch.long , device=UpperCAmelCase__ , )
UpperCamelCase_ = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
@slow
@unittest.skip('Model is not available.' )
def __lowerCamelCase ( self : str ) -> int:
SCREAMING_SNAKE_CASE__ :Tuple = 'nvidia/megatron-bert-uncased-345m'
if "MYDIR" in os.environ:
SCREAMING_SNAKE_CASE__ :Dict = os.path.join(os.environ['MYDIR'] , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Dict = MegatronBertModel.from_pretrained(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.half()
SCREAMING_SNAKE_CASE__ :Optional[int] = _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE__ :str = model(UpperCamelCase_ )[0]
SCREAMING_SNAKE_CASE__ :Dict = torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Tuple = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
SCREAMING_SNAKE_CASE__ :List[Any] = output[0, ii, jj]
SCREAMING_SNAKE_CASE__ :List[str] = expected[3 * ii + jj]
SCREAMING_SNAKE_CASE__ :List[Any] = 'ii={} jj={} a={} b={}'.format(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
self.assertTrue(math.isclose(UpperCamelCase_ , UpperCamelCase_ , rel_tol=UpperCamelCase_ , abs_tol=UpperCamelCase_ ) , msg=UpperCamelCase_ )
| 209
| 0
|
'''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,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
UpperCAmelCase_ : str = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
UpperCAmelCase_ : Optional[int] = TaTokenizerFast
UpperCAmelCase_ : Tuple = {"configuration_mt5": ["MT5Config", "MT5OnnxConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
"MT5EncoderModel",
"MT5ForConditionalGeneration",
"MT5ForQuestionAnswering",
"MT5Model",
"MT5PreTrainedModel",
"MT5Stack",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Tuple = ["TFMT5EncoderModel", "TFMT5ForConditionalGeneration", "TFMT5Model"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = ["FlaxMT5EncoderModel", "FlaxMT5ForConditionalGeneration", "FlaxMT5Model"]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
UpperCAmelCase_ : Tuple = _LazyModule(
__name__,
globals()["__file__"],
_import_structure,
extra_objects={"MT5Tokenizer": MTaTokenizer, "MT5TokenizerFast": MTaTokenizerFast},
module_spec=__spec__,
)
| 424
|
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : List[str] = Mock()
_a : str = conn, Mock()
_a : Union[str, Any] = iter([1, None] )
_a : List[str] = lambda A : next(A )
# ===== invoke =====
send_file(filename='mytext.txt' , testing=A )
# ===== ensurance =====
sock.assert_called_once()
sock.return_value.bind.assert_called_once()
sock.return_value.listen.assert_called_once()
sock.return_value.accept.assert_called_once()
conn.recv.assert_called_once()
file.return_value.__enter__.assert_called_once()
file.return_value.__enter__.return_value.read.assert_called()
conn.send.assert_called_once()
conn.close.assert_called_once()
sock.return_value.shutdown.assert_called_once()
sock.return_value.close.assert_called_once()
| 424
| 1
|
"""simple docstring"""
import argparse
import datetime
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : int = {
'''0''': '''Sunday''',
'''1''': '''Monday''',
'''2''': '''Tuesday''',
'''3''': '''Wednesday''',
'''4''': '''Thursday''',
'''5''': '''Friday''',
'''6''': '''Saturday''',
}
__lowercase : int = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(__UpperCamelCase ) < 11:
raise ValueError('''Must be 10 characters long''' )
# Get month
__lowercase : int = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError('''Month must be between 1 - 12''' )
__lowercase : str = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get day
__lowercase : int = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError('''Date must be between 1 - 31''' )
# Get second separator
__lowercase : str = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError('''Date separator must be \'-\' or \'/\'''' )
# Get year
__lowercase : int = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 85_00:
raise ValueError(
'''Year out of range. There has to be some sort of limit...right?''' )
# Get datetime obj for validation
__lowercase : List[Any] = datetime.date(int(__UpperCamelCase ) , int(__UpperCamelCase ) , int(__UpperCamelCase ) )
# Start math
if m <= 2:
__lowercase : Tuple = y - 1
__lowercase : Tuple = m + 12
# maths var
__lowercase : int = int(str(__UpperCamelCase )[:2] )
__lowercase : int = int(str(__UpperCamelCase )[2:] )
__lowercase : int = int(2.6 * m - 5.39 )
__lowercase : int = int(c / 4 )
__lowercase : int = int(k / 4 )
__lowercase : int = int(d + k )
__lowercase : int = int(t + u + v + x )
__lowercase : int = int(z - (2 * c) )
__lowercase : int = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError('''The date was evaluated incorrectly. Contact developer.''' )
# Response
__lowercase : str = f"""Your date {date_input}, is a {days[str(__UpperCamelCase )]}!"""
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
a_ = argparse.ArgumentParser(
description=(
'Find out what day of the week nearly any date is or was. Enter '
'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
)
)
parser.add_argument(
'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
)
a_ = parser.parse_args()
zeller(args.date_input)
| 76
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
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
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__snake_case = 16
__snake_case = 32
def _lowercase ( SCREAMING_SNAKE_CASE_ : Accelerator , SCREAMING_SNAKE_CASE_ : int = 16 ):
"""simple docstring"""
UpperCamelCase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
UpperCamelCase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(SCREAMING_SNAKE_CASE_ : str ):
# max_length=None => use the model max length (it's actually the default)
UpperCamelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ )
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():
UpperCamelCase = datasets.map(
SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , 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
UpperCamelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(SCREAMING_SNAKE_CASE_ : List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
UpperCamelCase = 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":
UpperCamelCase = 16
elif accelerator.mixed_precision != "no":
UpperCamelCase = 8
else:
UpperCamelCase = None
return tokenizer.pad(
SCREAMING_SNAKE_CASE_ , padding="""longest""" , max_length=SCREAMING_SNAKE_CASE_ , pad_to_multiple_of=SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
UpperCamelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=SCREAMING_SNAKE_CASE_ , collate_fn=SCREAMING_SNAKE_CASE_ , batch_size=SCREAMING_SNAKE_CASE_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__snake_case = mocked_dataloaders # noqa: F811
def _lowercase ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , SCREAMING_SNAKE_CASE_ ) == "1":
UpperCamelCase = 2
# New Code #
UpperCamelCase = int(args.gradient_accumulation_steps )
UpperCamelCase = int(args.local_sgd_steps )
# Initialize accelerator
UpperCamelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=SCREAMING_SNAKE_CASE_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
UpperCamelCase = config["""lr"""]
UpperCamelCase = int(config["""num_epochs"""] )
UpperCamelCase = int(config["""seed"""] )
UpperCamelCase = int(config["""batch_size"""] )
UpperCamelCase = evaluate.load("""glue""" , """mrpc""" )
set_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase , UpperCamelCase = get_dataloaders(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
UpperCamelCase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=SCREAMING_SNAKE_CASE_ )
# 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).
UpperCamelCase = model.to(accelerator.device )
# Instantiate optimizer
UpperCamelCase = AdamW(params=model.parameters() , lr=SCREAMING_SNAKE_CASE_ )
# Instantiate scheduler
UpperCamelCase = get_linear_schedule_with_warmup(
optimizer=SCREAMING_SNAKE_CASE_ , num_warmup_steps=100 , num_training_steps=(len(SCREAMING_SNAKE_CASE_ ) * num_epochs) , )
# 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.
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = accelerator.prepare(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Now we train the model
for epoch in range(SCREAMING_SNAKE_CASE_ ):
model.train()
with LocalSGD(
accelerator=SCREAMING_SNAKE_CASE_ , model=SCREAMING_SNAKE_CASE_ , local_sgd_steps=SCREAMING_SNAKE_CASE_ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase = output.loss
accelerator.backward(SCREAMING_SNAKE_CASE_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(SCREAMING_SNAKE_CASE_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ )
UpperCamelCase = outputs.logits.argmax(dim=-1 )
UpperCamelCase , UpperCamelCase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=SCREAMING_SNAKE_CASE_ , references=SCREAMING_SNAKE_CASE_ , )
UpperCamelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , SCREAMING_SNAKE_CASE_ )
def _lowercase ( ):
"""simple docstring"""
UpperCamelCase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=SCREAMING_SNAKE_CASE_ , default=SCREAMING_SNAKE_CASE_ , 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.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=SCREAMING_SNAKE_CASE_ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=SCREAMING_SNAKE_CASE_ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
UpperCamelCase = parser.parse_args()
UpperCamelCase = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
main()
| 386
| 0
|
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( snake_case_ :str ):
def decorator(snake_case_ :Any ):
__UpperCAmelCase = getattr(snake_case_ , '''handle_key''' , [] )
handle += [key]
setattr(snake_case_ , '''handle_key''' , snake_case_ )
return func
return decorator
def lowercase__ ( *snake_case_ :List[str] ):
def decorator(snake_case_ :List[Any] ):
__UpperCAmelCase = getattr(snake_case_ , '''handle_key''' , [] )
handle += keys
setattr(snake_case_ , '''handle_key''' , snake_case_ )
return func
return decorator
class _UpperCAmelCase ( _lowerCAmelCase ):
def __new__( cls : Optional[int] , _lowercase : int , _lowercase : Union[str, Any] , _lowercase : Union[str, Any] ):
__UpperCAmelCase = super().__new__(cls , _lowercase , _lowercase , _lowercase )
if not hasattr(_lowercase , '''key_handler''' ):
setattr(_lowercase , '''key_handler''' , {} )
setattr(_lowercase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
__UpperCAmelCase = getattr(_lowercase , '''handle_key''' , [] )
for key in handled_keys:
__UpperCAmelCase = value
return new_cls
@staticmethod
def a ( cls : Dict ):
__UpperCAmelCase = get_character()
if char != KEYMAP["undefined"]:
__UpperCAmelCase = ord(_lowercase )
__UpperCAmelCase = cls.key_handler.get(_lowercase )
if handler:
__UpperCAmelCase = char
return handler(cls )
else:
return None
def lowercase__ ( cls :List[Any] ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 397
|
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[Any] = {
'configuration_cpmant': ['CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CpmAntConfig'],
'tokenization_cpmant': ['CpmAntTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowercase : int = [
'CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST',
'CpmAntForCausalLM',
'CpmAntModel',
'CpmAntPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
_lowercase : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 397
| 1
|
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class _UpperCAmelCase ( _A ):
"""simple docstring"""
def __lt__( self , _lowerCAmelCase ):
'''simple docstring'''
return self[-1] < other[-1]
def __eq__( self , _lowerCAmelCase ):
'''simple docstring'''
return self[-1] == other[-1]
def snake_case__ ( UpperCAmelCase : list ):
lowerCAmelCase__ :list[Stack] = []
# sort into stacks
for element in collection:
lowerCAmelCase__ :int = Stack([element] )
lowerCAmelCase__ :Tuple = bisect_left(UpperCAmelCase , UpperCAmelCase )
if i != len(UpperCAmelCase ):
stacks[i].append(UpperCAmelCase )
else:
stacks.append(UpperCAmelCase )
# use a heap-based merge to merge stack efficiently
lowerCAmelCase__ :List[str] = merge(*(reversed(UpperCAmelCase ) for stack in stacks) )
return collection
if __name__ == "__main__":
_a : List[str] = input("""Enter numbers separated by a comma:\n""").strip()
_a : int = [int(item) for item in user_input.split(""",""")]
print(patience_sort(unsorted))
| 145
|
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_a : Optional[int] = 16
_a : List[Any] = 32
def snake_case__ ( UpperCAmelCase : Accelerator , UpperCAmelCase : int = 1_6 ):
lowerCAmelCase__ :Optional[int] = AutoTokenizer.from_pretrained("bert-base-cased" )
lowerCAmelCase__ :Dict = load_dataset("glue" , "mrpc" )
def tokenize_function(UpperCAmelCase : Optional[int] ):
# max_length=None => use the model max length (it's actually the default)
lowerCAmelCase__ :Any = 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():
lowerCAmelCase__ :Dict = 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
lowerCAmelCase__ :str = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(UpperCAmelCase : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowerCAmelCase__ :Dict = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowerCAmelCase__ :int = 1_6
elif accelerator.mixed_precision != "no":
lowerCAmelCase__ :List[str] = 8
else:
lowerCAmelCase__ :Dict = None
return tokenizer.pad(
UpperCAmelCase , padding="longest" , max_length=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_tensors="pt" , )
# Instantiate dataloaders.
lowerCAmelCase__ :int = DataLoader(
tokenized_datasets["train"] , shuffle=UpperCAmelCase , collate_fn=UpperCAmelCase , batch_size=UpperCAmelCase )
lowerCAmelCase__ :List[Any] = DataLoader(
tokenized_datasets["validation"] , shuffle=UpperCAmelCase , collate_fn=UpperCAmelCase , batch_size=UpperCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_a : List[str] = mocked_dataloaders # noqa: F811
def snake_case__ ( UpperCAmelCase : str , UpperCAmelCase : Dict ):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" , UpperCAmelCase ) == "1":
lowerCAmelCase__ :Union[str, Any] = 2
# New Code #
lowerCAmelCase__ :List[str] = int(args.gradient_accumulation_steps )
# Initialize accelerator
lowerCAmelCase__ :List[Any] = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=UpperCAmelCase )
if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
raise NotImplementedError(
"Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowerCAmelCase__ :Union[str, Any] = config["lr"]
lowerCAmelCase__ :Dict = int(config["num_epochs"] )
lowerCAmelCase__ :str = int(config["seed"] )
lowerCAmelCase__ :int = int(config["batch_size"] )
lowerCAmelCase__ :Any = evaluate.load("glue" , "mrpc" )
set_seed(UpperCAmelCase )
lowerCAmelCase__ ,lowerCAmelCase__ :Dict = get_dataloaders(UpperCAmelCase , UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowerCAmelCase__ :Any = 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).
lowerCAmelCase__ :str = model.to(accelerator.device )
# Instantiate optimizer
lowerCAmelCase__ :Tuple = AdamW(params=model.parameters() , lr=UpperCAmelCase )
# Instantiate scheduler
lowerCAmelCase__ :Optional[int] = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase , num_warmup_steps=1_0_0 , num_training_steps=(len(UpperCAmelCase ) * num_epochs) , )
# 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.
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ :List[Any] = 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 )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(UpperCAmelCase ):
lowerCAmelCase__ :Optional[int] = model(**UpperCAmelCase )
lowerCAmelCase__ :Any = output.loss
accelerator.backward(UpperCAmelCase )
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():
lowerCAmelCase__ :Optional[int] = model(**UpperCAmelCase )
lowerCAmelCase__ :int = outputs.logits.argmax(dim=-1 )
lowerCAmelCase__ ,lowerCAmelCase__ :Dict = accelerator.gather_for_metrics((predictions, batch["labels"]) )
metric.add_batch(
predictions=UpperCAmelCase , references=UpperCAmelCase , )
lowerCAmelCase__ :List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCAmelCase )
def snake_case__ ( ):
lowerCAmelCase__ :str = 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." , )
# New Code #
parser.add_argument(
"--gradient_accumulation_steps" , type=UpperCAmelCase , default=1 , help="The number of minibatches to be ran before gradients are accumulated." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU." )
lowerCAmelCase__ :int = parser.parse_args()
lowerCAmelCase__ :Union[str, Any] = {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
training_function(UpperCAmelCase , UpperCAmelCase )
if __name__ == "__main__":
main()
| 145
| 1
|
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase = "cpu" , __UpperCamelCase = None ):
'''simple docstring'''
UpperCAmelCase__ : int = torch.load(__UpperCamelCase , map_location=__UpperCamelCase )
for k, v in tqdm(state_dict.items() ):
if not isinstance(__UpperCamelCase , torch.Tensor ):
raise TypeError("""FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin""" )
UpperCAmelCase__ : Tuple = v.half()
if save_path is None: # overwrite src_path
UpperCAmelCase__ : List[Any] = src_path
torch.save(__UpperCamelCase , __UpperCamelCase )
if __name__ == "__main__":
fire.Fire(convert)
| 194
|
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
from .text_encoder import MultilingualCLIP
__UpperCAmelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
__UpperCAmelCase = '\n Examples:\n ```py\n >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n >>> import torch\n\n >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n >>> pipe_prior.to("cuda")\n\n >>> prompt = "red cat, 4k photo"\n >>> out = pipe_prior(prompt)\n >>> image_emb = out.image_embeds\n >>> negative_image_emb = out.negative_image_embeds\n\n >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n >>> pipe.to("cuda")\n\n >>> image = pipe(\n ... prompt,\n ... image_embeds=image_emb,\n ... negative_image_embeds=negative_image_emb,\n ... height=768,\n ... width=768,\n ... num_inference_steps=100,\n ... ).images\n\n >>> image[0].save("cat.png")\n ```\n'
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=8 ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = h // scale_factor**2
if h % scale_factor**2 != 0:
new_h += 1
UpperCAmelCase__ : Any = w // scale_factor**2
if w % scale_factor**2 != 0:
new_w += 1
return new_h * scale_factor, new_w * scale_factor
class __lowercase ( __lowerCamelCase ):
def __init__( self : str ,A : MultilingualCLIP ,A : XLMRobertaTokenizer ,A : UNetaDConditionModel ,A : Union[DDIMScheduler, DDPMScheduler] ,A : VQModel ,):
'''simple docstring'''
super().__init__()
self.register_modules(
text_encoder=A ,tokenizer=A ,unet=A ,scheduler=A ,movq=A ,)
UpperCAmelCase__ : Optional[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def __lowercase ( self : Dict ,A : Any ,A : Tuple ,A : Dict ,A : int ,A : str ,A : List[str] ):
'''simple docstring'''
if latents is None:
UpperCAmelCase__ : Any = randn_tensor(A ,generator=A ,device=A ,dtype=A )
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
UpperCAmelCase__ : int = latents.to(A )
UpperCAmelCase__ : Any = latents * scheduler.init_noise_sigma
return latents
def __lowercase ( self : Optional[int] ,A : List[Any] ,A : Optional[Any] ,A : str ,A : Optional[Any] ,A : str=None ,):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = len(A ) if isinstance(A ,A ) else 1
# get prompt text embeddings
UpperCAmelCase__ : List[Any] = self.tokenizer(
A ,padding="""max_length""" ,truncation=A ,max_length=77 ,return_attention_mask=A ,add_special_tokens=A ,return_tensors="""pt""" ,)
UpperCAmelCase__ : List[str] = text_inputs.input_ids
UpperCAmelCase__ : Any = self.tokenizer(A ,padding="""longest""" ,return_tensors="""pt""" ).input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A ,A ):
UpperCAmelCase__ : List[str] = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] )
logger.warning(
"""The following part of your input was truncated because CLIP can only handle sequences up to"""
f" {self.tokenizer.model_max_length} tokens: {removed_text}" )
UpperCAmelCase__ : str = text_input_ids.to(A )
UpperCAmelCase__ : Optional[Any] = text_inputs.attention_mask.to(A )
UpperCAmelCase__ , UpperCAmelCase__ : Tuple = self.text_encoder(
input_ids=A ,attention_mask=A )
UpperCAmelCase__ : Optional[int] = prompt_embeds.repeat_interleave(A ,dim=0 )
UpperCAmelCase__ : Optional[int] = text_encoder_hidden_states.repeat_interleave(A ,dim=0 )
UpperCAmelCase__ : List[str] = text_mask.repeat_interleave(A ,dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase__ : List[str]
if negative_prompt is None:
UpperCAmelCase__ : List[Any] = [""""""] * batch_size
elif type(A ) is not type(A ):
raise TypeError(
f"`negative_prompt` should be the same type to `prompt`, but got {type(A )} !="
f" {type(A )}." )
elif isinstance(A ,A ):
UpperCAmelCase__ : Any = [negative_prompt]
elif batch_size != len(A ):
raise ValueError(
f"`negative_prompt`: {negative_prompt} has batch size {len(A )}, but `prompt`:"
f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
""" the batch size of `prompt`.""" )
else:
UpperCAmelCase__ : List[Any] = negative_prompt
UpperCAmelCase__ : Any = self.tokenizer(
A ,padding="""max_length""" ,max_length=77 ,truncation=A ,return_attention_mask=A ,add_special_tokens=A ,return_tensors="""pt""" ,)
UpperCAmelCase__ : Optional[int] = uncond_input.input_ids.to(A )
UpperCAmelCase__ : str = uncond_input.attention_mask.to(A )
UpperCAmelCase__ , UpperCAmelCase__ : Dict = self.text_encoder(
input_ids=A ,attention_mask=A )
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
UpperCAmelCase__ : Any = negative_prompt_embeds.shape[1]
UpperCAmelCase__ : Any = negative_prompt_embeds.repeat(1 ,A )
UpperCAmelCase__ : str = negative_prompt_embeds.view(batch_size * num_images_per_prompt ,A )
UpperCAmelCase__ : Dict = uncond_text_encoder_hidden_states.shape[1]
UpperCAmelCase__ : Any = uncond_text_encoder_hidden_states.repeat(1 ,A ,1 )
UpperCAmelCase__ : Any = uncond_text_encoder_hidden_states.view(
batch_size * num_images_per_prompt ,A ,-1 )
UpperCAmelCase__ : List[Any] = uncond_text_mask.repeat_interleave(A ,dim=0 )
# done duplicates
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
UpperCAmelCase__ : Any = torch.cat([negative_prompt_embeds, prompt_embeds] )
UpperCAmelCase__ : int = torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
UpperCAmelCase__ : str = torch.cat([uncond_text_mask, text_mask] )
return prompt_embeds, text_encoder_hidden_states, text_mask
def __lowercase ( self : Tuple ,A : Dict=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
UpperCAmelCase__ : str = torch.device(f"cuda:{gpu_id}" )
UpperCAmelCase__ : Tuple = [
self.unet,
self.text_encoder,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(A ,A )
def __lowercase ( self : int ,A : Optional[Any]=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" ,"""0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
UpperCAmelCase__ : List[str] = torch.device(f"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to("""cpu""" ,silence_dtype_warnings=A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase__ : List[str] = None
for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
UpperCAmelCase__ , UpperCAmelCase__ : Union[str, Any] = cpu_offload_with_hook(A ,A ,prev_module_hook=A )
if self.safety_checker is not None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = cpu_offload_with_hook(self.safety_checker ,A ,prev_module_hook=A )
# We'll offload the last model manually.
UpperCAmelCase__ : Any = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __lowercase ( self : List[Any] ):
'''simple docstring'''
if not hasattr(self.unet ,"""_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(A ,"""_hf_hook""" )
and hasattr(module._hf_hook ,"""execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(A )
def __call__( self : Union[str, Any] ,A : Union[str, List[str]] ,A : Union[torch.FloatTensor, List[torch.FloatTensor]] ,A : Union[torch.FloatTensor, List[torch.FloatTensor]] ,A : Optional[Union[str, List[str]]] = None ,A : int = 512 ,A : int = 512 ,A : int = 100 ,A : float = 4.0 ,A : int = 1 ,A : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,A : Optional[torch.FloatTensor] = None ,A : Optional[str] = "pil" ,A : bool = True ,):
'''simple docstring'''
if isinstance(A ,A ):
UpperCAmelCase__ : str = 1
elif isinstance(A ,A ):
UpperCAmelCase__ : Tuple = len(A )
else:
raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(A )}" )
UpperCAmelCase__ : Optional[int] = self._execution_device
UpperCAmelCase__ : Dict = batch_size * num_images_per_prompt
UpperCAmelCase__ : Union[str, Any] = guidance_scale > 1.0
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = self._encode_prompt(
A ,A ,A ,A ,A )
if isinstance(A ,A ):
UpperCAmelCase__ : Dict = torch.cat(A ,dim=0 )
if isinstance(A ,A ):
UpperCAmelCase__ : Dict = torch.cat(A ,dim=0 )
if do_classifier_free_guidance:
UpperCAmelCase__ : Optional[int] = image_embeds.repeat_interleave(A ,dim=0 )
UpperCAmelCase__ : Tuple = negative_image_embeds.repeat_interleave(A ,dim=0 )
UpperCAmelCase__ : List[str] = torch.cat([negative_image_embeds, image_embeds] ,dim=0 ).to(
dtype=prompt_embeds.dtype ,device=A )
self.scheduler.set_timesteps(A ,device=A )
UpperCAmelCase__ : int = self.scheduler.timesteps
UpperCAmelCase__ : Union[str, Any] = self.unet.config.in_channels
UpperCAmelCase__ , UpperCAmelCase__ : Dict = get_new_h_w(A ,A ,self.movq_scale_factor )
# create initial latent
UpperCAmelCase__ : Dict = self.prepare_latents(
(batch_size, num_channels_latents, height, width) ,text_encoder_hidden_states.dtype ,A ,A ,A ,self.scheduler ,)
for i, t in enumerate(self.progress_bar(A ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ : Optional[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ : Optional[Any] = {"""text_embeds""": prompt_embeds, """image_embeds""": image_embeds}
UpperCAmelCase__ : Any = self.unet(
sample=A ,timestep=A ,encoder_hidden_states=A ,added_cond_kwargs=A ,return_dict=A ,)[0]
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ : List[str] = noise_pred.split(latents.shape[1] ,dim=1 )
UpperCAmelCase__ , UpperCAmelCase__ : Any = noise_pred.chunk(2 )
UpperCAmelCase__ , UpperCAmelCase__ : str = variance_pred.chunk(2 )
UpperCAmelCase__ : int = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase__ : List[Any] = torch.cat([noise_pred, variance_pred_text] ,dim=1 )
if not (
hasattr(self.scheduler.config ,"""variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase__ , UpperCAmelCase__ : List[Any] = noise_pred.split(latents.shape[1] ,dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ : Optional[Any] = self.scheduler.step(
A ,A ,A ,generator=A ,).prev_sample
# post-processing
UpperCAmelCase__ : List[Any] = self.movq.decode(A ,force_not_quantize=A )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
UpperCAmelCase__ : Union[str, Any] = image * 0.5 + 0.5
UpperCAmelCase__ : Optional[Any] = image.clamp(0 ,1 )
UpperCAmelCase__ : Dict = image.cpu().permute(0 ,2 ,3 ,1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase__ : List[str] = self.numpy_to_pil(A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=A )
| 194
| 1
|
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def UpperCamelCase ( snake_case__ : str , snake_case__ : str , **snake_case__ : Optional[Any] ) -> List[Any]:
UpperCamelCase : Optional[int] = AutoConfig.from_pretrained(snake_case__ , **snake_case__ )
UpperCamelCase : int = AutoModelForSeqaSeqLM.from_config(snake_case__ )
model.save_pretrained(snake_case__ )
AutoTokenizer.from_pretrained(snake_case__ ).save_pretrained(snake_case__ )
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)
| 40
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = '''▁'''
__UpperCAmelCase = {'''vocab_file''': '''sentencepiece.bpe.model'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
}
}
__UpperCAmelCase = {
'''facebook/xglm-564M''': 2_048,
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : int = VOCAB_FILES_NAMES
UpperCAmelCase__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : List[Any] = ["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_ = None, **SCREAMING_SNAKE_CASE_, ) -> None:
UpperCamelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
UpperCamelCase : Any = 7
UpperCamelCase : Optional[int] = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]
UpperCamelCase : Dict = kwargs.get('additional_special_tokens', [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
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_, sp_model_kwargs=self.sp_model_kwargs, **SCREAMING_SNAKE_CASE_, )
UpperCamelCase : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : Optional[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
UpperCamelCase : int = 1
# Mimic fairseq token-to-id alignment for the first 4 token
UpperCamelCase : Dict = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}
UpperCamelCase : Optional[int] = len(self.sp_model )
UpperCamelCase : Any = {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> List[Any]:
UpperCamelCase : int = self.__dict__.copy()
UpperCamelCase : Union[str, Any] = None
UpperCamelCase : int = self.sp_model.serialized_model_proto()
return state
def __setstate__( self, SCREAMING_SNAKE_CASE_ ) -> str:
UpperCamelCase : Any = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs' ):
UpperCamelCase : Any = {}
UpperCamelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
UpperCamelCase : Optional[int] = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = False ) -> List[int]:
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_ ))
return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_ ))
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
UpperCamelCase : str = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def snake_case_ ( self ) -> int:
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def snake_case_ ( self ) -> int:
UpperCamelCase : List[str] = {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 snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> List[str]:
return self.sp_model.encode(SCREAMING_SNAKE_CASE_, out_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
UpperCamelCase : Union[str, Any] = self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> str:
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
UpperCamelCase : Dict = ''.join(SCREAMING_SNAKE_CASE_ ).replace(SCREAMING_SNAKE_CASE_, ' ' ).strip()
return out_string
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCamelCase : Optional[int] = 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:
UpperCamelCase : List[str] = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
| 40
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/config.json'
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = '''fnet'''
def __init__( self ,_SCREAMING_SNAKE_CASE=32_000 ,_SCREAMING_SNAKE_CASE=768 ,_SCREAMING_SNAKE_CASE=12 ,_SCREAMING_SNAKE_CASE=3_072 ,_SCREAMING_SNAKE_CASE="gelu_new" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=512 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=0.02 ,_SCREAMING_SNAKE_CASE=1e-12 ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=512 ,_SCREAMING_SNAKE_CASE=3 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=2 ,**_SCREAMING_SNAKE_CASE ,) -> Union[str, Any]:
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE ,bos_token_id=_SCREAMING_SNAKE_CASE ,eos_token_id=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
UpperCAmelCase_ : List[str] = vocab_size
UpperCAmelCase_ : Optional[int] = max_position_embeddings
UpperCAmelCase_ : str = hidden_size
UpperCAmelCase_ : int = num_hidden_layers
UpperCAmelCase_ : List[str] = intermediate_size
UpperCAmelCase_ : List[str] = hidden_act
UpperCAmelCase_ : Tuple = hidden_dropout_prob
UpperCAmelCase_ : Tuple = initializer_range
UpperCAmelCase_ : int = type_vocab_size
UpperCAmelCase_ : str = layer_norm_eps
UpperCAmelCase_ : Optional[int] = use_tpu_fourier_optimizations
UpperCAmelCase_ : Any = tpu_short_seq_length
| 300
|
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
return (data["data"], data["target"])
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
UpperCAmelCase_ : List[str] = XGBClassifier()
classifier.fit(_lowercase , _lowercase )
return classifier
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = load_iris()
UpperCAmelCase_, UpperCAmelCase_ : Any = data_handling(_lowercase )
UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_ : Optional[int] = train_test_split(
_lowercase , _lowercase , test_size=0.25 )
UpperCAmelCase_ : Dict = iris['''target_names''']
# Create an XGBoost Classifier from the training data
UpperCAmelCase_ : int = xgboost(_lowercase , _lowercase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_lowercase , _lowercase , _lowercase , display_labels=_lowercase , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 300
| 1
|
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
UpperCAmelCase_ : List[str] = random.Random()
def A_ ( _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=1.0 , _lowerCAmelCase : Tuple=None , _lowerCAmelCase : Optional[Any]=None ):
"""simple docstring"""
if rng is None:
_lowerCamelCase : List[Any] = global_rng
_lowerCamelCase : Dict = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class UpperCAmelCase__ ( unittest.TestCase ):
def __init__( self : Dict,__A : List[Any],__A : Any=7,__A : Dict=4_0_0,__A : Union[str, Any]=2_0_0_0,__A : Any=1_0,__A : Dict=1_6_0,__A : List[Any]=8,__A : Optional[int]=0.0,__A : int=4_0_0_0,__A : Dict=False,__A : List[Any]=True,):
_lowerCamelCase : int = parent
_lowerCamelCase : Union[str, Any] = batch_size
_lowerCamelCase : List[Any] = min_seq_length
_lowerCamelCase : Optional[Any] = max_seq_length
_lowerCamelCase : Optional[int] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_lowerCamelCase : Optional[int] = padding_value
_lowerCamelCase : str = sampling_rate
_lowerCamelCase : int = return_attention_mask
_lowerCamelCase : List[Any] = do_normalize
_lowerCamelCase : str = feature_size
_lowerCamelCase : Tuple = chunk_length
_lowerCamelCase : List[Any] = hop_length
def lowerCamelCase_ ( self : str ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def lowerCamelCase_ ( self : int,__A : str=False,__A : Union[str, Any]=False ):
def _flatten(__A : Tuple ):
return list(itertools.chain(*__A ) )
if equal_length:
_lowerCamelCase : Optional[int] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_lowerCamelCase : Optional[int] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length,self.max_seq_length,self.seq_length_diff )
]
if numpify:
_lowerCamelCase : Dict = [np.asarray(__A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCAmelCase__ ( A , unittest.TestCase ):
lowerCAmelCase_ = WhisperFeatureExtractor if is_speech_available() else None
def lowerCamelCase_ ( self : Union[str, Any] ):
_lowerCamelCase : Tuple = WhisperFeatureExtractionTester(self )
def lowerCamelCase_ ( self : Optional[int] ):
_lowerCamelCase : Optional[Any] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : Optional[int] = feat_extract_first.save_pretrained(__A )[0]
check_json_file_has_correct_format(__A )
_lowerCamelCase : Union[str, Any] = self.feature_extraction_class.from_pretrained(__A )
_lowerCamelCase : Optional[int] = feat_extract_first.to_dict()
_lowerCamelCase : Dict = feat_extract_second.to_dict()
_lowerCamelCase : str = feat_extract_first.mel_filters
_lowerCamelCase : str = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A,__A ) )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : int ):
_lowerCamelCase : str = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
_lowerCamelCase : Any = os.path.join(__A,"feat_extract.json" )
feat_extract_first.to_json_file(__A )
_lowerCamelCase : int = self.feature_extraction_class.from_json_file(__A )
_lowerCamelCase : Tuple = feat_extract_first.to_dict()
_lowerCamelCase : Any = feat_extract_second.to_dict()
_lowerCamelCase : Dict = feat_extract_first.mel_filters
_lowerCamelCase : Union[str, Any] = feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A,__A ) )
self.assertEqual(__A,__A )
def lowerCamelCase_ ( self : Optional[Any] ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_lowerCamelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_lowerCamelCase : Union[str, Any] = [floats_list((1, x) )[0] for x in range(8_0_0,1_4_0_0,2_0_0 )]
_lowerCamelCase : Union[str, Any] = [np.asarray(__A ) for speech_input in speech_inputs]
# Test feature size
_lowerCamelCase : Optional[Any] = feature_extractor(__A,padding="max_length",return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
_lowerCamelCase : Union[str, Any] = feature_extractor(speech_inputs[0],return_tensors="np" ).input_features
_lowerCamelCase : Dict = feature_extractor(np_speech_inputs[0],return_tensors="np" ).input_features
self.assertTrue(np.allclose(__A,__A,atol=1e-3 ) )
# Test batched
_lowerCamelCase : str = feature_extractor(__A,return_tensors="np" ).input_features
_lowerCamelCase : List[Any] = feature_extractor(__A,return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__A,__A ):
self.assertTrue(np.allclose(__A,__A,atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
_lowerCamelCase : Optional[int] = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
_lowerCamelCase : Dict = np.asarray(__A )
_lowerCamelCase : int = feature_extractor(__A,return_tensors="np" ).input_features
_lowerCamelCase : Any = feature_extractor(__A,return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__A,__A ):
self.assertTrue(np.allclose(__A,__A,atol=1e-3 ) )
# Test truncation required
_lowerCamelCase : str = [floats_list((1, x) )[0] for x in range(2_0_0,(feature_extractor.n_samples + 5_0_0),2_0_0 )]
_lowerCamelCase : Dict = [np.asarray(__A ) for speech_input in speech_inputs]
_lowerCamelCase : Union[str, Any] = [x[: feature_extractor.n_samples] for x in speech_inputs]
_lowerCamelCase : Union[str, Any] = [np.asarray(__A ) for speech_input in speech_inputs_truncated]
_lowerCamelCase : Tuple = feature_extractor(__A,return_tensors="np" ).input_features
_lowerCamelCase : str = feature_extractor(__A,return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(__A,__A ):
self.assertTrue(np.allclose(__A,__A,atol=1e-3 ) )
def lowerCamelCase_ ( self : Any ):
import torch
_lowerCamelCase : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_lowerCamelCase : str = np.random.rand(1_0_0,3_2 ).astype(np.floataa )
_lowerCamelCase : Tuple = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_lowerCamelCase : List[Any] = feature_extractor.pad([{"input_features": inputs}],return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_lowerCamelCase : List[str] = feature_extractor.pad([{"input_features": inputs}],return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCamelCase_ ( self : Dict,__A : Optional[Any] ):
_lowerCamelCase : List[Any] = load_dataset("hf-internal-testing/librispeech_asr_dummy","clean",split="validation" )
# automatic decoding with librispeech
_lowerCamelCase : Dict = ds.sort("id" ).select(range(__A ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self : Any ):
# fmt: off
_lowerCamelCase : Optional[int] = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
_lowerCamelCase : Optional[Any] = self._load_datasamples(1 )
_lowerCamelCase : Union[str, Any] = WhisperFeatureExtractor()
_lowerCamelCase : str = feature_extractor(__A,return_tensors="pt" ).input_features
self.assertEqual(input_features.shape,(1, 8_0, 3_0_0_0) )
self.assertTrue(torch.allclose(input_features[0, 0, :3_0],__A,atol=1e-4 ) )
def lowerCamelCase_ ( self : Any ):
_lowerCamelCase : Optional[int] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_lowerCamelCase : List[Any] = self._load_datasamples(1 )[0]
_lowerCamelCase : List[str] = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5 # Rescale to [0, 65535] to show issue
_lowerCamelCase : Any = feat_extract.zero_mean_unit_var_norm([audio],attention_mask=__A )[0]
self.assertTrue(np.all(np.mean(__A ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(__A ) - 1 ) < 1e-3 ) )
| 44
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , unittest.TestCase ):
lowercase_ = StableDiffusionInstructPixaPixPipeline
lowercase_ = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
lowercase_ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase_ = IMAGE_TO_IMAGE_IMAGE_PARAMS
def __lowercase( self : str )-> int:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
SCREAMING_SNAKE_CASE__ : List[str] = PNDMScheduler(skip_prk_steps=a_ )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = 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 )
SCREAMING_SNAKE_CASE__ : Optional[int] = 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=1000 , )
SCREAMING_SNAKE_CASE__ : int = CLIPTextModel(a_ )
SCREAMING_SNAKE_CASE__ : Dict = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
SCREAMING_SNAKE_CASE__ : List[str] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __lowercase( self : List[Any] , a_ : Tuple , a_ : Optional[Any]=0 )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(a_ ) ).to(a_ )
SCREAMING_SNAKE_CASE__ : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = Image.fromarray(np.uinta(a_ ) ).convert('RGB' )
if str(a_ ).startswith('mps' ):
SCREAMING_SNAKE_CASE__ : str = torch.manual_seed(a_ )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.Generator(device=a_ ).manual_seed(a_ )
SCREAMING_SNAKE_CASE__ : Dict = {
'prompt': 'A painting of a squirrel eating a burger',
'image': image,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'image_guidance_scale': 1,
'output_type': 'numpy',
}
return inputs
def __lowercase( self : str )-> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : List[str] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : int = sd_pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : Dict = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : Optional[Any] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Dict = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = 'french fries'
SCREAMING_SNAKE_CASE__ : Optional[Any] = sd_pipe(**a_ , negative_prompt=a_ )
SCREAMING_SNAKE_CASE__ : Dict = output.images
SCREAMING_SNAKE_CASE__ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[str] = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : List[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[Any] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : int = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = [inputs['prompt']] * 2
SCREAMING_SNAKE_CASE__ : List[str] = np.array(inputs['image'] ).astype(np.floataa ) / 255.0
SCREAMING_SNAKE_CASE__ : Tuple = torch.from_numpy(a_ ).unsqueeze(0 ).to(a_ )
SCREAMING_SNAKE_CASE__ : Dict = image / 2 + 0.5
SCREAMING_SNAKE_CASE__ : Tuple = image.permute(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE__ : int = image.repeat(2 , 1 , 1 , 1 )
SCREAMING_SNAKE_CASE__ : Optional[int] = sd_pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Any = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : int = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = 'cpu' # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE__ : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Optional[Any] = EulerAncestralDiscreteScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' )
SCREAMING_SNAKE_CASE__ : List[Any] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : Dict = sd_pipe.to(a_ )
sd_pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = sd_pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Any = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Any = [round(a_ , 4 ) for x in image_slice.flatten().tolist()]
print(','.join([str(a_ ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[Any] = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def __lowercase( self : Union[str, Any] )-> Any:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def __lowercase( self : List[Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionInstructPixaPixPipeline(**a_ )
SCREAMING_SNAKE_CASE__ : int = VaeImageProcessor(do_resize=a_ , do_normalize=a_ )
SCREAMING_SNAKE_CASE__ : Tuple = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Any = pipe(**self.get_dummy_inputs_by_type(a_ , input_image_type='pt' ) )[0]
SCREAMING_SNAKE_CASE__ : Optional[int] = components['vae']
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_inputs_by_type(a_ , input_image_type='pt' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
SCREAMING_SNAKE_CASE__ : Union[str, Any] = vae.encode(inputs[image_param] ).latent_dist.mode()
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe(**a_ )[0]
SCREAMING_SNAKE_CASE__ : List[Any] = np.abs(out - out_latents_inputs ).max()
self.assertLess(a_ , 1e-4 , 'passing latents as image input generate different result from passing image' )
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
def __lowercase( self : Tuple )-> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowercase( self : List[Any] , a_ : Dict=0 )-> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = torch.manual_seed(a_ )
SCREAMING_SNAKE_CASE__ : List[str] = load_image(
'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
SCREAMING_SNAKE_CASE__ : Tuple = {
'prompt': 'turn him into a cyborg',
'image': image,
'generator': generator,
'num_inference_steps': 3,
'guidance_scale': 7.5,
'image_guidance_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def __lowercase( self : int )-> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : str = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowercase( self : Dict )-> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ )
SCREAMING_SNAKE_CASE__ : str = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Dict = pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : Optional[int] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : List[Any] = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowercase( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ )
SCREAMING_SNAKE_CASE__ : Dict = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : str = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Tuple = pipe(**a_ ).images
SCREAMING_SNAKE_CASE__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE__ : List[str] = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def __lowercase( self : int )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = 0
def callback_fn(a_ : int , a_ : int , a_ : torch.FloatTensor ) -> None:
SCREAMING_SNAKE_CASE__ : Tuple = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
SCREAMING_SNAKE_CASE__ : List[Any] = latents[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Optional[int] = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
SCREAMING_SNAKE_CASE__ : Optional[int] = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
SCREAMING_SNAKE_CASE__ : Tuple = latents[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE__ : Dict = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
SCREAMING_SNAKE_CASE__ : List[str] = False
SCREAMING_SNAKE_CASE__ : List[Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_inputs()
pipe(**a_ , callback=a_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def __lowercase( self : int )-> Any:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'timbrooks/instruct-pix2pix' , safety_checker=a_ , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Tuple = pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
SCREAMING_SNAKE_CASE__ : Tuple = self.get_inputs()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe(**a_ )
SCREAMING_SNAKE_CASE__ : Any = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def __lowercase( self : Tuple )-> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
SCREAMING_SNAKE_CASE__ : Dict = inputs['image'].resize((504, 504) )
SCREAMING_SNAKE_CASE__ : List[Any] = 'timbrooks/instruct-pix2pix'
SCREAMING_SNAKE_CASE__ : str = StableDiffusionInstructPixaPixPipeline.from_pretrained(
a_ , safety_checker=a_ , )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
pipe.enable_attention_slicing()
SCREAMING_SNAKE_CASE__ : Any = pipe(**a_ )
SCREAMING_SNAKE_CASE__ : List[str] = output.images[0]
SCREAMING_SNAKE_CASE__ : Any = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
SCREAMING_SNAKE_CASE__ : str = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 85
| 0
|
'''simple docstring'''
import re
def lowerCamelCase_ ( A_ ):
__lowerCamelCase = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(A_ , A_ ) )
if __name__ == "__main__":
_UpperCamelCase : Tuple ="0094702343221"
print(is_sri_lankan_phone_number(phone))
| 575
|
'''simple docstring'''
from statistics import mean
import numpy as np
def lowerCamelCase_ ( A_ , A_ , A_ , A_ ):
__lowerCamelCase = 0
# Number of processes finished
__lowerCamelCase = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
__lowerCamelCase = [0] * no_of_process
# List to include calculation results
__lowerCamelCase = [0] * no_of_process
# Sort by arrival time.
__lowerCamelCase = [burst_time[i] for i in np.argsort(A_ )]
__lowerCamelCase = [process_name[i] for i in np.argsort(A_ )]
arrival_time.sort()
while no_of_process > finished_process_count:
__lowerCamelCase = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
__lowerCamelCase = arrival_time[i]
__lowerCamelCase = 0
# Index showing the location of the process being performed
__lowerCamelCase = 0
# Saves the current response ratio.
__lowerCamelCase = 0
for i in range(0 , A_ ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
__lowerCamelCase = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
__lowerCamelCase = temp
__lowerCamelCase = i
# Calculate the turn around time
__lowerCamelCase = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
__lowerCamelCase = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def lowerCamelCase_ ( A_ , A_ , A_ , A_ ):
__lowerCamelCase = [0] * no_of_process
for i in range(0 , A_ ):
__lowerCamelCase = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
_UpperCamelCase : List[Any] =5
_UpperCamelCase : str =["A", "B", "C", "D", "E"]
_UpperCamelCase : int =[1, 2, 3, 4, 5]
_UpperCamelCase : Tuple =[1, 2, 3, 4, 5]
_UpperCamelCase : int =calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
_UpperCamelCase : Tuple =calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
for i in range(0, no_of_process):
print(
f'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
f'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
)
print(f'''average waiting time : {mean(waiting_time):.5f}''')
print(f'''average turn around time : {mean(turn_around_time):.5f}''')
| 575
| 1
|
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
UpperCamelCase = Mapping[str, np.ndarray]
UpperCamelCase = Mapping[str, Any] # Is a nested dict.
UpperCamelCase = 0.01
@dataclasses.dataclass(frozen=lowercase )
class lowerCAmelCase_ :
"""simple docstring"""
_snake_case : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_snake_case : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_snake_case : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_snake_case : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_snake_case : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_snake_case : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_snake_case : Optional[str] = None
# Templates used to generate this protein (prediction-only)
_snake_case : Optional[Sequence[str]] = None
# Chain corresponding to each parent
_snake_case : Optional[Sequence[int]] = None
def A ( lowercase__ : str ) -> Protein:
UpperCamelCase__ :Union[str, Any] = r"""(\[[A-Z]+\]\n)"""
UpperCamelCase__ :List[str] = [tag.strip() for tag in re.split(lowercase__ , lowercase__ ) if len(lowercase__ ) > 0]
UpperCamelCase__ :Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("""\n""" ) for l in tags[1::2]] )
UpperCamelCase__ :List[str] = ["N", "CA", "C"]
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :Optional[int] = None
UpperCamelCase__ :List[str] = None
for g in groups:
if "[PRIMARY]" == g[0]:
UpperCamelCase__ :List[Any] = g[1][0].strip()
for i in range(len(lowercase__ ) ):
if seq[i] not in residue_constants.restypes:
UpperCamelCase__ :List[str] = """X""" # FIXME: strings are immutable
UpperCamelCase__ :List[Any] = np.array(
[residue_constants.restype_order.get(lowercase__ , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
UpperCamelCase__ :List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(lowercase__ , g[1][axis].split() ) ) )
UpperCamelCase__ :Tuple = np.array(lowercase__ )
UpperCamelCase__ :Tuple = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(lowercase__ ):
UpperCamelCase__ :Optional[int] = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
UpperCamelCase__ :Dict = np.array(list(map({"""-""": 0, """+""": 1}.get , g[1][0].strip() ) ) )
UpperCamelCase__ :Any = np.zeros(
(
len(lowercase__ ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(lowercase__ ):
UpperCamelCase__ :List[Any] = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=lowercase__ , atom_mask=lowercase__ , aatype=lowercase__ , residue_index=np.arange(len(lowercase__ ) ) , b_factors=lowercase__ , )
def A ( lowercase__ : Protein , lowercase__ : int = 0 ) -> List[str]:
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Optional[Any] = prot.remark
if remark is not None:
pdb_headers.append(f"""REMARK {remark}""" )
UpperCamelCase__ :List[Any] = prot.parents
UpperCamelCase__ :List[Any] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
UpperCamelCase__ :List[Any] = [p for i, p in zip(lowercase__ , lowercase__ ) if i == chain_id]
if parents is None or len(lowercase__ ) == 0:
UpperCamelCase__ :str = ["""N/A"""]
pdb_headers.append(f"""PARENT {" ".join(lowercase__ )}""" )
return pdb_headers
def A ( lowercase__ : Protein , lowercase__ : str ) -> str:
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Optional[int] = pdb_str.split("""\n""" )
UpperCamelCase__ :Tuple = prot.remark
if remark is not None:
out_pdb_lines.append(f"""REMARK {remark}""" )
UpperCamelCase__ :List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
UpperCamelCase__ :Any = []
if prot.parents_chain_index is not None:
UpperCamelCase__ :Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(lowercase__ ) , [] )
parent_dict[str(lowercase__ )].append(lowercase__ )
UpperCamelCase__ :Optional[Any] = max([int(lowercase__ ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
UpperCamelCase__ :Union[str, Any] = parent_dict.get(str(lowercase__ ) , ["""N/A"""] )
parents_per_chain.append(lowercase__ )
else:
parents_per_chain.append(list(prot.parents ) )
else:
UpperCamelCase__ :Union[str, Any] = [["""N/A"""]]
def make_parent_line(lowercase__ : Sequence[str] ) -> str:
return f"""PARENT {" ".join(lowercase__ )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
UpperCamelCase__ :Optional[int] = 0
for i, l in enumerate(lowercase__ ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(lowercase__ )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(lowercase__ ):
UpperCamelCase__ :Optional[int] = parents_per_chain[chain_counter]
else:
UpperCamelCase__ :str = ["""N/A"""]
out_pdb_lines.append(make_parent_line(lowercase__ ) )
return "\n".join(lowercase__ )
def A ( lowercase__ : Protein ) -> str:
UpperCamelCase__ :Optional[int] = residue_constants.restypes + ["""X"""]
def res_atoa(lowercase__ : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , """UNK""" )
UpperCamelCase__ :Optional[Any] = residue_constants.atom_types
UpperCamelCase__ :List[str] = []
UpperCamelCase__ :Dict = prot.atom_mask
UpperCamelCase__ :Dict = prot.aatype
UpperCamelCase__ :List[str] = prot.atom_positions
UpperCamelCase__ :Dict = prot.residue_index.astype(np.intaa )
UpperCamelCase__ :Optional[int] = prot.b_factors
UpperCamelCase__ :Dict = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("""Invalid aatypes.""" )
UpperCamelCase__ :Any = get_pdb_headers(lowercase__ )
if len(lowercase__ ) > 0:
pdb_lines.extend(lowercase__ )
UpperCamelCase__ :Union[str, Any] = aatype.shape[0]
UpperCamelCase__ :Union[str, Any] = 1
UpperCamelCase__ :Tuple = 0
UpperCamelCase__ :Union[str, Any] = string.ascii_uppercase
UpperCamelCase__ :Tuple = None
# Add all atom sites.
for i in range(lowercase__ ):
UpperCamelCase__ :str = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(lowercase__ , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
UpperCamelCase__ :Optional[int] = """ATOM"""
UpperCamelCase__ :Union[str, Any] = atom_name if len(lowercase__ ) == 4 else f""" {atom_name}"""
UpperCamelCase__ :Union[str, Any] = """"""
UpperCamelCase__ :Dict = """"""
UpperCamelCase__ :List[Any] = 1.00
UpperCamelCase__ :Any = atom_name[0] # Protein supports only C, N, O, S, this works.
UpperCamelCase__ :int = """"""
UpperCamelCase__ :Union[str, Any] = """A"""
if chain_index is not None:
UpperCamelCase__ :List[Any] = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
UpperCamelCase__ :int = (
f"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
f"""{res_name_a:>3} {chain_tag:>1}"""
f"""{residue_index[i]:>4}{insertion_code:>1} """
f"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
f"""{occupancy:>6.2f}{b_factor:>6.2f} """
f"""{element:>2}{charge:>2}"""
)
pdb_lines.append(lowercase__ )
atom_index += 1
UpperCamelCase__ :Dict = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
UpperCamelCase__ :List[str] = True
UpperCamelCase__ :int = chain_index[i + 1]
if should_terminate:
# Close the chain.
UpperCamelCase__ :Tuple = """TER"""
UpperCamelCase__ :Any = (
f"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(lowercase__ )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(lowercase__ , lowercase__ ) )
pdb_lines.append("""END""" )
pdb_lines.append("""""" )
return "\n".join(lowercase__ )
def A ( lowercase__ : Protein ) -> np.ndarray:
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def A ( lowercase__ : FeatureDict , lowercase__ : ModelOutput , lowercase__ : Optional[np.ndarray] = None , lowercase__ : Optional[np.ndarray] = None , lowercase__ : Optional[str] = None , lowercase__ : Optional[Sequence[str]] = None , lowercase__ : Optional[Sequence[int]] = None , ) -> Protein:
return Protein(
aatype=features["""aatype"""] , atom_positions=result["""final_atom_positions"""] , atom_mask=result["""final_atom_mask"""] , residue_index=features["""residue_index"""] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""] ) , chain_index=lowercase__ , remark=lowercase__ , parents=lowercase__ , parents_chain_index=lowercase__ , )
| 45
|
import random
def A ( lowercase__ : Dict , lowercase__ : str , lowercase__ : Optional[Any] ) -> int:
UpperCamelCase__ :List[Any] = a[left_index]
UpperCamelCase__ :Dict = left_index + 1
for j in range(left_index + 1 , lowercase__ ):
if a[j] < pivot:
UpperCamelCase__ , UpperCamelCase__ :Optional[int] = a[i], a[j]
i += 1
UpperCamelCase__ , UpperCamelCase__ :Tuple = a[i - 1], a[left_index]
return i - 1
def A ( lowercase__ : Tuple , lowercase__ : Optional[int] , lowercase__ : Any ) -> Optional[int]:
if left < right:
UpperCamelCase__ :List[Any] = random.randint(lowercase__ , right - 1 )
UpperCamelCase__ , UpperCamelCase__ :Union[str, Any] = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
UpperCamelCase__ :int = partition(lowercase__ , lowercase__ , lowercase__ )
quick_sort_random(
lowercase__ , lowercase__ , lowercase__ ) # recursive quicksort to the left of the pivot point
quick_sort_random(
lowercase__ , pivot_index + 1 , lowercase__ ) # recursive quicksort to the right of the pivot point
def A ( ) -> List[Any]:
UpperCamelCase__ :str = input("""Enter numbers separated by a comma:\n""" ).strip()
UpperCamelCase__ :int = [int(lowercase__ ) for item in user_input.split(""",""" )]
quick_sort_random(lowercase__ , 0 , len(lowercase__ ) )
print(lowercase__ )
if __name__ == "__main__":
main()
| 45
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase_ = {
'''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''VivitImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''VivitModel''',
'''VivitPreTrainedModel''',
'''VivitForVideoClassification''',
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 58
|
'''simple docstring'''
lowercase_ = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
lowercase_ = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowercase_ = {
'''{processor_class}''': '''FakeProcessorClass''',
'''{model_class}''': '''FakeModelClass''',
'''{object_class}''': '''FakeObjectClass''',
}
| 58
| 1
|
"""simple docstring"""
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
lowercase_ = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здорово, не так ли?",
"de": "Maschinelles Lernen ist großartig, oder?",
}
# BLUE scores as follows:
# "pair": [fairseq, transformers]
lowercase_ = {
"ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
"en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
"en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
"de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
}
lowercase_ = F'''{src_lang}-{tgt_lang}'''
lowercase_ = F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---
# FSMT
## Model description
This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.
For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).
The abbreviation FSMT stands for FairSeqMachineTranslation
All four models are available:
* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)
## Intended uses & limitations
#### How to use
```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)
input = \"{texts[src_lang]}\"
input_ids = tokenizer.encode(input, return_tensors=\"pt\")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}
```
#### Limitations and bias
- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)
## Training data
Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).
## Eval results
pair | fairseq | transformers
-------|---------|----------
{pair} | {scores[pair][0]} | {scores[pair][1]}
The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking
The score was calculated using this code:
```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.
## Data Sources
- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)
### BibTeX entry and citation info
```bibtex
@inproceedings{{...,
year={{2020}},
title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
booktitle={{Proc. of WMT}},
}}
```
## TODO
- port model ensemble (fairseq uses 4 model checkpoints)
'''
os.makedirs(lowercase__ , exist_ok=lowercase__ )
lowercase_ = os.path.join(lowercase__ , """README.md""" )
print(F'''Generating {path}''' )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(lowercase__ )
# make sure we are under the root of the project
UpperCAmelCase : List[Any] = Path(__file__).resolve().parent.parent.parent
UpperCAmelCase : Optional[int] = repo_dir / "model_cards"
for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[Any] = model_name.split("-")
UpperCAmelCase : Dict = model_cards_dir / "facebook" / model_name
write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)
| 567
|
'''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class UpperCAmelCase_ ( A ):
'''simple docstring'''
lowercase_ : int = ["input_values", "attention_mask"]
def __init__( self : Any , snake_case__ : int = 1 , snake_case__ : int = 1_60_00 , snake_case__ : float = 0.0 , snake_case__ : bool = False , snake_case__ : int = 80 , snake_case__ : int = 16 , snake_case__ : int = 64 , snake_case__ : str = "hann_window" , snake_case__ : float = 1.0 , snake_case__ : float = 80 , snake_case__ : float = 76_00 , snake_case__ : float = 1e-10 , snake_case__ : int = 2 , snake_case__ : bool = True , **snake_case__ : List[Any] , ):
'''simple docstring'''
super().__init__(feature_size=snake_case__ , sampling_rate=snake_case__ , padding_value=snake_case__ , **snake_case__ )
UpperCAmelCase__ : int = do_normalize
UpperCAmelCase__ : Tuple = return_attention_mask
UpperCAmelCase__ : Union[str, Any] = num_mel_bins
UpperCAmelCase__ : List[str] = hop_length
UpperCAmelCase__ : List[str] = win_length
UpperCAmelCase__ : Union[str, Any] = win_function
UpperCAmelCase__ : str = frame_signal_scale
UpperCAmelCase__ : int = fmin
UpperCAmelCase__ : Union[str, Any] = fmax
UpperCAmelCase__ : Optional[Any] = mel_floor
UpperCAmelCase__ : List[str] = reduction_factor
UpperCAmelCase__ : str = win_length * sampling_rate // 10_00
UpperCAmelCase__ : Union[str, Any] = hop_length * sampling_rate // 10_00
UpperCAmelCase__ : Optional[Any] = optimal_fft_length(self.sample_size )
UpperCAmelCase__ : str = (self.n_fft // 2) + 1
UpperCAmelCase__ : Dict = window_function(window_length=self.sample_size , name=self.win_function , periodic=snake_case__ )
UpperCAmelCase__ : List[str] = mel_filter_bank(
num_frequency_bins=self.n_freqs , num_mel_filters=self.num_mel_bins , min_frequency=self.fmin , max_frequency=self.fmax , sampling_rate=self.sampling_rate , norm="slaney" , mel_scale="slaney" , )
if frame_signal_scale != 1.0:
warnings.warn(
"The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers" , snake_case__ , )
if reduction_factor != 2.0:
warnings.warn(
"The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers" , snake_case__ , )
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def UpperCamelCase ( snake_case__ : List[np.ndarray] , snake_case__ : List[np.ndarray] , snake_case__ : float = 0.0 ):
'''simple docstring'''
if attention_mask is not None:
UpperCAmelCase__ : Tuple = np.array(snake_case__ , np.intaa )
UpperCAmelCase__ : Tuple = []
for vector, length in zip(snake_case__ , attention_mask.sum(-1 ) ):
UpperCAmelCase__ : List[str] = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
UpperCAmelCase__ : Optional[int] = padding_value
normed_input_values.append(snake_case__ )
else:
UpperCAmelCase__ : Optional[int] = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def UpperCamelCase ( self : Tuple , snake_case__ : np.ndarray , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = spectrogram(
snake_case__ , window=self.window , frame_length=self.sample_size , hop_length=self.sample_stride , fft_length=self.n_fft , mel_filters=self.mel_filters , mel_floor=self.mel_floor , log_mel="log10" , )
return log_mel_spec.T
def __call__( self : List[Any] , snake_case__ : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , snake_case__ : Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None , snake_case__ : Union[bool, str, PaddingStrategy] = False , snake_case__ : Optional[int] = None , snake_case__ : bool = False , snake_case__ : Optional[int] = None , snake_case__ : Optional[bool] = None , snake_case__ : Optional[Union[str, TensorType]] = None , snake_case__ : Optional[int] = None , **snake_case__ : List[Any] , ):
'''simple docstring'''
if audio is None and audio_target is None:
raise ValueError("You must provide either `audio` or `audio_target` values." )
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
F""" {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
if audio is not None:
UpperCAmelCase__ : Union[str, Any] = self._process_audio(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__ , )
else:
UpperCAmelCase__ : str = None
if audio_target is not None:
UpperCAmelCase__ : str = self._process_audio(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , **snake_case__ , )
if inputs is None:
return inputs_target
else:
UpperCAmelCase__ : Union[str, Any] = inputs_target["input_values"]
UpperCAmelCase__ : Any = inputs_target.get("attention_mask" )
if decoder_attention_mask is not None:
UpperCAmelCase__ : Dict = decoder_attention_mask
return inputs
def UpperCamelCase ( self : List[Any] , snake_case__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , snake_case__ : bool = False , snake_case__ : Union[bool, str, PaddingStrategy] = False , snake_case__ : Optional[int] = None , snake_case__ : bool = False , snake_case__ : Optional[int] = None , snake_case__ : Optional[bool] = None , snake_case__ : Optional[Union[str, TensorType]] = None , **snake_case__ : int , ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = isinstance(snake_case__ , np.ndarray ) and len(speech.shape ) > 1
if is_batched_numpy and len(speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
UpperCAmelCase__ : Union[str, Any] = is_batched_numpy or (
isinstance(snake_case__ , (list, tuple) ) and (isinstance(speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
UpperCAmelCase__ : List[Any] = [np.asarray(snake_case__ , dtype=np.floataa ) for speech in speech]
elif not is_batched and not isinstance(snake_case__ , np.ndarray ):
UpperCAmelCase__ : int = np.asarray(snake_case__ , dtype=np.floataa )
elif isinstance(snake_case__ , np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : List[Any] = speech.astype(np.floataa )
# always return batch
if not is_batched:
UpperCAmelCase__ : str = [speech]
# needed to make pad() work on spectrogram inputs
UpperCAmelCase__ : str = self.feature_size
# convert into correct format for padding
if is_target:
UpperCAmelCase__ : List[Any] = [self._extract_mel_features(snake_case__ ) for waveform in speech]
UpperCAmelCase__ : List[Any] = BatchFeature({"input_values": features} )
UpperCAmelCase__ : List[str] = self.num_mel_bins
else:
UpperCAmelCase__ : List[str] = BatchFeature({"input_values": speech} )
UpperCAmelCase__ : Optional[Any] = self.pad(
snake_case__ , padding=snake_case__ , max_length=snake_case__ , truncation=snake_case__ , pad_to_multiple_of=snake_case__ , return_attention_mask=snake_case__ , **snake_case__ , )
UpperCAmelCase__ : List[str] = feature_size_hack
# convert input values to correct format
UpperCAmelCase__ : Tuple = padded_inputs["input_values"]
if not isinstance(input_values[0] , np.ndarray ):
UpperCAmelCase__ : List[Any] = [np.asarray(snake_case__ , dtype=np.floataa ) for array in input_values]
elif (
not isinstance(snake_case__ , np.ndarray )
and isinstance(input_values[0] , np.ndarray )
and input_values[0].dtype is np.dtype(np.floataa )
):
UpperCAmelCase__ : str = [array.astype(np.floataa ) for array in input_values]
elif isinstance(snake_case__ , np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
UpperCAmelCase__ : str = input_values.astype(np.floataa )
# convert attention_mask to correct format
UpperCAmelCase__ : Dict = padded_inputs.get("attention_mask" )
if attention_mask is not None:
UpperCAmelCase__ : int = [np.asarray(snake_case__ , dtype=np.intaa ) for array in attention_mask]
# zero-mean and unit-variance normalization
if not is_target and self.do_normalize:
UpperCAmelCase__ : Any = (
attention_mask
if self._get_padding_strategies(snake_case__ , max_length=snake_case__ ) is not PaddingStrategy.DO_NOT_PAD
else None
)
UpperCAmelCase__ : int = self.zero_mean_unit_var_norm(
padded_inputs["input_values"] , attention_mask=snake_case__ , padding_value=self.padding_value )
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(snake_case__ )
return padded_inputs
def UpperCamelCase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = super().to_dict()
# Don't serialize these as they are derived from the other properties.
UpperCAmelCase__ : Dict = ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
for name in names:
if name in output:
del output[name]
return output
| 199
| 0
|
"""simple docstring"""
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase : Any = get_tests_dir('fixtures/spiece.model')
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( A , unittest.TestCase ):
"""simple docstring"""
__a = AlbertTokenizer
__a = AlbertTokenizerFast
__a = True
__a = True
__a = True
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
__UpperCAmelCase : Dict = AlbertTokenizer(UpperCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__ ( self : Union[str, Any] , UpperCamelCase : Dict ):
'''simple docstring'''
__UpperCAmelCase : str = """this is a test"""
__UpperCAmelCase : Optional[int] = """this is a test"""
return input_text, output_text
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = """<pad>"""
__UpperCAmelCase : Tuple = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase ) , UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase ) , UpperCamelCase )
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
__UpperCAmelCase : Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<pad>""" )
self.assertEqual(vocab_keys[1] , """<unk>""" )
self.assertEqual(vocab_keys[-1] , """▁eloquent""" )
self.assertEqual(len(UpperCamelCase ) , 30_000 )
def lowerCamelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 30_000 )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
__UpperCAmelCase : List[Any] = self.get_tokenizer()
__UpperCAmelCase : Dict = self.get_rust_tokenizer()
__UpperCAmelCase : Dict = """I was born in 92000, and this is falsé."""
__UpperCAmelCase : Optional[Any] = tokenizer.tokenize(UpperCamelCase )
__UpperCAmelCase : Optional[int] = rust_tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : str = tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
__UpperCAmelCase : str = rust_tokenizer.encode(UpperCamelCase , add_special_tokens=UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
__UpperCAmelCase : Dict = self.get_rust_tokenizer()
__UpperCAmelCase : int = tokenizer.encode(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = rust_tokenizer.encode(UpperCamelCase )
self.assertListEqual(UpperCamelCase , UpperCamelCase )
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Dict = AlbertTokenizer(UpperCamelCase , keep_accents=UpperCamelCase )
__UpperCAmelCase : Optional[Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(UpperCamelCase , ["""▁this""", """▁is""", """▁a""", """▁test"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) , [48, 25, 21, 1_289] )
__UpperCAmelCase : List[Any] = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
UpperCamelCase , ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""] )
__UpperCAmelCase : Dict = tokenizer.convert_tokens_to_ids(UpperCamelCase )
self.assertListEqual(UpperCamelCase , [31, 23, 386, 19, 561, 3_050, 15, 17, 48, 25, 8_256, 18, 1, 9] )
__UpperCAmelCase : int = tokenizer.convert_ids_to_tokens(UpperCamelCase )
self.assertListEqual(
UpperCamelCase , ["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""] , )
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
__UpperCAmelCase : str = AlbertTokenizer(UpperCamelCase )
__UpperCAmelCase : Optional[int] = tokenizer.encode("""sequence builders""" )
__UpperCAmelCase : Dict = tokenizer.encode("""multi-sequence build""" )
__UpperCAmelCase : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(UpperCamelCase )
__UpperCAmelCase : int = tokenizer.build_inputs_with_special_tokens(UpperCamelCase , UpperCamelCase )
assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
tokenizer.sep_token_id
]
@slow
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = {"""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, 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]], """input_ids""": [[2, 21_970, 13, 5, 6_092, 167, 28, 7_103, 2_153, 673, 8, 7_028, 12_051, 18, 17, 7_103, 2_153, 673, 8, 3_515, 18_684, 8, 4_461, 6, 1_927, 297, 8, 12_060, 2_607, 18, 13, 5, 4_461, 15, 10_538, 38, 8, 135, 15, 822, 58, 15, 993, 10_363, 15, 1_460, 8_005, 4_461, 15, 993, 255, 2_328, 9, 9, 9, 6, 26, 1_112, 816, 3_260, 13, 5, 103, 2_377, 6, 17, 1_112, 816, 2_782, 13, 5, 103, 10_641, 6, 29, 84, 2_512, 2_430, 782, 18_684, 2_761, 19, 808, 2_430, 2_556, 17, 855, 1_480, 9_477, 4_091, 128, 11_712, 15, 7_103, 2_153, 673, 17, 24_883, 9_990, 9, 3], [2, 11_502, 25, 1_006, 20, 782, 8, 11_809, 855, 1_732, 19_393, 18_667, 37, 367, 21_018, 69, 1_854, 34, 11_860, 19_124, 27, 156, 225, 17, 193, 4_141, 19, 65, 9_124, 9, 3, 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], [2, 14, 2_231, 886, 2_385, 17_659, 84, 14, 16_792, 1_952, 9, 3, 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]], """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, 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, 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=UpperCamelCase , model_name="""albert-base-v2""" , revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e""" , )
| 299
|
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : str , UpperCamelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Any = data
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Any = None
def lowerCamelCase ( ) -> TreeNode:
'''simple docstring'''
print("""\n********Press N to stop entering at any point of time********\n""" )
__UpperCAmelCase : Optional[int] = input("""Enter the value of the root node: """ ).strip().lower()
__UpperCAmelCase : queue.Queue = queue.Queue()
__UpperCAmelCase : int = TreeNode(int(_UpperCamelCase ) )
q.put(_UpperCamelCase )
while not q.empty():
__UpperCAmelCase : List[str] = q.get()
__UpperCAmelCase : List[str] = f'''Enter the left node of {node_found.data}: '''
__UpperCAmelCase : Tuple = input(_UpperCamelCase ).strip().lower() or """n"""
if check == "n":
return tree_node
__UpperCAmelCase : str = TreeNode(int(_UpperCamelCase ) )
__UpperCAmelCase : List[Any] = left_node
q.put(_UpperCamelCase )
__UpperCAmelCase : List[str] = f'''Enter the right node of {node_found.data}: '''
__UpperCAmelCase : Tuple = input(_UpperCamelCase ).strip().lower() or """n"""
if check == "n":
return tree_node
__UpperCAmelCase : List[str] = TreeNode(int(_UpperCamelCase ) )
__UpperCAmelCase : Tuple = right_node
q.put(_UpperCamelCase )
raise
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : queue.Queue = queue.Queue()
q.put(_UpperCamelCase )
while not q.empty():
__UpperCAmelCase : str = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : queue.Queue = queue.Queue()
q.put(_UpperCamelCase )
while not q.empty():
__UpperCAmelCase : Union[str, Any] = []
while not q.empty():
__UpperCAmelCase : Optional[int] = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(_UpperCamelCase )
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : list[TreeNode] = []
__UpperCAmelCase : Optional[Any] = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(_UpperCamelCase )
__UpperCAmelCase : Dict = n.left
# end of while means current node doesn't have left child
__UpperCAmelCase : List[str] = stack.pop()
# start to traverse its right child
__UpperCAmelCase : List[str] = n.right
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase : list[TreeNode] = []
__UpperCAmelCase : Dict = node
while n or stack:
while n:
stack.append(_UpperCamelCase )
__UpperCAmelCase : Tuple = n.left
__UpperCAmelCase : Any = stack.pop()
print(n.data , end=""",""" )
__UpperCAmelCase : List[Any] = n.right
def lowerCamelCase ( _UpperCamelCase : TreeNode ) -> None:
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or not node:
return
__UpperCAmelCase ,__UpperCAmelCase : Optional[int] = [], []
__UpperCAmelCase : Optional[Any] = node
stacka.append(_UpperCamelCase )
while stacka: # to find the reversed order of post order, store it in stack2
__UpperCAmelCase : Tuple = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(_UpperCamelCase )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def lowerCamelCase ( _UpperCamelCase : str = "" , _UpperCamelCase : int=5_0 , _UpperCamelCase : Tuple="*" ) -> str:
'''simple docstring'''
if not s:
return "\n" + width * char
__UpperCAmelCase ,__UpperCAmelCase : Tuple = divmod(width - len(_UpperCamelCase ) - 2 , 2 )
return f'''{left * char} {s} {(left + extra) * char}'''
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('Binary Tree Traversals'))
UpperCAmelCase : TreeNode = build_tree()
print(prompt('Pre Order Traversal'))
pre_order(node)
print(prompt() + '\n')
print(prompt('In Order Traversal'))
in_order(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal'))
post_order(node)
print(prompt() + '\n')
print(prompt('Level Order Traversal'))
level_order(node)
print(prompt() + '\n')
print(prompt('Actual Level Order Traversal'))
level_order_actual(node)
print('*' * 50 + '\n')
print(prompt('Pre Order Traversal - Iteration Version'))
pre_order_iter(node)
print(prompt() + '\n')
print(prompt('In Order Traversal - Iteration Version'))
in_order_iter(node)
print(prompt() + '\n')
print(prompt('Post Order Traversal - Iteration Version'))
post_order_iter(node)
print(prompt())
| 299
| 1
|
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> str:
"""simple docstring"""
__UpperCAmelCase : Any = ""
for word_or_phrase in separated:
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += word_or_phrase + separator
return joined.strip(UpperCamelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 77
|
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"""
A_ = MBartConfig
A_ = {}
A_ = """gelu"""
def __init__( self , _UpperCAmelCase , _UpperCAmelCase=13 , _UpperCAmelCase=7 , _UpperCAmelCase=True , _UpperCAmelCase=False , _UpperCAmelCase=99 , _UpperCAmelCase=32 , _UpperCAmelCase=2 , _UpperCAmelCase=4 , _UpperCAmelCase=37 , _UpperCAmelCase=0.1 , _UpperCAmelCase=0.1 , _UpperCAmelCase=20 , _UpperCAmelCase=2 , _UpperCAmelCase=1 , _UpperCAmelCase=0 , ) -> Union[str, Any]:
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = seq_length
UpperCamelCase_ = is_training
UpperCamelCase_ = use_labels
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = eos_token_id
UpperCamelCase_ = pad_token_id
UpperCamelCase_ = bos_token_id
def _UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
UpperCamelCase_ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
UpperCamelCase_ = tf.concat([input_ids, eos_tensor] , axis=1 )
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = 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 , )
UpperCamelCase_ = prepare_mbart_inputs_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return config, inputs_dict
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase ) -> List[str]:
UpperCamelCase_ = TFMBartModel(config=_UpperCAmelCase ).get_decoder()
UpperCamelCase_ = inputs_dict['input_ids']
UpperCamelCase_ = input_ids[:1, :]
UpperCamelCase_ = inputs_dict['attention_mask'][:1, :]
UpperCamelCase_ = inputs_dict['head_mask']
UpperCamelCase_ = 1
# first forward pass
UpperCamelCase_ = model(_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase , use_cache=_UpperCAmelCase )
UpperCamelCase_ , UpperCamelCase_ = outputs.to_tuple()
UpperCamelCase_ = past_key_values[1]
def _snake_case (__lowercase , __lowercase , __lowercase , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , __lowercase=None , ):
if attention_mask is None:
UpperCamelCase_ = tf.cast(tf.math.not_equal(__lowercase , config.pad_token_id) , tf.inta)
if decoder_attention_mask is None:
UpperCamelCase_ = 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:
UpperCamelCase_ = tf.ones((config.encoder_layers, config.encoder_attention_heads))
if decoder_head_mask is None:
UpperCamelCase_ = tf.ones((config.decoder_layers, config.decoder_attention_heads))
if cross_attn_head_mask is None:
UpperCamelCase_ = 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"""
A_ = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
A_ = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
A_ = (
{
"""conversational""": TFMBartForConditionalGeneration,
"""feature-extraction""": TFMBartModel,
"""summarization""": TFMBartForConditionalGeneration,
"""text2text-generation""": TFMBartForConditionalGeneration,
"""translation""": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
A_ = True
A_ = False
A_ = False
def _UpperCAmelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def _UpperCAmelCase ( self ) -> Optional[int]:
UpperCamelCase_ = TFMBartModelTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=_UpperCAmelCase )
def _UpperCAmelCase ( self ) -> Optional[int]:
self.config_tester.run_common_tests()
def _UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase_ = 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"""
A_ = [
""" UN Chief Says There Is No Military Solution in Syria""",
]
A_ = [
"""Şeful ONU declară că nu există o soluţie militară în Siria""",
]
A_ = """facebook/mbart-large-en-ro"""
@cached_property
def _UpperCAmelCase ( self ) -> Any:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def _UpperCAmelCase ( self ) -> List[str]:
UpperCamelCase_ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def _UpperCAmelCase ( self , **_UpperCAmelCase ) -> int:
UpperCamelCase_ = self.translate_src_text(**_UpperCAmelCase )
self.assertListEqual(self.expected_text , _UpperCAmelCase )
def _UpperCAmelCase ( self , **_UpperCAmelCase ) -> List[str]:
UpperCamelCase_ = self.tokenizer(self.src_text , **_UpperCAmelCase , return_tensors='tf' )
UpperCamelCase_ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
UpperCamelCase_ = self.tokenizer.batch_decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
return generated_words
@slow
def _UpperCAmelCase ( self ) -> List[Any]:
self._assert_generated_batch_equal_expected()
| 23
| 0
|
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__snake_case =logging.get_logger(__name__)
class UpperCAmelCase_ :
def __init__( self : Any , UpperCAmelCase__ : str = None , UpperCAmelCase__ : uuid.UUID = None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Any=None ) -> Optional[Any]:
if not conversation_id:
lowerCAmelCase = uuid.uuida()
if past_user_inputs is None:
lowerCAmelCase = []
if generated_responses is None:
lowerCAmelCase = []
lowerCAmelCase = conversation_id
lowerCAmelCase = past_user_inputs
lowerCAmelCase = generated_responses
lowerCAmelCase = text
def __eq__( self : Optional[int] , UpperCAmelCase__ : List[str] ) -> Union[str, Any]:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : bool = False ) -> Union[str, Any]:
if self.new_user_input:
if overwrite:
logger.warning(
F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
F'''with: "{text}".''' )
lowerCAmelCase = text
else:
logger.warning(
F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
lowerCAmelCase = text
def __UpperCAmelCase ( self : Dict ) -> Any:
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
lowerCAmelCase = None
def __UpperCAmelCase ( self : List[Any] , UpperCAmelCase__ : str ) -> Any:
self.generated_responses.append(UpperCAmelCase__ )
def __UpperCAmelCase ( self : int ) -> Optional[int]:
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self : Optional[Any] ) -> Optional[Any]:
lowerCAmelCase = F'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
lowerCAmelCase = """user""" if is_user else """bot"""
output += F'''{name} >> {text} \n'''
return output
@add_end_docstrings(
UpperCAmelCase__ , r'''\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n ''' , )
class UpperCAmelCase_ ( UpperCAmelCase__ ):
def __init__( self : Tuple , *UpperCAmelCase__ : str , **UpperCAmelCase__ : str ) -> Any:
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
if self.tokenizer.pad_token_id is None:
lowerCAmelCase = self.tokenizer.eos_token
def __UpperCAmelCase ( self : int , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Any=None , **UpperCAmelCase__ : Dict ) -> Tuple:
lowerCAmelCase = {}
lowerCAmelCase = {}
lowerCAmelCase = {}
if min_length_for_response is not None:
lowerCAmelCase = min_length_for_response
if minimum_tokens is not None:
lowerCAmelCase = minimum_tokens
if "max_length" in generate_kwargs:
lowerCAmelCase = generate_kwargs["""max_length"""]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
lowerCAmelCase = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(UpperCAmelCase__ )
return preprocess_params, forward_params, postprocess_params
def __call__( self : Optional[int] , UpperCAmelCase__ : Union[Conversation, List[Conversation]] , UpperCAmelCase__ : Any=0 , **UpperCAmelCase__ : Optional[int] ) -> Optional[int]:
lowerCAmelCase = super().__call__(UpperCAmelCase__ , num_workers=UpperCAmelCase__ , **UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and len(UpperCAmelCase__ ) == 1:
return outputs[0]
return outputs
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Conversation , UpperCAmelCase__ : List[str]=3_2 ) -> Dict[str, Any]:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise ValueError('ConversationalPipeline, expects Conversation as inputs' )
if conversation.new_user_input is None:
raise ValueError(
F'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
'Add user inputs with the conversation\'s `add_user_input` method' )
if hasattr(self.tokenizer , '_build_conversation_input_ids' ):
lowerCAmelCase = self.tokenizer._build_conversation_input_ids(UpperCAmelCase__ )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
lowerCAmelCase = self._legacy_parse_and_tokenize(UpperCAmelCase__ )
if self.framework == "pt":
lowerCAmelCase = torch.LongTensor([input_ids] )
elif self.framework == "tf":
lowerCAmelCase = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : str=1_0 , **UpperCAmelCase__ : int ) -> Union[str, Any]:
lowerCAmelCase = generate_kwargs.get('max_length' , self.model.config.max_length )
lowerCAmelCase = model_inputs["""input_ids"""].shape[1]
if max_length - minimum_tokens < n:
logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
lowerCAmelCase = max_length - minimum_tokens
lowerCAmelCase = model_inputs["""input_ids"""][:, -trim:]
if "attention_mask" in model_inputs:
lowerCAmelCase = model_inputs["""attention_mask"""][:, -trim:]
lowerCAmelCase = model_inputs.pop('conversation' )
lowerCAmelCase = max_length
lowerCAmelCase = self.model.generate(**UpperCAmelCase__ , **UpperCAmelCase__ )
if self.model.config.is_encoder_decoder:
lowerCAmelCase = 1
else:
lowerCAmelCase = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def __UpperCAmelCase ( self : Optional[int] , UpperCAmelCase__ : List[str] , UpperCAmelCase__ : Union[str, Any]=True ) -> List[Any]:
lowerCAmelCase = model_outputs["""output_ids"""]
lowerCAmelCase = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=UpperCAmelCase__ , clean_up_tokenization_spaces=UpperCAmelCase__ , )
lowerCAmelCase = model_outputs["""conversation"""]
conversation.mark_processed()
conversation.append_response(UpperCAmelCase__ )
return conversation
def __UpperCAmelCase ( self : Union[str, Any] , UpperCAmelCase__ : Conversation ) -> Dict:
lowerCAmelCase = self.tokenizer.eos_token_id
lowerCAmelCase = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(UpperCAmelCase__ , add_special_tokens=UpperCAmelCase__ ) )
if len(UpperCAmelCase__ ) > self.tokenizer.model_max_length:
lowerCAmelCase = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 714
|
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__snake_case =logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : Optional[str] = field(
default='''cifar10''' , metadata={'''help''': '''Name of a dataset from the datasets package'''} )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={'''help''': '''The column name of the images in the files.'''} )
lowerCamelCase : Optional[str] = field(default=__lowercase , metadata={'''help''': '''A folder containing the training data.'''} )
lowerCamelCase : Optional[str] = field(default=__lowercase , metadata={'''help''': '''A folder containing the validation data.'''} )
lowerCamelCase : Optional[float] = field(
default=0.1_5 , metadata={'''help''': '''Percent to split off of train for validation.'''} )
lowerCamelCase : Optional[int] = field(
default=__lowercase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
lowerCamelCase : Optional[int] = field(
default=__lowercase , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def __UpperCAmelCase ( self : List[str] ) -> int:
lowerCAmelCase = {}
if self.train_dir is not None:
lowerCAmelCase = self.train_dir
if self.validation_dir is not None:
lowerCAmelCase = self.validation_dir
lowerCAmelCase = data_files if data_files else None
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : str = field(
default=__lowercase , metadata={
'''help''': (
'''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
)
} , )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''} )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={
'''help''': (
'''Override some existing default config settings when a model is trained from scratch. Example: '''
'''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
)
} , )
lowerCamelCase : Optional[str] = field(
default=__lowercase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''} )
lowerCamelCase : str = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
lowerCamelCase : str = field(default=__lowercase , metadata={'''help''': '''Name or path of preprocessor config.'''} )
lowerCamelCase : bool = field(
default=__lowercase , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
lowerCamelCase : float = field(
default=0.7_5 , metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''} )
lowerCamelCase : bool = field(
default=__lowercase , metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''} )
@dataclass
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : float = field(
default=1E-3 , metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''} )
def a_ ( lowerCamelCase : Optional[int] ):
lowerCAmelCase = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def a_ ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
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.
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , lowerCamelCase , lowerCamelCase )
# 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 )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase )
transformers.utils.logging.set_verbosity(lowerCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCAmelCase = 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 and training_args.resume_from_checkpoint is 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.' )
# Initialize our dataset.
lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowerCAmelCase = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowerCamelCase ) and data_args.train_val_split > 0.0:
lowerCAmelCase = ds['train'].train_test_split(data_args.train_val_split )
lowerCAmelCase = split['train']
lowerCAmelCase = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCAmelCase = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
lowerCAmelCase = ViTMAEConfig.from_pretrained(model_args.config_name , **lowerCamelCase )
elif model_args.model_name_or_path:
lowerCAmelCase = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **lowerCamelCase )
else:
lowerCAmelCase = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(f'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(f'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
lowerCAmelCase = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **lowerCamelCase )
elif model_args.model_name_or_path:
lowerCAmelCase = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **lowerCamelCase )
else:
lowerCAmelCase = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowerCAmelCase = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowerCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
lowerCAmelCase = ViTMAEForPreTraining(lowerCamelCase )
if training_args.do_train:
lowerCAmelCase = ds['train'].column_names
else:
lowerCAmelCase = ds['validation'].column_names
if data_args.image_column_name is not None:
lowerCAmelCase = data_args.image_column_name
elif "image" in column_names:
lowerCAmelCase = 'image'
elif "img" in column_names:
lowerCAmelCase = 'img'
else:
lowerCAmelCase = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
lowerCAmelCase = image_processor.size['shortest_edge']
else:
lowerCAmelCase = (image_processor.size['height'], image_processor.size['width'])
lowerCAmelCase = Compose(
[
Lambda(lambda lowerCamelCase : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(lowerCamelCase , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(lowerCamelCase : Union[str, Any] ):
lowerCAmelCase = [transforms(lowerCamelCase ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
lowerCAmelCase = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowerCamelCase )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
lowerCAmelCase = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowerCamelCase )
# Compute absolute learning rate
lowerCAmelCase = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowerCAmelCase = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
lowerCAmelCase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=lowerCamelCase , data_collator=lowerCamelCase , )
# Training
if training_args.do_train:
lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCAmelCase = last_checkpoint
lowerCAmelCase = trainer.train(resume_from_checkpoint=lowerCamelCase )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowerCAmelCase = trainer.evaluate()
trainer.log_metrics('eval' , lowerCamelCase )
trainer.save_metrics('eval' , lowerCamelCase )
# Write model card and (optionally) push to hub
lowerCAmelCase = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase )
else:
trainer.create_model_card(**lowerCamelCase )
def a_ ( lowerCamelCase : Optional[Any] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 513
| 0
|
a_ : Optional[int] = 9.80_665
def lowerCamelCase__ (_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = g):
if fluid_density <= 0:
raise ValueError('Impossible fluid density')
if volume < 0:
raise ValueError('Impossible Object volume')
if gravity <= 0:
raise ValueError('Impossible Gravity')
return fluid_density * gravity * volume
if __name__ == "__main__":
import doctest
# run doctest
doctest.testmod()
| 73
|
"""simple docstring"""
from typing import Dict
from .base import GenericTensor, Pipeline
class _UpperCAmelCase ( lowerCAmelCase__):
def _snake_case ( self : int , lowercase_ : Optional[Any]=None , lowercase_ : List[str]=None , lowercase_ : Optional[Any]=None , **lowercase_ : Any ):
if tokenize_kwargs is None:
snake_case_ : str = {}
if truncation is not None:
if "truncation" in tokenize_kwargs:
raise ValueError(
'''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)''' )
snake_case_ : int = truncation
snake_case_ : Union[str, Any] = tokenize_kwargs
snake_case_ : int = {}
if return_tensors is not None:
snake_case_ : str = return_tensors
return preprocess_params, {}, postprocess_params
def _snake_case ( self : List[Any] , lowercase_ : Optional[int] , **lowercase_ : int ):
snake_case_ : Union[str, Any] = self.framework
snake_case_ : List[Any] = self.tokenizer(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
return model_inputs
def _snake_case ( self : Union[str, Any] , lowercase_ : Tuple ):
snake_case_ : Union[str, Any] = self.model(**lowercase_ )
return model_outputs
def _snake_case ( self : str , lowercase_ : str , lowercase_ : List[str]=False ):
# [0] is the first available tensor, logits or last_hidden_state.
if return_tensors:
return model_outputs[0]
if self.framework == "pt":
return model_outputs[0].tolist()
elif self.framework == "tf":
return model_outputs[0].numpy().tolist()
def __call__( self : List[str] , *lowercase_ : int , **lowercase_ : Dict ):
return super().__call__(*lowercase_ , **lowercase_ )
| 123
| 0
|
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__snake_case = 6378137.0
__snake_case = 6356752.314245
__snake_case = 6378137
def a ( __a , __a , __a , __a ) -> Dict:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = (AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
UpperCamelCase__ :Union[str, Any] = atan((1 - flattening) * tan(radians(lowerCamelCase__ ) ) )
UpperCamelCase__ :Optional[int] = atan((1 - flattening) * tan(radians(lowerCamelCase__ ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
UpperCamelCase__ :str = haversine_distance(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
UpperCamelCase__ :Optional[Any] = (b_lata + b_lata) / 2
UpperCamelCase__ :List[Any] = (b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
UpperCamelCase__ :str = (sin(lowerCamelCase__ ) ** 2) * (cos(lowerCamelCase__ ) ** 2)
UpperCamelCase__ :List[Any] = cos(sigma / 2 ) ** 2
UpperCamelCase__ :Dict = (sigma - sin(lowerCamelCase__ )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
UpperCamelCase__ :Tuple = (cos(lowerCamelCase__ ) ** 2) * (sin(lowerCamelCase__ ) ** 2)
UpperCamelCase__ :Union[str, Any] = sin(sigma / 2 ) ** 2
UpperCamelCase__ :Any = (sigma + sin(lowerCamelCase__ )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class lowercase ( A__ ):
"""simple docstring"""
_a = 'transfo-xl'
_a = ['mems']
_a = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self , UpperCamelCase_=267735 , UpperCamelCase_=[20000, 40000, 200000] , UpperCamelCase_=1024 , UpperCamelCase_=1024 , UpperCamelCase_=16 , UpperCamelCase_=64 , UpperCamelCase_=4096 , UpperCamelCase_=4 , UpperCamelCase_=False , UpperCamelCase_=18 , UpperCamelCase_=1600 , UpperCamelCase_=1000 , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=0 , UpperCamelCase_=-1 , UpperCamelCase_=True , UpperCamelCase_=0.1 , UpperCamelCase_=0.0 , UpperCamelCase_=True , UpperCamelCase_="normal" , UpperCamelCase_=0.01 , UpperCamelCase_=0.01 , UpperCamelCase_=0.02 , UpperCamelCase_=1e-5 , UpperCamelCase_=0 , **UpperCamelCase_ , ):
'''simple docstring'''
UpperCamelCase__ :Dict = vocab_size
UpperCamelCase__ :Optional[int] = []
self.cutoffs.extend(UpperCamelCase_ )
if proj_share_all_but_first:
UpperCamelCase__ :Any = [False] + [True] * len(self.cutoffs )
else:
UpperCamelCase__ :Dict = [False] + [False] * len(self.cutoffs )
UpperCamelCase__ :Union[str, Any] = d_model
UpperCamelCase__ :List[Any] = d_embed
UpperCamelCase__ :Any = d_head
UpperCamelCase__ :Tuple = d_inner
UpperCamelCase__ :int = div_val
UpperCamelCase__ :int = pre_lnorm
UpperCamelCase__ :Any = n_layer
UpperCamelCase__ :int = n_head
UpperCamelCase__ :Tuple = mem_len
UpperCamelCase__ :List[str] = same_length
UpperCamelCase__ :Any = attn_type
UpperCamelCase__ :List[str] = clamp_len
UpperCamelCase__ :Union[str, Any] = sample_softmax
UpperCamelCase__ :List[Any] = adaptive
UpperCamelCase__ :List[Any] = dropout
UpperCamelCase__ :Optional[Any] = dropatt
UpperCamelCase__ :str = untie_r
UpperCamelCase__ :List[Any] = init
UpperCamelCase__ :Optional[Any] = init_range
UpperCamelCase__ :int = proj_init_std
UpperCamelCase__ :List[str] = init_std
UpperCamelCase__ :Optional[int] = layer_norm_epsilon
super().__init__(eos_token_id=UpperCamelCase_ , **UpperCamelCase_ )
@property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
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 lowerCAmelCase__ ( self , UpperCamelCase_ ):
'''simple docstring'''
raise NotImplementedError(
F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
| 280
| 0
|
"""simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
snake_case = '''src/transformers'''
snake_case = '''docs/source/en'''
snake_case = '''.'''
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
with open(lowerCAmelCase_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
_snake_case = f.readlines()
# Find the start prompt.
_snake_case = 0
while not lines[start_index].startswith(lowerCAmelCase_ ):
start_index += 1
start_index += 1
_snake_case = start_index
while not lines[end_index].startswith(lowerCAmelCase_ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# Add here suffixes that are used to identify models, separated by |
snake_case = '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case = re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case = re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
snake_case = re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# This is to make sure the transformers module imported is the one in the repo.
snake_case = direct_transformers_import(TRANSFORMERS_PATH)
def snake_case ( lowerCAmelCase_ ) -> str:
_snake_case = re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)''' , lowerCAmelCase_ )
return [m.group(0 ) for m in matches]
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> Any:
_snake_case = 2 if text == '''✅''' or text == '''❌''' else len(lowerCAmelCase_ )
_snake_case = (width - text_length) // 2
_snake_case = width - text_length - left_indent
return " " * left_indent + text + " " * right_indent
def snake_case ( ) -> List[Any]:
_snake_case = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_snake_case = {
name: config_maping_names[code]
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if code in config_maping_names
}
_snake_case = {name: config.replace('''Config''' , '''''' ) for name, config in model_name_to_config.items()}
# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
_snake_case = collections.defaultdict(lowerCAmelCase_ )
_snake_case = collections.defaultdict(lowerCAmelCase_ )
_snake_case = collections.defaultdict(lowerCAmelCase_ )
_snake_case = collections.defaultdict(lowerCAmelCase_ )
_snake_case = collections.defaultdict(lowerCAmelCase_ )
# Let's lookup through all transformers object (once).
for attr_name in dir(lowerCAmelCase_ ):
_snake_case = None
if attr_name.endswith('''Tokenizer''' ):
_snake_case = slow_tokenizers
_snake_case = attr_name[:-9]
elif attr_name.endswith('''TokenizerFast''' ):
_snake_case = fast_tokenizers
_snake_case = attr_name[:-13]
elif _re_tf_models.match(lowerCAmelCase_ ) is not None:
_snake_case = tf_models
_snake_case = _re_tf_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_flax_models.match(lowerCAmelCase_ ) is not None:
_snake_case = flax_models
_snake_case = _re_flax_models.match(lowerCAmelCase_ ).groups()[0]
elif _re_pt_models.match(lowerCAmelCase_ ) is not None:
_snake_case = pt_models
_snake_case = _re_pt_models.match(lowerCAmelCase_ ).groups()[0]
if lookup_dict is not None:
while len(lowerCAmelCase_ ) > 0:
if attr_name in model_name_to_prefix.values():
_snake_case = True
break
# Try again after removing the last word in the name
_snake_case = ''''''.join(camel_case_split(lowerCAmelCase_ )[:-1] )
# Let's build that table!
_snake_case = list(model_name_to_config.keys() )
model_names.sort(key=str.lower )
_snake_case = ['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
_snake_case = [len(lowerCAmelCase_ ) + 2 for c in columns]
_snake_case = max([len(lowerCAmelCase_ ) for name in model_names] ) + 2
# Build the table per se
_snake_case = '''|''' + '''|'''.join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for c, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + '''|\n'''
# Use ":-----:" format to center-aligned table cell texts
table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths] ) + "|\n"
_snake_case = {True: '''✅''', False: '''❌'''}
for name in model_names:
_snake_case = model_name_to_prefix[name]
_snake_case = [
name,
check[slow_tokenizers[prefix]],
check[fast_tokenizers[prefix]],
check[pt_models[prefix]],
check[tf_models[prefix]],
check[flax_models[prefix]],
]
table += "|" + "|".join([_center_text(lowerCAmelCase_ , lowerCAmelCase_ ) for l, w in zip(lowerCAmelCase_ , lowerCAmelCase_ )] ) + "|\n"
return table
def snake_case ( lowerCAmelCase_=False ) -> Union[str, Any]:
_snake_case , _snake_case , _snake_case , _snake_case = _find_text_in_file(
filename=os.path.join(lowerCAmelCase_ , '''index.md''' ) , start_prompt='''<!--This table is updated automatically from the auto modules''' , end_prompt='''<!-- End table-->''' , )
_snake_case = get_model_table_from_auto_modules()
if current_table != new_table:
if overwrite:
with open(os.path.join(lowerCAmelCase_ , '''index.md''' ) , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(lines[:start_index] + [new_table] + lines[end_index:] )
else:
raise ValueError(
'''The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.''' )
if __name__ == "__main__":
snake_case = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
snake_case = parser.parse_args()
check_model_table(args.fix_and_overwrite)
| 103
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case =logging.get_logger(__name__)
__snake_case ={
"""bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}
class UpperCAmelCase_ ( __lowercase ):
lowerCamelCase : Optional[Any] = '''gpt_bigcode'''
lowerCamelCase : Any = ['''past_key_values''']
lowerCamelCase : List[Any] = {
'''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__ : List[str]=5_0_2_5_7 , UpperCAmelCase__ : List[str]=1_0_2_4 , UpperCAmelCase__ : Optional[Any]=7_6_8 , UpperCAmelCase__ : int=1_2 , UpperCAmelCase__ : List[str]=1_2 , UpperCAmelCase__ : List[Any]=None , UpperCAmelCase__ : Optional[int]="gelu_pytorch_tanh" , UpperCAmelCase__ : Any=0.1 , UpperCAmelCase__ : Tuple=0.1 , UpperCAmelCase__ : str=0.1 , UpperCAmelCase__ : List[str]=1E-5 , UpperCAmelCase__ : int=0.02 , UpperCAmelCase__ : Optional[Any]=True , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Dict=5_0_2_5_6 , UpperCAmelCase__ : Any=5_0_2_5_6 , UpperCAmelCase__ : Dict=True , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : Union[str, Any]=True , **UpperCAmelCase__ : int , ) -> List[str]:
lowerCAmelCase = vocab_size
lowerCAmelCase = n_positions
lowerCAmelCase = n_embd
lowerCAmelCase = n_layer
lowerCAmelCase = n_head
lowerCAmelCase = n_inner
lowerCAmelCase = activation_function
lowerCAmelCase = resid_pdrop
lowerCAmelCase = embd_pdrop
lowerCAmelCase = attn_pdrop
lowerCAmelCase = layer_norm_epsilon
lowerCAmelCase = initializer_range
lowerCAmelCase = scale_attn_weights
lowerCAmelCase = use_cache
lowerCAmelCase = attention_softmax_in_fpaa
lowerCAmelCase = scale_attention_softmax_in_fpaa
lowerCAmelCase = multi_query
lowerCAmelCase = bos_token_id
lowerCAmelCase = eos_token_id
super().__init__(bos_token_id=UpperCAmelCase__ , eos_token_id=UpperCAmelCase__ , **UpperCAmelCase__ )
| 133
| 0
|
'''simple docstring'''
class UpperCAmelCase : # Public class to implement a graph
'''simple docstring'''
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_) -> None:
"""simple docstring"""
a_ =row
a_ =col
a_ =graph
def lowercase_ ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_) -> bool:
"""simple docstring"""
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def lowercase_ ( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_) -> None:
"""simple docstring"""
a_ =[-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
a_ =[-1, 0, 1, -1, 1, -1, 0, 1]
a_ =True # Make those cells visited
for k in range(8):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , lowerCAmelCase_):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , lowerCAmelCase_)
def lowercase_ ( self) -> int: # And finally, count all islands.
"""simple docstring"""
a_ =[[False for j in range(self.COL)] for i in range(self.ROW)]
a_ =0
for i in range(self.ROW):
for j in range(self.COL):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_)
count += 1
return count
| 41
|
'''simple docstring'''
import os
from math import logaa
def UpperCAmelCase_ ( lowercase__ = "base_exp.txt" ):
'''simple docstring'''
a_ =0
a_ =0
for i, line in enumerate(open(os.path.join(os.path.dirname(lowercase__ ) , lowercase__ ) ) ):
a_ , a_ =list(map(lowercase__ , line.split("," ) ) )
if x * logaa(lowercase__ ) > largest:
a_ =x * logaa(lowercase__ )
a_ =i + 1
return result
if __name__ == "__main__":
print(solution())
| 41
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''bert-base-uncased''': '''https://huggingface.co/bert-base-uncased/resolve/main/config.json''',
'''bert-large-uncased''': '''https://huggingface.co/bert-large-uncased/resolve/main/config.json''',
'''bert-base-cased''': '''https://huggingface.co/bert-base-cased/resolve/main/config.json''',
'''bert-large-cased''': '''https://huggingface.co/bert-large-cased/resolve/main/config.json''',
'''bert-base-multilingual-uncased''': '''https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json''',
'''bert-base-multilingual-cased''': '''https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json''',
'''bert-base-chinese''': '''https://huggingface.co/bert-base-chinese/resolve/main/config.json''',
'''bert-base-german-cased''': '''https://huggingface.co/bert-base-german-cased/resolve/main/config.json''',
'''bert-large-uncased-whole-word-masking''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'''
),
'''bert-large-uncased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-large-cased-whole-word-masking-finetuned-squad''': (
'''https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'''
),
'''bert-base-cased-finetuned-mrpc''': '''https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json''',
'''bert-base-german-dbmdz-cased''': '''https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json''',
'''bert-base-german-dbmdz-uncased''': '''https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese''': '''https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json''',
'''cl-tohoku/bert-base-japanese-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'''
),
'''cl-tohoku/bert-base-japanese-char-whole-word-masking''': (
'''https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-cased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'''
),
'''TurkuNLP/bert-base-finnish-uncased-v1''': (
'''https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'''
),
'''wietsedv/bert-base-dutch-cased''': '''https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json''',
# See all BERT models at https://huggingface.co/models?filter=bert
}
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''bert'''
def __init__( self , snake_case=30522 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case="gelu" , snake_case=0.1 , snake_case=0.1 , snake_case=512 , snake_case=2 , snake_case=0.02 , snake_case=1E-12 , snake_case=0 , snake_case="absolute" , snake_case=True , snake_case=None , **snake_case , ) -> List[Any]:
super().__init__(pad_token_id=snake_case , **snake_case )
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = hidden_act
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = type_vocab_size
_UpperCAmelCase = initializer_range
_UpperCAmelCase = layer_norm_eps
_UpperCAmelCase = position_embedding_type
_UpperCAmelCase = use_cache
_UpperCAmelCase = classifier_dropout
class lowercase__ ( A ):
'''simple docstring'''
@property
def lowerCamelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
_UpperCAmelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 573
|
"""simple docstring"""
from __future__ import annotations
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case ) -> None:
_UpperCAmelCase = order
# a_{0} ... a_{k}
_UpperCAmelCase = [1.0] + [0.0] * order
# b_{0} ... b_{k}
_UpperCAmelCase = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
_UpperCAmelCase = [0.0] * self.order
# y[n-1] ... y[n-k]
_UpperCAmelCase = [0.0] * self.order
def lowerCamelCase_ ( self , snake_case , snake_case ) -> None:
if len(snake_case ) < self.order:
_UpperCAmelCase = [1.0, *a_coeffs]
if len(snake_case ) != self.order + 1:
_UpperCAmelCase = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(snake_case )}'
)
raise ValueError(snake_case )
if len(snake_case ) != self.order + 1:
_UpperCAmelCase = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(snake_case )}'
)
raise ValueError(snake_case )
_UpperCAmelCase = a_coeffs
_UpperCAmelCase = b_coeffs
def lowerCamelCase_ ( self , snake_case ) -> float:
_UpperCAmelCase = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 , self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
_UpperCAmelCase = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
_UpperCAmelCase = self.input_history[:-1]
_UpperCAmelCase = self.output_history[:-1]
_UpperCAmelCase = sample
_UpperCAmelCase = result
return result
| 573
| 1
|
import pytest
UpperCAmelCase_ = "__dummy_dataset1__"
UpperCAmelCase_ = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def __magic_name__ ( ) -> Optional[Any]:
"""simple docstring"""
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def __magic_name__ ( ) -> Optional[int]:
"""simple docstring"""
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def __magic_name__ ( lowercase , lowercase , lowercase ) -> Dict:
"""simple docstring"""
lowercase_ : Optional[Any] = dataset_loading_script_name
lowercase_ : Optional[Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=__snake_case )
lowercase_ : List[Any] = script_dir / f"""{script_name}.py"""
with open(__snake_case , """w""" ) as f:
f.write(__snake_case )
return str(__snake_case )
| 713
|
import math
import flax.linen as nn
import jax.numpy as jnp
def __magic_name__ ( lowercase , lowercase , lowercase = 1 , lowercase = 1 , lowercase = 1.0E4 , lowercase = False , lowercase = 1.0 , ) -> jnp.ndarray:
"""simple docstring"""
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f"""Embedding dimension {embedding_dim} should be even"""
lowercase_ : List[Any] = float(embedding_dim // 2 )
lowercase_ : Any = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
lowercase_ : Union[str, Any] = min_timescale * jnp.exp(jnp.arange(lowercase , dtype=jnp.floataa ) * -log_timescale_increment )
lowercase_ : List[str] = jnp.expand_dims(lowercase , 1 ) * jnp.expand_dims(lowercase , 0 )
# scale embeddings
lowercase_ : int = scale * emb
if flip_sin_to_cos:
lowercase_ : List[str] = jnp.concatenate([jnp.cos(lowercase ), jnp.sin(lowercase )] , axis=1 )
else:
lowercase_ : List[str] = jnp.concatenate([jnp.sin(lowercase ), jnp.cos(lowercase )] , axis=1 )
lowercase_ : str = jnp.reshape(lowercase , [jnp.shape(lowercase )[0], embedding_dim] )
return signal
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
__a : int = 32
__a : jnp.dtype = jnp.floataa
@nn.compact
def __call__( self, snake_case__ ) -> Union[str, Any]:
"""simple docstring"""
lowercase_ : List[str] = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="""linear_1""" )(snake_case__ )
lowercase_ : List[Any] = nn.silu(snake_case__ )
lowercase_ : List[str] = nn.Dense(self.time_embed_dim, dtype=self.dtype, name="""linear_2""" )(snake_case__ )
return temb
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
__a : int = 32
__a : bool = False
__a : float = 1
@nn.compact
def __call__( self, snake_case__ ) -> Optional[Any]:
"""simple docstring"""
return get_sinusoidal_embeddings(
snake_case__, embedding_dim=self.dim, flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.freq_shift )
| 436
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class _A ( UpperCamelCase ):
"""simple docstring"""
lowerCamelCase : Tuple = 'ctrl'
lowerCamelCase : Any = ['past_key_values']
lowerCamelCase : Optional[int] = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]=246534 , __SCREAMING_SNAKE_CASE : int=256 , __SCREAMING_SNAKE_CASE : Optional[Any]=1280 , __SCREAMING_SNAKE_CASE : Optional[Any]=8192 , __SCREAMING_SNAKE_CASE : int=48 , __SCREAMING_SNAKE_CASE : Union[str, Any]=16 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : Dict=0.1 , __SCREAMING_SNAKE_CASE : List[Any]=1e-6 , __SCREAMING_SNAKE_CASE : List[str]=0.02 , __SCREAMING_SNAKE_CASE : Union[str, Any]=True , **__SCREAMING_SNAKE_CASE : int , ) -> Any:
__UpperCAmelCase =vocab_size
__UpperCAmelCase =n_positions
__UpperCAmelCase =n_embd
__UpperCAmelCase =n_layer
__UpperCAmelCase =n_head
__UpperCAmelCase =dff
__UpperCAmelCase =resid_pdrop
__UpperCAmelCase =embd_pdrop
__UpperCAmelCase =layer_norm_epsilon
__UpperCAmelCase =initializer_range
__UpperCAmelCase =use_cache
super().__init__(**__SCREAMING_SNAKE_CASE )
| 68
|
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowercase ( _UpperCAmelCase , unittest.TestCase):
"""simple docstring"""
_A : Optional[Any] = CTRLTokenizer
_A : Dict = False
_A : Any = False
def __UpperCamelCase (self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case_ : Tuple = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
snake_case_ : int = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
snake_case_ : List[str] = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
snake_case_ : Tuple = {"""unk_token""": """<unk>"""}
snake_case_ : Tuple = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
snake_case_ : Union[str, 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__ ) )
def __UpperCamelCase (self , **lowercase__ ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **lowercase__ )
def __UpperCamelCase (self , lowercase__ ):
snake_case_ : Tuple = """adapt react readapt apt"""
snake_case_ : Tuple = """adapt react readapt apt"""
return input_text, output_text
def __UpperCamelCase (self ):
snake_case_ : int = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
snake_case_ : Tuple = """adapt react readapt apt"""
snake_case_ : List[str] = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
snake_case_ : List[str] = tokenizer.tokenize(lowercase__ )
self.assertListEqual(lowercase__ , lowercase__ )
snake_case_ : Union[str, Any] = tokens + [tokenizer.unk_token]
snake_case_ : List[str] = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__ ) , lowercase__ )
| 480
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """roformer"""
def __init__( self : List[Any] ,_a : Tuple=50000 ,_a : List[str]=None ,_a : int=768 ,_a : List[str]=12 ,_a : Optional[Any]=12 ,_a : Union[str, Any]=3072 ,_a : Optional[int]="gelu" ,_a : Dict=0.1 ,_a : List[str]=0.1 ,_a : Any=1536 ,_a : Optional[Any]=2 ,_a : List[Any]=0.02 ,_a : Dict=1e-12 ,_a : Union[str, Any]=0 ,_a : List[str]=False ,_a : str=True ,**_a : Optional[Any] ,):
'''simple docstring'''
super().__init__(pad_token_id=_a ,**_a )
A_ : Optional[int] = vocab_size
A_ : str = hidden_size if embedding_size is None else embedding_size
A_ : int = hidden_size
A_ : Any = num_hidden_layers
A_ : Tuple = num_attention_heads
A_ : List[str] = hidden_act
A_ : str = intermediate_size
A_ : Union[str, Any] = hidden_dropout_prob
A_ : Any = attention_probs_dropout_prob
A_ : Union[str, Any] = max_position_embeddings
A_ : List[str] = type_vocab_size
A_ : List[str] = initializer_range
A_ : List[str] = layer_norm_eps
A_ : Optional[Any] = rotary_value
A_ : Union[str, Any] = use_cache
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Tuple = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : int = {0: """batch""", 1: """sequence"""}
A_ : Union[str, Any] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 27
|
'''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
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'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 __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = """deberta-v2"""
def __init__( self : Optional[Any] ,_a : Union[str, Any]=128100 ,_a : Optional[int]=1536 ,_a : Dict=24 ,_a : int=24 ,_a : Tuple=6144 ,_a : Union[str, Any]="gelu" ,_a : List[Any]=0.1 ,_a : Dict=0.1 ,_a : int=512 ,_a : int=0 ,_a : int=0.02 ,_a : int=1e-7 ,_a : List[str]=False ,_a : Union[str, Any]=-1 ,_a : List[Any]=0 ,_a : Optional[Any]=True ,_a : Tuple=None ,_a : Any=0 ,_a : int="gelu" ,**_a : Any ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Union[str, Any] = hidden_size
A_ : Dict = num_hidden_layers
A_ : Union[str, Any] = num_attention_heads
A_ : List[Any] = intermediate_size
A_ : List[Any] = hidden_act
A_ : Optional[int] = hidden_dropout_prob
A_ : Dict = attention_probs_dropout_prob
A_ : int = max_position_embeddings
A_ : Any = type_vocab_size
A_ : List[Any] = initializer_range
A_ : int = relative_attention
A_ : Tuple = max_relative_positions
A_ : int = pad_token_id
A_ : Tuple = position_biased_input
# Backwards compatibility
if type(_a ) == str:
A_ : str = [x.strip() for x in pos_att_type.lower().split("""|""" )]
A_ : Any = pos_att_type
A_ : Optional[int] = vocab_size
A_ : Tuple = layer_norm_eps
A_ : Any = kwargs.get("""pooler_hidden_size""" ,_a )
A_ : Union[str, Any] = pooler_dropout
A_ : List[Any] = pooler_hidden_act
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
def _a ( self : Any ):
'''simple docstring'''
if self.task == "multiple-choice":
A_ : Any = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
A_ : Any = {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 _a ( self : Optional[int] ):
'''simple docstring'''
return 12
def _a ( self : int ,_a : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] ,_a : int = -1 ,_a : int = -1 ,_a : int = -1 ,_a : bool = False ,_a : Optional["TensorType"] = None ,_a : int = 3 ,_a : int = 40 ,_a : int = 40 ,_a : "PreTrainedTokenizerBase" = None ,):
'''simple docstring'''
A_ : Any = super().generate_dummy_inputs(preprocessor=_a ,framework=_a )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 27
| 1
|
from __future__ import annotations
from collections.abc import Callable
__SCREAMING_SNAKE_CASE =list[list[float | int]]
def a (_lowerCAmelCase , _lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = len(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = [[0 for _ in range(size + 1 )] for _ in range(_lowerCAmelCase )]
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for row in range(_lowerCAmelCase ):
for col in range(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = matrix[row][col]
SCREAMING_SNAKE_CASE_ = vector[row][0]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
while row < size and col < size:
# pivoting
SCREAMING_SNAKE_CASE_ = max((abs(augmented[rowa][col] ), rowa) for rowa in range(_lowerCAmelCase , _lowerCAmelCase ) )[
1
]
if augmented[pivot_row][col] == 0:
col += 1
continue
else:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = augmented[pivot_row], augmented[row]
for rowa in range(row + 1 , _lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = augmented[rowa][col] / augmented[row][col]
SCREAMING_SNAKE_CASE_ = 0
for cola in range(col + 1 , size + 1 ):
augmented[rowa][cola] -= augmented[row][cola] * ratio
row += 1
col += 1
# back substitution
for col in range(1 , _lowerCAmelCase ):
for row in range(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = augmented[row][col] / augmented[col][col]
for cola in range(_lowerCAmelCase , size + 1 ):
augmented[row][cola] -= augmented[col][cola] * ratio
# round to get rid of numbers like 2.000000000000004
return [
[round(augmented[row][size] / augmented[row][row] , 1_0 )] for row in range(_lowerCAmelCase )
]
def a (_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = len(_lowerCAmelCase )
SCREAMING_SNAKE_CASE_ = [[0 for _ in range(_lowerCAmelCase )] for _ in range(_lowerCAmelCase )]
SCREAMING_SNAKE_CASE_ = [[0] for _ in range(_lowerCAmelCase )]
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for x_val, y_val in enumerate(_lowerCAmelCase ):
for col in range(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ = (x_val + 1) ** (size - col - 1)
SCREAMING_SNAKE_CASE_ = y_val
SCREAMING_SNAKE_CASE_ = solve(_lowerCAmelCase , _lowerCAmelCase )
def interpolated_func(_lowerCAmelCase ) -> int:
return sum(
round(coeffs[x_val][0] ) * (var ** (size - x_val - 1))
for x_val in range(_lowerCAmelCase ) )
return interpolated_func
def a (_lowerCAmelCase ):
return (
1
- variable
+ variable**2
- variable**3
+ variable**4
- variable**5
+ variable**6
- variable**7
+ variable**8
- variable**9
+ variable**1_0
)
def a (_lowerCAmelCase = question_function , _lowerCAmelCase = 1_0 ):
SCREAMING_SNAKE_CASE_ = [func(_lowerCAmelCase ) for x_val in range(1 , order + 1 )]
SCREAMING_SNAKE_CASE_ = [
interpolate(data_points[:max_coeff] ) for max_coeff in range(1 , order + 1 )
]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 42
SCREAMING_SNAKE_CASE_ = 42
for poly in polynomials:
SCREAMING_SNAKE_CASE_ = 1
while func(_lowerCAmelCase ) == poly(_lowerCAmelCase ):
x_val += 1
ret += poly(_lowerCAmelCase )
return ret
if __name__ == "__main__":
print(f"""{solution() = }""")
| 234
|
from ..utils import DummyObject, requires_backends
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["flax"]
def __init__( self: Dict , *_lowerCamelCase: Tuple , **_lowerCamelCase: List[str] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Dict , *_lowerCamelCase: Optional[Any] , **_lowerCamelCase: List[Any] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Tuple , *_lowerCamelCase: Tuple , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["flax"]
def __init__( self: Union[str, Any] , *_lowerCamelCase: Any , **_lowerCamelCase: Dict ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Union[str, Any] , *_lowerCamelCase: Any , **_lowerCamelCase: Tuple ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: int , *_lowerCamelCase: List[Any] , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = ["flax"]
def __init__( self: int , *_lowerCamelCase: Tuple , **_lowerCamelCase: List[Any] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Optional[Any] , *_lowerCamelCase: Optional[Any] , **_lowerCamelCase: List[str] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Optional[int] , *_lowerCamelCase: str , **_lowerCamelCase: int ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["flax"]
def __init__( self: int , *_lowerCamelCase: Dict , **_lowerCamelCase: Dict ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Optional[int] , *_lowerCamelCase: Any , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Any , *_lowerCamelCase: Optional[int] , **_lowerCamelCase: Union[str, Any] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = ["flax"]
def __init__( self: Tuple , *_lowerCamelCase: List[str] , **_lowerCamelCase: Union[str, Any] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: str , *_lowerCamelCase: Optional[Any] , **_lowerCamelCase: Optional[Any] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Any , *_lowerCamelCase: str , **_lowerCamelCase: List[Any] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ["flax"]
def __init__( self: Dict , *_lowerCamelCase: str , **_lowerCamelCase: List[Any] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Tuple , *_lowerCamelCase: List[Any] , **_lowerCamelCase: Optional[Any] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Union[str, Any] , *_lowerCamelCase: Union[str, Any] , **_lowerCamelCase: Any ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = ["flax"]
def __init__( self: str , *_lowerCamelCase: List[str] , **_lowerCamelCase: List[Any] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: List[Any] , *_lowerCamelCase: Dict , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: int , *_lowerCamelCase: Dict , **_lowerCamelCase: List[str] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = ["flax"]
def __init__( self: Dict , *_lowerCamelCase: Tuple , **_lowerCamelCase: int ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: List[Any] , *_lowerCamelCase: Dict , **_lowerCamelCase: Tuple ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Optional[int] , *_lowerCamelCase: Union[str, Any] , **_lowerCamelCase: Optional[Any] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any = ["flax"]
def __init__( self: int , *_lowerCamelCase: List[Any] , **_lowerCamelCase: Optional[int] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Union[str, Any] , *_lowerCamelCase: Optional[Any] , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Optional[Any] , *_lowerCamelCase: Any , **_lowerCamelCase: int ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = ["flax"]
def __init__( self: Optional[Any] , *_lowerCamelCase: Union[str, Any] , **_lowerCamelCase: Any ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Optional[Any] , *_lowerCamelCase: Union[str, Any] , **_lowerCamelCase: Tuple ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Union[str, Any] , *_lowerCamelCase: Tuple , **_lowerCamelCase: str ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = ["flax"]
def __init__( self: str , *_lowerCamelCase: Any , **_lowerCamelCase: int ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: List[Any] , *_lowerCamelCase: Optional[int] , **_lowerCamelCase: Tuple ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Dict , *_lowerCamelCase: str , **_lowerCamelCase: int ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = ["flax"]
def __init__( self: Any , *_lowerCamelCase: Union[str, Any] , **_lowerCamelCase: Any ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Tuple , *_lowerCamelCase: Union[str, Any] , **_lowerCamelCase: Union[str, Any] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: Dict , *_lowerCamelCase: List[Any] , **_lowerCamelCase: Optional[Any] ):
requires_backends(cls , ['''flax'''] )
class __magic_name__ ( metaclass=__UpperCAmelCase):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] = ["flax"]
def __init__( self: Tuple , *_lowerCamelCase: List[Any] , **_lowerCamelCase: Optional[Any] ):
requires_backends(self , ['''flax'''] )
@classmethod
def _A ( cls: Any , *_lowerCamelCase: List[str] , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
@classmethod
def _A ( cls: int , *_lowerCamelCase: Tuple , **_lowerCamelCase: Optional[int] ):
requires_backends(cls , ['''flax'''] )
| 234
| 1
|
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __lowerCamelCase ( __a :ndarray ) -> float:
"""simple docstring"""
return np.dot(__a , __a )
class A :
'''simple docstring'''
def __init__( self : Union[str, Any] , *,
__lowerCAmelCase : float = np.inf , __lowerCAmelCase : str = "linear" , __lowerCAmelCase : float = 0.0 , ) -> None:
"""simple docstring"""
A__ = regularization
A__ = gamma
if kernel == "linear":
A__ = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError("""rbf kernel requires gamma""" )
if not isinstance(self.gamma , (float, int) ):
raise ValueError("""gamma must be float or int""" )
if not self.gamma > 0:
raise ValueError("""gamma must be > 0""" )
A__ = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
A__ = f'Unknown kernel: {kernel}'
raise ValueError(__lowerCAmelCase )
def a_ ( self : Tuple , __lowerCAmelCase : ndarray , __lowerCAmelCase : ndarray ) -> float:
"""simple docstring"""
return np.dot(__lowerCAmelCase , __lowerCAmelCase )
def a_ ( self : str , __lowerCAmelCase : ndarray , __lowerCAmelCase : ndarray ) -> float:
"""simple docstring"""
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def a_ ( self : Union[str, Any] , __lowerCAmelCase : list[ndarray] , __lowerCAmelCase : ndarray ) -> None:
"""simple docstring"""
A__ = observations
A__ = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((A__) , ) = np.shape(__lowerCAmelCase )
def to_minimize(__lowerCAmelCase : ndarray ) -> float:
A__ = 0
((A__) , ) = np.shape(__lowerCAmelCase )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(__lowerCAmelCase )
A__ = LinearConstraint(__lowerCAmelCase , 0 , 0 )
A__ = Bounds(0 , self.regularization )
A__ = minimize(
__lowerCAmelCase , np.ones(__lowerCAmelCase ) , bounds=__lowerCAmelCase , constraints=[ly_contraint] ).x
A__ = l_star
# calculating mean offset of separation plane to points
A__ = 0
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
A__ = s / n
def a_ ( self : Tuple , __lowerCAmelCase : ndarray ) -> int:
"""simple docstring"""
A__ = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , __lowerCAmelCase )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 247
|
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A : int = '''src/diffusers'''
# Matches is_xxx_available()
A : Dict = re.compile(R'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
A : Optional[Any] = re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
A : str = '''
{0} = None
'''
A : str = '''
class {0}(metaclass=DummyObject):
_backends = {1}
def __init__(self, *args, **kwargs):
requires_backends(self, {1})
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, {1})
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, {1})
'''
A : Tuple = '''
def {0}(*args, **kwargs):
requires_backends({0}, {1})
'''
def __lowerCamelCase ( __a :str ) -> Any:
"""simple docstring"""
A__ = _re_backend.findall(__a )
if len(__a ) == 0:
return None
return "_and_".join(__a )
def __lowerCamelCase ( ) -> Union[str, Any]:
"""simple docstring"""
with open(os.path.join(__a , """__init__.py""" ) , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
A__ = f.readlines()
# Get to the point we do the actual imports for type checking
A__ = 0
A__ = {}
# Go through the end of the file
while line_index < len(__a ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
A__ = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith("""else:""" ):
line_index += 1
line_index += 1
A__ = []
# Until we unindent, add backend objects to the list
while line_index < len(__a ) and len(lines[line_index] ) > 1:
A__ = lines[line_index]
A__ = _re_single_line_import.search(__a )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(__a ) > 0:
A__ = objects
else:
line_index += 1
return backend_specific_objects
def __lowerCamelCase ( __a :Any , __a :List[str] ) -> int:
"""simple docstring"""
if name.isupper():
return DUMMY_CONSTANT.format(__a )
elif name.islower():
return DUMMY_FUNCTION.format(__a , __a )
else:
return DUMMY_CLASS.format(__a , __a )
def __lowerCamelCase ( __a :Optional[Any]=None ) -> Tuple:
"""simple docstring"""
if backend_specific_objects is None:
A__ = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
A__ = {}
for backend, objects in backend_specific_objects.items():
A__ = """[""" + """, """.join(F'"{b}"' for b in backend.split("""_and_""" ) ) + """]"""
A__ = """# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(__a , __a ) for o in objects] )
A__ = dummy_file
return dummy_files
def __lowerCamelCase ( __a :Union[str, Any]=False ) -> Optional[int]:
"""simple docstring"""
A__ = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
A__ = {"""torch""": """pt"""}
# Locate actual dummy modules and read their content.
A__ = os.path.join(__a , """utils""" )
A__ = {
backend: os.path.join(__a , F'dummy_{short_names.get(__a , __a )}_objects.py' )
for backend in dummy_files.keys()
}
A__ = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(__a ):
with open(__a , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
A__ = f.read()
else:
A__ = """"""
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
F'Updating diffusers.utils.dummy_{short_names.get(__a , __a )}_objects.py as the main '
"""__init__ has new objects.""" )
with open(dummy_file_paths[backend] , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
"""The main __init__ has objects that are not present in """
F'diffusers.utils.dummy_{short_names.get(__a , __a )}_objects.py. Run `make fix-copies` '
"""to fix this.""" )
if __name__ == "__main__":
A : Dict = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A : int = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 247
| 1
|
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __snake_case ( ) -> List[str]:
lowercase : List[Any] = HfArgumentParser(__A )
lowercase : int = parser.parse_args_into_dataclasses()[0]
lowercase : Union[str, Any] = TensorFlowBenchmark(args=__A )
try:
lowercase : str = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
lowercase : Union[str, Any] = """Arg --no_{0} is no longer used, please use --no-{0} instead."""
lowercase : Tuple = """ """.join(str(__A ).split(""" """ )[:-1] )
lowercase : Tuple = """"""
lowercase : Optional[int] = eval(str(__A ).split(""" """ )[-1] )
lowercase : Union[str, Any] = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(__A )
if len(__A ) > 0:
lowercase : Any = full_error_msg + begin_error_msg + str(__A )
raise ValueError(__A )
benchmark.run()
if __name__ == "__main__":
main()
| 607
|
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""parameters.json""" )
lowercase : Any = json.loads(open(__A ).read() )
if not params:
raise ValueError(
F'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.''' )
if not args.output.endswith(""".pt""" ):
lowercase : List[str] = args.output + """.pt"""
lowercase : Dict = OrderedDict()
with tf.device("""/CPU:0""" ):
lowercase : Any = tf.train.load_checkpoint(args.tf_model_dir )
lowercase : List[Any] = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
lowercase : Optional[Any] = reader.get_tensor(__A ).astype(np.floataa )
if key_name.endswith("""/adam_m""" ) or key_name.endswith("""/adam_v""" ):
continue
if key_name.startswith("""pasts/""" ):
if key_name.startswith("""pasts/mlp""" ):
lowercase : str = int(key_name[9] )
elif key_name.startswith("""pasts/out""" ):
lowercase : List[Any] = 8
lowercase : Optional[int] = """model.sqout.%d.weight""" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
lowercase : int = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase : int = torch.tensor(__A )
elif key_name.startswith("""model/moe""" ):
lowercase : Optional[int] = int(key_name[9:].split("""/""" )[0] )
if key_name.endswith("""/switch_gating/kernel""" ):
lowercase : Any = """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
lowercase : List[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase : Tuple = torch.tensor(__A )
elif key_name.endswith("""/softmlp/kernel""" ):
lowercase : str = """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
lowercase : Optional[int] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase : Optional[int] = torch.tensor(__A )
elif key_name.endswith("""/wo/kernel""" ) or key_name.endswith("""/wi/kernel""" ):
lowercase : Union[str, Any] = key_name[-9:-7]
for i in range(16 ):
lowercase : int = """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
lowercase : str = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
lowercase : List[Any] = torch.tensor(__A )
elif key_name.startswith("""model/mlp""" ):
lowercase : Any = int(key_name[9:].split("""/""" )[0] )
if key_name.endswith("""/p1/kernel""" ):
lowercase : Dict = """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
lowercase : Any = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase : int = torch.tensor(__A )
elif key_name.endswith("""/p1/bias""" ):
lowercase : Tuple = """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
lowercase : Tuple = vnp.copy() # same because it is one dimensional
lowercase : Tuple = torch.tensor(__A )
elif key_name.endswith("""/p2/kernel""" ):
lowercase : int = """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
lowercase : Any = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase : Optional[Any] = torch.tensor(__A )
elif key_name.endswith("""/p2/bias""" ):
lowercase : Any = """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
lowercase : str = vnp.copy() # same because it is one dimensional
lowercase : str = torch.tensor(__A )
elif key_name.startswith("""model/ln""" ):
lowercase : Union[str, Any] = int(key_name[8:].split("""/""" )[0] )
if key_name.endswith("""/b""" ):
lowercase : Any = """model.blocks.%d.feed_forward.norm.bias""" % player
lowercase : List[str] = vnp.copy() # same because it is one dimensional
lowercase : Any = torch.tensor(__A )
elif key_name.endswith("""/g""" ):
lowercase : int = """model.blocks.%d.feed_forward.norm.weight""" % player
lowercase : Union[str, Any] = vnp.copy() # same because it is one dimensional
lowercase : Optional[Any] = torch.tensor(__A )
elif key_name.startswith("""model/att""" ):
lowercase : Optional[int] = int(key_name[9:].split("""/""" )[0] )
if key_name.endswith("""/qkv/kernel""" ):
lowercase : Optional[int] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
lowercase : Any = state[:, 0, :, :]
lowercase : List[Any] = state[:, 1, :, :]
lowercase : Optional[Any] = state[:, 2, :, :]
lowercase : Dict = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase : List[str] = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase : Optional[Any] = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase : str = """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
lowercase : Optional[int] = torch.tensor(__A )
lowercase : int = """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
lowercase : Optional[Any] = torch.tensor(__A )
lowercase : Tuple = """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
lowercase : List[str] = torch.tensor(__A )
elif key_name.endswith("""/o/kernel""" ):
lowercase : Any = """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
lowercase : List[Any] = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
lowercase : Optional[int] = torch.tensor(__A )
elif key_name.startswith("""model/an""" ):
lowercase : Tuple = int(key_name[8:].split("""/""" )[0] )
if key_name.endswith("""/b""" ):
lowercase : List[str] = """model.blocks.%d.self_attn.norm.bias""" % player
lowercase : List[str] = vnp.copy() # same because it is one dimensional
lowercase : int = torch.tensor(__A )
elif key_name.endswith("""/g""" ):
lowercase : Any = """model.blocks.%d.self_attn.norm.weight""" % player
lowercase : Union[str, Any] = vnp.copy() # same because it is one dimensional
lowercase : int = torch.tensor(__A )
elif (
key_name.startswith("""model/wte""" )
or key_name.startswith("""model/wpe""" )
or key_name.startswith("""model/ete""" )
):
lowercase : Any = {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
key_name[-3:]
]
lowercase : Optional[int] = """model.%s.weight""" % nlayer
lowercase : Any = vnp.copy() # same in embedded
lowercase : Dict = torch.tensor(__A )
if key_name.startswith("""model/wte""" ):
lowercase : Optional[Any] = """lm_head.weight"""
lowercase : int = vnp.copy() # same in embedded
lowercase : str = torch.tensor(__A )
elif key_name.startswith("""model/wob""" ):
lowercase : str = """final_logits_bias"""
lowercase : List[str] = vnp.copy() # same in embedded
lowercase : str = state.reshape((1, -1) )
lowercase : Tuple = torch.tensor(__A )
elif key_name == "model/dense/kernel":
lowercase : Dict = """model.last_project.weight"""
lowercase : Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
lowercase : int = torch.tensor(__A )
elif key_name == "model/dense_1/bias":
lowercase : List[str] = """model.last_project.bias"""
lowercase : Dict = vnp.copy() # same because it is one dimensional
lowercase : List[str] = torch.tensor(__A )
torch.save(__A ,args.output )
if __name__ == "__main__":
lowerCAmelCase: Tuple =argparse.ArgumentParser(
description="model converter.", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("--tf_model_dir", metavar="PATH", type=str, required=True, help="import model")
parser.add_argument("--output", metavar="PATH", type=str, required=True, help="output model")
lowerCAmelCase: Tuple =parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 607
| 1
|
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
a_ = get_logger(__name__)
class lowercase__ :
a_ ='dummy_data'
a_ ='datasets'
a_ =False
def __init__( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = None , __UpperCAmelCase = False , __UpperCAmelCase = True , __UpperCAmelCase = None , )-> str:
'''simple docstring'''
lowerCAmelCase__ = 0
lowerCAmelCase__ = dataset_name
lowerCAmelCase__ = cache_dir
lowerCAmelCase__ = use_local_dummy_data
lowerCAmelCase__ = config
# download_callbacks take a single url as input
lowerCAmelCase__ = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowerCAmelCase__ = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowerCAmelCase__ = str(A_ )
# to be downloaded
lowerCAmelCase__ = None
lowerCAmelCase__ = None
@property
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
if self._dummy_file is None:
lowerCAmelCase__ = self.download_dummy_data()
return self._dummy_file
@property
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join("dummy" , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join("dummy" , self.version_name )
@property
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
return os.path.join(self.dummy_data_folder , "dummy_data.zip" )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
lowerCAmelCase__ = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowerCAmelCase__ = cached_path(
A_ , cache_dir=self.cache_dir , extract_compressed_file=A_ , force_extract=A_ )
return os.path.join(A_ , self.dummy_file_name )
@property
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
if self._bucket_url is None:
lowerCAmelCase__ = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , "/" ) )
return self._bucket_url
@property
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , "/" ).split("/" )[:-1] )
def UpperCAmelCase ( self , __UpperCAmelCase , *__UpperCAmelCase )-> List[str]:
'''simple docstring'''
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowerCAmelCase__ = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowerCAmelCase__ = self.dummy_file_name
# special case when data_url is a dict
if isinstance(A_ , A_ ):
return self.create_dummy_data_dict(A_ , A_ )
elif isinstance(A_ , (list, tuple) ):
return self.create_dummy_data_list(A_ , A_ )
else:
return self.create_dummy_data_single(A_ , A_ )
def UpperCAmelCase ( self , __UpperCAmelCase , *__UpperCAmelCase )-> Dict:
'''simple docstring'''
return self.download_and_extract(A_ )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> List[str]:
'''simple docstring'''
return self.download_and_extract(A_ )
def UpperCAmelCase ( self , __UpperCAmelCase , *__UpperCAmelCase , **__UpperCAmelCase )-> List[Any]:
'''simple docstring'''
return path
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
return {}
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(A_ , A_ ):
for single_url in single_urls:
download_callback(A_ )
else:
lowerCAmelCase__ = single_urls
download_callback(A_ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(A_ , A_ ):
lowerCAmelCase__ = [os.path.join(A_ , urllib.parse.quote_plus(Path(A_ ).name ) ) for x in single_urls]
else:
lowerCAmelCase__ = single_urls
lowerCAmelCase__ = os.path.join(A_ , urllib.parse.quote_plus(Path(A_ ).name ) )
lowerCAmelCase__ = value
# make sure that values are unique
if all(isinstance(A_ , A_ ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowerCAmelCase__ = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowerCAmelCase__ = all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}" , A_ ) ) for url in data_url )
lowerCAmelCase__ = all(
url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed" ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowerCAmelCase__ = [data_url[0]] * len(A_ )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(A_ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowerCAmelCase__ = os.path.join(A_ , urllib.parse.quote_plus(single_url.split("/" )[-1] ) )
dummy_data_list.append(A_ )
return dummy_data_list
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
for download_callback in self.download_callbacks:
download_callback(A_ )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowerCAmelCase__ = os.path.join(A_ , urllib.parse.quote_plus(data_url.split("/" )[-1] ) )
if os.path.exists(A_ ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
pass
def UpperCAmelCase ( self , __UpperCAmelCase )-> Tuple:
'''simple docstring'''
def _iter_archive_members(__UpperCAmelCase ):
# this preserves the order of the members inside the ZIP archive
lowerCAmelCase__ = Path(self.dummy_file ).parent
lowerCAmelCase__ = path.relative_to(A_ )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowerCAmelCase__ = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(A_ )
lowerCAmelCase__ = Path(A_ )
lowerCAmelCase__ = _iter_archive_members(A_ ) if self.use_local_dummy_data else path.rglob("*" )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith((".", "__") ):
yield file_path.relative_to(A_ ).as_posix(), file_path.open("rb" )
def UpperCAmelCase ( self , __UpperCAmelCase )-> Union[str, Any]:
'''simple docstring'''
if not isinstance(A_ , A_ ):
lowerCAmelCase__ = [paths]
for path in paths:
if os.path.isfile(A_ ):
if os.path.basename(A_ ).startswith((".", "__") ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(A_ ):
if os.path.basename(A_ ).startswith((".", "__") ):
continue
dirnames.sort()
for filename in sorted(A_ ):
if filename.startswith((".", "__") ):
continue
yield os.path.join(A_ , A_ )
| 716
|
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
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 (
MODEL_MAPPING,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerModel,
)
from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import SegformerImageProcessor
class lowercase__ ( _UpperCAmelCase ):
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "num_attention_heads" ) )
self.parent.assertTrue(hasattr(__UpperCAmelCase , "num_encoder_blocks" ) )
class lowercase__ :
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=13 , __UpperCAmelCase=64 , __UpperCAmelCase=3 , __UpperCAmelCase=4 , __UpperCAmelCase=[2, 2, 2, 2] , __UpperCAmelCase=[8, 4, 2, 1] , __UpperCAmelCase=[16, 32, 64, 128] , __UpperCAmelCase=[1, 4, 8, 16] , __UpperCAmelCase=[1, 2, 4, 8] , __UpperCAmelCase=True , __UpperCAmelCase=True , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.02 , __UpperCAmelCase=3 , __UpperCAmelCase=None , )-> str:
'''simple docstring'''
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = num_encoder_blocks
lowerCAmelCase__ = sr_ratios
lowerCAmelCase__ = depths
lowerCAmelCase__ = hidden_sizes
lowerCAmelCase__ = downsampling_rates
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = is_training
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = scope
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
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.image_size, self.image_size] , self.num_labels )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
return SegformerConfig(
image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = SegformerModel(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = lowerCAmelCase__ = self.image_size // (self.downsampling_rates[-1] * 2)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )-> str:
'''simple docstring'''
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = SegformerForSemanticSegmentation(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ = model(__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
lowerCAmelCase__ = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = 1
lowerCAmelCase__ = SegformerForSemanticSegmentation(config=__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
lowerCAmelCase__ = torch.randint(0 , 1 , (self.batch_size, self.image_size, self.image_size) ).to(__UpperCAmelCase )
lowerCAmelCase__ = model(__UpperCAmelCase , labels=__UpperCAmelCase )
self.parent.assertGreater(result.loss , 0.0 )
def UpperCAmelCase ( self )-> Optional[int]:
'''simple docstring'''
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowercase__ ( _UpperCAmelCase, _UpperCAmelCase, unittest.TestCase ):
a_ =(
(
SegformerModel,
SegformerForSemanticSegmentation,
SegformerForImageClassification,
)
if is_torch_available()
else ()
)
a_ =(
{
"""feature-extraction""": SegformerModel,
"""image-classification""": SegformerForImageClassification,
"""image-segmentation""": SegformerForSemanticSegmentation,
}
if is_torch_available()
else {}
)
a_ =True
a_ =False
a_ =False
a_ =False
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = SegformerModelTester(self )
lowerCAmelCase__ = SegformerConfigTester(self , config_class=__UpperCAmelCase )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__UpperCAmelCase )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCAmelCase )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase )
@unittest.skip("SegFormer does not use inputs_embeds" )
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
pass
@unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods" )
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(__UpperCAmelCase )
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] , __UpperCAmelCase )
def UpperCAmelCase ( self )-> str:
'''simple docstring'''
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
lowerCAmelCase__ = False
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ = outputs.attentions
lowerCAmelCase__ = sum(self.model_tester.depths )
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first attentions (first block, first layer)
lowerCAmelCase__ = (self.model_tester.image_size // 4) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
# verify the last attentions (last block, last layer)
lowerCAmelCase__ = (self.model_tester.image_size // 32) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
self.assertListEqual(
list(attentions[-1].shape[-3:] ) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
lowerCAmelCase__ = len(__UpperCAmelCase )
# Check attention is always last and order is fine
lowerCAmelCase__ = True
lowerCAmelCase__ = True
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
self.assertEqual(out_len + 1 , len(__UpperCAmelCase ) )
lowerCAmelCase__ = outputs.attentions
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first attentions (first block, first layer)
lowerCAmelCase__ = (self.model_tester.image_size // 4) ** 2
lowerCAmelCase__ = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
def UpperCAmelCase ( self )-> Tuple:
'''simple docstring'''
def check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase ) )
lowerCAmelCase__ = outputs.hidden_states
lowerCAmelCase__ = self.model_tester.num_encoder_blocks
self.assertEqual(len(__UpperCAmelCase ) , __UpperCAmelCase )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.hidden_sizes[0],
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(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ = True
check_hidden_states_output(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
def UpperCAmelCase ( self )-> Optional[Any]:
'''simple docstring'''
if not self.model_tester.is_training:
return
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase__ = True
for model_class in self.all_model_classes:
if model_class in get_values(__UpperCAmelCase ):
continue
lowerCAmelCase__ = model_class(__UpperCAmelCase )
model.to(__UpperCAmelCase )
model.train()
lowerCAmelCase__ = self._prepare_for_class(__UpperCAmelCase , __UpperCAmelCase , return_labels=__UpperCAmelCase )
lowerCAmelCase__ = model(**__UpperCAmelCase ).loss
loss.backward()
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCAmelCase ( self )-> Any:
'''simple docstring'''
pass
@slow
def UpperCAmelCase ( self )-> Union[str, Any]:
'''simple docstring'''
for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = SegformerModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
def _a ( ) -> Any:
"""simple docstring"""
lowerCAmelCase__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
class lowercase__ ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self )-> Dict:
'''simple docstring'''
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=__UpperCAmelCase , align=__UpperCAmelCase , do_random_crop=__UpperCAmelCase )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
__UpperCAmelCase )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__UpperCAmelCase , return_tensors="pt" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(__UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
lowerCAmelCase__ = torch.tensor(
[
[[-4.6_310, -5.5_232, -6.2_356], [-5.1_921, -6.1_444, -6.5_996], [-5.4_424, -6.2_790, -6.7_574]],
[[-12.1_391, -13.3_122, -13.9_554], [-12.8_732, -13.9_352, -14.3_563], [-12.9_438, -13.8_226, -14.2_513]],
[[-12.5_134, -13.4_686, -14.4_915], [-12.8_669, -14.4_343, -14.7_758], [-13.2_523, -14.5_819, -15.0_694]],
] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , __UpperCAmelCase , atol=1E-4 ) )
@slow
def UpperCAmelCase ( self )-> int:
'''simple docstring'''
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=__UpperCAmelCase , align=__UpperCAmelCase , do_random_crop=__UpperCAmelCase )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained(
"nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ).to(__UpperCAmelCase )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__UpperCAmelCase , return_tensors="pt" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(__UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = torch.Size((1, model.config.num_labels, 128, 128) )
self.assertEqual(outputs.logits.shape , __UpperCAmelCase )
lowerCAmelCase__ = torch.tensor(
[
[[-13.5_748, -13.9_111, -12.6_500], [-14.3_500, -15.3_683, -14.2_328], [-14.7_532, -16.0_424, -15.6_087]],
[[-17.1_651, -15.8_725, -12.9_653], [-17.2_580, -17.3_718, -14.8_223], [-16.6_058, -16.8_783, -16.7_452]],
[[-3.6_456, -3.0_209, -1.4_203], [-3.0_797, -3.1_959, -2.0_000], [-1.8_757, -1.9_217, -1.6_997]],
] ).to(__UpperCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3] , __UpperCAmelCase , atol=1E-1 ) )
@slow
def UpperCAmelCase ( self )-> List[Any]:
'''simple docstring'''
lowerCAmelCase__ = SegformerImageProcessor(
image_scale=(512, 512) , keep_ratio=__UpperCAmelCase , align=__UpperCAmelCase , do_random_crop=__UpperCAmelCase )
lowerCAmelCase__ = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512" ).to(
__UpperCAmelCase )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=__UpperCAmelCase , return_tensors="pt" )
lowerCAmelCase__ = encoded_inputs.pixel_values.to(__UpperCAmelCase )
with torch.no_grad():
lowerCAmelCase__ = model(__UpperCAmelCase )
lowerCAmelCase__ = outputs.logits.detach().cpu()
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase , target_sizes=[(500, 300)] )
lowerCAmelCase__ = torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , __UpperCAmelCase )
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=__UpperCAmelCase )
lowerCAmelCase__ = torch.Size((128, 128) )
self.assertEqual(segmentation[0].shape , __UpperCAmelCase )
| 115
| 0
|
'''simple docstring'''
def a_ ( __snake_case : Any , __snake_case : List[Any] , __snake_case : Optional[Any] ) -> float:
"""simple docstring"""
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if years_to_repay <= 0 or not isinstance(__lowercase , __lowercase ):
raise Exception('''Years to repay must be an integer > 0''' )
# Yearly rate is divided by 12 to get monthly rate
lowerCamelCase_ =rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
lowerCamelCase_ =years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 676
|
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig
lowerCamelCase_ = logging.get_logger(__name__)
# General docstring
lowerCamelCase_ = '''RegNetConfig'''
# Base docstring
lowerCamelCase_ = '''facebook/regnet-y-040'''
lowerCamelCase_ = [1, 10_88, 7, 7]
# Image classification docstring
lowerCamelCase_ = '''facebook/regnet-y-040'''
lowerCamelCase_ = '''tabby, tabby cat'''
lowerCamelCase_ = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : int , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int = 3 , __UpperCAmelCase : int = 1 , __UpperCAmelCase : int = 1 , __UpperCAmelCase : Optional[str] = "relu" , ):
'''simple docstring'''
super().__init__()
_A = nn.Convad(
__UpperCAmelCase , __UpperCAmelCase , kernel_size=__UpperCAmelCase , stride=__UpperCAmelCase , padding=kernel_size // 2 , groups=__UpperCAmelCase , bias=__UpperCAmelCase , )
_A = nn.BatchNormad(__UpperCAmelCase )
_A = ACTaFN[activation] if activation is not None else nn.Identity()
def lowerCAmelCase ( self : str , __UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
_A = self.convolution(__UpperCAmelCase )
_A = self.normalization(__UpperCAmelCase )
_A = self.activation(__UpperCAmelCase )
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Tuple , __UpperCAmelCase : RegNetConfig ):
'''simple docstring'''
super().__init__()
_A = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
_A = config.num_channels
def lowerCAmelCase ( self : int , __UpperCAmelCase : str ):
'''simple docstring'''
_A = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration." )
_A = self.embedder(__UpperCAmelCase )
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Optional[int] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int = 2 ):
'''simple docstring'''
super().__init__()
_A = nn.Convad(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , stride=__UpperCAmelCase , bias=__UpperCAmelCase )
_A = nn.BatchNormad(__UpperCAmelCase )
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Tensor ):
'''simple docstring'''
_A = self.convolution(__UpperCAmelCase )
_A = self.normalization(__UpperCAmelCase )
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __UpperCAmelCase : int , __UpperCAmelCase : int ):
'''simple docstring'''
super().__init__()
_A = nn.AdaptiveAvgPoolad((1, 1) )
_A = nn.Sequential(
nn.Convad(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 ) , nn.ReLU() , nn.Convad(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 ) , nn.Sigmoid() , )
def lowerCAmelCase ( self : Optional[Any] , __UpperCAmelCase : str ):
'''simple docstring'''
_A = self.pooler(__UpperCAmelCase )
_A = self.attention(__UpperCAmelCase )
_A = hidden_state * attention
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Any , __UpperCAmelCase : RegNetConfig , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int = 1 ):
'''simple docstring'''
super().__init__()
_A = in_channels != out_channels or stride != 1
_A = max(1 , out_channels // config.groups_width )
_A = (
RegNetShortCut(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
_A = nn.Sequential(
RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase , groups=__UpperCAmelCase , activation=config.hidden_act ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=__UpperCAmelCase ) , )
_A = ACTaFN[config.hidden_act]
def lowerCAmelCase ( self : Optional[int] , __UpperCAmelCase : Any ):
'''simple docstring'''
_A = hidden_state
_A = self.layer(__UpperCAmelCase )
_A = self.shortcut(__UpperCAmelCase )
hidden_state += residual
_A = self.activation(__UpperCAmelCase )
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __UpperCAmelCase : RegNetConfig , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int = 1 ):
'''simple docstring'''
super().__init__()
_A = in_channels != out_channels or stride != 1
_A = max(1 , out_channels // config.groups_width )
_A = (
RegNetShortCut(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase ) if should_apply_shortcut else nn.Identity()
)
_A = nn.Sequential(
RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase , groups=__UpperCAmelCase , activation=config.hidden_act ) , RegNetSELayer(__UpperCAmelCase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(__UpperCAmelCase , __UpperCAmelCase , kernel_size=1 , activation=__UpperCAmelCase ) , )
_A = ACTaFN[config.hidden_act]
def lowerCAmelCase ( self : List[str] , __UpperCAmelCase : Any ):
'''simple docstring'''
_A = hidden_state
_A = self.layer(__UpperCAmelCase )
_A = self.shortcut(__UpperCAmelCase )
hidden_state += residual
_A = self.activation(__UpperCAmelCase )
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : List[str] , __UpperCAmelCase : RegNetConfig , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : int = 2 , __UpperCAmelCase : int = 2 , ):
'''simple docstring'''
super().__init__()
_A = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
_A = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , stride=__UpperCAmelCase , ) , *[layer(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) for _ in range(depth - 1 )] , )
def lowerCAmelCase ( self : int , __UpperCAmelCase : List[str] ):
'''simple docstring'''
_A = self.layers(__UpperCAmelCase )
return hidden_state
class _UpperCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self : Union[str, Any] , __UpperCAmelCase : RegNetConfig ):
'''simple docstring'''
super().__init__()
_A = nn.ModuleList([] )
# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
self.stages.append(
RegNetStage(
__UpperCAmelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_A = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(__UpperCAmelCase , config.depths[1:] ):
self.stages.append(RegNetStage(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , depth=__UpperCAmelCase ) )
def lowerCAmelCase ( self : Any , __UpperCAmelCase : Tensor , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = True ):
'''simple docstring'''
_A = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_A = hidden_states + (hidden_state,)
_A = stage_module(__UpperCAmelCase )
if output_hidden_states:
_A = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None )
return BaseModelOutputWithNoAttention(last_hidden_state=__UpperCAmelCase , hidden_states=__UpperCAmelCase )
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
snake_case = RegNetConfig
snake_case = '''regnet'''
snake_case = '''pixel_values'''
snake_case = True
def lowerCAmelCase ( self : Union[str, Any] , __UpperCAmelCase : Union[str, Any] ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="fan_out" , nonlinearity="relu" )
elif isinstance(__UpperCAmelCase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def lowerCAmelCase ( self : Dict , __UpperCAmelCase : List[Any] , __UpperCAmelCase : List[str]=False ):
'''simple docstring'''
if isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_A = value
lowerCamelCase_ = r'''
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`RegNetConfig`]): 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.
'''
lowerCamelCase_ = r'''
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`ConvNextImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''
@add_start_docstrings(
'''The bare RegNet model outputting raw features without any specific head on top.''' , snake_case_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self : int , __UpperCAmelCase : Dict ):
'''simple docstring'''
super().__init__(__UpperCAmelCase )
_A = config
_A = RegNetEmbeddings(__UpperCAmelCase )
_A = RegNetEncoder(__UpperCAmelCase )
_A = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , modality="vision" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCAmelCase ( self : Tuple , __UpperCAmelCase : Tensor , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None ):
'''simple docstring'''
_A = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_A = return_dict if return_dict is not None else self.config.use_return_dict
_A = self.embedder(__UpperCAmelCase )
_A = self.encoder(
__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , return_dict=__UpperCAmelCase )
_A = encoder_outputs[0]
_A = self.pooler(__UpperCAmelCase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__UpperCAmelCase , pooler_output=__UpperCAmelCase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'''
RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , snake_case_ , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __init__( self : Tuple , __UpperCAmelCase : Optional[int] ):
'''simple docstring'''
super().__init__(__UpperCAmelCase )
_A = config.num_labels
_A = RegNetModel(__UpperCAmelCase )
# classification head
_A = nn.Sequential(
nn.Flatten() , nn.Linear(config.hidden_sizes[-1] , config.num_labels ) if config.num_labels > 0 else nn.Identity() , )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__UpperCAmelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__UpperCAmelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCAmelCase ( self : str , __UpperCAmelCase : Optional[torch.FloatTensor] = None , __UpperCAmelCase : Optional[torch.LongTensor] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , ):
'''simple docstring'''
_A = return_dict if return_dict is not None else self.config.use_return_dict
_A = self.regnet(__UpperCAmelCase , output_hidden_states=__UpperCAmelCase , return_dict=__UpperCAmelCase )
_A = outputs.pooler_output if return_dict else outputs[1]
_A = self.classifier(__UpperCAmelCase )
_A = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_A = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_A = "single_label_classification"
else:
_A = "multi_label_classification"
if self.config.problem_type == "regression":
_A = MSELoss()
if self.num_labels == 1:
_A = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_A = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
elif self.config.problem_type == "single_label_classification":
_A = CrossEntropyLoss()
_A = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_A = BCEWithLogitsLoss()
_A = loss_fct(__UpperCAmelCase , __UpperCAmelCase )
if not return_dict:
_A = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase , logits=__UpperCAmelCase , hidden_states=outputs.hidden_states )
| 330
| 0
|
def __UpperCAmelCase ( a_):
snake_case_ = [int(a_) for i in ip_va_address.split('.') if i.isdigit()]
return len(a_) == 4 and all(0 <= int(a_) <= 2_54 for octet in octets)
if __name__ == "__main__":
lowercase = input().strip()
lowercase = "valid" if is_ip_va_address_valid(ip) else "invalid"
print(f'{ip} is a {valid_or_invalid} IP v4 address.')
| 607
|
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class UpperCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ) -> str:
snake_case_ = tf.convert_to_tensor(
[
[
8.2_220_991, # 3rd highest value; idx. 0
-0.5_620_044,
5.23_229_752,
4.0_386_393,
-6.8_798_378,
-0.54_785_802,
-3.2_012_153,
2.92_777_176,
1.88_171_953,
7.35_341_276, # 5th highest value; idx. 9
8.43_207_833, # 2nd highest value; idx. 10
-9.85_711_836,
-5.96_209_236,
-1.13_039_161,
-7.1_115_294,
-0.8_369_633,
-5.3_186_408,
7.06_427_407,
0.81_369_344,
-0.82_023_817,
-5.9_179_796,
0.58_813_443,
-6.99_778_438,
4.71_551_189,
-0.18_771_637,
7.44_020_759, # 4th highest value; idx. 25
9.38_450_987, # 1st highest value; idx. 26
2.12_662_941,
-9.32_562_038,
2.35_652_522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58_425_518,
4.53_139_238,
-5.57_510_464,
-6.28_030_699,
-7.19_529_503,
-4.02_122_551,
1.39_337_037,
-6.06_707_057,
1.59_480_517,
-9.643_119,
0.03_907_799,
0.67_231_762,
-8.88_206_726,
6.27_115_922, # 4th highest value; idx. 13
2.28_520_723,
4.82_767_506,
4.30_421_368,
8.8_275_313, # 2nd highest value; idx. 17
5.44_029_958, # 5th highest value; idx. 18
-4.4_735_794,
7.38_579_536, # 3rd highest value; idx. 20
-2.91_051_663,
2.61_946_077,
-2.5_674_762,
-9.48_959_302,
-4.02_922_645,
-1.35_416_918,
9.67_702_323, # 1st highest value; idx. 27
-5.89_478_553,
1.85_370_467,
], # cummulative prob of 5 highest values <= 0.6
] , dtype=tf.floataa , )
snake_case_ = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]] , dtype=tf.intaa , ) # expected non filtered idx as noted above
snake_case_ = tf.convert_to_tensor(
[8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023] , dtype=tf.floataa , ) # expected non filtered values as noted above
snake_case_ = tf_top_k_top_p_filtering(a , top_k=10 , top_p=0.6 , min_tokens_to_keep=4 )
snake_case_ = output[output != -float('inf' )]
snake_case_ = tf.cast(
tf.where(tf.not_equal(a , tf.constant(-float('inf' ) , dtype=tf.floataa ) ) ) , dtype=tf.intaa , )
tf.debugging.assert_near(a , a , rtol=1E-12 )
tf.debugging.assert_equal(a , a )
@require_tf
class UpperCamelCase_ ( unittest.TestCase , snake_case_ ):
'''simple docstring'''
if is_tf_available():
lowerCAmelCase = {
'''AutoModelForCausalLM''': TFAutoModelForCausalLM,
'''AutoModelForSpeechSeq2Seq''': TFAutoModelForSpeechSeqaSeq,
'''AutoModelForSeq2SeqLM''': TFAutoModelForSeqaSeqLM,
'''AutoModelForVision2Seq''': TFAutoModelForVisionaSeq,
'''LogitsProcessorList''': TFLogitsProcessorList,
'''MinLengthLogitsProcessor''': TFMinLengthLogitsProcessor,
'''create_tensor_fn''': tf.convert_to_tensor,
'''floats_tensor''': floats_tensor,
'''return_tensors''': '''tf''',
}
@slow
def _UpperCamelCase ( self ) -> Optional[int]:
# TF-only test: tf.saved_model export
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 2
snake_case_ = 2
class UpperCamelCase_ ( tf.Module ):
'''simple docstring'''
def __init__( self , a ) -> Any:
super(a , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length) , tf.intaa , name='input_ids' ),
tf.TensorSpec((None, input_length) , tf.intaa , name='attention_mask' ),
) , jit_compile=a , )
def _UpperCamelCase ( self , a , a ) -> Optional[Any]:
snake_case_ = self.model.generate(
input_ids=a , attention_mask=a , max_new_tokens=a , return_dict_in_generate=a , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2, 0], [1_02, 1_03]]
snake_case_ = [[1, 0], [1, 1]]
snake_case_ = DummyModel(model=a )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(a , a , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(a ).signatures['serving_default']
for batch_size in range(1 , len(a ) + 1 ):
snake_case_ = {
'input_ids': tf.constant(dummy_input_ids[:batch_size] ),
'attention_mask': tf.constant(dummy_attention_masks[:batch_size] ),
}
snake_case_ = serving_func(**a )['sequences']
snake_case_ = test_model.generate(**a , max_new_tokens=a )
tf.debugging.assert_equal(a , a )
@slow
def _UpperCamelCase ( self ) -> Dict:
# TF-only test: tf.saved_model export
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 1
snake_case_ = 2
class UpperCamelCase_ ( tf.Module ):
'''simple docstring'''
def __init__( self , a ) -> int:
super(a , self ).__init__()
snake_case_ = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None) , tf.intaa , name='input_ids' ),
tf.TensorSpec((batch_size, None) , tf.intaa , name='attention_mask' ),
) , jit_compile=a , )
def _UpperCamelCase ( self , a , a ) -> Union[str, Any]:
snake_case_ = self.model.generate(
input_ids=a , attention_mask=a , max_new_tokens=a , return_dict_in_generate=a , )
return {"sequences": outputs["sequences"]}
snake_case_ = [[2], [1_02, 1_03]]
snake_case_ = [[1], [1, 1]]
snake_case_ = DummyModel(model=a )
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(a , a , signatures={'serving_default': dummy_model.serving} )
snake_case_ = tf.saved_model.load(a ).signatures['serving_default']
for input_row in range(len(a ) ):
snake_case_ = {
'input_ids': tf.constant([dummy_input_ids[input_row]] ),
'attention_mask': tf.constant([dummy_attention_masks[input_row]] ),
}
snake_case_ = serving_func(**a )['sequences']
snake_case_ = test_model.generate(**a , max_new_tokens=a )
tf.debugging.assert_equal(a , a )
@slow
@require_tensorflow_text
def _UpperCamelCase ( self ) -> Any:
# TF-only test: tf.saved_model export
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id='google/flan-t5-small' , filename='spiece.model' , local_dir=a )
class UpperCamelCase_ ( tf.keras.layers.Layer ):
'''simple docstring'''
def __init__( self ) -> Any:
super().__init__()
snake_case_ = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(a , 'spiece.model' ) , 'rb' ).read() )
snake_case_ = TFAutoModelForSeqaSeqLM.from_pretrained('hf-internal-testing/tiny-random-t5' )
def _UpperCamelCase ( self , a , *a , **a ) -> int:
snake_case_ = self.tokenizer.tokenize(a )
snake_case_ , snake_case_ = text.pad_model_inputs(
a , max_seq_length=64 , pad_value=self.model.config.pad_token_id )
snake_case_ = self.model.generate(input_ids=a , attention_mask=a )
return self.tokenizer.detokenize(a )
snake_case_ = CompleteSentenceTransformer()
snake_case_ = tf.keras.layers.Input(shape=(1,) , dtype=tf.string , name='inputs' )
snake_case_ = complete_model(a )
snake_case_ = tf.keras.Model(a , a )
keras_model.save(a )
def _UpperCamelCase ( self ) -> Union[str, Any]:
# Has PT equivalent: this test relies on random sampling
snake_case_ = {
'do_sample': True,
'num_beams': 1,
'top_p': 0.7,
'top_k': 10,
'temperature': 0.7,
}
snake_case_ = 14
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 'Hello, my dog is cute and'
snake_case_ = tokenizer(a , return_tensors='tf' )
snake_case_ = TFAutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2' )
snake_case_ = 6_38
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**a , eos_token_id=a , **a )
self.assertTrue(expectation == len(generated_tokens[0] ) )
snake_case_ = [6_38, 1_98]
with tf.device(':/CPU:0' ):
tf.random.set_seed(0 )
snake_case_ = model.generate(**a , eos_token_id=a , **a )
self.assertTrue(expectation == len(generated_tokens[0] ) )
def _UpperCamelCase ( self ) -> Any:
# Has PT equivalent: ample use of framework-specific code
snake_case_ = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = 'Hugging Face is a technology company based in New York and Paris.'
snake_case_ = bart_tokenizer(a , return_tensors='tf' ).input_ids
snake_case_ = TFBartForConditionalGeneration.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(a ).numpy()
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def _UpperCamelCase ( self , a , a=None , **a ) -> List[str]:
return super().call(a , **a )
snake_case_ = FakeBart.from_pretrained('hf-internal-testing/tiny-random-bart' )
snake_case_ = bart_model.generate(a , foo='bar' ).numpy()
self.assertTrue(np.array_equal(a , a ) )
class UpperCamelCase_ ( bart_model.model.encoder.__class__ ):
'''simple docstring'''
def _UpperCamelCase ( self , a , **a ) -> List[Any]:
return super().call(a , **a )
snake_case_ = FakeEncoder(bart_model.config , bart_model.model.shared )
snake_case_ = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
snake_case_ = bart_model.generate(a ).numpy()
with self.assertRaises(a ):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(a , foo='bar' )
| 607
| 1
|
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def UpperCAmelCase_ ( __UpperCAmelCase : Dict ) -> List[Any]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = image.size
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
SCREAMING_SNAKE_CASE_ = image.resize((w, h) , resample=PIL_INTERPOLATION['lanczos'] )
SCREAMING_SNAKE_CASE_ = np.array(__UpperCamelCase ).astype(np.floataa ) / 2_5_5.0
SCREAMING_SNAKE_CASE_ = image[None].transpose(0 , 3 , 1 , 2 )
SCREAMING_SNAKE_CASE_ = torch.from_numpy(__UpperCamelCase )
return 2.0 * image - 1.0
class lowerCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : str , _lowerCAmelCase : str , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] , ):
super().__init__()
self.register_modules(vqvae=SCREAMING_SNAKE_CASE_ , unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__( self : Union[str, Any] , _lowerCAmelCase : Optional[Any] = None , _lowerCAmelCase : Dict = 1 , _lowerCAmelCase : List[str] = 100 , _lowerCAmelCase : Optional[Any] = 0.0 , _lowerCAmelCase : Optional[Any] = None , _lowerCAmelCase : Union[str, Any] = "pil" , _lowerCAmelCase : Optional[Any] = True , ):
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE_ = 1
elif isinstance(SCREAMING_SNAKE_CASE_ , torch.Tensor ):
SCREAMING_SNAKE_CASE_ = image.shape[0]
else:
raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(SCREAMING_SNAKE_CASE_ )}" )
if isinstance(SCREAMING_SNAKE_CASE_ , PIL.Image.Image ):
SCREAMING_SNAKE_CASE_ = preprocess(SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
SCREAMING_SNAKE_CASE_ = (batch_size, self.unet.config.in_channels // 2, height, width)
SCREAMING_SNAKE_CASE_ = next(self.unet.parameters() ).dtype
SCREAMING_SNAKE_CASE_ = randn_tensor(SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ , device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
SCREAMING_SNAKE_CASE_ = image.to(device=self.device , dtype=SCREAMING_SNAKE_CASE_ )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ , device=self.device )
SCREAMING_SNAKE_CASE_ = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
SCREAMING_SNAKE_CASE_ = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
SCREAMING_SNAKE_CASE_ = 'eta' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
SCREAMING_SNAKE_CASE_ = {}
if accepts_eta:
SCREAMING_SNAKE_CASE_ = eta
for t in self.progress_bar(SCREAMING_SNAKE_CASE_ ):
# concat latents and low resolution image in the channel dimension.
SCREAMING_SNAKE_CASE_ = torch.cat([latents, image] , dim=1 )
SCREAMING_SNAKE_CASE_ = self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# predict the noise residual
SCREAMING_SNAKE_CASE_ = self.unet(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ).sample
# compute the previous noisy sample x_t -> x_t-1
SCREAMING_SNAKE_CASE_ = self.scheduler.step(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ).prev_sample
# decode the image latents with the VQVAE
SCREAMING_SNAKE_CASE_ = self.vqvae.decode(SCREAMING_SNAKE_CASE_ ).sample
SCREAMING_SNAKE_CASE_ = torch.clamp(SCREAMING_SNAKE_CASE_ , -1.0 , 1.0 )
SCREAMING_SNAKE_CASE_ = image / 2 + 0.5
SCREAMING_SNAKE_CASE_ = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
SCREAMING_SNAKE_CASE_ = self.numpy_to_pil(SCREAMING_SNAKE_CASE_ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_ )
| 31
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = GPTSanJapaneseTokenizer
SCREAMING_SNAKE_CASE_ = False
SCREAMING_SNAKE_CASE_ = {'do_clean_text': False, 'add_prefix_space': False}
def UpperCamelCase( self ) -> Optional[Any]:
'''simple docstring'''
super().setUp()
# fmt: off
lowerCamelCase_ = ['こん', 'こんに', 'にちは', 'ばんは', '世界,㔺界', '、', '。', '<BR>', '<SP>', '<TAB>', '<URL>', '<EMAIL>', '<TEL>', '<DATE>', '<PRICE>', '<BLOCK>', '<KIGOU>', '<U2000U2BFF>', '<|emoji1|>', '<unk>', '<|bagoftoken|>', '<|endoftext|>']
# fmt: on
lowerCamelCase_ = {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}} # 😀
lowerCamelCase_ = {'unk_token': '<unk>'}
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCamelCase_ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['emoji_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
with open(self.emoji_file , 'w' ) as emoji_writer:
emoji_writer.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
def UpperCamelCase( self , **SCREAMING_SNAKE_CASE_ ) -> Dict:
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。 \nこんばんは、世界。😀'
return input_text, output_text
def UpperCamelCase( self , SCREAMING_SNAKE_CASE_ ) -> int:
'''simple docstring'''
lowerCamelCase_ ,lowerCamelCase_ = self.get_input_output_texts(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , add_special_tokens=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ , clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ )
return text, ids
def UpperCamelCase( self ) -> Tuple:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass # TODO add if relevant
def UpperCamelCase( self ) -> int:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。 こんばんは、㔺界。'
lowerCamelCase_ = ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
lowerCamelCase_ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids without special tokens
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Testing conversion to ids with special tokens
lowerCamelCase_ = tokens + [tokenizer.unk_token]
lowerCamelCase_ = [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
lowerCamelCase_ = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.get_tokenizer()
# Testing tokenization
lowerCamelCase_ = 'こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。'
lowerCamelCase_ = 'こんにちは、、、、世界。こんばんは、、、、世界。'
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = 'こんにちは、世界。こんばんは、世界。😀'
lowerCamelCase_ = tokenizer.encode(prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode('' , prefix_text=prefix_text + input_text )
lowerCamelCase_ = tokenizer.encode(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.decode(SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
# Testing tokenization
lowerCamelCase_ = 'こんにちは、世界。'
lowerCamelCase_ = 'こんばんは、㔺界。😀'
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = len(tokenizer.encode(SCREAMING_SNAKE_CASE_ ) ) - 2
lowerCamelCase_ = [1] + [0] * (len_prefix + len_text + 1)
lowerCamelCase_ = [1] * (len_prefix + len_text + 1) + [0]
lowerCamelCase_ = [1] + [1] * (len_prefix) + [0] * (len_text + 1)
lowerCamelCase_ = tokenizer(prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer('' , prefix_text=prefix_text + input_text ).token_type_ids
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , prefix_text=SCREAMING_SNAKE_CASE_ ).token_type_ids
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
@slow
def UpperCamelCase( self ) -> Dict:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = tokenizer.encode('あンいワ' )
lowerCamelCase_ = tokenizer.encode('' , prefix_text='あンいワ' )
lowerCamelCase_ = tokenizer.encode('いワ' , prefix_text='あン' )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertEqual(tokenizer.decode(SCREAMING_SNAKE_CASE_ ) , tokenizer.decode(SCREAMING_SNAKE_CASE_ ) )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(x_token_a[1] , x_token_a[-1] ) # SEG token
self.assertEqual(x_token_a[1] , x_token_a[3] ) # SEG token
@slow
def UpperCamelCase( self ) -> List[str]:
'''simple docstring'''
lowerCamelCase_ = self.tokenizer_class.from_pretrained('Tanrei/GPTSAN-japanese' )
lowerCamelCase_ = [['武田信玄', 'は、'], ['織田信長', 'の配下の、']]
lowerCamelCase_ = tokenizer(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
lowerCamelCase_ = tokenizer.batch_encode_plus(SCREAMING_SNAKE_CASE_ , padding=SCREAMING_SNAKE_CASE_ )
# fmt: off
lowerCamelCase_ = [[35993, 8640, 25948, 35998, 30647, 35675, 35999, 35999], [35993, 10382, 9868, 35998, 30646, 9459, 30646, 35675]]
lowerCamelCase_ = [[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
lowerCamelCase_ = [[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
# fmt: on
self.assertListEqual(x_token.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token.attention_mask , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.input_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.token_type_ids , SCREAMING_SNAKE_CASE_ )
self.assertListEqual(x_token_a.attention_mask , SCREAMING_SNAKE_CASE_ )
def UpperCamelCase( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase( self ) -> List[Any]:
'''simple docstring'''
pass
| 42
| 0
|
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all MVP models at https://huggingface.co/models?filter=mvp
_lowercase = {
'''vocab_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
},
'''added_tokens.json''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
},
'''merges_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
},
}
_lowercase = {
'''RUCAIBox/mvp''': 1024,
}
class __snake_case ( snake_case__ ):
"""simple docstring"""
UpperCamelCase_ = VOCAB_FILES_NAMES
UpperCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ = ['input_ids', 'attention_mask']
UpperCamelCase_ = MvpTokenizer
def __init__( self : Any ,lowerCAmelCase__ : Dict=None ,lowerCAmelCase__ : int=None ,lowerCAmelCase__ : Any=None ,lowerCAmelCase__ : Union[str, Any]="replace" ,lowerCAmelCase__ : Union[str, Any]="<s>" ,lowerCAmelCase__ : List[str]="</s>" ,lowerCAmelCase__ : Optional[int]="</s>" ,lowerCAmelCase__ : List[Any]="<s>" ,lowerCAmelCase__ : Optional[Any]="<unk>" ,lowerCAmelCase__ : Union[str, Any]="<pad>" ,lowerCAmelCase__ : Tuple="<mask>" ,lowerCAmelCase__ : Any=False ,lowerCAmelCase__ : Optional[Any]=True ,**lowerCAmelCase__ : Optional[Any] ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__(
lowerCAmelCase__ ,lowerCAmelCase__ ,tokenizer_file=lowerCAmelCase__ ,errors=lowerCAmelCase__ ,bos_token=lowerCAmelCase__ ,eos_token=lowerCAmelCase__ ,sep_token=lowerCAmelCase__ ,cls_token=lowerCAmelCase__ ,unk_token=lowerCAmelCase__ ,pad_token=lowerCAmelCase__ ,mask_token=lowerCAmelCase__ ,add_prefix_space=lowerCAmelCase__ ,trim_offsets=lowerCAmelCase__ ,**lowerCAmelCase__ ,)
lowerCAmelCase_ : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space:
lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,pre_tok_state.pop("type" ) )
lowerCAmelCase_ : Tuple = add_prefix_space
lowerCAmelCase_ : List[Any] = pre_tok_class(**lowerCAmelCase__ )
lowerCAmelCase_ : Union[str, Any] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowerCAmelCase_ : Optional[Any] = "post_processor"
lowerCAmelCase_ : Union[str, Any] = getattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ )
if tokenizer_component_instance:
lowerCAmelCase_ : List[Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowerCAmelCase_ : int = tuple(state["sep"] )
if "cls" in state:
lowerCAmelCase_ : List[str] = tuple(state["cls"] )
lowerCAmelCase_ : List[Any] = False
if state.get("add_prefix_space" ,lowerCAmelCase__ ) != add_prefix_space:
lowerCAmelCase_ : Tuple = add_prefix_space
lowerCAmelCase_ : Tuple = True
if state.get("trim_offsets" ,lowerCAmelCase__ ) != trim_offsets:
lowerCAmelCase_ : str = trim_offsets
lowerCAmelCase_ : int = True
if changes_to_apply:
lowerCAmelCase_ : List[str] = getattr(lowerCAmelCase__ ,state.pop("type" ) )
lowerCAmelCase_ : Dict = component_class(**lowerCAmelCase__ )
setattr(self.backend_tokenizer ,lowerCAmelCase__ ,lowerCAmelCase__ )
@property
def UpperCAmelCase_ ( self : Any ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def UpperCAmelCase_ ( self : Tuple ,lowerCAmelCase__ : Dict ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ : Tuple = AddedToken(lowerCAmelCase__ ,lstrip=lowerCAmelCase__ ,rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ) else value
lowerCAmelCase_ : Optional[int] = value
def UpperCAmelCase_ ( self : int ,*lowerCAmelCase__ : int ,**lowerCAmelCase__ : Optional[Any] ) -> BatchEncoding:
'''simple docstring'''
lowerCAmelCase_ : Dict = kwargs.get("is_split_into_words" ,lowerCAmelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,*lowerCAmelCase__ : List[str] ,**lowerCAmelCase__ : Union[str, Any] ) -> BatchEncoding:
'''simple docstring'''
lowerCAmelCase_ : List[str] = kwargs.get("is_split_into_words" ,lowerCAmelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
"to use it with pretokenized inputs." )
return super()._encode_plus(*lowerCAmelCase__ ,**lowerCAmelCase__ )
def UpperCAmelCase_ ( self : str ,lowerCAmelCase__ : str ,lowerCAmelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
lowerCAmelCase_ : str = self._tokenizer.model.save(lowerCAmelCase__ ,name=lowerCAmelCase__ )
return tuple(lowerCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ,lowerCAmelCase__ : Optional[Any] ,lowerCAmelCase__ : Dict=None ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : str = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def UpperCAmelCase_ ( self : List[str] ,lowerCAmelCase__ : List[int] ,lowerCAmelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
lowerCAmelCase_ : int = [self.sep_token_id]
lowerCAmelCase_ : 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 + sep + token_ids_a + sep ) * [0]
| 683
|
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = list(snake_case__)
lowerCAmelCase_ : Tuple = list(snake_case__)
lowerCAmelCase_ : List[str] = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count += 1
lowerCAmelCase_ : Dict = "_"
if count > 1:
return False
else:
return "".join(snake_case__)
def UpperCamelCase ( snake_case__):
lowerCAmelCase_ : Union[str, Any] = []
while True:
lowerCAmelCase_ : Tuple = ["$"] * len(snake_case__)
lowerCAmelCase_ : Tuple = []
for i in range(len(snake_case__)):
for j in range(i + 1 , len(snake_case__)):
lowerCAmelCase_ : Optional[int] = compare_string(binary[i] , binary[j])
if k is False:
lowerCAmelCase_ : str = "*"
lowerCAmelCase_ : Tuple = "*"
temp.append("X")
for i in range(len(snake_case__)):
if checka[i] == "$":
pi.append(binary[i])
if len(snake_case__) == 0:
return pi
lowerCAmelCase_ : List[Any] = list(set(snake_case__))
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[int] = []
for minterm in minterms:
lowerCAmelCase_ : Dict = ""
for _ in range(snake_case__):
lowerCAmelCase_ : Dict = str(minterm % 2) + string
minterm //= 2
temp.append(snake_case__)
return temp
def UpperCamelCase ( snake_case__ , snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = list(snake_case__)
lowerCAmelCase_ : Dict = list(snake_case__)
lowerCAmelCase_ : Dict = 0
for i in range(len(snake_case__)):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : Optional[Any] = []
lowerCAmelCase_ : Dict = [0] * len(snake_case__)
for i in range(len(chart[0])):
lowerCAmelCase_ : List[Any] = 0
lowerCAmelCase_ : int = -1
for j in range(len(snake_case__)):
if chart[j][i] == 1:
count += 1
lowerCAmelCase_ : Optional[int] = j
if count == 1:
lowerCAmelCase_ : Union[str, Any] = 1
for i in range(len(snake_case__)):
if select[i] == 1:
for j in range(len(chart[0])):
if chart[i][j] == 1:
for k in range(len(snake_case__)):
lowerCAmelCase_ : Tuple = 0
temp.append(prime_implicants[i])
while True:
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : Dict = -1
lowerCAmelCase_ : Tuple = 0
for i in range(len(snake_case__)):
lowerCAmelCase_ : Dict = chart[i].count(1)
if count_n > max_n:
lowerCAmelCase_ : Optional[int] = count_n
lowerCAmelCase_ : Optional[Any] = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem])
for i in range(len(chart[0])):
if chart[rem][i] == 1:
for j in range(len(snake_case__)):
lowerCAmelCase_ : Any = 0
def UpperCamelCase ( snake_case__ , snake_case__):
lowerCAmelCase_ : str = [[0 for x in range(len(snake_case__))] for x in range(len(snake_case__))]
for i in range(len(snake_case__)):
lowerCAmelCase_ : Optional[Any] = prime_implicants[i].count("_")
for j in range(len(snake_case__)):
if is_for_table(prime_implicants[i] , binary[j] , snake_case__):
lowerCAmelCase_ : Dict = 1
return chart
def UpperCamelCase ( ):
lowerCAmelCase_ : Optional[Any] = int(input("Enter the no. of variables\n"))
lowerCAmelCase_ : Tuple = [
float(snake_case__)
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n").split()
]
lowerCAmelCase_ : Any = decimal_to_binary(snake_case__ , snake_case__)
lowerCAmelCase_ : Dict = check(snake_case__)
print("Prime Implicants are:")
print(snake_case__)
lowerCAmelCase_ : int = prime_implicant_chart(snake_case__ , snake_case__)
lowerCAmelCase_ : List[str] = selection(snake_case__ , snake_case__)
print("Essential Prime Implicants are:")
print(snake_case__)
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 683
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST",
"UniSpeechForCTC",
"UniSpeechForPreTraining",
"UniSpeechForSequenceClassification",
"UniSpeechModel",
"UniSpeechPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 267
|
'''simple docstring'''
def lowerCamelCase ( _snake_case : int ,_snake_case : int ):
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 ,number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 267
| 1
|
'''simple docstring'''
import functools
def _a( UpperCamelCase__ : list[int], UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not isinstance(UpperCamelCase__, UpperCamelCase__ ) or not all(isinstance(UpperCamelCase__, UpperCamelCase__ ) for day in days ):
raise ValueError('''The parameter days should be a list of integers''' )
if len(UpperCamelCase__ ) != 3 or not all(isinstance(UpperCamelCase__, UpperCamelCase__ ) for cost in costs ):
raise ValueError('''The parameter costs should be a list of three integers''' )
if len(UpperCamelCase__ ) == 0:
return 0
if min(UpperCamelCase__ ) <= 0:
raise ValueError('''All days elements should be greater than 0''' )
if max(UpperCamelCase__ ) >= 3_6_6:
raise ValueError('''All days elements should be less than 366''' )
SCREAMING_SNAKE_CASE__ : Dict =set(UpperCamelCase__ )
@functools.cache
def dynamic_programming(UpperCamelCase__ : int ) -> int:
if index > 3_6_5:
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 + 3_0 ), )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 665
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = ShapEImgaImgPipeline
snake_case_ = ["""image"""]
snake_case_ = ["""image"""]
snake_case_ = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
snake_case_ = False
@property
def __magic_name__ ( self : List[Any] ) -> List[Any]:
return 32
@property
def __magic_name__ ( self : List[str] ) -> Optional[int]:
return 32
@property
def __magic_name__ ( self : Optional[int] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
return 8
@property
def __magic_name__ ( self : Optional[int] ) -> Union[str, Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict =CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
SCREAMING_SNAKE_CASE__ : str =CLIPVisionModel(__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
SCREAMING_SNAKE_CASE__ : int =CLIPImageProcessor(
crop_size=2_24 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48145466, 0.4578275, 0.40821073] , image_std=[0.26862954, 0.26130258, 0.27577711] , resample=3 , size=2_24 , )
return image_processor
@property
def __magic_name__ ( self : List[str] ) -> Dict:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str ={
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
SCREAMING_SNAKE_CASE__ : str =PriorTransformer(**__lowercase )
return model
@property
def __magic_name__ ( self : Tuple ) -> List[str]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[Any] ={
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
SCREAMING_SNAKE_CASE__ : str =ShapERenderer(**__lowercase )
return model
def __magic_name__ ( self : List[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : int =self.dummy_prior
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_encoder
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.dummy_image_processor
SCREAMING_SNAKE_CASE__ : Tuple =self.dummy_renderer
SCREAMING_SNAKE_CASE__ : int =HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=10_24 , prediction_type='''sample''' , use_karras_sigmas=__lowercase , clip_sample=__lowercase , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE__ : Any ={
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def __magic_name__ ( self : Any , __lowercase : List[str] , __lowercase : Any=0 ) -> Any:
SCREAMING_SNAKE_CASE__ : int =floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase ) ).to(__lowercase )
if str(__lowercase ).startswith('''mps''' ):
SCREAMING_SNAKE_CASE__ : List[str] =torch.manual_seed(__lowercase )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Generator(device=__lowercase ).manual_seed(__lowercase )
SCREAMING_SNAKE_CASE__ : Any ={
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def __magic_name__ ( self : List[str] ) -> str:
SCREAMING_SNAKE_CASE__ : int ='''cpu'''
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : str =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Any =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Dict =pipe(**self.get_dummy_inputs(__lowercase ) )
SCREAMING_SNAKE_CASE__ : Tuple =output.images[0]
SCREAMING_SNAKE_CASE__ : List[Any] =image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE__ : List[Any] =np.array(
[
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
0.00039216,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __magic_name__ ( self : List[Any] ) -> List[str]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __magic_name__ ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch_device == '''cpu'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__lowercase , relax_max_difference=__lowercase , )
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Any =self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict =self.pipeline_class(**__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : List[str] =2
SCREAMING_SNAKE_CASE__ : Dict =self.get_dummy_inputs(__lowercase )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE__ : Tuple =batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE__ : List[Any] =pipe(**__lowercase , num_images_per_prompt=__lowercase )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __magic_name__ ( self : Optional[Any] ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ ( self : int ) -> Dict:
SCREAMING_SNAKE_CASE__ : List[str] =load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
SCREAMING_SNAKE_CASE__ : Dict =load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''' )
SCREAMING_SNAKE_CASE__ : List[Any] =ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
SCREAMING_SNAKE_CASE__ : Tuple =pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =torch.Generator(device=__lowercase ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple =pipe(
__lowercase , generator=__lowercase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__lowercase , __lowercase )
| 665
| 1
|
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
lowerCAmelCase_ : Tuple = '''
Examples:
```py
>>> from PIL import Image
>>> import torch
>>> from diffusers import DiffusionPipeline
>>> from diffusers.utils import export_to_gif, load_image
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> repo = "openai/shap-e-img2img"
>>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
>>> guidance_scale = 3.0
>>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
>>> image = load_image(image_url).convert("RGB")
>>> images = pipe(
... image,
... guidance_scale=guidance_scale,
... num_inference_steps=64,
... frame_size=256,
... ).images
>>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
```
'''
@dataclass
class __lowerCAmelCase ( __a ):
snake_case : Union[PIL.Image.Image, np.ndarray]
class __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
super().__init__()
self.register_modules(
prior=lowerCAmelCase__ , image_encoder=lowerCAmelCase__ , image_processor=lowerCAmelCase__ , scheduler=lowerCAmelCase__ , renderer=lowerCAmelCase__ , )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if latents is None:
_UpperCAmelCase : Tuple = randn_tensor(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=lowerCAmelCase__ , dtype=lowerCAmelCase__ )
else:
if latents.shape != shape:
raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
_UpperCAmelCase : str = latents.to(lowerCAmelCase__ )
_UpperCAmelCase : List[Any] = latents * scheduler.init_noise_sigma
return latents
def snake_case_ (self , lowerCAmelCase__=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_UpperCAmelCase : Tuple = torch.device(F"cuda:{gpu_id}" )
_UpperCAmelCase : Tuple = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCAmelCase__ , lowerCAmelCase__ )
@property
def snake_case_ (self ):
if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(lowerCAmelCase__ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , ):
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(image[0] , torch.Tensor ):
_UpperCAmelCase : int = torch.cat(lowerCAmelCase__ , axis=0 ) if image[0].ndim == 4 else torch.stack(lowerCAmelCase__ , axis=0 )
if not isinstance(lowerCAmelCase__ , torch.Tensor ):
_UpperCAmelCase : List[str] = self.image_processor(lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )
_UpperCAmelCase : Optional[int] = image.to(dtype=self.image_encoder.dtype , device=lowerCAmelCase__ )
_UpperCAmelCase : Dict = self.image_encoder(lowerCAmelCase__ )["""last_hidden_state"""]
_UpperCAmelCase : Union[str, Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
_UpperCAmelCase : List[str] = image_embeds.repeat_interleave(lowerCAmelCase__ , dim=0 )
if do_classifier_free_guidance:
_UpperCAmelCase : Tuple = torch.zeros_like(lowerCAmelCase__ )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_UpperCAmelCase : List[str] = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(lowerCAmelCase__ )
def __call__(self , lowerCAmelCase__ , lowerCAmelCase__ = 1 , lowerCAmelCase__ = 2_5 , lowerCAmelCase__ = None , lowerCAmelCase__ = None , lowerCAmelCase__ = 4.0 , lowerCAmelCase__ = 6_4 , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , ):
if isinstance(lowerCAmelCase__ , PIL.Image.Image ):
_UpperCAmelCase : List[Any] = 1
elif isinstance(lowerCAmelCase__ , torch.Tensor ):
_UpperCAmelCase : Any = image.shape[0]
elif isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
_UpperCAmelCase : List[Any] = len(lowerCAmelCase__ )
else:
raise ValueError(
F"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowerCAmelCase__ )}" )
_UpperCAmelCase : List[str] = self._execution_device
_UpperCAmelCase : Tuple = batch_size * num_images_per_prompt
_UpperCAmelCase : Optional[int] = guidance_scale > 1.0
_UpperCAmelCase : List[str] = self._encode_image(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# prior
self.scheduler.set_timesteps(lowerCAmelCase__ , device=lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = self.scheduler.timesteps
_UpperCAmelCase : Union[str, Any] = self.prior.config.num_embeddings
_UpperCAmelCase : Optional[Any] = self.prior.config.embedding_dim
_UpperCAmelCase : Any = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
_UpperCAmelCase : Union[str, Any] = latents.reshape(latents.shape[0] , lowerCAmelCase__ , lowerCAmelCase__ )
for i, t in enumerate(self.progress_bar(lowerCAmelCase__ ) ):
# expand the latents if we are doing classifier free guidance
_UpperCAmelCase : Dict = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_UpperCAmelCase : List[str] = self.scheduler.scale_model_input(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = self.prior(
lowerCAmelCase__ , timestep=lowerCAmelCase__ , proj_embedding=lowerCAmelCase__ , ).predicted_image_embedding
# remove the variance
_UpperCAmelCase , _UpperCAmelCase : int = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = noise_pred.chunk(2 )
_UpperCAmelCase : str = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
_UpperCAmelCase : Dict = self.scheduler.step(
lowerCAmelCase__ , timestep=lowerCAmelCase__ , sample=lowerCAmelCase__ , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=lowerCAmelCase__ )
_UpperCAmelCase : str = []
for i, latent in enumerate(lowerCAmelCase__ ):
print()
_UpperCAmelCase : Any = self.renderer.decode(
latent[None, :] , lowerCAmelCase__ , size=lowerCAmelCase__ , ray_batch_size=4_0_9_6 , n_coarse_samples=6_4 , n_fine_samples=1_2_8 , )
images.append(lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = torch.stack(lowerCAmelCase__ )
if output_type not in ["np", "pil"]:
raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}" )
_UpperCAmelCase : Optional[int] = images.cpu().numpy()
if output_type == "pil":
_UpperCAmelCase : int = [self.numpy_to_pil(lowerCAmelCase__ ) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=lowerCAmelCase__ )
| 414
|
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __lowerCAmelCase ( __a ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ ):
super().__init__()
# make sure scheduler can always be converted to DDIM
_UpperCAmelCase : Any = DDIMScheduler.from_config(scheduler.config )
self.register_modules(unet=lowerCAmelCase__ , scheduler=lowerCAmelCase__ )
@torch.no_grad()
def __call__(self , lowerCAmelCase__ = 1 , lowerCAmelCase__ = None , lowerCAmelCase__ = 0.0 , lowerCAmelCase__ = 5_0 , lowerCAmelCase__ = None , lowerCAmelCase__ = "pil" , lowerCAmelCase__ = True , ):
# Sample gaussian noise to begin loop
if isinstance(self.unet.config.sample_size , lowerCAmelCase__ ):
_UpperCAmelCase : Tuple = (
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size,
self.unet.config.sample_size,
)
else:
_UpperCAmelCase : Tuple = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and len(lowerCAmelCase__ ) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(lowerCAmelCase__ )}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators." )
_UpperCAmelCase : List[str] = randn_tensor(lowerCAmelCase__ , generator=lowerCAmelCase__ , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(lowerCAmelCase__ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
_UpperCAmelCase : Any = self.unet(lowerCAmelCase__ , lowerCAmelCase__ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
_UpperCAmelCase : Dict = self.scheduler.step(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , eta=lowerCAmelCase__ , use_clipped_model_output=lowerCAmelCase__ , generator=lowerCAmelCase__ ).prev_sample
_UpperCAmelCase : Tuple = (image / 2 + 0.5).clamp(0 , 1 )
_UpperCAmelCase : str = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_UpperCAmelCase : int = self.numpy_to_pil(lowerCAmelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCAmelCase__ )
| 414
| 1
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
def lowerCamelCase_ ( UpperCAmelCase_ : Optional[int] ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : List[Any] = SwinConfig(
embed_dim=1_9_2 , depths=(2, 2, 1_8, 2) , num_heads=(6, 1_2, 2_4, 4_8) , window_size=1_2 , out_features=['stage2', 'stage3', 'stage4'] , )
_UpperCamelCase : Optional[int] = DetaConfig(
backbone_config=UpperCAmelCase_ , num_queries=9_0_0 , encoder_ffn_dim=2_0_4_8 , decoder_ffn_dim=2_0_4_8 , num_feature_levels=5 , assign_first_stage=UpperCAmelCase_ , with_box_refine=UpperCAmelCase_ , two_stage=UpperCAmelCase_ , )
# set labels
_UpperCamelCase : Union[str, Any] = 'huggingface/label-files'
if "o365" in model_name:
_UpperCamelCase : Tuple = 3_6_6
_UpperCamelCase : int = 'object365-id2label.json'
else:
_UpperCamelCase : int = 9_1
_UpperCamelCase : Tuple = 'coco-detection-id2label.json'
_UpperCamelCase : int = num_labels
_UpperCamelCase : Dict = json.load(open(cached_download(hf_hub_url(UpperCAmelCase_ , UpperCAmelCase_ , repo_type='dataset' ) ) , 'r' ) )
_UpperCamelCase : Dict = {int(UpperCAmelCase_ ): v for k, v in idalabel.items()}
_UpperCamelCase : Any = idalabel
_UpperCamelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowerCamelCase_ ( UpperCAmelCase_ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
_UpperCamelCase : str = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.patch_embed.proj.weight', 'model.backbone.model.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.0.body.patch_embed.proj.bias', 'model.backbone.model.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.0.body.patch_embed.norm.weight', 'model.backbone.model.embeddings.norm.weight') )
rename_keys.append(('backbone.0.body.patch_embed.norm.bias', 'model.backbone.model.embeddings.norm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.reduction.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.weight''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((F'''backbone.0.body.layers.{i}.downsample.norm.bias''', F'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append(('backbone.0.body.norm1.weight', 'model.backbone.model.hidden_states_norms.stage2.weight') )
rename_keys.append(('backbone.0.body.norm1.bias', 'model.backbone.model.hidden_states_norms.stage2.bias') )
rename_keys.append(('backbone.0.body.norm2.weight', 'model.backbone.model.hidden_states_norms.stage3.weight') )
rename_keys.append(('backbone.0.body.norm2.bias', 'model.backbone.model.hidden_states_norms.stage3.bias') )
rename_keys.append(('backbone.0.body.norm3.weight', 'model.backbone.model.hidden_states_norms.stage4.weight') )
rename_keys.append(('backbone.0.body.norm3.bias', 'model.backbone.model.hidden_states_norms.stage4.bias') )
# transformer encoder
for i in range(config.encoder_layers ):
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', F'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', F'''model.encoder.layers.{i}.self_attn.attention_weights.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', F'''model.encoder.layers.{i}.self_attn.attention_weights.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', F'''model.encoder.layers.{i}.self_attn.value_proj.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', F'''model.encoder.layers.{i}.self_attn.value_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', F'''model.encoder.layers.{i}.self_attn.output_proj.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', F'''model.encoder.layers.{i}.self_attn.output_proj.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.weight''', F'''model.encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''model.encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''model.encoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''model.encoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''model.encoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''model.encoder.layers.{i}.fc2.bias''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''model.encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''model.encoder.layers.{i}.final_layer_norm.bias''') )
# transformer decoder
for i in range(config.decoder_layers ):
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', F'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', F'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', F'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.weight''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm1.bias''', F'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''model.decoder.layers.{i}.self_attn.out_proj.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''model.decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.weight''', F'''model.decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm2.bias''', F'''model.decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.weight''', F'''model.decoder.layers.{i}.fc1.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear1.bias''', F'''model.decoder.layers.{i}.fc1.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.weight''', F'''model.decoder.layers.{i}.fc2.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.linear2.bias''', F'''model.decoder.layers.{i}.fc2.bias''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.weight''', F'''model.decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((F'''transformer.decoder.layers.{i}.norm3.bias''', F'''model.decoder.layers.{i}.final_layer_norm.bias''') )
# fmt: on
return rename_keys
def lowerCamelCase_ ( UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : Optional[int] = dct.pop(UpperCAmelCase_ )
_UpperCamelCase : List[str] = val
def lowerCamelCase_ ( UpperCAmelCase_ : Dict , UpperCAmelCase_ : Tuple ) -> str:
'''simple docstring'''
_UpperCamelCase : str = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCamelCase : Union[str, Any] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCamelCase : Tuple = state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight''' )
_UpperCamelCase : Optional[Any] = state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCamelCase : List[str] = in_proj_weight[:dim, :]
_UpperCamelCase : str = in_proj_bias[: dim]
_UpperCamelCase : List[str] = in_proj_weight[
dim : dim * 2, :
]
_UpperCamelCase : Union[str, Any] = in_proj_bias[
dim : dim * 2
]
_UpperCamelCase : Any = in_proj_weight[
-dim :, :
]
_UpperCamelCase : Tuple = in_proj_bias[-dim :]
# fmt: on
def lowerCamelCase_ ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any ) -> int:
'''simple docstring'''
_UpperCamelCase : List[str] = config.d_model
for i in range(config.decoder_layers ):
# read in weights + bias of input projection layer of self-attention
_UpperCamelCase : Union[str, Any] = state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
_UpperCamelCase : Optional[Any] = state_dict.pop(F'''transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
_UpperCamelCase : Any = in_proj_weight[:hidden_size, :]
_UpperCamelCase : str = in_proj_bias[:hidden_size]
_UpperCamelCase : str = in_proj_weight[
hidden_size : hidden_size * 2, :
]
_UpperCamelCase : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCamelCase : str = in_proj_weight[-hidden_size:, :]
_UpperCamelCase : Optional[int] = in_proj_bias[-hidden_size:]
def lowerCamelCase_ ( ) -> str:
'''simple docstring'''
_UpperCamelCase : Dict = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCamelCase : str = Image.open(requests.get(UpperCAmelCase_ , stream=UpperCAmelCase_ ).raw )
return im
@torch.no_grad()
def lowerCamelCase_ ( UpperCAmelCase_ : Any , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str] ) -> Optional[int]:
'''simple docstring'''
_UpperCamelCase : int = get_deta_config(UpperCAmelCase_ )
# load original state dict
if model_name == "deta-swin-large":
_UpperCamelCase : int = hf_hub_download(repo_id='nielsr/deta-checkpoints' , filename='adet_swin_ft.pth' )
elif model_name == "deta-swin-large-o365":
_UpperCamelCase : str = hf_hub_download(repo_id='jozhang97/deta-swin-l-o365' , filename='deta_swin_pt_o365.pth' )
else:
raise ValueError(F'''Model name {model_name} not supported''' )
_UpperCamelCase : str = torch.load(UpperCAmelCase_ , map_location='cpu' )['model']
# original state dict
for name, param in state_dict.items():
print(UpperCAmelCase_ , param.shape )
# rename keys
_UpperCamelCase : Union[str, Any] = create_rename_keys(UpperCAmelCase_ )
for src, dest in rename_keys:
rename_key(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
read_in_swin_q_k_v(UpperCAmelCase_ , config.backbone_config )
read_in_decoder_q_k_v(UpperCAmelCase_ , UpperCAmelCase_ )
# fix some prefixes
for key in state_dict.copy().keys():
if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
_UpperCamelCase : str = state_dict.pop(UpperCAmelCase_ )
_UpperCamelCase : List[str] = val
if "input_proj" in key:
_UpperCamelCase : int = state_dict.pop(UpperCAmelCase_ )
_UpperCamelCase : List[Any] = val
if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
_UpperCamelCase : Union[str, Any] = state_dict.pop(UpperCAmelCase_ )
_UpperCamelCase : Any = val
# finally, create HuggingFace model and load state dict
_UpperCamelCase : Any = DetaForObjectDetection(UpperCAmelCase_ )
model.load_state_dict(UpperCAmelCase_ )
model.eval()
_UpperCamelCase : List[str] = 'cuda' if torch.cuda.is_available() else 'cpu'
model.to(UpperCAmelCase_ )
# load image processor
_UpperCamelCase : Any = DetaImageProcessor(format='coco_detection' )
# verify our conversion on image
_UpperCamelCase : int = prepare_img()
_UpperCamelCase : List[str] = processor(images=UpperCAmelCase_ , return_tensors='pt' )
_UpperCamelCase : List[str] = encoding['pixel_values']
_UpperCamelCase : Optional[Any] = model(pixel_values.to(UpperCAmelCase_ ) )
# verify logits
print('Logits:' , outputs.logits[0, :3, :3] )
print('Boxes:' , outputs.pred_boxes[0, :3, :3] )
if model_name == "deta-swin-large":
_UpperCamelCase : Union[str, Any] = torch.tensor(
[[-7.6_3_0_8, -2.8_4_8_5, -5.3_7_3_7], [-7.2_0_3_7, -4.5_5_0_5, -4.8_0_2_7], [-7.2_9_4_3, -4.2_6_1_1, -4.6_6_1_7]] )
_UpperCamelCase : Optional[Any] = torch.tensor([[0.4_9_8_7, 0.4_9_6_9, 0.9_9_9_9], [0.2_5_4_9, 0.5_4_9_8, 0.4_8_0_5], [0.5_4_9_8, 0.2_7_5_7, 0.0_5_6_9]] )
elif model_name == "deta-swin-large-o365":
_UpperCamelCase : Dict = torch.tensor(
[[-8.0_1_2_2, -3.5_7_2_0, -4.9_7_1_7], [-8.1_5_4_7, -3.6_8_8_6, -4.6_3_8_9], [-7.6_6_1_0, -3.6_1_9_4, -5.0_1_3_4]] )
_UpperCamelCase : Optional[int] = torch.tensor([[0.2_5_2_3, 0.5_5_4_9, 0.4_8_8_1], [0.7_7_1_5, 0.4_1_4_9, 0.4_6_0_1], [0.5_5_0_3, 0.2_7_5_3, 0.0_5_7_5]] )
assert torch.allclose(outputs.logits[0, :3, :3] , expected_logits.to(UpperCAmelCase_ ) , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , expected_boxes.to(UpperCAmelCase_ ) , atol=1e-4 )
print('Everything ok!' )
if pytorch_dump_folder_path:
# Save model and processor
logger.info(F'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...''' )
Path(UpperCAmelCase_ ).mkdir(exist_ok=UpperCAmelCase_ )
model.save_pretrained(UpperCAmelCase_ )
processor.save_pretrained(UpperCAmelCase_ )
# Push to hub
if push_to_hub:
print('Pushing model and processor to hub...' )
model.push_to_hub(F'''jozhang97/{model_name}''' )
processor.push_to_hub(F'''jozhang97/{model_name}''' )
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
type=str,
default="""deta-swin-large""",
choices=["""deta-swin-large""", """deta-swin-large-o365"""],
help="""Name of the 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 or not to push the converted model to the 🤗 hub."""
)
lowerCAmelCase__ = parser.parse_args()
convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 648
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase__ = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlipConfig""",
"""BlipTextConfig""",
"""BlipVisionConfig""",
],
"""processing_blip""": ["""BlipProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ["""BlipImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlipModel""",
"""BlipPreTrainedModel""",
"""BlipForConditionalGeneration""",
"""BlipForQuestionAnswering""",
"""BlipVisionModel""",
"""BlipTextModel""",
"""BlipForImageTextRetrieval""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFBlipModel""",
"""TFBlipPreTrainedModel""",
"""TFBlipForConditionalGeneration""",
"""TFBlipForQuestionAnswering""",
"""TFBlipVisionModel""",
"""TFBlipTextModel""",
"""TFBlipForImageTextRetrieval""",
]
if TYPE_CHECKING:
from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
from .processing_blip import BlipProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_blip import BlipImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blip import (
BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
BlipForConditionalGeneration,
BlipForImageTextRetrieval,
BlipForQuestionAnswering,
BlipModel,
BlipPreTrainedModel,
BlipTextModel,
BlipVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blip import (
TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFBlipForConditionalGeneration,
TFBlipForImageTextRetrieval,
TFBlipForQuestionAnswering,
TFBlipModel,
TFBlipPreTrainedModel,
TFBlipTextModel,
TFBlipVisionModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 648
| 1
|
lowerCAmelCase__ = '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
lowerCAmelCase__ = [{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__ = {
'{processor_class}': 'FakeProcessorClass',
'{model_class}': 'FakeModelClass',
'{object_class}': 'FakeObjectClass',
}
| 321
|
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
lowerCAmelCase__ = 'true'
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase=82 , _UpperCAmelCase=16 ) -> int:
'''simple docstring'''
set_seed(42 )
__lowercase = RegressionModel()
__lowercase = deepcopy(_UpperCAmelCase )
__lowercase = RegressionDataset(length=_UpperCAmelCase )
__lowercase = DataLoader(_UpperCAmelCase , batch_size=_UpperCAmelCase )
model.to(accelerator.device )
__lowercase , __lowercase = accelerator.prepare(_UpperCAmelCase , _UpperCAmelCase )
return model, ddp_model, dataloader
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase=False ) -> int:
'''simple docstring'''
__lowercase = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
__lowercase = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(_UpperCAmelCase ):
__lowercase = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase )
return outputs
with accelerator.main_process_first():
__lowercase = dataset.map(
_UpperCAmelCase , batched=_UpperCAmelCase , remove_columns=["idx", "sentence1", "sentence2"] , )
__lowercase = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(_UpperCAmelCase ):
if use_longest:
return tokenizer.pad(_UpperCAmelCase , padding="longest" , return_tensors="pt" )
return tokenizer.pad(_UpperCAmelCase , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(_UpperCAmelCase , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=16 )
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__lowercase = Accelerator(dispatch_batches=_UpperCAmelCase , split_batches=_UpperCAmelCase )
__lowercase = get_dataloader(_UpperCAmelCase , not dispatch_batches )
__lowercase = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=_UpperCAmelCase )
__lowercase , __lowercase = accelerator.prepare(_UpperCAmelCase , _UpperCAmelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
__lowercase = []
for batch in dataloader:
__lowercase , __lowercase = batch.values()
with torch.no_grad():
__lowercase = model(_UpperCAmelCase )
__lowercase , __lowercase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowercase , __lowercase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCAmelCase )
targs.append(_UpperCAmelCase )
__lowercase , __lowercase = torch.cat(_UpperCAmelCase ), torch.cat(_UpperCAmelCase )
return logits, targs
def __lowercase ( _UpperCAmelCase , _UpperCAmelCase=82 , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=16 ) -> Any:
'''simple docstring'''
__lowercase , __lowercase , __lowercase = get_basic_setup(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
__lowercase , __lowercase = generate_predictions(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
assert (
len(_UpperCAmelCase ) == num_samples
), f'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCAmelCase )}'''
def __lowercase ( _UpperCAmelCase = False , _UpperCAmelCase = False ) -> str:
'''simple docstring'''
__lowercase = evaluate.load("glue" , "mrpc" )
__lowercase , __lowercase = get_mrpc_setup(_UpperCAmelCase , _UpperCAmelCase )
# First do baseline
__lowercase , __lowercase , __lowercase = setup["no"]
model.to(_UpperCAmelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCAmelCase )
with torch.inference_mode():
__lowercase = model(**_UpperCAmelCase )
__lowercase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCAmelCase , references=batch["labels"] )
__lowercase = metric.compute()
# Then do distributed
__lowercase , __lowercase , __lowercase = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowercase = model(**_UpperCAmelCase )
__lowercase = outputs.logits.argmax(dim=-1 )
__lowercase = batch["labels"]
__lowercase , __lowercase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCAmelCase , references=_UpperCAmelCase )
__lowercase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def __lowercase ( ) -> Dict:
'''simple docstring'''
__lowercase = Accelerator(split_batches=_UpperCAmelCase , dispatch_batches=_UpperCAmelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(_UpperCAmelCase , _UpperCAmelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowercase = Accelerator(split_batches=_UpperCAmelCase , dispatch_batches=_UpperCAmelCase )
if accelerator.is_local_main_process:
print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(_UpperCAmelCase , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
__lowercase = Accelerator()
test_torch_metrics(_UpperCAmelCase , 512 )
accelerator.state._reset_state()
def __lowercase ( _UpperCAmelCase ) -> List[str]:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 321
| 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)
snake_case_ : Any = 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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(3_2, (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=1_2_8, 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')
snake_case_ : Tuple = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
snake_case_ : str = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)
snake_case_ : str = train_datagen.flow_from_directory(
'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
)
snake_case_ : Union[str, Any] = test_datagen.flow_from_directory(
'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
)
classifier.save('cnn.h5')
# Part 3 - Making new predictions
snake_case_ : List[str] = tf.keras.preprocessing.image.load_img(
'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
)
snake_case_ : str = tf.keras.preprocessing.image.img_to_array(test_image)
snake_case_ : Union[str, Any] = np.expand_dims(test_image, axis=0)
snake_case_ : int = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
snake_case_ : Dict = 'Normal'
if result[0][0] == 1:
snake_case_ : Optional[int] = 'Abnormality detected'
| 166
|
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
snake_case_ : str = logging.get_logger(__name__) # pylint: disable=invalid-name
snake_case_ : Optional[int] = '\n Examples:\n ```py\n >>> import torch\n >>> import numpy as np\n\n >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n >>> from transformers import pipeline\n >>> from diffusers.utils import load_image\n\n\n >>> def make_hint(image, depth_estimator):\n ... image = depth_estimator(image)["depth"]\n ... image = np.array(image)\n ... image = image[:, :, None]\n ... image = np.concatenate([image, image, image], axis=2)\n ... detected_map = torch.from_numpy(image).float() / 255.0\n ... hint = detected_map.permute(2, 0, 1)\n ... return hint\n\n\n >>> depth_estimator = pipeline("depth-estimation")\n\n >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n ... )\n >>> pipe_prior = pipe_prior.to("cuda")\n\n >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n ... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n ... )\n >>> pipe = pipe.to("cuda")\n\n\n >>> img = load_image(\n ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n ... "/kandinsky/cat.png"\n ... ).resize((768, 768))\n\n >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n >>> prompt = "A robot, 4k photo"\n >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n >>> image_emb, zero_image_emb = pipe_prior(\n ... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n ... ).to_tuple()\n\n >>> images = pipe(\n ... image_embeds=image_emb,\n ... negative_image_embeds=zero_image_emb,\n ... hint=hint,\n ... num_inference_steps=50,\n ... generator=generator,\n ... height=768,\n ... width=768,\n ... ).images\n\n >>> images[0].save("robot_cat.png")\n ```\n'
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Tuple , snake_case_ : Optional[int]=8 ):
'''simple docstring'''
UpperCAmelCase: Any = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
UpperCAmelCase: Dict = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __lowerCamelCase ( lowercase ):
def __init__( self , __snake_case , __snake_case , __snake_case , ) -> List[str]:
"""simple docstring"""
super().__init__()
self.register_modules(
unet=__snake_case , scheduler=__snake_case , movq=__snake_case , )
UpperCAmelCase: List[Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A__ ( self , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case ) -> Union[str, Any]:
"""simple docstring"""
if latents is None:
UpperCAmelCase: Tuple = randn_tensor(__snake_case , generator=__snake_case , device=__snake_case , dtype=__snake_case )
else:
if latents.shape != shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}' )
UpperCAmelCase: str = latents.to(__snake_case )
UpperCAmelCase: Tuple = latents * scheduler.init_noise_sigma
return latents
def A__ ( self , __snake_case=0 ) -> str:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
UpperCAmelCase: Union[str, Any] = torch.device(F'cuda:{gpu_id}' )
UpperCAmelCase: Optional[int] = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__snake_case , __snake_case )
def A__ ( self , __snake_case=0 ) -> List[Any]:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
UpperCAmelCase: Optional[Any] = torch.device(F'cuda:{gpu_id}' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=__snake_case )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase: int = None
for cpu_offloaded_model in [self.unet, self.movq]:
UpperCAmelCase , UpperCAmelCase: str = cpu_offload_with_hook(__snake_case , __snake_case , prev_module_hook=__snake_case )
# We'll offload the last model manually.
UpperCAmelCase: Union[str, Any] = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A__ ( self ) -> Any:
"""simple docstring"""
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__snake_case , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__snake_case )
def __call__( self , __snake_case , __snake_case , __snake_case , __snake_case = 5_1_2 , __snake_case = 5_1_2 , __snake_case = 1_0_0 , __snake_case = 4.0 , __snake_case = 1 , __snake_case = None , __snake_case = None , __snake_case = "pil" , __snake_case = True , ) -> Dict:
"""simple docstring"""
UpperCAmelCase: Optional[int] = self._execution_device
UpperCAmelCase: Optional[int] = guidance_scale > 1.0
if isinstance(__snake_case , __snake_case ):
UpperCAmelCase: int = torch.cat(__snake_case , dim=0 )
if isinstance(__snake_case , __snake_case ):
UpperCAmelCase: List[Any] = torch.cat(__snake_case , dim=0 )
if isinstance(__snake_case , __snake_case ):
UpperCAmelCase: List[Any] = torch.cat(__snake_case , dim=0 )
UpperCAmelCase: Dict = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
UpperCAmelCase: Dict = image_embeds.repeat_interleave(__snake_case , dim=0 )
UpperCAmelCase: Dict = negative_image_embeds.repeat_interleave(__snake_case , dim=0 )
UpperCAmelCase: Tuple = hint.repeat_interleave(__snake_case , dim=0 )
UpperCAmelCase: Any = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__snake_case )
UpperCAmelCase: Any = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=__snake_case )
self.scheduler.set_timesteps(__snake_case , device=__snake_case )
UpperCAmelCase: Any = self.scheduler.timesteps
UpperCAmelCase: List[str] = self.movq.config.latent_channels
UpperCAmelCase , UpperCAmelCase: Union[str, Any] = downscale_height_and_width(__snake_case , __snake_case , self.movq_scale_factor )
# create initial latent
UpperCAmelCase: Any = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __snake_case , __snake_case , __snake_case , self.scheduler , )
for i, t in enumerate(self.progress_bar(__snake_case ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase: List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase: str = {"image_embeds": image_embeds, "hint": hint}
UpperCAmelCase: Any = self.unet(
sample=__snake_case , timestep=__snake_case , encoder_hidden_states=__snake_case , added_cond_kwargs=__snake_case , return_dict=__snake_case , )[0]
if do_classifier_free_guidance:
UpperCAmelCase , UpperCAmelCase: Optional[Any] = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase , UpperCAmelCase: str = noise_pred.chunk(2 )
UpperCAmelCase , UpperCAmelCase: Dict = variance_pred.chunk(2 )
UpperCAmelCase: List[Any] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase: Union[str, Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase , UpperCAmelCase: Any = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase: int = self.scheduler.step(
__snake_case , __snake_case , __snake_case , generator=__snake_case , )[0]
# post-processing
UpperCAmelCase: Optional[Any] = self.movq.decode(__snake_case , force_not_quantize=__snake_case )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )
if output_type in ["np", "pil"]:
UpperCAmelCase: Optional[Any] = image * 0.5 + 0.5
UpperCAmelCase: Union[str, Any] = image.clamp(0 , 1 )
UpperCAmelCase: List[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase: Dict = self.numpy_to_pil(__snake_case )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__snake_case )
| 166
| 1
|
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _A , _A , _A ):
# Initialise PyTorch model
a : Any = TaConfig.from_json_file(_A )
print(f"""Building PyTorch model from configuration: {config}""" )
a : Optional[int] = TaForConditionalGeneration(_A )
# Load weights from tf checkpoint
load_tf_weights_in_ta(_A , _A , _A )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(_A )
if __name__ == "__main__":
lowerCAmelCase: Any = 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(
'--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.'
)
lowerCAmelCase: str = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 526
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A: Dict = {
"configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
"feature_extraction_whisper": ["WhisperFeatureExtractor"],
"processing_whisper": ["WhisperProcessor"],
"tokenization_whisper": ["WhisperTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: Union[str, Any] = ["WhisperTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: str = [
"WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
"WhisperForConditionalGeneration",
"WhisperModel",
"WhisperPreTrainedModel",
"WhisperForAudioClassification",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: str = [
"TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFWhisperForConditionalGeneration",
"TFWhisperModel",
"TFWhisperPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A: str = [
"FlaxWhisperForConditionalGeneration",
"FlaxWhisperModel",
"FlaxWhisperPreTrainedModel",
"FlaxWhisperForAudioClassification",
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
A: Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 160
| 0
|
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
A : Union[str, Any] = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
def _lowerCamelCase ( _UpperCamelCase : Any = "dhaka" , _UpperCamelCase : List[Any] = 5 ):
'''simple docstring'''
__lowerCAmelCase = min(_UpperCamelCase , 50 ) # Prevent abuse!
__lowerCAmelCase = {
"q": query,
"tbm": "isch",
"hl": "en",
"ijn": "0",
}
__lowerCAmelCase = requests.get("https://www.google.com/search" , params=_UpperCamelCase , headers=_UpperCamelCase )
__lowerCAmelCase = BeautifulSoup(html.text , "html.parser" )
__lowerCAmelCase = "".join(
re.findall(R"AF_initDataCallback\(([^<]+)\);" , str(soup.select("script" ) ) ) )
__lowerCAmelCase = json.dumps(_UpperCamelCase )
__lowerCAmelCase = json.loads(_UpperCamelCase )
__lowerCAmelCase = re.findall(
R"\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\"," , _UpperCamelCase , )
if not matched_google_image_data:
return 0
__lowerCAmelCase = re.sub(
R"\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]" , "" , str(_UpperCamelCase ) , )
__lowerCAmelCase = re.findall(
R"(?:'|,),\[\"(https:|http.*?)\",\d+,\d+\]" , _UpperCamelCase , )
for index, fixed_full_res_image in enumerate(_UpperCamelCase ):
if index >= max_images:
return index
__lowerCAmelCase = bytes(_UpperCamelCase , "ascii" ).decode(
"unicode-escape" )
__lowerCAmelCase = bytes(_UpperCamelCase , "ascii" ).decode(
"unicode-escape" )
__lowerCAmelCase = urllib.request.build_opener()
__lowerCAmelCase = [
(
"User-Agent",
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582",
)
]
urllib.request.install_opener(_UpperCamelCase )
__lowerCAmelCase = f"query_{query.replace(' ' , '_' )}"
if not os.path.exists(_UpperCamelCase ):
os.makedirs(_UpperCamelCase )
urllib.request.urlretrieve( # noqa: S310
_UpperCamelCase , f"{path_name}/original_size_img_{index}.jpg" )
return index
if __name__ == "__main__":
try:
A : Any = download_images_from_google_query(sys.argv[1])
print(f'''{image_count} images were downloaded to disk.''')
except IndexError:
print("Please provide a search term.")
raise
| 701
|
"""simple docstring"""
from itertools import product
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = sides_number
__lowerCAmelCase = max_face_number * dice_number
__lowerCAmelCase = [0] * (max_total + 1)
__lowerCAmelCase = 1
__lowerCAmelCase = range(_UpperCamelCase , max_face_number + 1 )
for dice_numbers in product(_UpperCamelCase , repeat=_UpperCamelCase ):
__lowerCAmelCase = sum(_UpperCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
__lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
__lowerCAmelCase = 0
__lowerCAmelCase = 9
__lowerCAmelCase = 4 * 9
__lowerCAmelCase = 6
for peter_total in range(_UpperCamelCase , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
__lowerCAmelCase = (4**9) * (6**6)
__lowerCAmelCase = peter_wins_count / total_games_number
__lowerCAmelCase = round(_UpperCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(f'''{solution() = }''')
| 282
| 0
|
'''simple docstring'''
from __future__ import annotations
import os
from typing import Any
import requests
A : Tuple = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
A : int = BASE_URL + '''/user'''
# https://github.com/settings/tokens
A : Tuple = os.environ.get('''USER_TOKEN''', '''''')
def lowerCAmelCase__ ( lowerCamelCase : str ):
_A : Optional[int] = {
'Authorization': F'token {auth_token}',
'Accept': 'application/vnd.github.v3+json',
}
return requests.get(lowerCamelCase ,headers=lowerCamelCase ).json()
if __name__ == "__main__": # pragma: no cover
if USER_TOKEN:
for key, value in fetch_github_info(USER_TOKEN).items():
print(f"""{key}: {value}""")
else:
raise ValueError('''\'USER_TOKEN\' field cannot be empty.''')
| 128
|
'''simple docstring'''
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
A : List[str] = get_logger(__name__)
class __lowerCamelCase :
"""simple docstring"""
a = "dummy_data"
a = "datasets"
a = False
def __init__( self : List[str] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Union[Version, str] , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = True , SCREAMING_SNAKE_CASE : Optional[List[Callable]] = None , ):
_A : Dict = 0
_A : Dict = dataset_name
_A : Any = cache_dir
_A : List[str] = use_local_dummy_data
_A : Optional[Any] = config
# download_callbacks take a single url as input
_A : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
_A : int = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
_A : Any = str(SCREAMING_SNAKE_CASE)
# to be downloaded
_A : Optional[Any] = None
_A : List[str] = None
@property
def A ( self : str):
if self._dummy_file is None:
_A : Tuple = self.download_dummy_data()
return self._dummy_file
@property
def A ( self : Optional[Any]):
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('dummy' , self.config.name , self.version_name)
# structure is dummy / version_name
return os.path.join('dummy' , self.version_name)
@property
def A ( self : Tuple):
return os.path.join(self.dummy_data_folder , 'dummy_data.zip')
def A ( self : int):
_A : Dict = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
_A : Optional[int] = cached_path(
SCREAMING_SNAKE_CASE , cache_dir=self.cache_dir , extract_compressed_file=SCREAMING_SNAKE_CASE , force_extract=SCREAMING_SNAKE_CASE)
return os.path.join(SCREAMING_SNAKE_CASE , self.dummy_file_name)
@property
def A ( self : List[str]):
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file)
@property
def A ( self : str):
if self._bucket_url is None:
_A : Tuple = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '/'))
return self._bucket_url
@property
def A ( self : str):
# return full path if its a dir
if os.path.isdir(self.dummy_file):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '/').split('/')[:-1])
def A ( self : List[str] , SCREAMING_SNAKE_CASE : List[Any] , *SCREAMING_SNAKE_CASE : Optional[Any]):
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
_A : Any = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
_A : Dict = self.dummy_file_name
# special case when data_url is a dict
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
return self.create_dummy_data_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
elif isinstance(SCREAMING_SNAKE_CASE , (list, tuple)):
return self.create_dummy_data_list(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
else:
return self.create_dummy_data_single(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
def A ( self : Optional[Any] , SCREAMING_SNAKE_CASE : str , *SCREAMING_SNAKE_CASE : str):
return self.download_and_extract(SCREAMING_SNAKE_CASE)
def A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Tuple):
return self.download_and_extract(SCREAMING_SNAKE_CASE)
def A ( self : Tuple , SCREAMING_SNAKE_CASE : Optional[Any] , *SCREAMING_SNAKE_CASE : Union[str, Any] , **SCREAMING_SNAKE_CASE : Any):
return path
def A ( self : str):
return {}
def A ( self : Any , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Union[str, Any]):
_A : List[str] = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
for single_url in single_urls:
download_callback(SCREAMING_SNAKE_CASE)
else:
_A : Optional[Any] = single_urls
download_callback(SCREAMING_SNAKE_CASE)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
_A : List[Any] = [os.path.join(SCREAMING_SNAKE_CASE , urllib.parse.quote_plus(Path(SCREAMING_SNAKE_CASE).name)) for x in single_urls]
else:
_A : str = single_urls
_A : Optional[Any] = os.path.join(SCREAMING_SNAKE_CASE , urllib.parse.quote_plus(Path(SCREAMING_SNAKE_CASE).name))
_A : Tuple = value
# make sure that values are unique
if all(isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE) for i in dummy_data_dict.values()) and len(set(dummy_data_dict.values())) < len(
dummy_data_dict.values()):
# append key to value to make its name unique
_A : List[Any] = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def A ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int):
_A : List[str] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
_A : Union[str, Any] = all(bool(re.findall('[0-9]{3,}-of-[0-9]{3,}' , SCREAMING_SNAKE_CASE)) for url in data_url)
_A : Optional[Any] = all(
url.startswith('https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed') for url in data_url)
if data_url and (is_tf_records or is_pubmed_records):
_A : Optional[Any] = [data_url[0]] * len(SCREAMING_SNAKE_CASE)
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(SCREAMING_SNAKE_CASE)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_A : Any = os.path.join(SCREAMING_SNAKE_CASE , urllib.parse.quote_plus(single_url.split('/')[-1]))
dummy_data_list.append(SCREAMING_SNAKE_CASE)
return dummy_data_list
def A ( self : List[Any] , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Any):
for download_callback in self.download_callbacks:
download_callback(SCREAMING_SNAKE_CASE)
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
_A : Tuple = os.path.join(SCREAMING_SNAKE_CASE , urllib.parse.quote_plus(data_url.split('/')[-1]))
if os.path.exists(SCREAMING_SNAKE_CASE) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def A ( self : str):
pass
def A ( self : str):
pass
def A ( self : Tuple , SCREAMING_SNAKE_CASE : Optional[Any]):
def _iter_archive_members(SCREAMING_SNAKE_CASE : str):
# this preserves the order of the members inside the ZIP archive
_A : Dict = Path(self.dummy_file).parent
_A : Union[str, Any] = path.relative_to(SCREAMING_SNAKE_CASE)
with ZipFile(self.local_path_to_dummy_data) as zip_file:
_A : Any = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix()):
yield dummy_parent_path.joinpath(SCREAMING_SNAKE_CASE)
_A : str = Path(SCREAMING_SNAKE_CASE)
_A : Any = _iter_archive_members(SCREAMING_SNAKE_CASE) if self.use_local_dummy_data else path.rglob('*')
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('.', '__')):
yield file_path.relative_to(SCREAMING_SNAKE_CASE).as_posix(), file_path.open('rb')
def A ( self : int , SCREAMING_SNAKE_CASE : Tuple):
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE):
_A : Tuple = [paths]
for path in paths:
if os.path.isfile(SCREAMING_SNAKE_CASE):
if os.path.basename(SCREAMING_SNAKE_CASE).startswith(('.', '__')):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(SCREAMING_SNAKE_CASE):
if os.path.basename(SCREAMING_SNAKE_CASE).startswith(('.', '__')):
continue
dirnames.sort()
for filename in sorted(SCREAMING_SNAKE_CASE):
if filename.startswith(('.', '__')):
continue
yield os.path.join(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE)
| 128
| 1
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase_ = {
'vocab_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
},
'merges_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
},
'tokenizer_file': {
'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
},
}
UpperCamelCase_ = {
'gpt2': 10_24,
'gpt2-medium': 10_24,
'gpt2-large': 10_24,
'gpt2-xl': 10_24,
'distilgpt2': 10_24,
}
class snake_case_ ( a ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ['input_ids', 'attention_mask']
__UpperCamelCase = GPTaTokenizer
def __init__( self, A_=None, A_=None, A_=None, A_="<|endoftext|>", A_="<|endoftext|>", A_="<|endoftext|>", A_=False, **A_, ) -> Optional[Any]:
super().__init__(
A_, A_, tokenizer_file=A_, unk_token=A_, bos_token=A_, eos_token=A_, add_prefix_space=A_, **A_, )
UpperCAmelCase__ =kwargs.pop("add_bos_token", A_ )
UpperCAmelCase__ =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space", A_ ) != add_prefix_space:
UpperCAmelCase__ =getattr(A_, pre_tok_state.pop("type" ) )
UpperCAmelCase__ =add_prefix_space
UpperCAmelCase__ =pre_tok_class(**A_ )
UpperCAmelCase__ =add_prefix_space
def __UpperCAmelCase ( self, *A_, **A_ ) -> BatchEncoding:
UpperCAmelCase__ =kwargs.get("is_split_into_words", A_ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*A_, **A_ )
def __UpperCAmelCase ( self, *A_, **A_ ) -> BatchEncoding:
UpperCAmelCase__ =kwargs.get("is_split_into_words", A_ )
assert self.add_prefix_space or not is_split_into_words, (
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*A_, **A_ )
def __UpperCAmelCase ( self, A_, A_ = None ) -> Tuple[str]:
UpperCAmelCase__ =self._tokenizer.model.save(A_, name=A_ )
return tuple(A_ )
def __UpperCAmelCase ( self, A_ ) -> List[int]:
UpperCAmelCase__ =[]
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(A_, add_special_tokens=A_ ) + [self.eos_token_id] )
if len(A_ ) > self.model_max_length:
UpperCAmelCase__ =input_ids[-self.model_max_length :]
return input_ids
| 510
|
# using dfs for finding eulerian path traversal
def _UpperCAmelCase ( A , A , A , A=None ):
'''simple docstring'''
UpperCAmelCase__ =(path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
UpperCAmelCase__ , UpperCAmelCase__ =True, True
UpperCAmelCase__ =dfs(A , A , A , A )
return path
def _UpperCAmelCase ( A , A ):
'''simple docstring'''
UpperCAmelCase__ =0
UpperCAmelCase__ =-1
for i in range(A ):
if i not in graph.keys():
continue
if len(graph[i] ) % 2 == 1:
odd_degree_nodes += 1
UpperCAmelCase__ =i
if odd_degree_nodes == 0:
return 1, odd_node
if odd_degree_nodes == 2:
return 2, odd_node
return 3, odd_node
def _UpperCAmelCase ( A , A ):
'''simple docstring'''
UpperCAmelCase__ =[[False for _ in range(max_node + 1 )] for _ in range(max_node + 1 )]
UpperCAmelCase__ , UpperCAmelCase__ =check_circuit_or_path(A , A )
if check == 3:
print("graph is not Eulerian" )
print("no path" )
return
UpperCAmelCase__ =1
if check == 2:
UpperCAmelCase__ =odd_node
print("graph has a Euler path" )
if check == 1:
print("graph has a Euler cycle" )
UpperCAmelCase__ =dfs(A , A , A )
print(A )
def _UpperCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase__ ={1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
UpperCAmelCase__ ={1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
UpperCAmelCase__ ={1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
UpperCAmelCase__ ={1: [2, 3], 2: [1, 3], 3: [1, 2]}
UpperCAmelCase__ ={
1: [],
2: []
# all degree is zero
}
UpperCAmelCase__ =10
check_euler(A , A )
check_euler(A , A )
check_euler(A , A )
check_euler(A , A )
check_euler(A , A )
if __name__ == "__main__":
main()
| 510
| 1
|
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any]=5 ):
"""simple docstring"""
assert masked_input.count("""<mask>""" ) == 1
snake_case_ : Tuple = torch.tensor(tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ ) ).unsqueeze(0 ) # Batch size 1
snake_case_ : int = model(SCREAMING_SNAKE_CASE__ )[0] # The last hidden-state is the first element of the output tuple
snake_case_ : Optional[int] = (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
snake_case_ : str = logits[0, masked_index, :]
snake_case_ : Optional[int] = logits.softmax(dim=0 )
snake_case_ , snake_case_ : Tuple = prob.topk(k=SCREAMING_SNAKE_CASE__ , dim=0 )
snake_case_ : List[str] = """ """.join(
[tokenizer.convert_ids_to_tokens(indices[i].item() ) for i in range(len(SCREAMING_SNAKE_CASE__ ) )] )
snake_case_ : Optional[int] = tokenizer.mask_token
snake_case_ : Optional[int] = []
for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """ ) ):
snake_case_ : Optional[Any] = predicted_token_bpe.replace("""\u2581""" , """ """ )
if " {0}".format(SCREAMING_SNAKE_CASE__ ) in masked_input:
topk_filled_outputs.append(
(
masked_input.replace(""" {0}""".format(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ ),
values[index].item(),
predicted_token,
) )
else:
topk_filled_outputs.append(
(
masked_input.replace(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ),
values[index].item(),
predicted_token,
) )
return topk_filled_outputs
a_ = CamembertTokenizer.from_pretrained('''camembert-base''')
a_ = CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()
a_ = '''Le camembert est <mask> :)'''
print(fill_mask(masked_input, model, tokenizer, topk=3))
| 480
|
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(SCREAMING_SNAKE_CASE__ ) )
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list[list[int]] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
if index == len(SCREAMING_SNAKE_CASE__ ):
return True
# Recursive Step
for i in range(SCREAMING_SNAKE_CASE__ ):
if valid_coloring(graph[index] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# Color current vertex
snake_case_ : Dict = i
# Validate coloring
if util_color(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index + 1 ):
return True
# Backtrack
snake_case_ : List[Any] = -1
return False
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list[list[int]] , SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : int = [-1] * len(SCREAMING_SNAKE_CASE__ )
if util_color(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , 0 ):
return colored_vertices
return []
| 480
| 1
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_UpperCAmelCase = None
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = "▁"
_UpperCAmelCase = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_UpperCAmelCase = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
"tokenizer_file": {
"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
},
}
_UpperCAmelCase = {
"google/pegasus-xsum": 512,
}
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = VOCAB_FILES_NAMES
lowerCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ = PegasusTokenizer
lowerCamelCase_ = ['''input_ids''', '''attention_mask''']
def __init__( self , lowercase=None , lowercase=None , lowercase="<pad>" , lowercase="</s>" , lowercase="<unk>" , lowercase="<mask_2>" , lowercase="<mask_1>" , lowercase=None , lowercase=1_0_3 , **lowercase , ):
"""simple docstring"""
A_ : Tuple = offset
if additional_special_tokens is not None:
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
raise TypeError(
F'''additional_special_tokens should be of type {type(UpperCAmelCase__ )}, but is'''
F''' {type(UpperCAmelCase__ )}''' )
A_ : Optional[Any] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F'''<unk_{i}>''' for i in range(len(UpperCAmelCase__ ) , self.offset - 1 )
]
if len(set(UpperCAmelCase__ ) ) != len(UpperCAmelCase__ ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
A_ : Optional[int] = additional_special_tokens_extended
else:
A_ : Union[str, Any] = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F'''<unk_{i}>''' for i in range(2 , self.offset )]
super().__init__(
UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , mask_token_sent=UpperCAmelCase__ , offset=UpperCAmelCase__ , additional_special_tokens=UpperCAmelCase__ , **UpperCAmelCase__ , )
A_ : Union[str, Any] = vocab_file
A_ : Optional[int] = False if not self.vocab_file else True
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
A_ : Optional[int] = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
F''' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}''' )
return [1 if x in all_special_ids else 0 for x in seq]
def lowerCAmelCase_ ( self , lowercase , lowercase = None , lowercase = False ):
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(UpperCAmelCase__ )
elif token_ids_a is None:
return self._special_token_mask(UpperCAmelCase__ ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowerCAmelCase_ ( self , lowercase , lowercase=None ):
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowerCAmelCase_ ( self , lowercase , lowercase = None ):
"""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(UpperCAmelCase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
A_ : Optional[int] = os.path.join(
UpperCAmelCase__ , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCAmelCase__ ):
copyfile(self.vocab_file , UpperCAmelCase__ )
return (out_vocab_file,)
| 721
|
import numpy as np
_UpperCAmelCase = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class UpperCAmelCase :
'''simple docstring'''
def __init__( self ):
"""simple docstring"""
A_ : Any = np.array(lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
A_ , A_ : Optional[Any] = np.where(letter == self.SQUARE )
A_ : List[str] = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def lowerCAmelCase_ ( self , lowercase , lowercase ):
"""simple docstring"""
A_ : int = self.SQUARE[indexa - 1, indexa - 1]
return letter
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
A_ : int = message.lower()
A_ : Tuple = message.replace(' ' , '' )
A_ : int = message.replace('j' , 'i' )
A_ : Any = np.empty((2, len(lowercase )) )
for letter_index in range(len(lowercase ) ):
A_ : Optional[int] = self.letter_to_numbers(message[letter_index] )
A_ : Union[str, Any] = numbers[0]
A_ : Union[str, Any] = numbers[1]
A_ : Optional[int] = first_step.reshape(2 * len(lowercase ) )
A_ : int = ''
for numbers_index in range(len(lowercase ) ):
A_ : str = int(second_step[numbers_index * 2] )
A_ : str = int(second_step[(numbers_index * 2) + 1] )
A_ : Tuple = self.numbers_to_letter(lowercase , lowercase )
A_ : Tuple = encoded_message + letter
return encoded_message
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
A_ : Optional[int] = message.lower()
message.replace(' ' , '' )
A_ : Tuple = np.empty(2 * len(lowercase ) )
for letter_index in range(len(lowercase ) ):
A_ : Optional[Any] = self.letter_to_numbers(message[letter_index] )
A_ : Optional[int] = numbers[0]
A_ : Dict = numbers[1]
A_ : Optional[int] = first_step.reshape((2, len(lowercase )) )
A_ : List[str] = ''
for numbers_index in range(len(lowercase ) ):
A_ : List[Any] = int(second_step[0, numbers_index] )
A_ : Optional[int] = int(second_step[1, numbers_index] )
A_ : Tuple = self.numbers_to_letter(lowercase , lowercase )
A_ : str = decoded_message + letter
return decoded_message
| 70
| 0
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase: Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase: str = {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
"""umberto-commoncrawl-cased-v1""": (
"""https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
),
"""umberto-wikipedia-uncased-v1""": (
"""https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
),
}
class __lowerCAmelCase ( _UpperCamelCase ):
'''simple docstring'''
_A = "camembert"
def __init__( self: Union[str, Any], lowerCamelCase_: List[Any]=30522, lowerCamelCase_: List[str]=768, lowerCamelCase_: List[str]=12, lowerCamelCase_: Tuple=12, lowerCamelCase_: List[Any]=3072, lowerCamelCase_: Union[str, Any]="gelu", lowerCamelCase_: Dict=0.1, lowerCamelCase_: Union[str, Any]=0.1, lowerCamelCase_: Any=512, lowerCamelCase_: int=2, lowerCamelCase_: int=0.0_2, lowerCamelCase_: Optional[Any]=1E-12, lowerCamelCase_: Optional[int]=1, lowerCamelCase_: Union[str, Any]=0, lowerCamelCase_: Optional[int]=2, lowerCamelCase_: Dict="absolute", lowerCamelCase_: Optional[Any]=True, lowerCamelCase_: Union[str, Any]=None, **lowerCamelCase_: str, ):
super().__init__(pad_token_id=lowerCamelCase_, bos_token_id=lowerCamelCase_, eos_token_id=lowerCamelCase_, **lowerCamelCase_ )
lowercase__ : Optional[Any] = vocab_size
lowercase__ : Dict = hidden_size
lowercase__ : Tuple = num_hidden_layers
lowercase__ : List[Any] = num_attention_heads
lowercase__ : Tuple = hidden_act
lowercase__ : Dict = intermediate_size
lowercase__ : Optional[Any] = hidden_dropout_prob
lowercase__ : Any = attention_probs_dropout_prob
lowercase__ : List[str] = max_position_embeddings
lowercase__ : Optional[int] = type_vocab_size
lowercase__ : Tuple = initializer_range
lowercase__ : Dict = layer_norm_eps
lowercase__ : str = position_embedding_type
lowercase__ : Any = use_cache
lowercase__ : Tuple = classifier_dropout
class __lowerCAmelCase ( _UpperCamelCase ):
'''simple docstring'''
@property
def snake_case__( self: Tuple ):
if self.task == "multiple-choice":
lowercase__ : List[str] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowercase__ : Any = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 266
|
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase: List[Any] = {
"""configuration_efficientnet""": [
"""EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EfficientNetConfig""",
"""EfficientNetOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase: Dict = ["""EfficientNetImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase: Dict = [
"""EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EfficientNetForImageClassification""",
"""EfficientNetModel""",
"""EfficientNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_efficientnet import (
EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
EfficientNetConfig,
EfficientNetOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientnet import EfficientNetImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientnet import (
EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientNetForImageClassification,
EfficientNetModel,
EfficientNetPreTrainedModel,
)
else:
import sys
__UpperCamelCase: List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 266
| 1
|
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Any =logging.get_logger(__name__)
# TODO Update this
lowerCAmelCase : Optional[int] ={
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/config.json',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class _a ( snake_case_ ):
_UpperCamelCase: str = "esm"
def __init__( self , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_=768 , lowercase_=12 , lowercase_=12 , lowercase_=3072 , lowercase_=0.1 , lowercase_=0.1 , lowercase_=1026 , lowercase_=0.0_2 , lowercase_=1e-12 , lowercase_="absolute" , lowercase_=True , lowercase_=None , lowercase_=False , lowercase_=False , lowercase_=None , lowercase_=None , **lowercase_ , ) -> Optional[Any]:
super().__init__(pad_token_id=lowercase_ , mask_token_id=lowercase_ , **lowercase_ )
lowerCAmelCase : Any = vocab_size
lowerCAmelCase : Dict = hidden_size
lowerCAmelCase : Optional[int] = num_hidden_layers
lowerCAmelCase : int = num_attention_heads
lowerCAmelCase : Optional[Any] = intermediate_size
lowerCAmelCase : str = hidden_dropout_prob
lowerCAmelCase : List[str] = attention_probs_dropout_prob
lowerCAmelCase : List[Any] = max_position_embeddings
lowerCAmelCase : Optional[int] = initializer_range
lowerCAmelCase : List[str] = layer_norm_eps
lowerCAmelCase : Tuple = position_embedding_type
lowerCAmelCase : Tuple = use_cache
lowerCAmelCase : Optional[Any] = emb_layer_norm_before
lowerCAmelCase : List[str] = token_dropout
lowerCAmelCase : List[str] = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
lowerCAmelCase : Any = EsmFoldConfig()
elif isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase : Optional[Any] = EsmFoldConfig(**lowercase_ )
lowerCAmelCase : str = esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
lowerCAmelCase : List[Any] = get_default_vocab_list()
else:
lowerCAmelCase : int = vocab_list
else:
lowerCAmelCase : int = None
lowerCAmelCase : Dict = None
if self.esmfold_config is not None and getattr(self.esmfold_config , """use_esm_attn_map""" , lowercase_ ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase : str = super().to_dict()
if isinstance(self.esmfold_config , lowercase_ ):
lowerCAmelCase : Optional[int] = self.esmfold_config.to_dict()
return output
@dataclass
class _a :
_UpperCamelCase: List[str] = None
_UpperCamelCase: List[Any] = True
_UpperCamelCase: Optional[int] = False
_UpperCamelCase: Optional[Any] = False
_UpperCamelCase: List[Any] = False
_UpperCamelCase: Dict = 0
_UpperCamelCase: int = True
_UpperCamelCase: List[str] = False
_UpperCamelCase: List[Any] = 128
_UpperCamelCase: Optional[Any] = None
def _snake_case ( self ) -> Any:
if self.trunk is None:
lowerCAmelCase : Optional[int] = TrunkConfig()
elif isinstance(self.trunk , lowercase_ ):
lowerCAmelCase : Optional[Any] = TrunkConfig(**self.trunk )
def _snake_case ( self ) -> int:
lowerCAmelCase : int = asdict(self )
lowerCAmelCase : Optional[Any] = self.trunk.to_dict()
return output
@dataclass
class _a :
_UpperCamelCase: Union[str, Any] = 48
_UpperCamelCase: Optional[int] = 1024
_UpperCamelCase: Dict = 128
_UpperCamelCase: Tuple = 32
_UpperCamelCase: Union[str, Any] = 32
_UpperCamelCase: Dict = 32
_UpperCamelCase: List[Any] = 0
_UpperCamelCase: List[Any] = 0
_UpperCamelCase: Union[str, Any] = False
_UpperCamelCase: Tuple = 4
_UpperCamelCase: Union[str, Any] = 128
_UpperCamelCase: Optional[Any] = None
def _snake_case ( self ) -> int:
if self.structure_module is None:
lowerCAmelCase : Optional[int] = StructureModuleConfig()
elif isinstance(self.structure_module , lowercase_ ):
lowerCAmelCase : Tuple = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
f""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
lowerCAmelCase : Optional[Any] = self.sequence_state_dim // self.sequence_head_width
lowerCAmelCase : Dict = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def _snake_case ( self ) -> List[str]:
lowerCAmelCase : Dict = asdict(self )
lowerCAmelCase : Dict = self.structure_module.to_dict()
return output
@dataclass
class _a :
_UpperCamelCase: Tuple = 384
_UpperCamelCase: Union[str, Any] = 128
_UpperCamelCase: Any = 16
_UpperCamelCase: Optional[int] = 128
_UpperCamelCase: Any = 12
_UpperCamelCase: Any = 4
_UpperCamelCase: Any = 8
_UpperCamelCase: int = 0.1
_UpperCamelCase: Any = 8
_UpperCamelCase: str = 1
_UpperCamelCase: Union[str, Any] = 2
_UpperCamelCase: Dict = 7
_UpperCamelCase: Optional[int] = 10
_UpperCamelCase: Optional[int] = 1e-8
_UpperCamelCase: List[Any] = 1e5
def _snake_case ( self ) -> List[Any]:
return asdict(self )
def _UpperCAmelCase ( ):
'''simple docstring'''
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 716
|
from math import factorial
class _a :
def __init__( self , lowercase_ , lowercase_ ) -> Optional[Any]:
lowerCAmelCase : Union[str, Any] = real
if isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase : Tuple = [1] * rank
else:
lowerCAmelCase : Any = rank
def __repr__( self ) -> int:
return (
f"""{self.real}+"""
f"""{'+'.join(str(lowercase_ )+'E'+str(n+1 )for n,dual in enumerate(self.duals ) )}"""
)
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase : List[Any] = self.duals.copy()
while cur[-1] == 0:
cur.pop(-1 )
return Dual(self.real , lowercase_ )
def __add__( self , lowercase_ ) -> Tuple:
if not isinstance(lowercase_ , lowercase_ ):
return Dual(self.real + other , self.duals )
lowerCAmelCase : int = self.duals.copy()
lowerCAmelCase : Tuple = other.duals.copy()
if len(lowercase_ ) > len(lowercase_ ):
o_dual.extend([1] * (len(lowercase_ ) - len(lowercase_ )) )
elif len(lowercase_ ) < len(lowercase_ ):
s_dual.extend([1] * (len(lowercase_ ) - len(lowercase_ )) )
lowerCAmelCase : List[Any] = []
for i in range(len(lowercase_ ) ):
new_duals.append(s_dual[i] + o_dual[i] )
return Dual(self.real + other.real , lowercase_ )
_UpperCamelCase: List[Any] = __add__
def __sub__( self , lowercase_ ) -> Union[str, Any]:
return self + other * -1
def __mul__( self , lowercase_ ) -> Optional[int]:
if not isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase : Union[str, Any] = []
for i in self.duals:
new_duals.append(i * other )
return Dual(self.real * other , lowercase_ )
lowerCAmelCase : Union[str, Any] = [0] * (len(self.duals ) + len(other.duals ) + 1)
for i, item in enumerate(self.duals ):
for j, jtem in enumerate(other.duals ):
new_duals[i + j + 1] += item * jtem
for k in range(len(self.duals ) ):
new_duals[k] += self.duals[k] * other.real
for index in range(len(other.duals ) ):
new_duals[index] += other.duals[index] * self.real
return Dual(self.real * other.real , lowercase_ )
_UpperCamelCase: str = __mul__
def __truediv__( self , lowercase_ ) -> Optional[Any]:
if not isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase : List[str] = []
for i in self.duals:
new_duals.append(i / other )
return Dual(self.real / other , lowercase_ )
raise ValueError
def __floordiv__( self , lowercase_ ) -> int:
if not isinstance(lowercase_ , lowercase_ ):
lowerCAmelCase : List[Any] = []
for i in self.duals:
new_duals.append(i // other )
return Dual(self.real // other , lowercase_ )
raise ValueError
def __pow__( self , lowercase_ ) -> str:
if n < 0 or isinstance(lowercase_ , lowercase_ ):
raise ValueError("""power must be a positive integer""" )
if n == 0:
return 1
if n == 1:
return self
lowerCAmelCase : int = self
for _ in range(n - 1 ):
x *= self
return x
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
if not callable(SCREAMING_SNAKE_CASE__ ):
raise ValueError("""differentiate() requires a function as input for func""" )
if not isinstance(SCREAMING_SNAKE_CASE__ ,(float, int) ):
raise ValueError("""differentiate() requires a float as input for position""" )
if not isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
raise ValueError("""differentiate() requires an int as input for order""" )
lowerCAmelCase : List[Any] = Dual(SCREAMING_SNAKE_CASE__ ,1 )
lowerCAmelCase : Optional[Any] = func(SCREAMING_SNAKE_CASE__ )
if order == 0:
return result.real
return result.duals[order - 1] * factorial(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
return y**2 * y**4
print(differentiate(f, 9, 2))
| 693
| 0
|
'''simple docstring'''
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 _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : int=2 , lowerCAmelCase__ : str=3 , lowerCAmelCase__ : List[Any]=4 , lowerCAmelCase__ : Optional[Any]=2 , lowerCAmelCase__ : Dict=7 , lowerCAmelCase__ : Tuple=True , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : List[Any]=True , lowerCAmelCase__ : Tuple=True , lowerCAmelCase__ : Optional[Any]=9_9 , lowerCAmelCase__ : List[str]=3_6 , lowerCAmelCase__ : List[str]=3 , lowerCAmelCase__ : Any=4 , lowerCAmelCase__ : Optional[Any]=3_7 , lowerCAmelCase__ : int="gelu" , lowerCAmelCase__ : Optional[Any]=0.1 , lowerCAmelCase__ : Union[str, Any]=0.1 , lowerCAmelCase__ : List[str]=5_1_2 , lowerCAmelCase__ : List[str]=1_6 , lowerCAmelCase__ : Optional[int]=2 , lowerCAmelCase__ : Optional[Any]=0.02 , lowerCAmelCase__ : Optional[Any]=6 , lowerCAmelCase__ : Union[str, Any]=6 , lowerCAmelCase__ : Optional[Any]=3 , lowerCAmelCase__ : Optional[Any]=4 , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : Optional[int]=1_0_0_0 , ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = parent
__SCREAMING_SNAKE_CASE : Union[str, Any] = batch_size
__SCREAMING_SNAKE_CASE : List[str] = num_channels
__SCREAMING_SNAKE_CASE : List[str] = image_size
__SCREAMING_SNAKE_CASE : int = patch_size
__SCREAMING_SNAKE_CASE : Dict = text_seq_length
__SCREAMING_SNAKE_CASE : Union[str, Any] = is_training
__SCREAMING_SNAKE_CASE : Any = use_input_mask
__SCREAMING_SNAKE_CASE : Union[str, Any] = use_token_type_ids
__SCREAMING_SNAKE_CASE : str = use_labels
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : Tuple = hidden_size
__SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
__SCREAMING_SNAKE_CASE : List[str] = num_attention_heads
__SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_act
__SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Union[str, Any] = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[str] = type_vocab_size
__SCREAMING_SNAKE_CASE : List[str] = type_sequence_label_size
__SCREAMING_SNAKE_CASE : List[str] = initializer_range
__SCREAMING_SNAKE_CASE : Union[str, Any] = coordinate_size
__SCREAMING_SNAKE_CASE : Optional[int] = shape_size
__SCREAMING_SNAKE_CASE : str = num_labels
__SCREAMING_SNAKE_CASE : Any = num_choices
__SCREAMING_SNAKE_CASE : Union[str, Any] = scope
__SCREAMING_SNAKE_CASE : Optional[Any] = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
__SCREAMING_SNAKE_CASE : Union[str, Any] = text_seq_length
__SCREAMING_SNAKE_CASE : List[Any] = (image_size // patch_size) ** 2 + 1
__SCREAMING_SNAKE_CASE : Dict = self.text_seq_length + self.image_seq_length
def UpperCamelCase__ ( self : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : Union[str, Any] = 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]:
__SCREAMING_SNAKE_CASE : List[Any] = bbox[i, j, 3]
__SCREAMING_SNAKE_CASE : str = bbox[i, j, 1]
__SCREAMING_SNAKE_CASE : str = t
if bbox[i, j, 2] < bbox[i, j, 0]:
__SCREAMING_SNAKE_CASE : int = bbox[i, j, 2]
__SCREAMING_SNAKE_CASE : Dict = bbox[i, j, 0]
__SCREAMING_SNAKE_CASE : List[Any] = t
__SCREAMING_SNAKE_CASE : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_input_mask:
__SCREAMING_SNAKE_CASE : Any = random_attention_mask([self.batch_size, self.text_seq_length] )
__SCREAMING_SNAKE_CASE : Tuple = None
if self.use_token_type_ids:
__SCREAMING_SNAKE_CASE : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
__SCREAMING_SNAKE_CASE : Any = None
__SCREAMING_SNAKE_CASE : Tuple = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
__SCREAMING_SNAKE_CASE : Optional[int] = 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 UpperCamelCase__ ( self : Optional[int] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Any , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = LayoutLMvaModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
# text + image
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(UpperCAmelCase_ , pixel_values=UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : Any = model(
UpperCAmelCase_ , bbox=UpperCAmelCase_ , pixel_values=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : List[Any] = model(UpperCAmelCase_ , bbox=UpperCAmelCase_ , pixel_values=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : List[str] = model(UpperCAmelCase_ , bbox=UpperCAmelCase_ , pixel_values=UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
__SCREAMING_SNAKE_CASE : str = model(UpperCAmelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(pixel_values=UpperCAmelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def UpperCamelCase__ ( self : Tuple , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Tuple , lowerCAmelCase__ : str , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = self.num_labels
__SCREAMING_SNAKE_CASE : Optional[int] = LayoutLMvaForSequenceClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__SCREAMING_SNAKE_CASE : int = model(
UpperCAmelCase_ , bbox=UpperCAmelCase_ , pixel_values=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCamelCase__ ( self : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any , lowerCAmelCase__ : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = self.num_labels
__SCREAMING_SNAKE_CASE : Tuple = LayoutLMvaForTokenClassification(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__SCREAMING_SNAKE_CASE : Tuple = model(
UpperCAmelCase_ , bbox=UpperCAmelCase_ , pixel_values=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def UpperCamelCase__ ( self : List[Any] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : int , lowerCAmelCase__ : List[str] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = LayoutLMvaForQuestionAnswering(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
__SCREAMING_SNAKE_CASE : Optional[Any] = model(
UpperCAmelCase_ , bbox=UpperCAmelCase_ , pixel_values=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , start_positions=UpperCAmelCase_ , end_positions=UpperCAmelCase_ , )
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 UpperCamelCase__ ( self : str ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.prepare_config_and_inputs()
(
(
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) , (
__SCREAMING_SNAKE_CASE
) ,
) : List[str] = config_and_inputs
__SCREAMING_SNAKE_CASE : Tuple = {
"""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 _UpperCamelCase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : List[str] = False
_A : Optional[Any] = False
_A : Optional[Any] = False
_A : Optional[Any] = (
(
LayoutLMvaModel,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaForQuestionAnswering,
)
if is_torch_available()
else ()
)
_A : Optional[Any] = (
{"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
if is_torch_available()
else {}
)
def UpperCamelCase__ ( self : str , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : str ):
"""simple docstring"""
return True
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = LayoutLMvaModelTester(self )
__SCREAMING_SNAKE_CASE : Optional[Any] = ConfigTester(self , config_class=UpperCAmelCase_ , hidden_size=3_7 )
def UpperCamelCase__ ( self : Any , lowerCAmelCase__ : Dict , lowerCAmelCase__ : str , lowerCAmelCase__ : List[str]=False ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = copy.deepcopy(UpperCAmelCase_ )
if model_class in get_values(UpperCAmelCase_ ):
__SCREAMING_SNAKE_CASE : str = {
k: v.unsqueeze(1 ).expand(-1 , self.model_tester.num_choices , -1 ).contiguous()
if isinstance(UpperCAmelCase_ , torch.Tensor ) and v.ndim > 1
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(UpperCAmelCase_ ):
__SCREAMING_SNAKE_CASE : List[str] = torch.ones(self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase_ )
elif model_class in get_values(UpperCAmelCase_ ):
__SCREAMING_SNAKE_CASE : int = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase_ )
elif model_class in [
*get_values(UpperCAmelCase_ ),
]:
__SCREAMING_SNAKE_CASE : Dict = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=UpperCAmelCase_ )
elif model_class in [
*get_values(UpperCAmelCase_ ),
]:
__SCREAMING_SNAKE_CASE : str = torch.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=torch.long , device=UpperCAmelCase_ , )
return inputs_dict
def UpperCamelCase__ ( self : Optional[int] ):
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self : int ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__SCREAMING_SNAKE_CASE : Dict = type
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def UpperCamelCase__ ( self : Any ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_ )
def UpperCamelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_ )
def UpperCamelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_ )
@slow
def UpperCamelCase__ ( self : Dict ):
"""simple docstring"""
for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : List[str] = LayoutLMvaModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def lowerCAmelCase_ ( ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def UpperCamelCase__ ( self : List[str] ):
"""simple docstring"""
return LayoutLMvaImageProcessor(apply_ocr=UpperCAmelCase_ ) if is_vision_available() else None
@slow
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base""" ).to(UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : List[str] = self.default_image_processor
__SCREAMING_SNAKE_CASE : str = prepare_img()
__SCREAMING_SNAKE_CASE : int = image_processor(images=UpperCAmelCase_ , return_tensors="""pt""" ).pixel_values.to(UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : Optional[int] = torch.tensor([[1, 2]] )
__SCREAMING_SNAKE_CASE : Any = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]] ).unsqueeze(0 )
# forward pass
__SCREAMING_SNAKE_CASE : Optional[int] = model(
input_ids=input_ids.to(UpperCAmelCase_ ) , bbox=bbox.to(UpperCAmelCase_ ) , pixel_values=pixel_values.to(UpperCAmelCase_ ) , )
# verify the logits
__SCREAMING_SNAKE_CASE : List[str] = torch.Size((1, 1_9_9, 7_6_8) )
self.assertEqual(outputs.last_hidden_state.shape , UpperCAmelCase_ )
__SCREAMING_SNAKE_CASE : int = torch.tensor(
[[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase_ , atol=1E-4 ) )
| 578
|
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _lowercase ( UpperCamelCase_ ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'_float_tensor',
'decoder.output_projection.weight',
]
for k in ignore_keys:
state_dict.pop(UpperCamelCase_ , UpperCamelCase_ )
def _lowercase ( UpperCamelCase_ ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = emb.weight.shape
SCREAMING_SNAKE_CASE__ = nn.Linear(UpperCamelCase_ , UpperCamelCase_ , bias=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = emb.weight.data
return lin_layer
def _lowercase ( UpperCamelCase_ , UpperCamelCase_="facebook/mbart-large-en-ro" , UpperCamelCase_=False , UpperCamelCase_=False ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = torch.load(UpperCamelCase_ , map_location='cpu' )['model']
remove_ignore_keys_(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = state_dict['encoder.embed_tokens.weight'].shape[0]
SCREAMING_SNAKE_CASE__ = MBartConfig.from_pretrained(UpperCamelCase_ , vocab_size=UpperCamelCase_ )
if mbart_aa and finetuned:
SCREAMING_SNAKE_CASE__ = 'relu'
SCREAMING_SNAKE_CASE__ = state_dict['decoder.embed_tokens.weight']
SCREAMING_SNAKE_CASE__ = MBartForConditionalGeneration(UpperCamelCase_ )
model.model.load_state_dict(UpperCamelCase_ )
if finetuned:
SCREAMING_SNAKE_CASE__ = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
)
parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--hf_config""",
default="""facebook/mbart-large-cc25""",
type=str,
help="""Which huggingface architecture to use: mbart-large""",
)
parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
__snake_case = parser.parse_args()
__snake_case = convert_fairseq_mbart_checkpoint_from_disk(
args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
)
model.save_pretrained(args.pytorch_dump_folder_path)
| 472
| 0
|
'''simple docstring'''
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _lowerCAmelCase ( lowerCamelCase_ : Union[dict, list, tuple, torch.Tensor] ):
__lowercase = []
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
for v in tree.values():
shapes.extend(_fetch_dims(lowerCamelCase_ ) )
elif isinstance(lowerCamelCase_ , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(lowerCamelCase_ ) )
elif isinstance(lowerCamelCase_ , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def _lowerCAmelCase ( lowerCamelCase_ : int , lowerCamelCase_ : Tuple[int, ...] ):
__lowercase = []
for d in reversed(lowerCamelCase_ ):
idx.append(flat_idx % d )
__lowercase = flat_idx // d
return tuple(reversed(lowerCamelCase_ ) )
@torch.jit.ignore
def _lowerCAmelCase ( lowerCamelCase_ : Sequence[int] , lowerCamelCase_ : Sequence[int] , lowerCamelCase_ : Sequence[int] , lowerCamelCase_ : Optional[Sequence[bool]] = None , lowerCamelCase_ : Optional[Sequence[bool]] = None , ):
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(lowerCamelCase_ : List[bool] ) -> None:
__lowercase = True
for i in range(len(lowerCamelCase_ ) ):
__lowercase = -1 * (i + 1)
l[reversed_idx] &= tally
__lowercase = l[reversed_idx]
if start_edges is None:
__lowercase = [s == 0 for s in start]
reduce_edge_list(lowerCamelCase_ )
if end_edges is None:
__lowercase = [e == (d - 1) for e, d in zip(lowerCamelCase_ , lowerCamelCase_ )]
reduce_edge_list(lowerCamelCase_ )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(lowerCamelCase_ ) == 0:
return [()]
elif len(lowerCamelCase_ ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
__lowercase = []
__lowercase = []
# Dimensions common to start and end can be selected directly
for s, e in zip(lowerCamelCase_ , lowerCamelCase_ ):
if s == e:
path_list.append(slice(lowerCamelCase_ , s + 1 ) )
else:
break
__lowercase = tuple(lowerCamelCase_ )
__lowercase = len(lowerCamelCase_ )
# start == end, and we're done
if divergence_idx == len(lowerCamelCase_ ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__lowercase = start[divergence_idx]
return tuple(
path + (slice(lowerCamelCase_ , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
__lowercase = end[divergence_idx]
return tuple(
path + (slice(lowerCamelCase_ , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
__lowercase = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def _lowerCAmelCase ( lowerCamelCase_ : torch.Tensor , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : int ):
__lowercase = t.shape[:no_batch_dims]
__lowercase = list(_flat_idx_to_idx(lowerCamelCase_ , lowerCamelCase_ ) )
# _get_minimal_slice_set is inclusive
__lowercase = list(_flat_idx_to_idx(flat_end - 1 , lowerCamelCase_ ) )
# Get an ordered list of slices to perform
__lowercase = _get_minimal_slice_set(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , )
__lowercase = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def _lowerCAmelCase ( lowerCamelCase_ : Callable , lowerCamelCase_ : Dict[str, Any] , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : bool = False , lowerCamelCase_ : Any = None , lowerCamelCase_ : bool = False , ):
if not (len(lowerCamelCase_ ) > 0):
raise ValueError('''Must provide at least one input''' )
__lowercase = [shape[:no_batch_dims] for shape in _fetch_dims(lowerCamelCase_ )]
__lowercase = tuple([max(lowerCamelCase_ ) for s in zip(*lowerCamelCase_ )] )
def _prep_inputs(lowerCamelCase_ : torch.Tensor ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
__lowercase = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
__lowercase = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
__lowercase = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
__lowercase = tensor_tree_map(_prep_inputs , lowerCamelCase_ )
__lowercase = None
if _out is not None:
__lowercase = tensor_tree_map(lambda lowerCamelCase_ : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
__lowercase = 1
for d in orig_batch_dims:
flat_batch_dim *= d
__lowercase = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(lowerCamelCase_ : torch.Tensor ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
__lowercase = 0
__lowercase = prepped_outputs
for _ in range(lowerCamelCase_ ):
# Chunk the input
if not low_mem:
__lowercase = _select_chunk
else:
__lowercase = partial(
_chunk_slice , flat_start=lowerCamelCase_ , flat_end=min(lowerCamelCase_ , i + chunk_size ) , no_batch_dims=len(lowerCamelCase_ ) , )
__lowercase = tensor_tree_map(lowerCamelCase_ , lowerCamelCase_ )
# Run the layer on the chunk
__lowercase = layer(**lowerCamelCase_ )
# Allocate space for the output
if out is None:
__lowercase = tensor_tree_map(lambda lowerCamelCase_ : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , lowerCamelCase_ )
# Put the chunk in its pre-allocated space
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
def assign(lowerCamelCase_ : dict , lowerCamelCase_ : dict ) -> None:
for k, v in da.items():
if isinstance(lowerCamelCase_ , lowerCamelCase_ ):
assign(lowerCamelCase_ , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
__lowercase = da[k]
assign(lowerCamelCase_ , lowerCamelCase_ )
elif isinstance(lowerCamelCase_ , lowerCamelCase_ ):
for xa, xa in zip(lowerCamelCase_ , lowerCamelCase_ ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
__lowercase = xa
elif isinstance(lowerCamelCase_ , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
__lowercase = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
__lowercase = tensor_tree_map(lambda lowerCamelCase_ : t.view(orig_batch_dims + t.shape[1:] ) , lowerCamelCase_ )
return out
class __lowercase :
'''simple docstring'''
def __init__(self ,_lowerCamelCase = 512 ,) -> List[str]:
'''simple docstring'''
__lowercase = max_chunk_size
__lowercase = None
__lowercase = None
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ) -> int:
'''simple docstring'''
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
__lowercase = [2**l for l in range(int(math.log(self.max_chunk_size ,2 ) ) + 1 )]
__lowercase = [c for c in candidates if c > min_chunk_size]
__lowercase = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(_lowerCamelCase ) -> bool:
try:
with torch.no_grad():
fn(*_lowerCamelCase ,chunk_size=_lowerCamelCase )
return True
except RuntimeError:
return False
__lowercase = 0
__lowercase = len(_lowerCamelCase ) - 1
while i > min_viable_chunk_size_index:
__lowercase = test_chunk_size(candidates[i] )
if not viable:
__lowercase = (min_viable_chunk_size_index + i) // 2
else:
__lowercase = i
__lowercase = (i + len(_lowerCamelCase ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ) -> bool:
'''simple docstring'''
__lowercase = True
for aa, aa in zip(_lowerCamelCase ,_lowerCamelCase ):
assert type(_lowerCamelCase ) == type(_lowerCamelCase )
if isinstance(_lowerCamelCase ,(list, tuple) ):
consistent &= self._compare_arg_caches(_lowerCamelCase ,_lowerCamelCase )
elif isinstance(_lowerCamelCase ,_lowerCamelCase ):
__lowercase = [v for _, v in sorted(aa.items() ,key=lambda _lowerCamelCase : x[0] )]
__lowercase = [v for _, v in sorted(aa.items() ,key=lambda _lowerCamelCase : x[0] )]
consistent &= self._compare_arg_caches(_lowerCamelCase ,_lowerCamelCase )
else:
consistent &= aa == aa
return consistent
def _UpperCAmelCase (self ,_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,) -> int:
'''simple docstring'''
__lowercase = True
__lowercase = tree_map(lambda _lowerCamelCase : a.shape if isinstance(_lowerCamelCase ,torch.Tensor ) else a ,_lowerCamelCase ,_lowerCamelCase )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_lowerCamelCase )
__lowercase = self._compare_arg_caches(self.cached_arg_data ,_lowerCamelCase )
else:
# Otherwise, we can reuse the precomputed value
__lowercase = False
if not consistent:
__lowercase = self._determine_favorable_chunk_size(
_lowerCamelCase ,_lowerCamelCase ,_lowerCamelCase ,)
__lowercase = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 56
|
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def _lowerCAmelCase ( lowerCamelCase_ : int ):
__lowercase = torch.load(lowerCamelCase_ , map_location='''cpu''' )
if "model" in sd.keys():
__lowercase = torch.load(lowerCamelCase_ , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__lowercase = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(lowerCamelCase_ )
__lowercase = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__lowercase = sd.pop(lowerCamelCase_ )
__lowercase = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__lowercase = sd[key]
# We split QKV in separate Q,K,V
__lowercase = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__lowercase = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__lowercase = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__lowercase = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__lowercase , __lowercase , __lowercase = torch.split(lowerCamelCase_ , depth // 3 , dim=0 )
__lowercase = q
__lowercase = k
__lowercase = v
del sd[key]
return sd
@torch.no_grad()
def _lowerCAmelCase ( lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Tuple , lowerCamelCase_ : Union[str, Any]=None ):
__lowercase = load_checkpoint(lowerCamelCase_ )
if config is not None:
__lowercase = OPTConfig.from_pretrained(lowerCamelCase_ )
else:
__lowercase = OPTConfig()
__lowercase = OPTModel(lowerCamelCase_ ).half().eval()
model.load_state_dict(lowerCamelCase_ )
# Check results
Path(lowerCamelCase_ ).mkdir(exist_ok=lowerCamelCase_ )
model.save_pretrained(lowerCamelCase_ )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--fairseq_path''',
type=str,
help=(
'''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
''' https://huggingface.co/models?other=opt_metasq'''
),
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 56
| 1
|
"""simple docstring"""
import math
import os
import sys
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = ""
try:
with open(lowerCAmelCase__ , "rb" ) as binary_file:
UpperCAmelCase_ = binary_file.read()
for dat in data:
UpperCAmelCase_ = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print("File not accessible" )
sys.exit()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
lexicon.pop(lowerCAmelCase__ )
UpperCAmelCase_ = last_match_id
if math.loga(lowerCAmelCase__ ).is_integer():
for curr_key in lexicon:
UpperCAmelCase_ = "0" + lexicon[curr_key]
UpperCAmelCase_ = bin(lowerCAmelCase__ )[2:]
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = {"0": "0", "1": "1"}
UpperCAmelCase_ = "", ""
UpperCAmelCase_ = len(lowerCAmelCase__ )
for i in range(len(lowerCAmelCase__ ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
UpperCAmelCase_ = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
index += 1
UpperCAmelCase_ = ""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
UpperCAmelCase_ = lexicon[curr_string]
result += last_match_id
return result
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = os.path.getsize(lowerCAmelCase__ )
UpperCAmelCase_ = bin(lowerCAmelCase__ )[2:]
UpperCAmelCase_ = len(lowerCAmelCase__ )
return "0" * (length_length - 1) + file_length_binary + compressed
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = 8
try:
with open(lowerCAmelCase__ , "wb" ) as opened_file:
UpperCAmelCase_ = [
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:
opened_file.write(int(lowerCAmelCase__ , 2 ).to_bytes(1 , byteorder="big" ) )
except OSError:
print("File not accessible" )
sys.exit()
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ):
UpperCAmelCase_ = read_file_binary(lowerCAmelCase__ )
UpperCAmelCase_ = compress_data(lowerCAmelCase__ )
UpperCAmelCase_ = add_file_length(lowerCAmelCase__ , lowerCAmelCase__ )
write_file_binary(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 82
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = ["PLBartTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ = [
"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
A_ = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 143
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ : str = {
"configuration_layoutlmv3": [
"LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP",
"LayoutLMv3Config",
"LayoutLMv3OnnxConfig",
],
"processing_layoutlmv3": ["LayoutLMv3Processor"],
"tokenization_layoutlmv3": ["LayoutLMv3Tokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = ["LayoutLMv3TokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = [
"LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"LayoutLMv3ForQuestionAnswering",
"LayoutLMv3ForSequenceClassification",
"LayoutLMv3ForTokenClassification",
"LayoutLMv3Model",
"LayoutLMv3PreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Tuple = [
"TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMv3ForQuestionAnswering",
"TFLayoutLMv3ForSequenceClassification",
"TFLayoutLMv3ForTokenClassification",
"TFLayoutLMv3Model",
"TFLayoutLMv3PreTrainedModel",
]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] = ["LayoutLMv3FeatureExtractor"]
SCREAMING_SNAKE_CASE__ : str = ["LayoutLMv3ImageProcessor"]
if TYPE_CHECKING:
from .configuration_layoutlmva import (
LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
LayoutLMvaConfig,
LayoutLMvaOnnxConfig,
)
from .processing_layoutlmva import LayoutLMvaProcessor
from .tokenization_layoutlmva import LayoutLMvaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_layoutlmva import (
LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
LayoutLMvaForQuestionAnswering,
LayoutLMvaForSequenceClassification,
LayoutLMvaForTokenClassification,
LayoutLMvaModel,
LayoutLMvaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_layoutlmva import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
TFLayoutLMvaPreTrainedModel,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
else:
import sys
SCREAMING_SNAKE_CASE__ : Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 700
|
"""simple docstring"""
from torch import nn
class A_ ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
super().__init__()
a : List[str] = class_size
a : Tuple = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
a : int = nn.Linear(__UpperCAmelCase , __UpperCAmelCase )
def lowercase_ ( self , __UpperCAmelCase ) -> Any:
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
a : int = self.mlp(__UpperCAmelCase )
return logits
| 509
| 0
|
def __A ( _A ):
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is a Perfect number or not...""")
SCREAMING_SNAKE_CASE : Dict = int(input("""Enter number: """).strip())
print(f'''{number} is {"" if perfect(number) else "not "}a Perfect Number.''')
| 197
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCAmelCase : Optional[int] = {'vocab_file': 'sentencepiece.model'}
__UpperCAmelCase : Dict = {
'vocab_file': {
'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
},
}
__UpperCAmelCase : str = {
'google/rembert': 2_56,
}
class __lowerCAmelCase (__UpperCamelCase ):
'''simple docstring'''
a__ = VOCAB_FILES_NAMES
a__ = PRETRAINED_VOCAB_FILES_MAP
a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , a , a=False , a=True , a=True , a="[CLS]" , a="[SEP]" , a="[UNK]" , a="[SEP]" , a="[PAD]" , a="[CLS]" , a="[MASK]" , **a , ):
"""simple docstring"""
super().__init__(
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 , **a , )
snake_case_ :Dict = do_lower_case
snake_case_ :Tuple = remove_space
snake_case_ :List[Any] = keep_accents
snake_case_ :Union[str, Any] = vocab_file
snake_case_ :Optional[Any] = spm.SentencePieceProcessor()
self.sp_model.Load(a )
@property
def _a ( self ):
"""simple docstring"""
return len(self.sp_model )
def _a ( self ):
"""simple docstring"""
snake_case_ :List[Any] = {self.convert_ids_to_tokens(a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
snake_case_ :Tuple = self.__dict__.copy()
snake_case_ :List[Any] = None
return state
def __setstate__( self , a ):
"""simple docstring"""
snake_case_ :List[Any] = d
snake_case_ :Dict = spm.SentencePieceProcessor()
self.sp_model.Load(self.vocab_file )
def _a ( self , a , a=False ):
"""simple docstring"""
snake_case_ :str = self.sp_model.EncodeAsPieces(a )
return pieces
def _a ( self , a ):
"""simple docstring"""
return self.sp_model.PieceToId(a )
def _a ( self , a ):
"""simple docstring"""
return self.sp_model.IdToPiece(a )
def _a ( self , a ):
"""simple docstring"""
snake_case_ :int = self.sp_model.decode_pieces(a )
return out_string
def _a ( self , a , a = None ):
"""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 cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _a ( self , a , a = None , a = False ):
"""simple docstring"""
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(a )) + [1] + ([0] * len(a )) + [1]
return [1] + ([0] * len(a )) + [1]
def _a ( self , a , a = None ):
"""simple docstring"""
snake_case_ :Any = [self.sep_token_id]
snake_case_ :Dict = [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 , a , a = None ):
"""simple docstring"""
if not os.path.isdir(a ):
logger.error("Vocabulary path ({}) should be a directory".format(a ) )
return
snake_case_ :str = 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,)
| 584
| 0
|
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__UpperCAmelCase : List[str] = TypeVar("T")
class _snake_case ( Generic[T] ):
def __init__( self ,UpperCamelCase ,UpperCamelCase ) -> None:
snake_case__ :Any | T = None
snake_case__ :int = len(UpperCamelCase )
snake_case__ :list[T] = [any_type for _ in range(self.N )] + arr
snake_case__ :Optional[Any] = fnc
self.build()
def lowerCAmelCase_ ( self ) -> None:
for p in range(self.N - 1 ,0 ,-1 ):
snake_case__ :Optional[Any] = self.fn(self.st[p * 2] ,self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> None:
p += self.N
snake_case__ :Optional[Any] = v
while p > 1:
snake_case__ :List[str] = p // 2
snake_case__ :List[Any] = self.fn(self.st[p * 2] ,self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> T | None: # noqa: E741
snake_case__ , snake_case__ :List[str] = l + self.N, r + self.N
snake_case__ :T | None = None
while l <= r:
if l % 2 == 1:
snake_case__ :List[str] = self.st[l] if res is None else self.fn(UpperCamelCase ,self.st[l] )
if r % 2 == 0:
snake_case__ :Union[str, Any] = self.st[r] if res is None else self.fn(UpperCamelCase ,self.st[r] )
snake_case__ , snake_case__ :List[str] = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__UpperCAmelCase : Optional[Any] = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
__UpperCAmelCase : Dict = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
__UpperCAmelCase : Optional[int] = SegmentTree(test_array, min)
__UpperCAmelCase : Dict = SegmentTree(test_array, max)
__UpperCAmelCase : Dict = SegmentTree(test_array, lambda a, b: a + b)
def lowercase_ ( ) -> None:
'''simple docstring'''
for i in range(len(__snake_case ) ):
for j in range(__snake_case , len(__snake_case ) ):
snake_case__ :Optional[Any] = reduce(__snake_case , test_array[i : j + 1] )
snake_case__ :str = reduce(__snake_case , test_array[i : j + 1] )
snake_case__ :List[str] = reduce(lambda __snake_case , __snake_case : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__snake_case , __snake_case )
assert max_range == max_segment_tree.query(__snake_case , __snake_case )
assert sum_range == sum_segment_tree.query(__snake_case , __snake_case )
test_all_segments()
for index, value in test_updates.items():
__UpperCAmelCase : Optional[Any] = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 57
|
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case__ :List[str] = 4
snake_case__ :Optional[int] = (1 << p) - 1
for _ in range(p - 2 ):
snake_case__ :List[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCamelCase = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""InstructBlipQFormerConfig""",
"""InstructBlipVisionConfig""",
],
"""processing_instructblip""": ["""InstructBlipProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"""INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InstructBlipQFormerModel""",
"""InstructBlipPreTrainedModel""",
"""InstructBlipForConditionalGeneration""",
"""InstructBlipVisionModel""",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 204
|
def UpperCamelCase ( __lowerCamelCase : int = 1 , __lowerCamelCase : int = 1000 ):
snake_case : int = 1
snake_case : int = 0
for divide_by_number in range(__lowerCamelCase , digit + 1 ):
snake_case : list[int] = []
snake_case : Optional[int] = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(__lowerCamelCase ):
snake_case : List[Any] = len(__lowerCamelCase )
snake_case : List[str] = divide_by_number
else:
has_been_divided.append(__lowerCamelCase )
snake_case : Any = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 204
| 1
|
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a = logging.get_logger(__name__)
a = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
a = {
"vocab_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
},
"merges_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
},
"tokenizer_file": {
"gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
"gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
"gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
"gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
"distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
},
}
a = {
"gpt2": 1024,
"gpt2-medium": 1024,
"gpt2-large": 1024,
"gpt2-xl": 1024,
"distilgpt2": 1024,
}
class _A ( __lowercase ):
__a = VOCAB_FILES_NAMES
__a = PRETRAINED_VOCAB_FILES_MAP
__a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = ["""input_ids""", """attention_mask"""]
__a = GPTaTokenizer
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="<|endoftext|>" , _SCREAMING_SNAKE_CASE="<|endoftext|>" , _SCREAMING_SNAKE_CASE="<|endoftext|>" , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ):
super().__init__(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = kwargs.pop("""add_bos_token""" , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , _SCREAMING_SNAKE_CASE ) != add_prefix_space:
_UpperCAmelCase = getattr(_SCREAMING_SNAKE_CASE , pre_tok_state.pop("""type""" ) )
_UpperCAmelCase = add_prefix_space
_UpperCAmelCase = pre_tok_class(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = add_prefix_space
def UpperCAmelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = kwargs.get("""is_split_into_words""" , _SCREAMING_SNAKE_CASE )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = kwargs.get("""is_split_into_words""" , _SCREAMING_SNAKE_CASE )
assert self.add_prefix_space or not is_split_into_words, (
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs."
)
return super()._encode_plus(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ):
_UpperCAmelCase = self._tokenizer.model.save(_SCREAMING_SNAKE_CASE , name=_SCREAMING_SNAKE_CASE )
return tuple(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE ) + [self.eos_token_id] )
if len(_SCREAMING_SNAKE_CASE ) > self.model_max_length:
_UpperCAmelCase = input_ids[-self.model_max_length :]
return input_ids
| 703
|
import math
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> float:
if (
not isinstance(snake_case , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 and 1.""" )
return apparent_power * power_factor
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> float:
if (
not isinstance(snake_case , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("""power_factor must be a valid float value between -1 and 1.""" )
return apparent_power * math.sqrt(1 - power_factor**2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 175
| 0
|
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class __lowerCAmelCase ( unittest.TestCase ):
def snake_case_ (self ):
_UpperCAmelCase : List[Any] = 0
def snake_case_ (self ):
_UpperCAmelCase : Any = AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Any = Path(lowerCAmelCase__ ) / """preprocessor_config.json"""
_UpperCAmelCase : Optional[Any] = Path(lowerCAmelCase__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(lowerCAmelCase__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(lowerCAmelCase__ , """w""" ) )
_UpperCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Optional[int] = Path(lowerCAmelCase__ ) / """preprocessor_config.json"""
_UpperCAmelCase : str = Path(lowerCAmelCase__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(lowerCAmelCase__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(lowerCAmelCase__ , """w""" ) )
_UpperCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : Optional[Any] = CLIPConfig()
# Create a dummy config file with image_proceesor_type
_UpperCAmelCase : str = Path(lowerCAmelCase__ ) / """preprocessor_config.json"""
_UpperCAmelCase : Union[str, Any] = Path(lowerCAmelCase__ ) / """config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(lowerCAmelCase__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(lowerCAmelCase__ , """w""" ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
_UpperCAmelCase : int = AutoImageProcessor.from_pretrained(lowerCAmelCase__ ).to_dict()
config_dict.pop("""image_processor_type""" )
_UpperCAmelCase : Any = CLIPImageProcessor(**lowerCAmelCase__ )
# save in new folder
model_config.save_pretrained(lowerCAmelCase__ )
config.save_pretrained(lowerCAmelCase__ )
_UpperCAmelCase : Dict = AutoImageProcessor.from_pretrained(lowerCAmelCase__ )
# make sure private variable is not incorrectly saved
_UpperCAmelCase : str = json.loads(config.to_json_string() )
self.assertTrue("""_processor_class""" not in dict_as_saved )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : List[str] = Path(lowerCAmelCase__ ) / """preprocessor_config.json"""
json.dump(
{"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""} , open(lowerCAmelCase__ , """w""" ) , )
_UpperCAmelCase : str = AutoImageProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
def snake_case_ (self ):
with self.assertRaisesRegex(
lowerCAmelCase__ , """clip-base is not a local folder and is not a valid model identifier""" ):
_UpperCAmelCase : str = AutoImageProcessor.from_pretrained("""clip-base""" )
def snake_case_ (self ):
with self.assertRaisesRegex(
lowerCAmelCase__ , r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
_UpperCAmelCase : List[Any] = AutoImageProcessor.from_pretrained(lowerCAmelCase__ , revision="""aaaaaa""" )
def snake_case_ (self ):
with self.assertRaisesRegex(
lowerCAmelCase__ , """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.""" , ):
_UpperCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model""" )
def snake_case_ (self ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(lowerCAmelCase__ ):
_UpperCAmelCase : Any = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCAmelCase__ ):
_UpperCAmelCase : List[str] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(lowerCAmelCase__ )
_UpperCAmelCase : List[str] = AutoImageProcessor.from_pretrained(lowerCAmelCase__ , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , """NewImageProcessor""" )
def snake_case_ (self ):
try:
AutoConfig.register("""custom""" , lowerCAmelCase__ )
AutoImageProcessor.register(lowerCAmelCase__ , lowerCAmelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCAmelCase__ ):
AutoImageProcessor.register(lowerCAmelCase__ , lowerCAmelCase__ )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase : int = Path(lowerCAmelCase__ ) / """preprocessor_config.json"""
_UpperCAmelCase : List[Any] = Path(lowerCAmelCase__ ) / """config.json"""
json.dump(
{"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""} , open(lowerCAmelCase__ , """w""" ) , )
json.dump({"""model_type""": """clip"""} , open(lowerCAmelCase__ , """w""" ) )
_UpperCAmelCase : Tuple = CustomImageProcessor.from_pretrained(lowerCAmelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(lowerCAmelCase__ )
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def snake_case_ (self ):
class __lowerCAmelCase ( __a ):
snake_case : List[str] = True
try:
AutoConfig.register("""custom""" , lowerCAmelCase__ )
AutoImageProcessor.register(lowerCAmelCase__ , lowerCAmelCase__ )
# If remote code is not set, the default is to use local
_UpperCAmelCase : int = AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor""" )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
_UpperCAmelCase : Union[str, Any] = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/test_dynamic_image_processor""" , trust_remote_code=lowerCAmelCase__ )
self.assertEqual(image_processor.__class__.__name__ , """NewImageProcessor""" )
self.assertTrue(not hasattr(lowerCAmelCase__ , """is_local""" ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 414
|
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class __lowerCAmelCase ( unittest.TestCase ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__=1_3 , lowerCAmelCase__=3_0 , lowerCAmelCase__=2 , lowerCAmelCase__=3 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=3_2 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=3_7 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=1_0 , lowerCAmelCase__=0.0_2 , ):
_UpperCAmelCase : Union[str, Any] = parent
_UpperCAmelCase : Optional[int] = batch_size
_UpperCAmelCase : Dict = image_size
_UpperCAmelCase : str = patch_size
_UpperCAmelCase : List[Any] = num_channels
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : List[str] = intermediate_size
_UpperCAmelCase : List[str] = hidden_act
_UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
_UpperCAmelCase : List[str] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[Any] = type_sequence_label_size
_UpperCAmelCase : Optional[Any] = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase : int = (image_size // patch_size) ** 2
_UpperCAmelCase : Tuple = num_patches + 1
def snake_case_ (self ):
_UpperCAmelCase : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : int = ViTConfig(
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=lowerCAmelCase__ , initializer_range=self.initializer_range , )
return config, pixel_values
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : Any = FlaxViTModel(config=lowerCAmelCase__ )
_UpperCAmelCase : Any = model(lowerCAmelCase__ )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase : Union[str, Any] = (self.image_size, self.image_size)
_UpperCAmelCase : Union[str, Any] = (self.patch_size, self.patch_size)
_UpperCAmelCase : Tuple = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def snake_case_ (self , lowerCAmelCase__ , lowerCAmelCase__ ):
_UpperCAmelCase : List[Any] = self.type_sequence_label_size
_UpperCAmelCase : Union[str, Any] = FlaxViTForImageClassification(config=lowerCAmelCase__ )
_UpperCAmelCase : Tuple = model(lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_UpperCAmelCase : Union[str, Any] = 1
_UpperCAmelCase : Dict = FlaxViTForImageClassification(lowerCAmelCase__ )
_UpperCAmelCase : Optional[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_UpperCAmelCase : Optional[int] = model(lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : Tuple = config_and_inputs
_UpperCAmelCase : Dict = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class __lowerCAmelCase ( __a , unittest.TestCase ):
snake_case : Any = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def snake_case_ (self ):
_UpperCAmelCase : Optional[int] = FlaxViTModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ , hidden_size=3_7 )
def snake_case_ (self ):
self.config_tester.run_common_tests()
def snake_case_ (self ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : str = model_class(lowerCAmelCase__ )
_UpperCAmelCase : str = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Dict = [*signature.parameters.keys()]
_UpperCAmelCase : Tuple = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def snake_case_ (self ):
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_UpperCAmelCase : List[str] = self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ )
_UpperCAmelCase : List[str] = model_class(lowerCAmelCase__ )
@jax.jit
def model_jitted(lowerCAmelCase__ , **lowerCAmelCase__ ):
return model(pixel_values=lowerCAmelCase__ , **lowerCAmelCase__ )
with self.subTest("""JIT Enabled""" ):
_UpperCAmelCase : Any = model_jitted(**lowerCAmelCase__ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_UpperCAmelCase : str = model_jitted(**lowerCAmelCase__ ).to_tuple()
self.assertEqual(len(lowerCAmelCase__ ) , len(lowerCAmelCase__ ) )
for jitted_output, output in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def snake_case_ (self ):
for model_class_name in self.all_model_classes:
_UpperCAmelCase : List[str] = model_class_name.from_pretrained("""google/vit-base-patch16-224""" )
_UpperCAmelCase : List[Any] = model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
self.assertIsNotNone(lowerCAmelCase__ )
| 414
| 1
|
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = "char"
a_ = "bpe"
a_ = "wp"
__lowerCamelCase : Any = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase_ ):
"""simple docstring"""
a_ = ["image_processor", "char_tokenizer"]
a_ = "ViTImageProcessor"
a_ = "MgpstrTokenizer"
def __init__( self : Union[str, Any] , __A : Any=None , __A : Dict=None , **__A : Any ):
snake_case__ : List[Any] = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __A , )
snake_case__ : List[str] = kwargs.pop("feature_extractor" )
snake_case__ : List[str] = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
snake_case__ : List[Any] = tokenizer
snake_case__ : List[str] = AutoTokenizer.from_pretrained("gpt2" )
snake_case__ : Dict = AutoTokenizer.from_pretrained("bert-base-uncased" )
super().__init__(__A , __A )
def __call__( self : List[str] , __A : str=None , __A : Optional[Any]=None , __A : Dict=None , **__A : Optional[Any] ):
if images is None and text is None:
raise ValueError("You need to specify either an `images` or `text` input to process." )
if images is not None:
snake_case__ : Union[str, Any] = self.image_processor(__A , return_tensors=__A , **__A )
if text is not None:
snake_case__ : List[str] = self.char_tokenizer(__A , return_tensors=__A , **__A )
if text is None:
return inputs
elif images is None:
return encodings
else:
snake_case__ : Any = encodings["input_ids"]
return inputs
def _lowercase ( self : Optional[Any] , __A : Optional[int] ):
snake_case__, snake_case__, snake_case__ : List[str] = sequences
snake_case__ : Optional[Any] = char_preds.size(0 )
snake_case__, snake_case__ : List[Any] = self._decode_helper(__A , "char" )
snake_case__, snake_case__ : Tuple = self._decode_helper(__A , "bpe" )
snake_case__, snake_case__ : int = self._decode_helper(__A , "wp" )
snake_case__ : Dict = []
snake_case__ : Dict = []
for i in range(__A ):
snake_case__ : List[str] = [char_scores[i], bpe_scores[i], wp_scores[i]]
snake_case__ : str = [char_strs[i], bpe_strs[i], wp_strs[i]]
snake_case__ : str = scores.index(max(__A ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
snake_case__ : Any = {}
snake_case__ : Union[str, Any] = final_strs
snake_case__ : Tuple = final_scores
snake_case__ : Optional[Any] = char_strs
snake_case__ : Tuple = bpe_strs
snake_case__ : Dict = wp_strs
return out
def _lowercase ( self : str , __A : List[Any] , __A : Union[str, Any] ):
if format == DecodeType.CHARACTER:
snake_case__ : int = self.char_decode
snake_case__ : List[str] = 1
snake_case__ : Optional[int] = "[s]"
elif format == DecodeType.BPE:
snake_case__ : Optional[int] = self.bpe_decode
snake_case__ : List[str] = 2
snake_case__ : Dict = "#"
elif format == DecodeType.WORDPIECE:
snake_case__ : Union[str, Any] = self.wp_decode
snake_case__ : Tuple = 1_0_2
snake_case__ : Optional[Any] = "[SEP]"
else:
raise ValueError(f'''Format {format} is not supported.''' )
snake_case__, snake_case__ : Union[str, Any] = [], []
snake_case__ : Tuple = pred_logits.size(0 )
snake_case__ : int = pred_logits.size(1 )
snake_case__, snake_case__ : Any = pred_logits.topk(1 , dim=-1 , largest=__A , sorted=__A )
snake_case__ : Tuple = preds_index.view(-1 , __A )[:, 1:]
snake_case__ : List[Any] = decoder(__A )
snake_case__, snake_case__ : Optional[Any] = torch.nn.functional.softmax(__A , dim=2 ).max(dim=2 )
snake_case__ : str = preds_max_prob[:, 1:]
for index in range(__A ):
snake_case__ : int = preds_str[index].find(__A )
snake_case__ : Union[str, Any] = preds_str[index][:pred_eos]
snake_case__ : Any = preds_index[index].cpu().tolist()
snake_case__ : Union[str, Any] = pred_index.index(__A ) if eos_token in pred_index else -1
snake_case__ : Union[str, Any] = preds_max_prob[index][: pred_eos_index + 1]
snake_case__ : List[str] = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__A )
conf_scores.append(__A )
return dec_strs, conf_scores
def _lowercase ( self : Union[str, Any] , __A : Tuple ):
snake_case__ : Tuple = [seq.replace(" " , "" ) for seq in self.char_tokenizer.batch_decode(__A )]
return decode_strs
def _lowercase ( self : List[Any] , __A : Any ):
return self.bpe_tokenizer.batch_decode(__A )
def _lowercase ( self : Optional[int] , __A : List[str] ):
snake_case__ : Optional[Any] = [seq.replace(" " , "" ) for seq in self.wp_tokenizer.batch_decode(__A )]
return decode_strs
| 25
|
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
snake_case__ : Any = [0] * len(snake_case_ )
for i in range(1 , len(snake_case_ ) ):
# use last results for better performance - dynamic programming
snake_case__ : Union[str, Any] = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
snake_case__ : str = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
snake_case__ : int = j
return prefix_result
def SCREAMING_SNAKE_CASE ( snake_case_ : str ):
return max(prefix_function(snake_case_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 25
| 1
|
from typing import Any
def snake_case_ (__A : list , __A : list , __A : dict , __A : dict , __A : dict , ) -> list:
_validation(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
# Creates data structures and fill initial step
__lowerCAmelCase : dict = {}
__lowerCAmelCase : dict = {}
for state in states_space:
__lowerCAmelCase : int = observations_space[0]
__lowerCAmelCase : Union[str, Any] = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
__lowerCAmelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase : Optional[int] = observations_space[o]
__lowerCAmelCase : Optional[int] = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
__lowerCAmelCase : Optional[int] = ""
__lowerCAmelCase : List[str] = -1
for k_state in states_space:
__lowerCAmelCase : Any = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
__lowerCAmelCase : str = probability
__lowerCAmelCase : Dict = k_state
# Update probabilities and pointers dicts
__lowerCAmelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
__lowerCAmelCase : int = arg_max
# The final observation
__lowerCAmelCase : int = observations_space[len(_SCREAMING_SNAKE_CASE ) - 1]
# argmax for given final observation
__lowerCAmelCase : Any = ""
__lowerCAmelCase : int = -1
for k_state in states_space:
__lowerCAmelCase : Any = probabilities[(k_state, final_observation)]
if probability > max_probability:
__lowerCAmelCase : Tuple = probability
__lowerCAmelCase : List[str] = k_state
__lowerCAmelCase : Tuple = arg_max
# Process pointers backwards
__lowerCAmelCase : Optional[Any] = last_state
__lowerCAmelCase : str = []
for o in range(len(_SCREAMING_SNAKE_CASE ) - 1 , -1 , -1 ):
result.append(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = pointers[previous, observations_space[o]]
result.reverse()
return result
def snake_case_ (__A : Any , __A : Any , __A : Any , __A : Any , __A : Any , ) -> None:
_validate_not_empty(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
_validate_lists(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_validate_dicts(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ (__A : Any , __A : Any , __A : Any , __A : Any , __A : Any , ) -> None:
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("""There's an empty parameter""" )
def snake_case_ (__A : Any , __A : Any ) -> None:
_validate_list(_SCREAMING_SNAKE_CASE , """observations_space""" )
_validate_list(_SCREAMING_SNAKE_CASE , """states_space""" )
def snake_case_ (__A : Any , __A : str ) -> None:
if not isinstance(_object , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = f'''{var_name} must be a list'''
raise ValueError(_SCREAMING_SNAKE_CASE )
else:
for x in _object:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : int = f'''{var_name} must be a list of strings'''
raise ValueError(_SCREAMING_SNAKE_CASE )
def snake_case_ (__A : Any , __A : Any , __A : Any , ) -> None:
_validate_dict(_SCREAMING_SNAKE_CASE , """initial_probabilities""" , _SCREAMING_SNAKE_CASE )
_validate_nested_dict(_SCREAMING_SNAKE_CASE , """transition_probabilities""" )
_validate_nested_dict(_SCREAMING_SNAKE_CASE , """emission_probabilities""" )
def snake_case_ (__A : Any , __A : str ) -> None:
_validate_dict(_object , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for x in _object.values():
_validate_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def snake_case_ (__A : Any , __A : str , __A : type , __A : bool = False ) -> None:
if not isinstance(_object , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Dict = f'''{var_name} must be a dict'''
raise ValueError(_SCREAMING_SNAKE_CASE )
if not all(isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for x in _object ):
__lowerCAmelCase : Tuple = f'''{var_name} all keys must be strings'''
raise ValueError(_SCREAMING_SNAKE_CASE )
if not all(isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for x in _object.values() ):
__lowerCAmelCase : Tuple = "nested dictionary " if nested else ""
__lowerCAmelCase : int = f'''{var_name} {nested_text}all values must be {value_type.__name__}'''
raise ValueError(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 651
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class _snake_case :
__A : Dict =BlenderbotConfig
__A : Union[str, Any] ={}
__A : Any ="gelu"
def __init__( self ,_snake_case ,_snake_case=13 ,_snake_case=7 ,_snake_case=True ,_snake_case=False ,_snake_case=99 ,_snake_case=32 ,_snake_case=2 ,_snake_case=4 ,_snake_case=37 ,_snake_case=0.1 ,_snake_case=0.1 ,_snake_case=20 ,_snake_case=2 ,_snake_case=1 ,_snake_case=0 ,):
UpperCAmelCase_ : List[Any] = parent
UpperCAmelCase_ : str = batch_size
UpperCAmelCase_ : Dict = seq_length
UpperCAmelCase_ : int = is_training
UpperCAmelCase_ : Optional[Any] = use_labels
UpperCAmelCase_ : Any = vocab_size
UpperCAmelCase_ : Optional[int] = hidden_size
UpperCAmelCase_ : Optional[int] = num_hidden_layers
UpperCAmelCase_ : int = num_attention_heads
UpperCAmelCase_ : Tuple = intermediate_size
UpperCAmelCase_ : Any = hidden_dropout_prob
UpperCAmelCase_ : Optional[int] = attention_probs_dropout_prob
UpperCAmelCase_ : List[Any] = max_position_embeddings
UpperCAmelCase_ : str = eos_token_id
UpperCAmelCase_ : List[Any] = pad_token_id
UpperCAmelCase_ : List[Any] = bos_token_id
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Any = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
UpperCAmelCase_ : int = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
UpperCAmelCase_ : Optional[Any] = tf.concat([input_ids, eos_tensor] ,axis=1 )
UpperCAmelCase_ : int = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
UpperCAmelCase_ : Optional[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 ,**self.config_updates ,)
UpperCAmelCase_ : List[str] = prepare_blenderbot_inputs_dict(_snake_case ,_snake_case ,_snake_case )
return config, inputs_dict
def UpperCamelCase__ ( self ,_snake_case ,_snake_case ):
UpperCAmelCase_ : Tuple = TFBlenderbotModel(config=_snake_case ).get_decoder()
UpperCAmelCase_ : int = inputs_dict["input_ids"]
UpperCAmelCase_ : Dict = input_ids[:1, :]
UpperCAmelCase_ : Any = inputs_dict["attention_mask"][:1, :]
UpperCAmelCase_ : int = inputs_dict["head_mask"]
UpperCAmelCase_ : Optional[int] = 1
# first forward pass
UpperCAmelCase_ : List[str] = model(_snake_case ,attention_mask=_snake_case ,head_mask=_snake_case ,use_cache=_snake_case )
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase_ : Optional[int] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
UpperCAmelCase_ : Any = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
UpperCAmelCase_ : Union[str, Any] = tf.concat([input_ids, next_tokens] ,axis=-1 )
UpperCAmelCase_ : Any = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
UpperCAmelCase_ : Any = model(_snake_case ,attention_mask=_snake_case )[0]
UpperCAmelCase_ : List[Any] = model(_snake_case ,attention_mask=_snake_case ,past_key_values=_snake_case )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
UpperCAmelCase_ : str = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
UpperCAmelCase_ : List[str] = output_from_no_past[:, -3:, random_slice_idx]
UpperCAmelCase_ : Union[str, Any] = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_snake_case ,_snake_case ,rtol=1E-3 )
def a__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : str=None , _SCREAMING_SNAKE_CASE : Any=None , _SCREAMING_SNAKE_CASE : Any=None , _SCREAMING_SNAKE_CASE : List[str]=None , _SCREAMING_SNAKE_CASE : Dict=None , ) -> Union[str, Any]:
"""simple docstring"""
if attention_mask is None:
UpperCAmelCase_ : Dict = tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
UpperCAmelCase_ : Optional[int] = 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:
UpperCAmelCase_ : List[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
UpperCAmelCase_ : Optional[int] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
UpperCAmelCase_ : str = 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 _snake_case (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase):
__A : Union[str, Any] =(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__A : List[str] =(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__A : Dict =(
{
"conversational": TFBlenderbotForConditionalGeneration,
"feature-extraction": TFBlenderbotModel,
"summarization": TFBlenderbotForConditionalGeneration,
"text2text-generation": TFBlenderbotForConditionalGeneration,
"translation": TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__A : Any =True
__A : Dict =False
__A : Dict =False
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Optional[int] = TFBlenderbotModelTester(self )
UpperCAmelCase_ : int = ConfigTester(self ,config_class=_snake_case )
def UpperCamelCase__ ( self ):
self.config_tester.run_common_tests()
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_snake_case )
@require_tokenizers
@require_tf
class _snake_case (unittest.TestCase):
__A : Optional[int] =["My friends are cool but they eat too many carbs."]
__A : Optional[Any] ="facebook/blenderbot-400M-distill"
@cached_property
def UpperCamelCase__ ( self ):
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def UpperCamelCase__ ( self ):
UpperCAmelCase_ : List[Any] = self.tokenizer(self.src_text ,return_tensors="tf" )
UpperCAmelCase_ : Union[str, Any] = self.model.generate(
model_inputs.input_ids ,)
UpperCAmelCase_ : str = self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=_snake_case )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 71
| 0
|
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase : Dict = HfApi()
lowercase : List[str] = {}
# fmt: off
lowercase : Optional[Any] = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
-1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
lowercase : str = torch.tensor([
-2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
-2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
lowercase : List[str] = torch.tensor([
-0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
-0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
-0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
lowercase : List[Any] = torch.tensor([
0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
-0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
-0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
lowercase : Any = torch.tensor([
0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
-0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
-0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
lowercase : Dict = torch.tensor([
0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
-0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
-0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
lowercase : Union[str, Any] = torch.tensor([
0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
-0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
-0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
lowercase : Dict = torch.tensor([
0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
-0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
-0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
lowercase : Dict = torch.tensor([
-1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
-2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
lowercase : List[str] = torch.tensor([
-1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
-2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
lowercase : List[Any] = torch.tensor([
-1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
-2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
lowercase : Optional[Any] = torch.tensor([
-2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
-3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
lowercase : Tuple = torch.tensor([
-2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
-2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
lowercase : Dict = torch.tensor([
-2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
-3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
lowercase : Optional[Any] = torch.tensor([
-1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
-2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on
lowercase : Optional[int] = api.list_models(filter='diffusers')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
lowercase : int = '/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]
print(f"Started running {mod.modelId}!!!")
if mod.modelId.startswith('CompVis'):
lowercase : Tuple = UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
else:
lowercase : Any = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
lowercase : List[str] = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
lowercase : Any = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
lowercase : Tuple = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :30], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1E-3
)
print(f"{mod.modelId} has passed successfully!!!")
| 94
|
from __future__ import annotations
lowercase : str = list[tuple[int, int]]
lowercase : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
lowercase : int = ([-1, 0], [0, -1], [1, 0], [0, 1]) # up, left, down, right
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :Optional[int] , a :int , a :int , a :int , a :int , a :float , a :Node | None , ) -> List[Any]:
__UpperCamelCase : List[Any] = pos_x
__UpperCamelCase : List[str] = pos_y
__UpperCamelCase : str = (pos_y, pos_x)
__UpperCamelCase : Optional[int] = goal_x
__UpperCamelCase : str = goal_y
__UpperCamelCase : int = g_cost
__UpperCamelCase : Dict = parent
__UpperCamelCase : str = self.calculate_heuristic()
def _lowerCamelCase ( self :List[Any] ) -> float:
__UpperCamelCase : Any = abs(self.pos_x - self.goal_x )
__UpperCamelCase : Tuple = abs(self.pos_y - self.goal_y )
return dx + dy
def __lt__( self :Union[str, Any] , a :Dict ) -> bool:
return self.f_cost < other.f_cost
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self :List[str] , a :tuple[int, int] , a :tuple[int, int] ) -> List[str]:
__UpperCamelCase : Any = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , a )
__UpperCamelCase : List[str] = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9_9_9_9 , a )
__UpperCamelCase : Optional[int] = [self.start]
__UpperCamelCase : list[Node] = []
__UpperCamelCase : Any = False
def _lowerCamelCase ( self :List[str] ) -> Path | None:
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
__UpperCamelCase : Optional[int] = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
__UpperCamelCase : Dict = True
return self.retrace_path(a )
self.closed_nodes.append(a )
__UpperCamelCase : Union[str, Any] = self.get_successors(a )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(a )
else:
# retrieve the best current path
__UpperCamelCase : Union[str, Any] = self.open_nodes.pop(self.open_nodes.index(a ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(a )
else:
self.open_nodes.append(a )
if not self.reached:
return [self.start.pos]
return None
def _lowerCamelCase ( self :str , a :Node ) -> list[Node]:
__UpperCamelCase : List[Any] = []
for action in delta:
__UpperCamelCase : Optional[Any] = parent.pos_x + action[1]
__UpperCamelCase : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(a ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
a , a , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , a , ) )
return successors
def _lowerCamelCase ( self :Optional[Any] , a :Node | None ) -> Path:
__UpperCamelCase : str = node
__UpperCamelCase : int = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__UpperCamelCase : str = current_node.parent
path.reverse()
return path
if __name__ == "__main__":
lowercase : List[str] = (0, 0)
lowercase : int = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
print('------')
lowercase : Any = GreedyBestFirst(init, goal)
lowercase : List[str] = greedy_bf.search()
if path:
for pos_x, pos_y in path:
lowercase : Optional[int] = 2
for elem in grid:
print(elem)
| 94
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCamelCase__: Tuple = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__: Optional[Any] = [
"MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
"MegaForCausalLM",
"MegaForMaskedLM",
"MegaForMultipleChoice",
"MegaForQuestionAnswering",
"MegaForSequenceClassification",
"MegaForTokenClassification",
"MegaModel",
"MegaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
UpperCamelCase__: Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 127
|
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE__ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : int = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
SCREAMING_SNAKE_CASE__ : Dict = {
"vocab_file": {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
"bert-base-multilingual-uncased": (
"https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
),
"bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
"bert-large-uncased-whole-word-masking": (
"https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
),
"bert-large-cased-whole-word-masking": (
"https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
),
"bert-large-uncased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
),
"bert-large-cased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
),
"bert-base-cased-finetuned-mrpc": (
"https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
),
"bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
"bert-base-german-dbmdz-uncased": (
"https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
),
"TurkuNLP/bert-base-finnish-cased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
),
"TurkuNLP/bert-base-finnish-uncased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
),
"wietsedv/bert-base-dutch-cased": (
"https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
"bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
"bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
"bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
"bert-base-multilingual-uncased": (
"https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
),
"bert-base-multilingual-cased": (
"https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
),
"bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
"bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
"bert-large-uncased-whole-word-masking": (
"https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
),
"bert-large-cased-whole-word-masking": (
"https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
),
"bert-large-uncased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
),
"bert-large-cased-whole-word-masking-finetuned-squad": (
"https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
),
"bert-base-cased-finetuned-mrpc": (
"https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
),
"bert-base-german-dbmdz-cased": (
"https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
),
"bert-base-german-dbmdz-uncased": (
"https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
),
"TurkuNLP/bert-base-finnish-cased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
),
"TurkuNLP/bert-base-finnish-uncased-v1": (
"https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
),
"wietsedv/bert-base-dutch-cased": (
"https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
),
},
}
SCREAMING_SNAKE_CASE__ : List[Any] = {
"bert-base-uncased": 5_12,
"bert-large-uncased": 5_12,
"bert-base-cased": 5_12,
"bert-large-cased": 5_12,
"bert-base-multilingual-uncased": 5_12,
"bert-base-multilingual-cased": 5_12,
"bert-base-chinese": 5_12,
"bert-base-german-cased": 5_12,
"bert-large-uncased-whole-word-masking": 5_12,
"bert-large-cased-whole-word-masking": 5_12,
"bert-large-uncased-whole-word-masking-finetuned-squad": 5_12,
"bert-large-cased-whole-word-masking-finetuned-squad": 5_12,
"bert-base-cased-finetuned-mrpc": 5_12,
"bert-base-german-dbmdz-cased": 5_12,
"bert-base-german-dbmdz-uncased": 5_12,
"TurkuNLP/bert-base-finnish-cased-v1": 5_12,
"TurkuNLP/bert-base-finnish-uncased-v1": 5_12,
"wietsedv/bert-base-dutch-cased": 5_12,
}
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"bert-base-uncased": {"do_lower_case": True},
"bert-large-uncased": {"do_lower_case": True},
"bert-base-cased": {"do_lower_case": False},
"bert-large-cased": {"do_lower_case": False},
"bert-base-multilingual-uncased": {"do_lower_case": True},
"bert-base-multilingual-cased": {"do_lower_case": False},
"bert-base-chinese": {"do_lower_case": False},
"bert-base-german-cased": {"do_lower_case": False},
"bert-large-uncased-whole-word-masking": {"do_lower_case": True},
"bert-large-cased-whole-word-masking": {"do_lower_case": False},
"bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
"bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
"bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
"bert-base-german-dbmdz-cased": {"do_lower_case": False},
"bert-base-german-dbmdz-uncased": {"do_lower_case": True},
"TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
"TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
"wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}
class a_ ( SCREAMING_SNAKE_CASE__ ):
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_INIT_CONFIGURATION
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = BertTokenizer
def __init__( self , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=None , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE="[UNK]" , SCREAMING_SNAKE_CASE="[SEP]" , SCREAMING_SNAKE_CASE="[PAD]" , SCREAMING_SNAKE_CASE="[CLS]" , SCREAMING_SNAKE_CASE="[MASK]" , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=None , **SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
super().__init__(
SCREAMING_SNAKE_CASE , tokenizer_file=SCREAMING_SNAKE_CASE , do_lower_case=SCREAMING_SNAKE_CASE , unk_token=SCREAMING_SNAKE_CASE , sep_token=SCREAMING_SNAKE_CASE , pad_token=SCREAMING_SNAKE_CASE , cls_token=SCREAMING_SNAKE_CASE , mask_token=SCREAMING_SNAKE_CASE , tokenize_chinese_chars=SCREAMING_SNAKE_CASE , strip_accents=SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE , )
SCREAMING_SNAKE_CASE_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , SCREAMING_SNAKE_CASE ) != do_lower_case
or normalizer_state.get('strip_accents' , SCREAMING_SNAKE_CASE ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , SCREAMING_SNAKE_CASE ) != tokenize_chinese_chars
):
SCREAMING_SNAKE_CASE_ = getattr(SCREAMING_SNAKE_CASE , normalizer_state.pop('type' ) )
SCREAMING_SNAKE_CASE_ = do_lower_case
SCREAMING_SNAKE_CASE_ = strip_accents
SCREAMING_SNAKE_CASE_ = tokenize_chinese_chars
SCREAMING_SNAKE_CASE_ = normalizer_class(**SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ = do_lower_case
def A_( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [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 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = [self.sep_token_id]
SCREAMING_SNAKE_CASE_ = [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 , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = self._tokenizer.model.save(SCREAMING_SNAKE_CASE , name=SCREAMING_SNAKE_CASE )
return tuple(SCREAMING_SNAKE_CASE )
| 205
| 0
|
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __snake_case ( nn.Module ):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE = 1_6 , __SCREAMING_SNAKE_CASE = 8_8 , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = 1 , __SCREAMING_SNAKE_CASE = 0.0 , __SCREAMING_SNAKE_CASE = 3_2 , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = False , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = None , __SCREAMING_SNAKE_CASE = "geglu" , __SCREAMING_SNAKE_CASE = None , ):
super().__init__()
snake_case__ : Dict = 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
snake_case__ : Optional[Any] = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
snake_case__ : Any = [7_7, 2_5_7]
# 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])`
snake_case__ : Dict = [1, 0]
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE=None , __SCREAMING_SNAKE_CASE = True , ):
snake_case__ : List[str] = hidden_states
snake_case__ : Optional[Any] = []
snake_case__ : int = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
snake_case__ : int = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
snake_case__ : int = self.transformer_index_for_condition[i]
snake_case__ : List[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]
snake_case__ : Union[str, Any] = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
snake_case__ : Dict = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE )
| 419
|
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
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 __snake_case ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ = KandinskyVaaImgaImgPipeline
lowerCamelCase__ = ['''image_embeds''', '''negative_image_embeds''', '''image''']
lowerCamelCase__ = [
'''image_embeds''',
'''negative_image_embeds''',
'''image''',
]
lowerCamelCase__ = [
'''generator''',
'''height''',
'''width''',
'''strength''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
lowerCamelCase__ = False
@property
def __UpperCamelCase ( self ):
return 3_2
@property
def __UpperCamelCase ( self ):
return 3_2
@property
def __UpperCamelCase ( self ):
return self.time_input_dim
@property
def __UpperCamelCase ( self ):
return self.time_input_dim * 4
@property
def __UpperCamelCase ( self ):
return 1_0_0
@property
def __UpperCamelCase ( self ):
torch.manual_seed(0 )
snake_case__ : int = {
"""in_channels""": 4,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """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""": """image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
snake_case__ : Optional[int] = UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
return model
@property
def __UpperCamelCase ( self ):
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 __UpperCamelCase ( self ):
torch.manual_seed(0 )
snake_case__ : List[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __UpperCamelCase ( self ):
snake_case__ : Dict = self.dummy_unet
snake_case__ : Any = self.dummy_movq
snake_case__ : Any = {
"""num_train_timesteps""": 1_0_0_0,
"""beta_schedule""": """linear""",
"""beta_start""": 0.0_0085,
"""beta_end""": 0.012,
"""clip_sample""": False,
"""set_alpha_to_one""": False,
"""steps_offset""": 0,
"""prediction_type""": """epsilon""",
"""thresholding""": False,
}
snake_case__ : int = DDIMScheduler(**__SCREAMING_SNAKE_CASE )
snake_case__ : str = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE=0 ):
snake_case__ : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
snake_case__ : int = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__SCREAMING_SNAKE_CASE )
# create init_image
snake_case__ : Any = floats_tensor((1, 3, 6_4, 6_4) , rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
snake_case__ : List[str] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
snake_case__ : str = Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" ).resize((2_5_6, 2_5_6) )
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
snake_case__ : Union[str, Any] = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
snake_case__ : List[Any] = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
snake_case__ : List[Any] = {
"""image""": init_image,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 6_4,
"""width""": 6_4,
"""num_inference_steps""": 1_0,
"""guidance_scale""": 7.0,
"""strength""": 0.2,
"""output_type""": """np""",
}
return inputs
def __UpperCamelCase ( self ):
snake_case__ : Optional[Any] = """cpu"""
snake_case__ : Dict = self.get_dummy_components()
snake_case__ : List[Any] = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
snake_case__ : Any = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
snake_case__ : int = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
snake_case__ : Union[str, Any] = output.images
snake_case__ : Dict = pipe(
**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) , return_dict=__SCREAMING_SNAKE_CASE , )[0]
snake_case__ : List[str] = image[0, -3:, -3:, -1]
snake_case__ : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
snake_case__ : Optional[int] = np.array(
[0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263] )
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()}"
@slow
@require_torch_gpu
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self ):
snake_case__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_img2img_frog.npy""" )
snake_case__ : Union[str, Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
snake_case__ : int = """A red cartoon frog, 4k"""
snake_case__ : Tuple = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(__SCREAMING_SNAKE_CASE )
snake_case__ : List[Any] = KandinskyVaaImgaImgPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder""" , torch_dtype=torch.floataa )
snake_case__ : Any = pipeline.to(__SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
snake_case__ : Optional[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
snake_case__ , snake_case__ : int = pipe_prior(
__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
snake_case__ : str = pipeline(
image=__SCREAMING_SNAKE_CASE , image_embeds=__SCREAMING_SNAKE_CASE , negative_image_embeds=__SCREAMING_SNAKE_CASE , generator=__SCREAMING_SNAKE_CASE , num_inference_steps=1_0_0 , height=7_6_8 , width=7_6_8 , strength=0.2 , output_type="""np""" , )
snake_case__ : List[Any] = output.images[0]
assert image.shape == (7_6_8, 7_6_8, 3)
assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
| 419
| 1
|
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __magic_name__ ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
UpperCamelCase_ = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]
def _lowerCAmelCase ( ):
"""simple docstring"""
if os.name == "nt":
_lowercase: List[Any] = CursorInfo()
_lowercase: Union[str, Any] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
_lowercase: Tuple = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def _lowerCAmelCase ( ):
"""simple docstring"""
if os.name == "nt":
_lowercase: Tuple = CursorInfo()
_lowercase: Optional[int] = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
_lowercase: List[str] = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(_UpperCamelCase , ctypes.byref(_UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def _lowerCAmelCase ( ):
"""simple docstring"""
try:
hide_cursor()
yield
finally:
show_cursor()
| 353
|
"""simple docstring"""
# Imports
import numpy as np
class __a :
def __init__( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
def snake_case_ ( self , a__=None , a__=None , a__=None , a__=None , a__=None ):
if red is not None:
_lowerCamelCase = red
if green is not None:
_lowerCamelCase = green
if blue is not None:
_lowerCamelCase = blue
if red_edge is not None:
_lowerCamelCase = red_edge
if nir is not None:
_lowerCamelCase = nir
return True
def snake_case_ ( self , a__="" , a__=None , a__=None , a__=None , a__=None , a__=None ):
self.set_matricies(red=a__ , green=a__ , blue=a__ , red_edge=a__ , nir=a__ )
_lowerCamelCase = {
'ARVI2': self.arvaa,
'CCCI': self.ccci,
'CVI': self.cvi,
'GLI': self.gli,
'NDVI': self.ndvi,
'BNDVI': self.bndvi,
'redEdgeNDVI': self.red_edge_ndvi,
'GNDVI': self.gndvi,
'GBNDVI': self.gbndvi,
'GRNDVI': self.grndvi,
'RBNDVI': self.rbndvi,
'PNDVI': self.pndvi,
'ATSAVI': self.atsavi,
'BWDRVI': self.bwdrvi,
'CIgreen': self.ci_green,
'CIrededge': self.ci_rededge,
'CI': self.ci,
'CTVI': self.ctvi,
'GDVI': self.gdvi,
'EVI': self.evi,
'GEMI': self.gemi,
'GOSAVI': self.gosavi,
'GSAVI': self.gsavi,
'Hue': self.hue,
'IVI': self.ivi,
'IPVI': self.ipvi,
'I': self.i,
'RVI': self.rvi,
'MRVI': self.mrvi,
'MSAVI': self.m_savi,
'NormG': self.norm_g,
'NormNIR': self.norm_nir,
'NormR': self.norm_r,
'NGRDI': self.ngrdi,
'RI': self.ri,
'S': self.s,
'IF': self._if,
'DVI': self.dvi,
'TVI': self.tvi,
'NDRE': self.ndre,
}
try:
return funcs[index]()
except KeyError:
print('Index not in the list!' )
return False
def snake_case_ ( self ):
return -0.18 + (1.17 * ((self.nir - self.red) / (self.nir + self.red)))
def snake_case_ ( self ):
return ((self.nir - self.redEdge) / (self.nir + self.redEdge)) / (
(self.nir - self.red) / (self.nir + self.red)
)
def snake_case_ ( self ):
return self.nir * (self.red / (self.green**2))
def snake_case_ ( self ):
return (2 * self.green - self.red - self.blue) / (
2 * self.green + self.red + self.blue
)
def snake_case_ ( self ):
return (self.nir - self.red) / (self.nir + self.red)
def snake_case_ ( self ):
return (self.nir - self.blue) / (self.nir + self.blue)
def snake_case_ ( self ):
return (self.redEdge - self.red) / (self.redEdge + self.red)
def snake_case_ ( self ):
return (self.nir - self.green) / (self.nir + self.green)
def snake_case_ ( self ):
return (self.nir - (self.green + self.blue)) / (
self.nir + (self.green + self.blue)
)
def snake_case_ ( self ):
return (self.nir - (self.green + self.red)) / (
self.nir + (self.green + self.red)
)
def snake_case_ ( self ):
return (self.nir - (self.blue + self.red)) / (self.nir + (self.blue + self.red))
def snake_case_ ( self ):
return (self.nir - (self.green + self.red + self.blue)) / (
self.nir + (self.green + self.red + self.blue)
)
def snake_case_ ( self , a__=0.08 , a__=1.22 , a__=0.03 ):
return a * (
(self.nir - a * self.red - b)
/ (a * self.nir + self.red - a * b + x * (1 + a**2))
)
def snake_case_ ( self ):
return (0.1 * self.nir - self.blue) / (0.1 * self.nir + self.blue)
def snake_case_ ( self ):
return (self.nir / self.green) - 1
def snake_case_ ( self ):
return (self.nir / self.redEdge) - 1
def snake_case_ ( self ):
return (self.red - self.blue) / self.red
def snake_case_ ( self ):
_lowerCamelCase = self.ndvi()
return ((ndvi + 0.5) / (abs(ndvi + 0.5 ))) * (abs(ndvi + 0.5 ) ** (1 / 2))
def snake_case_ ( self ):
return self.nir - self.green
def snake_case_ ( self ):
return 2.5 * (
(self.nir - self.red) / (self.nir + 6 * self.red - 7.5 * self.blue + 1)
)
def snake_case_ ( self ):
_lowerCamelCase = (2 * (self.nir**2 - self.red**2) + 1.5 * self.nir + 0.5 * self.red) / (
self.nir + self.red + 0.5
)
return n * (1 - 0.25 * n) - (self.red - 0.125) / (1 - self.red)
def snake_case_ ( self , a__=0.16 ):
return (self.nir - self.green) / (self.nir + self.green + y)
def snake_case_ ( self , a__=0.5 ):
return ((self.nir - self.green) / (self.nir + self.green + n)) * (1 + n)
def snake_case_ ( self ):
return np.arctan(
((2 * self.red - self.green - self.blue) / 30.5) * (self.green - self.blue) )
def snake_case_ ( self , a__=None , a__=None ):
return (self.nir - b) / (a * self.red)
def snake_case_ ( self ):
return (self.nir / ((self.nir + self.red) / 2)) * (self.ndvi() + 1)
def snake_case_ ( self ):
return (self.red + self.green + self.blue) / 30.5
def snake_case_ ( self ):
return self.nir / self.red
def snake_case_ ( self ):
return (self.rvi() - 1) / (self.rvi() + 1)
def snake_case_ ( self ):
return (
(2 * self.nir + 1)
- ((2 * self.nir + 1) ** 2 - 8 * (self.nir - self.red)) ** (1 / 2)
) / 2
def snake_case_ ( self ):
return self.green / (self.nir + self.red + self.green)
def snake_case_ ( self ):
return self.nir / (self.nir + self.red + self.green)
def snake_case_ ( self ):
return self.red / (self.nir + self.red + self.green)
def snake_case_ ( self ):
return (self.green - self.red) / (self.green + self.red)
def snake_case_ ( self ):
return (self.red - self.green) / (self.red + self.green)
def snake_case_ ( self ):
_lowerCamelCase = np.max([np.max(self.red ), np.max(self.green ), np.max(self.blue )] )
_lowerCamelCase = np.min([np.min(self.red ), np.min(self.green ), np.min(self.blue )] )
return (max_value - min_value) / max_value
def snake_case_ ( self ):
return (2 * self.red - self.green - self.blue) / (self.green - self.blue)
def snake_case_ ( self ):
return self.nir / self.red
def snake_case_ ( self ):
return (self.ndvi() + 0.5) ** (1 / 2)
def snake_case_ ( self ):
return (self.nir - self.redEdge) / (self.nir + self.redEdge)
| 650
| 0
|
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
for attribute in key.split('''.''' ):
__UpperCAmelCase : str = getattr(lowercase_ , lowercase_ )
if weight_type is not None:
__UpperCAmelCase : Optional[int] = getattr(lowercase_ , lowercase_ ).shape
else:
__UpperCAmelCase : List[Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
f" {value.shape} for {full_name}"
)
if weight_type == "weight":
__UpperCAmelCase : Union[str, Any] = value
elif weight_type == "weight_g":
__UpperCAmelCase : Dict = value
elif weight_type == "weight_v":
__UpperCAmelCase : Tuple = value
elif weight_type == "bias":
__UpperCAmelCase : str = value
else:
__UpperCAmelCase : Optional[int] = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
__UpperCAmelCase : int = []
__UpperCAmelCase : str = fairseq_model.state_dict()
__UpperCAmelCase : Optional[Any] = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
__UpperCAmelCase : Tuple = False
if "conv_layers" in name:
load_conv_layer(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , hf_model.config.feat_extract_norm == '''group''' , )
__UpperCAmelCase : str = True
else:
for key, mapped_key in MAPPING.items():
__UpperCAmelCase : Any = '''hubert.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
if key in name or (key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0] and not is_finetuned):
__UpperCAmelCase : List[str] = True
if "*" in mapped_key:
__UpperCAmelCase : List[Any] = name.split(lowercase_ )[0].split('''.''' )[-2]
__UpperCAmelCase : Optional[Any] = mapped_key.replace('''*''' , lowercase_ )
if "weight_g" in name:
__UpperCAmelCase : Optional[int] = '''weight_g'''
elif "weight_v" in name:
__UpperCAmelCase : Any = '''weight_v'''
elif "weight" in name:
__UpperCAmelCase : Union[str, Any] = '''weight'''
elif "bias" in name:
__UpperCAmelCase : List[str] = '''bias'''
else:
__UpperCAmelCase : Optional[int] = None
set_recursively(lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
continue
if not is_used:
unused_weights.append(lowercase_ )
logger.warning(f"Unused weights: {unused_weights}" )
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
__UpperCAmelCase : Dict = full_name.split('''conv_layers.''' )[-1]
__UpperCAmelCase : Dict = name.split('''.''' )
__UpperCAmelCase : Tuple = int(items[0] )
__UpperCAmelCase : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
)
__UpperCAmelCase : Optional[Any] = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
)
__UpperCAmelCase : Optional[int] = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
" found."
)
__UpperCAmelCase : List[Any] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f"{full_name} has size {value.shape}, but"
f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
)
__UpperCAmelCase : Optional[Any] = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(lowercase_ )
@torch.no_grad()
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_=None , lowercase_=None , lowercase_=True ) -> Union[str, Any]:
'''simple docstring'''
if config_path is not None:
__UpperCAmelCase : List[str] = HubertConfig.from_pretrained(lowercase_ )
else:
__UpperCAmelCase : str = HubertConfig()
if is_finetuned:
if dict_path:
__UpperCAmelCase : Dict = Dictionary.load(lowercase_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__UpperCAmelCase : Any = target_dict.pad_index
__UpperCAmelCase : List[Any] = target_dict.bos_index
__UpperCAmelCase : Tuple = target_dict.eos_index
__UpperCAmelCase : Union[str, Any] = len(target_dict.symbols )
__UpperCAmelCase : List[Any] = os.path.join(lowercase_ , '''vocab.json''' )
if not os.path.isdir(lowercase_ ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowercase_ ) )
return
os.makedirs(lowercase_ , exist_ok=lowercase_ )
with open(lowercase_ , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(target_dict.indices , lowercase_ )
__UpperCAmelCase : List[str] = WavaVecaCTCTokenizer(
lowercase_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token='''|''' , do_lower_case=lowercase_ , )
__UpperCAmelCase : Union[str, Any] = True if config.feat_extract_norm == '''layer''' else False
__UpperCAmelCase : Union[str, Any] = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=lowercase_ , return_attention_mask=lowercase_ , )
__UpperCAmelCase : Optional[Any] = WavaVecaProcessor(feature_extractor=lowercase_ , tokenizer=lowercase_ )
processor.save_pretrained(lowercase_ )
__UpperCAmelCase : int = HubertForCTC(lowercase_ )
else:
__UpperCAmelCase : Optional[Any] = HubertModel(lowercase_ )
if is_finetuned:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Any = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
__UpperCAmelCase : Any = model[0].eval()
recursively_load_weights(lowercase_ , lowercase_ , lowercase_ )
hf_wavavec.save_pretrained(lowercase_ )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
lowerCAmelCase = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 701
|
from random import shuffle
import tensorflow as tf
from numpy import array
def __SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = int(lowercase_ )
assert noofclusters < len(lowercase_ )
# Find out the dimensionality
__UpperCAmelCase : str = len(vectors[0] )
# Will help select random centroids from among the available vectors
__UpperCAmelCase : Union[str, Any] = list(range(len(lowercase_ ) ) )
shuffle(lowercase_ )
# 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.
__UpperCAmelCase : Union[str, Any] = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
__UpperCAmelCase : str = 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
__UpperCAmelCase : List[str] = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(lowercase_ )
]
##These nodes will assign the centroid Variables the appropriate
##values
__UpperCAmelCase : str = tf.placeholder('''float64''' , [dim] )
__UpperCAmelCase : Tuple = []
for centroid in centroids:
cent_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
__UpperCAmelCase : Union[str, Any] = [tf.Variable(0 ) for i in range(len(lowercase_ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
__UpperCAmelCase : Dict = tf.placeholder('''int32''' )
__UpperCAmelCase : Optional[Any] = []
for assignment in assignments:
cluster_assigns.append(tf.assign(lowercase_ , lowercase_ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
__UpperCAmelCase : Union[str, Any] = 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
__UpperCAmelCase : Any = tf.reduce_mean(lowercase_ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
__UpperCAmelCase : Tuple = tf.placeholder('''float''' , [dim] )
__UpperCAmelCase : Any = tf.placeholder('''float''' , [dim] )
__UpperCAmelCase : Any = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase_ , lowercase_ ) , 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
__UpperCAmelCase : Union[str, Any] = tf.placeholder('''float''' , [noofclusters] )
__UpperCAmelCase : Optional[Any] = tf.argmin(lowercase_ , 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.
__UpperCAmelCase : Optional[Any] = tf.initialize_all_variables()
# Initialize all variables
sess.run(lowercase_ )
##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.
__UpperCAmelCase : Union[str, Any] = 100
for _ in range(lowercase_ ):
##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(lowercase_ ) ):
__UpperCAmelCase : List[str] = 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.
__UpperCAmelCase : List[Any] = [
sess.run(lowercase_ , feed_dict={va: vect, va: sess.run(lowercase_ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
__UpperCAmelCase : Optional[Any] = sess.run(
lowercase_ , 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(lowercase_ ):
# Collect all the vectors assigned to this cluster
__UpperCAmelCase : Optional[Any] = [
vectors[i]
for i in range(len(lowercase_ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
__UpperCAmelCase : str = sess.run(
lowercase_ , feed_dict={mean_input: array(lowercase_ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
__UpperCAmelCase : List[str] = sess.run(lowercase_ )
__UpperCAmelCase : Tuple = sess.run(lowercase_ )
return centroids, assignments
| 675
| 0
|
import sys
SCREAMING_SNAKE_CASE__ : Optional[Any] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66896648950445244523161731856403098711121722383113"""
"""62229893423380308135336276614282806444486645238749"""
"""30358907296290491560440772390713810515859307960866"""
"""70172427121883998797908792274921901699720888093776"""
"""65727333001053367881220235421809751254540594752243"""
"""52584907711670556013604839586446706324415722155397"""
"""53697817977846174064955149290862569321978468622482"""
"""83972241375657056057490261407972968652414535100474"""
"""82166370484403199890008895243450658541227588666881"""
"""16427171479924442928230863465674813919123162824586"""
"""17866458359124566529476545682848912883142607690042"""
"""24219022671055626321111109370544217506941658960408"""
"""07198403850962455444362981230987879927244284909188"""
"""84580156166097919133875499200524063689912560717606"""
"""05886116467109405077541002256983155200055935729725"""
"""71636269561882670428252483600823257530420752963450"""
)
def __lowercase ( snake_case = N ):
"""simple docstring"""
__magic_name__ :Optional[int] = -sys.maxsize - 1
for i in range(len(snake_case ) - 1_2 ):
__magic_name__ :List[Any] = 1
for j in range(1_3 ):
product *= int(n[i + j] )
if product > largest_product:
__magic_name__ :str = product
return largest_product
if __name__ == "__main__":
print(f"{solution() = }")
| 0
|
from string import ascii_lowercase, ascii_uppercase
def UpperCAmelCase__ ( lowerCamelCase_ : str ):
if not sentence:
return ""
__a : Union[str, Any] = dict(zip(lowerCamelCase_ , lowerCamelCase_ ) )
return lower_to_upper.get(sentence[0] , sentence[0] ) + sentence[1:]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 47
| 0
|
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
def __UpperCAmelCase ( _snake_case : List[Any] ):
_lowercase = torch.load(lowercase__, map_location="cpu" )
if "model" in sd.keys():
_lowercase = torch.load(lowercase__, map_location="cpu" )["model"]
# pop unnecessary weights
_lowercase = [
"decoder.version",
"decoder.output_projection.weight",
]
for key in keys_to_delete:
if key in sd:
sd.pop(lowercase__ )
_lowercase = {
"decoder.project_in_dim.weight": "decoder.project_in.weight",
"decoder.project_out_dim.weight": "decoder.project_out.weight",
"decoder.layer_norm.weight": "decoder.final_layer_norm.weight",
"decoder.layer_norm.bias": "decoder.final_layer_norm.bias",
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
_lowercase = sd.pop(lowercase__ )
_lowercase = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
_lowercase = sd[key]
# We split QKV in separate Q,K,V
_lowercase = key.replace(".qkv_proj.", ".q_proj." )
_lowercase = key.replace(".qkv_proj.", ".k_proj." )
_lowercase = key.replace(".qkv_proj.", ".v_proj." )
_lowercase = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
_lowercase , _lowercase , _lowercase = torch.split(lowercase__, depth // 3, dim=0 )
_lowercase = q
_lowercase = k
_lowercase = v
del sd[key]
return sd
@torch.no_grad()
def __UpperCAmelCase ( _snake_case : Tuple, _snake_case : List[str], _snake_case : Optional[Any]=None ):
_lowercase = load_checkpoint(lowercase__ )
if config is not None:
_lowercase = OPTConfig.from_pretrained(lowercase__ )
else:
_lowercase = OPTConfig()
_lowercase = OPTModel(lowercase__ ).half().eval()
model.load_state_dict(lowercase__ )
# Check results
Path(lowercase__ ).mkdir(exist_ok=lowercase__ )
model.save_pretrained(lowercase__ )
if __name__ == "__main__":
__UpperCamelCase : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--fairseq_path",
type=str,
help=(
"path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
" https://huggingface.co/models?other=opt_metasq"
),
)
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
__UpperCamelCase : List[Any] = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)
| 719
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"MIT/ast-finetuned-audioset-10-10-0.4593": (
"https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"
),
}
class UpperCAmelCase_ ( lowercase__ ):
snake_case_ = """audio-spectrogram-transformer"""
def __init__( self : Optional[Any] , _lowercase : Union[str, Any]=7_6_8 , _lowercase : Any=1_2 , _lowercase : List[Any]=1_2 , _lowercase : Tuple=3_0_7_2 , _lowercase : List[str]="gelu" , _lowercase : List[Any]=0.0 , _lowercase : Optional[Any]=0.0 , _lowercase : List[Any]=0.02 , _lowercase : Any=1e-1_2 , _lowercase : Any=1_6 , _lowercase : int=True , _lowercase : Optional[int]=1_0 , _lowercase : Optional[int]=1_0 , _lowercase : str=1_0_2_4 , _lowercase : Dict=1_2_8 , **_lowercase : Optional[int] , ) -> Any:
super().__init__(**_lowercase )
_lowercase = hidden_size
_lowercase = num_hidden_layers
_lowercase = num_attention_heads
_lowercase = intermediate_size
_lowercase = hidden_act
_lowercase = hidden_dropout_prob
_lowercase = attention_probs_dropout_prob
_lowercase = initializer_range
_lowercase = layer_norm_eps
_lowercase = patch_size
_lowercase = qkv_bias
_lowercase = frequency_stride
_lowercase = time_stride
_lowercase = max_length
_lowercase = num_mel_bins
| 227
| 0
|
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __snake_case :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
snake_case__ : List[Any] = str(id_ )
snake_case__ : Dict = None
snake_case__ : List[Any] = None
snake_case__ : Optional[int] = []
snake_case__ : Tuple = {} # {vertex:distance}
def __lt__( self , __SCREAMING_SNAKE_CASE ):
return self.key < other.key
def __repr__( self ):
return self.id
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE ):
self.neighbors.append(__SCREAMING_SNAKE_CASE )
def __UpperCamelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
snake_case__ : Tuple = weight
def UpperCamelCase__ ( __magic_name__ : Optional[Any] , __magic_name__ : List[str] , __magic_name__ : int , __magic_name__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
graph[a - 1].add_neighbor(graph[b - 1] )
graph[b - 1].add_neighbor(graph[a - 1] )
# add the edges:
graph[a - 1].add_edge(graph[b - 1] , __magic_name__ )
graph[b - 1].add_edge(graph[a - 1] , __magic_name__ )
def UpperCamelCase__ ( __magic_name__ : list , __magic_name__ : Vertex ) -> list:
'''simple docstring'''
snake_case__ : Optional[int] = []
for u in graph:
snake_case__ : str = math.inf
snake_case__ : List[Any] = None
snake_case__ : Dict = 0
snake_case__ : Tuple = graph[:]
while q:
snake_case__ : Any = min(__magic_name__ )
q.remove(__magic_name__ )
for v in u.neighbors:
if (v in q) and (u.edges[v.id] < v.key):
snake_case__ : Optional[int] = u
snake_case__ : Dict = u.edges[v.id]
for i in range(1 , len(__magic_name__ ) ):
a.append((int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1) )
return a
def UpperCamelCase__ ( __magic_name__ : list , __magic_name__ : Vertex ) -> Iterator[tuple]:
'''simple docstring'''
for u in graph:
snake_case__ : Tuple = math.inf
snake_case__ : Tuple = None
snake_case__ : Optional[int] = 0
snake_case__ : str = list(__magic_name__ )
hq.heapify(__magic_name__ )
while h:
snake_case__ : str = hq.heappop(__magic_name__ )
for v in u.neighbors:
if (v in h) and (u.edges[v.id] < v.key):
snake_case__ : Union[str, Any] = u
snake_case__ : Dict = u.edges[v.id]
hq.heapify(__magic_name__ )
for i in range(1 , len(__magic_name__ ) ):
yield (int(graph[i].id ) + 1, int(graph[i].pi.id ) + 1)
def UpperCamelCase__ ( ) -> None:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38
|
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_snake_case = '''src/diffusers'''
_snake_case = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
_snake_case = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
_snake_case = spec.loader.load_module()
def __snake_case ( SCREAMING_SNAKE_CASE: Optional[Any] , SCREAMING_SNAKE_CASE: Optional[Any] ):
"""simple docstring"""
return line.startswith(SCREAMING_SNAKE_CASE ) or len(SCREAMING_SNAKE_CASE ) <= 1 or re.search(R'^\s*\)(\s*->.*:|:)\s*$' , SCREAMING_SNAKE_CASE ) is not None
def __snake_case ( SCREAMING_SNAKE_CASE: List[str] ):
"""simple docstring"""
_lowerCAmelCase = object_name.split('.' )
_lowerCAmelCase = 0
# First let's find the module where our object lives.
_lowerCAmelCase = parts[i]
while i < len(SCREAMING_SNAKE_CASE ) and not os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE , f"""{module}.py""" ) ):
i += 1
if i < len(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = os.path.join(SCREAMING_SNAKE_CASE , parts[i] )
if i >= len(SCREAMING_SNAKE_CASE ):
raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}.""" )
with open(os.path.join(SCREAMING_SNAKE_CASE , f"""{module}.py""" ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
_lowerCAmelCase = f.readlines()
# Now let's find the class / func in the code!
_lowerCAmelCase = ''
_lowerCAmelCase = 0
for name in parts[i + 1 :]:
while (
line_index < len(SCREAMING_SNAKE_CASE ) and re.search(Rf"""^{indent}(class|def)\s+{name}(\(|\:)""" , lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(SCREAMING_SNAKE_CASE ):
raise ValueError(f""" {object_name} does not match any function or class in {module}.""" )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
_lowerCAmelCase = line_index
while line_index < len(SCREAMING_SNAKE_CASE ) and _should_continue(lines[line_index] , SCREAMING_SNAKE_CASE ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_lowerCAmelCase = lines[start_index:line_index]
return "".join(SCREAMING_SNAKE_CASE )
_snake_case = re.compile(R'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
_snake_case = re.compile(R'''^\s*(\S+)->(\S+)(\s+.*|$)''')
_snake_case = re.compile(R'''<FILL\s+[^>]*>''')
def __snake_case ( SCREAMING_SNAKE_CASE: List[str] ):
"""simple docstring"""
_lowerCAmelCase = code.split('\n' )
_lowerCAmelCase = 0
while idx < len(SCREAMING_SNAKE_CASE ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(SCREAMING_SNAKE_CASE ):
return re.search(R'^(\s*)\S' , lines[idx] ).groups()[0]
return ""
def __snake_case ( SCREAMING_SNAKE_CASE: Optional[Any] ):
"""simple docstring"""
_lowerCAmelCase = len(get_indent(SCREAMING_SNAKE_CASE ) ) > 0
if has_indent:
_lowerCAmelCase = f"""class Bla:\n{code}"""
_lowerCAmelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=119 , preview=SCREAMING_SNAKE_CASE )
_lowerCAmelCase = black.format_str(SCREAMING_SNAKE_CASE , mode=SCREAMING_SNAKE_CASE )
_lowerCAmelCase , _lowerCAmelCase = style_docstrings_in_code(SCREAMING_SNAKE_CASE )
return result[len('class Bla:\n' ) :] if has_indent else result
def __snake_case ( SCREAMING_SNAKE_CASE: Optional[int] , SCREAMING_SNAKE_CASE: List[str]=False ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE , 'r' , encoding='utf-8' , newline='\n' ) as f:
_lowerCAmelCase = f.readlines()
_lowerCAmelCase = []
_lowerCAmelCase = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = search.groups()
_lowerCAmelCase = find_code_in_diffusers(SCREAMING_SNAKE_CASE )
_lowerCAmelCase = get_indent(SCREAMING_SNAKE_CASE )
_lowerCAmelCase = line_index + 1 if indent == theoretical_indent else line_index + 2
_lowerCAmelCase = theoretical_indent
_lowerCAmelCase = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
_lowerCAmelCase = True
while line_index < len(SCREAMING_SNAKE_CASE ) and should_continue:
line_index += 1
if line_index >= len(SCREAMING_SNAKE_CASE ):
break
_lowerCAmelCase = lines[line_index]
_lowerCAmelCase = _should_continue(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) and re.search(f"""^{indent}# End copy""" , SCREAMING_SNAKE_CASE ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
_lowerCAmelCase = lines[start_index:line_index]
_lowerCAmelCase = ''.join(SCREAMING_SNAKE_CASE )
# Remove any nested `Copied from` comments to avoid circular copies
_lowerCAmelCase = [line for line in theoretical_code.split('\n' ) if _re_copy_warning.search(SCREAMING_SNAKE_CASE ) is None]
_lowerCAmelCase = '\n'.join(SCREAMING_SNAKE_CASE )
# Before comparing, use the `replace_pattern` on the original code.
if len(SCREAMING_SNAKE_CASE ) > 0:
_lowerCAmelCase = replace_pattern.replace('with' , '' ).split(',' )
_lowerCAmelCase = [_re_replace_pattern.search(SCREAMING_SNAKE_CASE ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = pattern.groups()
_lowerCAmelCase = re.sub(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if option.strip() == "all-casing":
_lowerCAmelCase = re.sub(obja.lower() , obja.lower() , SCREAMING_SNAKE_CASE )
_lowerCAmelCase = re.sub(obja.upper() , obja.upper() , SCREAMING_SNAKE_CASE )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
_lowerCAmelCase = blackify(lines[start_index - 1] + theoretical_code )
_lowerCAmelCase = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
_lowerCAmelCase = lines[:start_index] + [theoretical_code] + lines[line_index:]
_lowerCAmelCase = start_index + 1
if overwrite and len(SCREAMING_SNAKE_CASE ) > 0:
# Warn the user a file has been modified.
print(f"""Detected changes, rewriting {filename}.""" )
with open(SCREAMING_SNAKE_CASE , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(SCREAMING_SNAKE_CASE )
return diffs
def __snake_case ( SCREAMING_SNAKE_CASE: bool = False ):
"""simple docstring"""
_lowerCAmelCase = glob.glob(os.path.join(SCREAMING_SNAKE_CASE , '**/*.py' ) , recursive=SCREAMING_SNAKE_CASE )
_lowerCAmelCase = []
for filename in all_files:
_lowerCAmelCase = is_copy_consistent(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
if not overwrite and len(SCREAMING_SNAKE_CASE ) > 0:
_lowerCAmelCase = '\n'.join(SCREAMING_SNAKE_CASE )
raise Exception(
'Found the following copy inconsistencies:\n'
+ diff
+ '\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.' )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_snake_case = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 580
| 0
|
"""simple docstring"""
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 lowerCamelCase :
'''simple docstring'''
a = MBartConfig
a = {}
a = "gelu"
def __init__( self : List[str] , _snake_case : Any , _snake_case : str=13 , _snake_case : str=7 , _snake_case : Optional[Any]=True , _snake_case : int=False , _snake_case : Tuple=99 , _snake_case : Tuple=32 , _snake_case : Optional[Any]=2 , _snake_case : str=4 , _snake_case : Dict=37 , _snake_case : int=0.1 , _snake_case : int=0.1 , _snake_case : List[Any]=20 , _snake_case : List[str]=2 , _snake_case : Tuple=1 , _snake_case : Dict=0 , ) -> List[str]:
SCREAMING_SNAKE_CASE__ = parent
SCREAMING_SNAKE_CASE__ = batch_size
SCREAMING_SNAKE_CASE__ = seq_length
SCREAMING_SNAKE_CASE__ = is_training
SCREAMING_SNAKE_CASE__ = use_labels
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = eos_token_id
SCREAMING_SNAKE_CASE__ = pad_token_id
SCREAMING_SNAKE_CASE__ = bos_token_id
def lowerCAmelCase_ ( self : List[str] ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
SCREAMING_SNAKE_CASE__ = tf.concat([input_ids, eos_tensor] , axis=1 )
SCREAMING_SNAKE_CASE__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE__ = 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 , )
SCREAMING_SNAKE_CASE__ = prepare_mbart_inputs_dict(_snake_case , _snake_case , _snake_case )
return config, inputs_dict
def lowerCAmelCase_ ( self : int , _snake_case : Any , _snake_case : List[str] ) -> Dict:
SCREAMING_SNAKE_CASE__ = TFMBartModel(config=_snake_case ).get_decoder()
SCREAMING_SNAKE_CASE__ = inputs_dict["input_ids"]
SCREAMING_SNAKE_CASE__ = input_ids[:1, :]
SCREAMING_SNAKE_CASE__ = inputs_dict["attention_mask"][:1, :]
SCREAMING_SNAKE_CASE__ = inputs_dict["head_mask"]
SCREAMING_SNAKE_CASE__ = 1
# first forward pass
SCREAMING_SNAKE_CASE__ = model(_snake_case , attention_mask=_snake_case , head_mask=_snake_case , use_cache=_snake_case )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = outputs.to_tuple()
SCREAMING_SNAKE_CASE__ = past_key_values[1]
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , ) -> Optional[int]:
if attention_mask is None:
SCREAMING_SNAKE_CASE__ = tf.cast(tf.math.not_equal(__UpperCAmelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
SCREAMING_SNAKE_CASE__ = 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:
SCREAMING_SNAKE_CASE__ = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
SCREAMING_SNAKE_CASE__ = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
SCREAMING_SNAKE_CASE__ = 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 lowerCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
a = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
a = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
a = (
{
"conversational": TFMBartForConditionalGeneration,
"feature-extraction": TFMBartModel,
"summarization": TFMBartForConditionalGeneration,
"text2text-generation": TFMBartForConditionalGeneration,
"translation": TFMBartForConditionalGeneration,
}
if is_tf_available()
else {}
)
a = True
a = False
a = False
def lowerCAmelCase_ ( self : int , _snake_case : Tuple , _snake_case : Optional[Any] , _snake_case : Dict , _snake_case : int , _snake_case : Union[str, Any] ) -> Tuple:
if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
# Exception encountered when calling layer '...'
return True
return False
def lowerCAmelCase_ ( self : Union[str, Any] ) -> int:
SCREAMING_SNAKE_CASE__ = TFMBartModelTester(self )
SCREAMING_SNAKE_CASE__ = ConfigTester(self , config_class=_snake_case )
def lowerCAmelCase_ ( self : Dict ) -> List[str]:
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_snake_case )
@require_sentencepiece
@require_tokenizers
@require_tf
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
a = [
" UN Chief Says There Is No Military Solution in Syria",
]
a = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
]
a = "facebook/mbart-large-en-ro"
@cached_property
def lowerCAmelCase_ ( self : Union[str, Any] ) -> int:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCAmelCase_ ( self : Tuple ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def lowerCAmelCase_ ( self : List[Any] , **_snake_case : str ) -> Any:
SCREAMING_SNAKE_CASE__ = self.translate_src_text(**_snake_case )
self.assertListEqual(self.expected_text , _snake_case )
def lowerCAmelCase_ ( self : Any , **_snake_case : List[str] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ = self.tokenizer(self.src_text , **_snake_case , return_tensors="tf" )
SCREAMING_SNAKE_CASE__ = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 )
SCREAMING_SNAKE_CASE__ = self.tokenizer.batch_decode(_snake_case , skip_special_tokens=_snake_case )
return generated_words
@slow
def lowerCAmelCase_ ( self : Optional[int] ) -> Union[str, Any]:
self._assert_generated_batch_equal_expected()
| 703
|
"""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 SCREAMING_SNAKE_CASE ( ) -> List[str]:
SCREAMING_SNAKE_CASE__ = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
SCREAMING_SNAKE_CASE__ = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw ).convert("RGB" )
return image
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ = []
# 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 SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ = dct.pop(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = val
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> Dict:
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
SCREAMING_SNAKE_CASE__ = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias""" )
SCREAMING_SNAKE_CASE__ = state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
SCREAMING_SNAKE_CASE__ = torch.cat((q_bias, torch.zeros_like(__UpperCAmelCase , requires_grad=__UpperCAmelCase ), v_bias) )
SCREAMING_SNAKE_CASE__ = qkv_bias
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
SCREAMING_SNAKE_CASE__ = 364 if "coco" in model_name else 224
SCREAMING_SNAKE_CASE__ = BlipaVisionConfig(image_size=__UpperCAmelCase ).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:
SCREAMING_SNAKE_CASE__ = OPTConfig.from_pretrained("facebook/opt-2.7b" , eos_token_id=__UpperCAmelCase ).to_dict()
elif "opt-6.7b" in model_name:
SCREAMING_SNAKE_CASE__ = OPTConfig.from_pretrained("facebook/opt-6.7b" , eos_token_id=__UpperCAmelCase ).to_dict()
elif "t5-xl" in model_name:
SCREAMING_SNAKE_CASE__ = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
SCREAMING_SNAKE_CASE__ = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
SCREAMING_SNAKE_CASE__ = BlipaConfig(vision_config=__UpperCAmelCase , text_config=__UpperCAmelCase )
return config, image_size
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=False ) -> List[str]:
SCREAMING_SNAKE_CASE__ = (
AutoTokenizer.from_pretrained("facebook/opt-2.7b" )
if "opt" in model_name
else AutoTokenizer.from_pretrained("google/flan-t5-xl" )
)
SCREAMING_SNAKE_CASE__ = tokenizer("\n" , add_special_tokens=__UpperCAmelCase ).input_ids[0]
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = get_blipa_config(__UpperCAmelCase , eos_token_id=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = BlipaForConditionalGeneration(__UpperCAmelCase ).eval()
SCREAMING_SNAKE_CASE__ = {
"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"),
}
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
SCREAMING_SNAKE_CASE__ = "cuda" if torch.cuda.is_available() else "cpu"
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = load_model_and_preprocess(
name=__UpperCAmelCase , model_type=__UpperCAmelCase , is_eval=__UpperCAmelCase , device=__UpperCAmelCase )
original_model.eval()
print("Done!" )
# update state dict keys
SCREAMING_SNAKE_CASE__ = original_model.state_dict()
SCREAMING_SNAKE_CASE__ = create_rename_keys(__UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
SCREAMING_SNAKE_CASE__ = state_dict.pop(__UpperCAmelCase )
if key.startswith("Qformer.bert" ):
SCREAMING_SNAKE_CASE__ = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
SCREAMING_SNAKE_CASE__ = key.replace("self" , "attention" )
if "opt_proj" in key:
SCREAMING_SNAKE_CASE__ = key.replace("opt_proj" , "language_projection" )
if "t5_proj" in key:
SCREAMING_SNAKE_CASE__ = key.replace("t5_proj" , "language_projection" )
if key.startswith("opt" ):
SCREAMING_SNAKE_CASE__ = key.replace("opt" , "language" )
if key.startswith("t5" ):
SCREAMING_SNAKE_CASE__ = key.replace("t5" , "language" )
SCREAMING_SNAKE_CASE__ = val
# read in qv biases
read_in_q_v_bias(__UpperCAmelCase , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = hf_model.load_state_dict(__UpperCAmelCase , strict=__UpperCAmelCase )
assert len(__UpperCAmelCase ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
SCREAMING_SNAKE_CASE__ = load_demo_image()
SCREAMING_SNAKE_CASE__ = vis_processors["eval"](__UpperCAmelCase ).unsqueeze(0 ).to(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = tokenizer(["\n"] , return_tensors="pt" ).input_ids.to(__UpperCAmelCase )
# create processor
SCREAMING_SNAKE_CASE__ = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=__UpperCAmelCase , image_std=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = BlipaProcessor(image_processor=__UpperCAmelCase , tokenizer=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = processor(images=__UpperCAmelCase , return_tensors="pt" ).pixel_values.to(__UpperCAmelCase )
# make sure processor creates exact same pixel values
assert torch.allclose(__UpperCAmelCase , __UpperCAmelCase )
original_model.to(__UpperCAmelCase )
hf_model.to(__UpperCAmelCase )
with torch.no_grad():
if "opt" in model_name:
SCREAMING_SNAKE_CASE__ = original_model({"image": original_pixel_values, "text_input": [""]} ).logits
SCREAMING_SNAKE_CASE__ = hf_model(__UpperCAmelCase , __UpperCAmelCase ).logits
else:
SCREAMING_SNAKE_CASE__ = original_model(
{"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]} ).logits
SCREAMING_SNAKE_CASE__ = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -100 )
SCREAMING_SNAKE_CASE__ = hf_model(__UpperCAmelCase , __UpperCAmelCase , labels=__UpperCAmelCase ).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":
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-41.5_850, -4.4_440, -8.9_922], [-47.4_322, -5.9_143, -1.7_340]] , device=__UpperCAmelCase )
assert torch.allclose(logits[0, :3, :3] , __UpperCAmelCase , atol=1e-4 )
elif model_name == "blip2-flan-t5-xl-coco":
SCREAMING_SNAKE_CASE__ = torch.tensor(
[[-57.0_109, -9.8_967, -12.6_280], [-68.6_578, -12.7_191, -10.5_065]] , device=__UpperCAmelCase )
else:
# cast to same type
SCREAMING_SNAKE_CASE__ = logits.dtype
assert torch.allclose(original_logits.to(__UpperCAmelCase ) , __UpperCAmelCase , atol=1e-2 )
print("Looks ok!" )
print("Generating a caption..." )
SCREAMING_SNAKE_CASE__ = ""
SCREAMING_SNAKE_CASE__ = tokenizer(__UpperCAmelCase , return_tensors="pt" ).input_ids.to(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = original_model.generate({"image": original_pixel_values} )
SCREAMING_SNAKE_CASE__ = hf_model.generate(
__UpperCAmelCase , __UpperCAmelCase , do_sample=__UpperCAmelCase , 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:" , __UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = input_ids.shape[1]
SCREAMING_SNAKE_CASE__ = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = [text.strip() for text in output_text]
print("HF generation:" , __UpperCAmelCase )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__UpperCAmelCase )
hf_model.save_pretrained(__UpperCAmelCase )
if push_to_hub:
processor.push_to_hub(F"""nielsr/{model_name}""" )
hf_model.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
_A = [
'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',
)
_A = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 538
| 0
|
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