code
stringlengths 86
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| code_codestyle
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| style_context
stringlengths 87
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| style_context_codestyle
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|---|---|---|---|---|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__snake_case :Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__snake_case :Any = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f'{len(upper_files)} files contain uppercase characters:')
print('''\n'''.join(upper_files) + '''\n''')
__snake_case :Tuple = [file for file in filepaths if ''' ''' in file]
if space_files:
print(f'{len(space_files)} files contain space characters:')
print('''\n'''.join(space_files) + '''\n''')
__snake_case :Optional[int] = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(f'{len(hyphen_files)} files contain hyphen characters:')
print('''\n'''.join(hyphen_files) + '''\n''')
__snake_case :Optional[int] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f'{len(nodir_files)} files are not in a directory:')
print('''\n'''.join(nodir_files) + '''\n''')
__snake_case :int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 49
|
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : str = CpmAntTokenizer
__lowercase : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
super().setUp()
lowercase__: Any = [
'<d>',
'</d>',
'<s>',
'</s>',
'</_>',
'<unk>',
'<pad>',
'</n>',
'我',
'是',
'C',
'P',
'M',
'A',
'n',
't',
]
lowercase__: List[Any] = 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] ) )
@tooslow
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
lowercase__: Optional[int] = CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b' )
lowercase__: Optional[Any] = '今天天气真好！'
lowercase__: str = ['今天', '天气', '真', '好', '！']
lowercase__: Optional[Any] = tokenizer.tokenize(lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
lowercase__: List[str] = '今天天气真好！'
lowercase__: List[str] = [tokenizer.bos_token] + tokens
lowercase__: Tuple = [6, 9_802, 14_962, 2_082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ ) , lowerCAmelCase__ )
lowercase__: Any = tokenizer.decode(lowerCAmelCase__ )
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
| 196
| 0
|
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
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 (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _snake_case :
def __init__( self , _a , _a=13 , _a=30 , _a=2 , _a=3 , _a=True , _a=True , _a=32 , _a=5 , _a=4 , _a=37 , _a="gelu" , _a=0.1 , _a=0.1 , _a=10 , _a=0.02 , _a=3 , _a=None , _a=2 , ):
__magic_name__ : Tuple = parent
__magic_name__ : Any = batch_size
__magic_name__ : List[str] = image_size
__magic_name__ : List[Any] = patch_size
__magic_name__ : Any = num_channels
__magic_name__ : List[str] = is_training
__magic_name__ : int = use_labels
__magic_name__ : Optional[Any] = hidden_size
__magic_name__ : Union[str, Any] = num_hidden_layers
__magic_name__ : int = num_attention_heads
__magic_name__ : List[Any] = intermediate_size
__magic_name__ : Union[str, Any] = hidden_act
__magic_name__ : Optional[Any] = hidden_dropout_prob
__magic_name__ : Any = attention_probs_dropout_prob
__magic_name__ : Tuple = type_sequence_label_size
__magic_name__ : List[Any] = initializer_range
__magic_name__ : Union[str, Any] = scope
__magic_name__ : str = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
__magic_name__ : Union[str, Any] = (image_size // patch_size) ** 2
__magic_name__ : Optional[Any] = num_patches + 2
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ : int = None
if self.use_labels:
__magic_name__ : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ : List[str] = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE ( self ):
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_a , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : List[Any] = DeiTModel(config=_a )
model.to(_a )
model.eval()
__magic_name__ : Dict = model(_a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : List[Any] = DeiTForMaskedImageModeling(config=_a )
model.to(_a )
model.eval()
__magic_name__ : Any = model(_a )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
__magic_name__ : Union[str, Any] = 1
__magic_name__ : Optional[Any] = DeiTForMaskedImageModeling(_a )
model.to(_a )
model.eval()
__magic_name__ : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__magic_name__ : Any = model(_a )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a ):
__magic_name__ : Any = self.type_sequence_label_size
__magic_name__ : List[Any] = DeiTForImageClassification(_a )
model.to(_a )
model.eval()
__magic_name__ : Optional[int] = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
__magic_name__ : Tuple = 1
__magic_name__ : Optional[Any] = DeiTForImageClassification(_a )
model.to(_a )
model.eval()
__magic_name__ : int = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
__magic_name__ : str = model(_a , labels=_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = self.prepare_config_and_inputs()
(
(
__magic_name__
) , (
__magic_name__
) , (
__magic_name__
) ,
) : Optional[Any] = config_and_inputs
__magic_name__ : Tuple = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _snake_case ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase__ = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
UpperCamelCase__ = (
{
'feature-extraction': DeiTModel,
'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
UpperCamelCase__ = False
UpperCamelCase__ = False
UpperCamelCase__ = False
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Optional[Any] = DeiTModelTester(self )
__magic_name__ : Union[str, Any] = ConfigTester(self , config_class=_a , has_text_modality=_a , hidden_size=37 )
def SCREAMING_SNAKE_CASE ( self ):
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def SCREAMING_SNAKE_CASE ( self ):
pass
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : int = model_class(_a )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__magic_name__ : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_a , nn.Linear ) )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Dict = model_class(_a )
__magic_name__ : Union[str, Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : List[str] = [*signature.parameters.keys()]
__magic_name__ : str = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_a )
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_a )
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a=False ):
__magic_name__ : Dict = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def SCREAMING_SNAKE_CASE ( self ):
if not self.model_tester.is_training:
return
__magic_name__ , __magic_name__ : str = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : int = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_a )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
__magic_name__ : str = model_class(_a )
model.to(_a )
model.train()
__magic_name__ : int = self._prepare_for_class(_a , _a , return_labels=_a )
__magic_name__ : Tuple = model(**_a ).loss
loss.backward()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
__magic_name__ : Tuple = False
__magic_name__ : List[str] = True
for model_class in self.all_model_classes:
if model_class in get_values(_a ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
__magic_name__ : str = model_class(_a )
model.gradient_checkpointing_enable()
model.to(_a )
model.train()
__magic_name__ : Union[str, Any] = self._prepare_for_class(_a , _a , return_labels=_a )
__magic_name__ : Optional[int] = model(**_a ).loss
loss.backward()
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ , __magic_name__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : int = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_a ),
*get_values(_a ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}''' ):
__magic_name__ : List[Any] = problem_type["title"]
__magic_name__ : List[Any] = problem_type["num_labels"]
__magic_name__ : Any = model_class(_a )
model.to(_a )
model.train()
__magic_name__ : Optional[Any] = self._prepare_for_class(_a , _a , return_labels=_a )
if problem_type["num_labels"] > 1:
__magic_name__ : Optional[int] = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
__magic_name__ : List[Any] = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_a ) as warning_list:
__magic_name__ : Optional[int] = model(**_a ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def SCREAMING_SNAKE_CASE ( self ):
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Union[str, Any] = DeiTModel.from_pretrained(_a )
self.assertIsNotNone(_a )
def lowerCAmelCase_ ( ) -> Any:
'''simple docstring'''
__magic_name__ : Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
@cached_property
def SCREAMING_SNAKE_CASE ( self ):
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
_a )
__magic_name__ : Any = self.default_image_processor
__magic_name__ : str = prepare_img()
__magic_name__ : Union[str, Any] = image_processor(images=_a , return_tensors="pt" ).to(_a )
# forward pass
with torch.no_grad():
__magic_name__ : Optional[int] = model(**_a )
# verify the logits
__magic_name__ : Union[str, Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , _a )
__magic_name__ : Any = torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(_a )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _a , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def SCREAMING_SNAKE_CASE ( self ):
__magic_name__ : List[str] = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224" , torch_dtype=torch.floataa , device_map="auto" )
__magic_name__ : Optional[int] = self.default_image_processor
__magic_name__ : Dict = prepare_img()
__magic_name__ : Union[str, Any] = image_processor(images=_a , return_tensors="pt" )
__magic_name__ : Optional[Any] = inputs.pixel_values.to(_a )
# forward pass to make sure inference works in fp16
with torch.no_grad():
__magic_name__ : Tuple = model(_a )
| 41
|
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
snake_case : Union[str, Any] = logging.get_logger(__name__)
class _snake_case :
UpperCamelCase__ = 42
UpperCamelCase__ = None
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE ( self , _a ):
raise NotImplementedError
def SCREAMING_SNAKE_CASE ( self ):
if not self.is_available():
raise RuntimeError(
f'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def SCREAMING_SNAKE_CASE ( cls ):
return f'''`pip install {cls.pip_package or cls.name}`'''
class _snake_case ( snake_case ):
UpperCamelCase__ = 'optuna'
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_optuna_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_optuna(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_optuna(_a )
class _snake_case ( snake_case ):
UpperCamelCase__ = 'ray'
UpperCamelCase__ = '\'ray[tune]\''
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_ray_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_ray(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_ray(_a )
class _snake_case ( snake_case ):
UpperCamelCase__ = 'sigopt'
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_sigopt_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_sigopt(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_sigopt(_a )
class _snake_case ( snake_case ):
UpperCamelCase__ = 'wandb'
@staticmethod
def SCREAMING_SNAKE_CASE ( ):
return is_wandb_available()
def SCREAMING_SNAKE_CASE ( self , _a , _a , _a , **_a ):
return run_hp_search_wandb(_a , _a , _a , **_a )
def SCREAMING_SNAKE_CASE ( self , _a ):
return default_hp_space_wandb(_a )
snake_case : int = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def lowerCAmelCase_ ( ) -> str:
'''simple docstring'''
__magic_name__ : List[Any] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(_snake_case ) > 0:
__magic_name__ : Dict = available_backends[0].name
if len(_snake_case ) > 1:
logger.info(
F'''{len(_snake_case )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
"No hyperparameter search backend available.\n"
+ "\n".join(
F''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 41
| 1
|
"""simple docstring"""
from collections import defaultdict
from math import gcd
def a_ ( _lowerCAmelCase : int = 150_0000 ):
'''simple docstring'''
lowercase__ : defaultdict = defaultdict(_lowerCAmelCase )
lowercase__ : int = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _lowerCAmelCase , 2 ):
if gcd(_lowerCAmelCase , _lowerCAmelCase ) > 1:
continue
lowercase__ : Tuple = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_lowerCAmelCase , limit + 1 , _lowerCAmelCase ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 77
|
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args )
| 97
| 0
|
"""simple docstring"""
import random
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = False ) -> dict:
snake_case_ = {i: [] for i in range(_SCREAMING_SNAKE_CASE )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(_SCREAMING_SNAKE_CASE )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(i + 1 , _SCREAMING_SNAKE_CASE ):
if random.random() < probability:
graph[i].append(_SCREAMING_SNAKE_CASE )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(_SCREAMING_SNAKE_CASE )
return graph
def _a ( _SCREAMING_SNAKE_CASE ) -> dict:
return {
i: [j for j in range(_SCREAMING_SNAKE_CASE ) if i != j] for i in range(_SCREAMING_SNAKE_CASE )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 233
|
"""simple docstring"""
import math
class __A :
'''simple docstring'''
def __init__( self : List[str] , UpperCAmelCase_ : Tuple=0 ) ->Optional[int]: # a graph with Node 0,1,...,N-1
"""simple docstring"""
snake_case_ = n
snake_case_ = [
[math.inf for j in range(0 , UpperCAmelCase_ )] for i in range(0 , UpperCAmelCase_ )
] # adjacency matrix for weight
snake_case_ = [
[math.inf for j in range(0 , UpperCAmelCase_ )] for i in range(0 , UpperCAmelCase_ )
] # dp[i][j] stores minimum distance from i to j
def lowerCAmelCase ( self : List[str] , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] ) ->Optional[int]:
"""simple docstring"""
snake_case_ = w
def lowerCAmelCase ( self : Optional[int] ) ->Any:
"""simple docstring"""
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
snake_case_ = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def lowerCAmelCase ( self : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[Any] ) ->Union[str, Any]:
"""simple docstring"""
return self.dp[u][v]
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 233
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:Tuple = {"""configuration_unispeech""": ["""UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP""", """UniSpeechConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_:List[Any] = [
"""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_:List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 116
|
from __future__ import annotations
from math import ceil, floor, sqrt
def __UpperCamelCase ( _lowerCAmelCase = 200_0000 ) -> int:
"""simple docstring"""
A : list[int] = [0]
A : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbers.append(triangle_numbers[-1] + idx )
# we want this to be as close as possible to target
A : int = 0
# the area corresponding to the grid that gives the product closest to target
A : int = 0
# an estimate of b, using the quadratic formula
A : float
# the largest integer less than b_estimate
A : int
# the largest integer less than b_estimate
A : int
# the triangle number corresponding to b_floor
A : int
# the triangle number corresponding to b_ceil
A : int
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1 ):
A : Union[str, Any] = (-1 + sqrt(1 + 8 * target / triangle_a )) / 2
A : List[Any] = floor(_lowerCAmelCase )
A : Tuple = ceil(_lowerCAmelCase )
A : int = triangle_numbers[b_floor]
A : Dict = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a ) < abs(
target - best_product ):
A : Optional[int] = triangle_b_first_guess * triangle_a
A : Optional[int] = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a ) < abs(
target - best_product ):
A : Tuple = triangle_b_second_guess * triangle_a
A : Tuple = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 116
| 1
|
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> int:
"""simple docstring"""
__lowerCamelCase = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
__lowerCamelCase = n - k
# Calculate C(n,k)
for i in range(UpperCamelCase__ ):
result *= n - i
result //= i + 1
return result
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , UpperCamelCase__ ) // (node_count + 1)
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError('factorial() not defined for negative values' )
__lowerCamelCase = 1
for i in range(1 , n + 1 ):
result *= i
return result
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> int:
"""simple docstring"""
return catalan_number(UpperCamelCase__ ) * factorial(UpperCamelCase__ )
if __name__ == "__main__":
__A = int(input("Enter the number of nodes: ").strip() or 0)
if node_count <= 0:
raise ValueError("We need some nodes to work with.")
print(
f'''Given {node_count} nodes, there are {binary_tree_count(node_count)} '''
f'''binary trees and {catalan_number(node_count)} binary search trees.'''
)
| 348
|
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
__A = logging.get_logger(__name__)
__A = TypeVar("DatasetType", Dataset, IterableDataset)
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[List[float]] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : Literal["first_exhausted", "all_exhausted"] = "first_exhausted" , ) -> DatasetType:
"""simple docstring"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError('Unable to interleave an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy.""" )
if dataset_type is Dataset:
return _interleave_map_style_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
else:
return _interleave_iterable_datasets(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , stopping_strategy=UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : List[DatasetType] , UpperCamelCase__ : Optional[DatasetInfo] = None , UpperCamelCase__ : Optional[NamedSplit] = None , UpperCamelCase__ : int = 0 , ) -> DatasetType:
"""simple docstring"""
if not dsets:
raise ValueError('Unable to concatenate an empty list of datasets.' )
for i, dataset in enumerate(UpperCamelCase__ ):
if not isinstance(UpperCamelCase__ , (Dataset, IterableDataset) ):
if isinstance(UpperCamelCase__ , (DatasetDict, IterableDatasetDict) ):
if not dataset:
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
'is an empty dataset dictionary.' )
raise ValueError(
F"""Dataset at position {i} has at least one split: {list(UpperCamelCase__ )}\n"""
F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(UpperCamelCase__ ) )}']""" )
raise ValueError(
F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase__ ).__name__}.""" )
if i == 0:
__lowerCamelCase , __lowerCamelCase = (
(Dataset, IterableDataset) if isinstance(UpperCamelCase__ , UpperCamelCase__ ) else (IterableDataset, Dataset)
)
elif not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError(
F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.""" )
if dataset_type is Dataset:
return _concatenate_map_style_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
else:
return _concatenate_iterable_datasets(UpperCamelCase__ , info=UpperCamelCase__ , split=UpperCamelCase__ , axis=UpperCamelCase__ )
| 348
| 1
|
import unittest
from transformers import AlbertConfig, is_torch_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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
)
from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class _lowercase :
'''simple docstring'''
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=7 , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=True , snake_case__=99 , snake_case__=16 , snake_case__=36 , snake_case__=6 , snake_case__=6 , snake_case__=6 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=512 , snake_case__=16 , snake_case__=2 , snake_case__=0.02 , snake_case__=3 , snake_case__=4 , snake_case__=None , ):
'''simple docstring'''
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = seq_length
UpperCamelCase_ = is_training
UpperCamelCase_ = use_input_mask
UpperCamelCase_ = use_token_type_ids
UpperCamelCase_ = use_labels
UpperCamelCase_ = vocab_size
UpperCamelCase_ = embedding_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_hidden_groups
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_act
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = type_vocab_size
UpperCamelCase_ = type_sequence_label_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = num_labels
UpperCamelCase_ = num_choices
UpperCamelCase_ = scope
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = None
if self.use_input_mask:
UpperCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase_ = None
if self.use_token_type_ids:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase_ = None
UpperCamelCase_ = None
UpperCamelCase_ = None
if self.use_labels:
UpperCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase_ = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _lowerCamelCase ( self ):
'''simple docstring'''
return AlbertConfig(
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 , num_hidden_groups=self.num_hidden_groups , )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = AlbertModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ )
UpperCamelCase_ = model(snake_case__ , token_type_ids=snake_case__ )
UpperCamelCase_ = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = AlbertForPreTraining(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ , sentence_order_label=snake_case__ , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.sop_logits.shape , (self.batch_size, config.num_labels) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = AlbertForMaskedLM(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = AlbertForQuestionAnswering(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , start_positions=snake_case__ , end_positions=snake_case__ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = self.num_labels
UpperCamelCase_ = AlbertForSequenceClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = self.num_labels
UpperCamelCase_ = AlbertForTokenClassification(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = self.num_choices
UpperCamelCase_ = AlbertForMultipleChoice(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCamelCase_ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
UpperCamelCase_ = model(
snake_case__ , attention_mask=snake_case__ , token_type_ids=snake_case__ , labels=snake_case__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.prepare_config_and_inputs()
(
(
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) , (
UpperCamelCase_
) ,
) = config_and_inputs
UpperCamelCase_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _lowercase (a_ , a_ , unittest.TestCase ):
'''simple docstring'''
lowercase__ = (
(
AlbertModel,
AlbertForPreTraining,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowercase__ = (
{
"""feature-extraction""": AlbertModel,
"""fill-mask""": AlbertForMaskedLM,
"""question-answering""": AlbertForQuestionAnswering,
"""text-classification""": AlbertForSequenceClassification,
"""token-classification""": AlbertForTokenClassification,
"""zero-shot""": AlbertForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = True
def _lowerCamelCase ( self , snake_case__ , snake_case__ , snake_case__=False ):
'''simple docstring'''
UpperCamelCase_ = super()._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
if return_labels:
if model_class in get_values(snake_case__ ):
UpperCamelCase_ = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=snake_case__ )
UpperCamelCase_ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=snake_case__ )
return inputs_dict
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = AlbertModelTester(self )
UpperCamelCase_ = ConfigTester(self , config_class=snake_case__ , hidden_size=37 )
def _lowerCamelCase ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*snake_case__ )
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCamelCase_ = type
self.model_tester.create_and_check_model(*snake_case__ )
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase_ = AlbertModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
@require_torch
class _lowercase (unittest.TestCase ):
'''simple docstring'''
@slow
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = AlbertModel.from_pretrained("albert-base-v2" )
UpperCamelCase_ = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
UpperCamelCase_ = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
UpperCamelCase_ = model(snake_case__ , attention_mask=snake_case__ )[0]
UpperCamelCase_ = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , snake_case__ )
UpperCamelCase_ = torch.tensor(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , snake_case__ , atol=1e-4 ) )
| 128
|
UpperCAmelCase : dict[tuple[int, int, int], int] ={}
def _lowerCAmelCase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
UpperCamelCase_ = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
UpperCamelCase_ = _calculate(days - 1 , _lowerCAmelCase , late + 1)
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
UpperCamelCase_ = _calculate(days - 1 , absent + 1 , 0)
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
UpperCamelCase_ = _calculate(days - 1 , _lowerCAmelCase , 0)
UpperCamelCase_ = state_late + state_absent + state_ontime
UpperCamelCase_ = prizestrings
return prizestrings
def _lowerCAmelCase (_lowerCAmelCase = 30):
return _calculate(_lowerCAmelCase , absent=0 , late=0)
if __name__ == "__main__":
print(solution())
| 128
| 1
|
'''simple docstring'''
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _lowercase = "" , _lowercase = False ):
"""simple docstring"""
_lowerCAmelCase = {}
# A node will be a leaf if the tree contains its word
_lowerCAmelCase = is_leaf
_lowerCAmelCase = prefix
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = 0
for q, w in zip(self.prefix , _lowercase ):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def _lowercase ( self , _lowercase ):
"""simple docstring"""
for word in words:
self.insert(_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
if self.prefix == word:
_lowerCAmelCase = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
_lowerCAmelCase = RadixNode(prefix=_lowercase , is_leaf=_lowercase )
else:
_lowerCAmelCase = self.nodes[word[0]]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = incoming_node.match(
_lowercase )
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(_lowercase )
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
_lowerCAmelCase = remaining_prefix
_lowerCAmelCase = self.nodes[matching_string[0]]
_lowerCAmelCase = RadixNode(_lowercase , _lowercase )
_lowerCAmelCase = aux_node
if remaining_word == "":
_lowerCAmelCase = True
else:
self.nodes[matching_string[0]].insert(_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.nodes.get(word[0] , _lowercase )
if not incoming_node:
return False
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = incoming_node.match(
_lowercase )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(_lowercase )
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = self.nodes.get(word[0] , _lowercase )
if not incoming_node:
return False
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = incoming_node.match(
_lowercase )
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(_lowercase )
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes ) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes ) == 1 and not self.is_leaf:
_lowerCAmelCase = list(self.nodes.values() )[0]
_lowerCAmelCase = merging_node.is_leaf
self.prefix += merging_node.prefix
_lowerCAmelCase = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes ) > 1:
_lowerCAmelCase = False
# If there is 1 edge, we merge it with its child
else:
_lowerCAmelCase = list(incoming_node.nodes.values() )[0]
_lowerCAmelCase = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
_lowerCAmelCase = merging_node.nodes
return True
def _lowercase ( self , _lowercase = 0 ):
"""simple docstring"""
if self.prefix != "":
print("""-""" * height , self.prefix , """ (leaf)""" if self.is_leaf else """""" )
for value in self.nodes.values():
value.print_tree(height + 1 )
def A ():
_lowerCAmelCase = """banana bananas bandana band apple all beast""".split()
_lowerCAmelCase = RadixNode()
root.insert_many(__lowerCamelCase )
assert all(root.find(__lowerCamelCase ) for word in words )
assert not root.find("""bandanas""" )
assert not root.find("""apps""" )
root.delete("""all""" )
assert not root.find("""all""" )
root.delete("""banana""" )
assert not root.find("""banana""" )
assert root.find("""bananas""" )
return True
def A ():
assert test_trie()
def A ():
_lowerCAmelCase = RadixNode()
_lowerCAmelCase = """banana bananas bandanas bandana band apple all beast""".split()
root.insert_many(__lowerCamelCase )
print("""Words:""" , __lowerCamelCase )
print("""Tree:""" )
root.print_tree()
if __name__ == "__main__":
main()
| 229
|
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : jnp.ndarray
@flax_register_to_config
class UpperCAmelCase_ ( nn.Module , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : int = 3_2
_lowercase : int = 4
_lowercase : int = 4
_lowercase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_lowercase : Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
_lowercase : Union[bool, Tuple[bool]] = False
_lowercase : Tuple[int] = (3_2_0, 6_4_0, 1_2_8_0, 1_2_8_0)
_lowercase : int = 2
_lowercase : Union[int, Tuple[int]] = 8
_lowercase : Optional[Union[int, Tuple[int]]] = None
_lowercase : int = 1_2_8_0
_lowercase : float = 0.0
_lowercase : bool = False
_lowercase : jnp.dtype = jnp.floataa
_lowercase : bool = True
_lowercase : int = 0
_lowercase : bool = False
def _lowercase ( self , _lowercase ):
"""simple docstring"""
_lowerCAmelCase = (1, self.in_channels, self.sample_size, self.sample_size)
_lowerCAmelCase = jnp.zeros(_lowercase , dtype=jnp.floataa )
_lowerCAmelCase = jnp.ones((1,) , dtype=jnp.intaa )
_lowerCAmelCase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
_lowerCAmelCase , _lowerCAmelCase = jax.random.split(_lowercase )
_lowerCAmelCase = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(_lowercase , _lowercase , _lowercase , _lowercase )["params"]
def _lowercase ( self ):
"""simple docstring"""
_lowerCAmelCase = self.block_out_channels
_lowerCAmelCase = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"""At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
_lowerCAmelCase = self.num_attention_heads or self.attention_head_dim
# input
_lowerCAmelCase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
_lowerCAmelCase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
_lowerCAmelCase = FlaxTimestepEmbedding(_lowercase , dtype=self.dtype )
_lowerCAmelCase = self.only_cross_attention
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = (num_attention_heads,) * len(self.down_block_types )
# down
_lowerCAmelCase = []
_lowerCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
_lowerCAmelCase = output_channel
_lowerCAmelCase = block_out_channels[i]
_lowerCAmelCase = i == len(_lowercase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
_lowerCAmelCase = FlaxCrossAttnDownBlockaD(
in_channels=_lowercase , out_channels=_lowercase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_lowerCAmelCase = FlaxDownBlockaD(
in_channels=_lowercase , out_channels=_lowercase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(_lowercase )
_lowerCAmelCase = down_blocks
# mid
_lowerCAmelCase = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
_lowerCAmelCase = []
_lowerCAmelCase = list(reversed(_lowercase ) )
_lowerCAmelCase = list(reversed(_lowercase ) )
_lowerCAmelCase = list(reversed(_lowercase ) )
_lowerCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
_lowerCAmelCase = output_channel
_lowerCAmelCase = reversed_block_out_channels[i]
_lowerCAmelCase = reversed_block_out_channels[min(i + 1 , len(_lowercase ) - 1 )]
_lowerCAmelCase = i == len(_lowercase ) - 1
if up_block_type == "CrossAttnUpBlock2D":
_lowerCAmelCase = FlaxCrossAttnUpBlockaD(
in_channels=_lowercase , out_channels=_lowercase , prev_output_channel=_lowercase , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
_lowerCAmelCase = FlaxUpBlockaD(
in_channels=_lowercase , out_channels=_lowercase , prev_output_channel=_lowercase , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(_lowercase )
_lowerCAmelCase = output_channel
_lowerCAmelCase = up_blocks
# out
_lowerCAmelCase = nn.GroupNorm(num_groups=32 , epsilon=1e-5 )
_lowerCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self , _lowercase , _lowercase , _lowercase , _lowercase=None , _lowercase=None , _lowercase = True , _lowercase = False , ):
"""simple docstring"""
if not isinstance(_lowercase , jnp.ndarray ):
_lowerCAmelCase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(_lowercase , jnp.ndarray ) and len(timesteps.shape ) == 0:
_lowerCAmelCase = timesteps.astype(dtype=jnp.floataa )
_lowerCAmelCase = jnp.expand_dims(_lowercase , 0 )
_lowerCAmelCase = self.time_proj(_lowercase )
_lowerCAmelCase = self.time_embedding(_lowercase )
# 2. pre-process
_lowerCAmelCase = jnp.transpose(_lowercase , (0, 2, 3, 1) )
_lowerCAmelCase = self.conv_in(_lowercase )
# 3. down
_lowerCAmelCase = (sample,)
for down_block in self.down_blocks:
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase , _lowerCAmelCase = down_block(_lowercase , _lowercase , _lowercase , deterministic=not train )
else:
_lowerCAmelCase , _lowerCAmelCase = down_block(_lowercase , _lowercase , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
_lowerCAmelCase = ()
for down_block_res_sample, down_block_additional_residual in zip(
_lowercase , _lowercase ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
_lowerCAmelCase = new_down_block_res_samples
# 4. mid
_lowerCAmelCase = self.mid_block(_lowercase , _lowercase , _lowercase , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
_lowerCAmelCase = down_block_res_samples[-(self.layers_per_block + 1) :]
_lowerCAmelCase = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(_lowercase , _lowercase ):
_lowerCAmelCase = up_block(
_lowercase , temb=_lowercase , encoder_hidden_states=_lowercase , res_hidden_states_tuple=_lowercase , deterministic=not train , )
else:
_lowerCAmelCase = up_block(_lowercase , temb=_lowercase , res_hidden_states_tuple=_lowercase , deterministic=not train )
# 6. post-process
_lowerCAmelCase = self.conv_norm_out(_lowercase )
_lowerCAmelCase = nn.silu(_lowercase )
_lowerCAmelCase = self.conv_out(_lowercase )
_lowerCAmelCase = jnp.transpose(_lowercase , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=_lowercase )
| 229
| 1
|
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
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 (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _lowercase ( _lowercase , _lowercase , _lowercase , unittest.TestCase ):
a = StableDiffusionControlNetImgaImgPipeline
a = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
a = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
a = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""} )
a = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowerCamelCase_ ( self: Any ):
torch.manual_seed(0 )
lowerCamelCase__ : List[str] = 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 , )
torch.manual_seed(0 )
lowerCamelCase__ : List[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
lowerCamelCase__ : List[Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
lowerCamelCase__ : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase__ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
lowerCamelCase__ : str = CLIPTextModel(UpperCamelCase__ )
lowerCamelCase__ : Any = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCamelCase__ : Dict = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase_ ( self: str , UpperCamelCase__: List[Any] , UpperCamelCase__: Tuple=0 ):
if str(UpperCamelCase__ ).startswith("""mps""" ):
lowerCamelCase__ : Any = torch.manual_seed(UpperCamelCase__ )
else:
lowerCamelCase__ : int = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
lowerCamelCase__ : Tuple = 2
lowerCamelCase__ : Optional[Any] = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCamelCase__ , device=torch.device(UpperCamelCase__ ) , )
lowerCamelCase__ : Optional[Any] = floats_tensor(control_image.shape , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
lowerCamelCase__ : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase__ : List[str] = Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert("""RGB""" ).resize((64, 64) )
lowerCamelCase__ : Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def lowerCamelCase_ ( self: Union[str, Any] ):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCamelCase_ ( self: Optional[int] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
def lowerCamelCase_ ( self: Any ):
self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
class _lowercase ( _lowercase , _lowercase , unittest.TestCase ):
a = StableDiffusionControlNetImgaImgPipeline
a = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
a = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
a = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def lowerCamelCase_ ( self: str ):
torch.manual_seed(0 )
lowerCamelCase__ : List[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 , )
torch.manual_seed(0 )
def init_weights(UpperCamelCase__: Tuple ):
if isinstance(UpperCamelCase__ , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
lowerCamelCase__ : Optional[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(UpperCamelCase__ )
torch.manual_seed(0 )
lowerCamelCase__ : Tuple = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(UpperCamelCase__ )
torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
lowerCamelCase__ : List[Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
lowerCamelCase__ : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
lowerCamelCase__ : Optional[int] = CLIPTextModel(UpperCamelCase__ )
lowerCamelCase__ : List[str] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
lowerCamelCase__ : Any = MultiControlNetModel([controlneta, controlneta] )
lowerCamelCase__ : List[Any] = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase__: str , UpperCamelCase__: Optional[Any]=0 ):
if str(UpperCamelCase__ ).startswith("""mps""" ):
lowerCamelCase__ : int = torch.manual_seed(UpperCamelCase__ )
else:
lowerCamelCase__ : Optional[Any] = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
lowerCamelCase__ : Any = 2
lowerCamelCase__ : List[str] = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCamelCase__ , device=torch.device(UpperCamelCase__ ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=UpperCamelCase__ , device=torch.device(UpperCamelCase__ ) , ),
]
lowerCamelCase__ : Tuple = floats_tensor(control_image[0].shape , rng=random.Random(UpperCamelCase__ ) ).to(UpperCamelCase__ )
lowerCamelCase__ : Dict = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase__ : Any = Image.fromarray(np.uinta(UpperCamelCase__ ) ).convert("""RGB""" ).resize((64, 64) )
lowerCamelCase__ : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def lowerCamelCase_ ( self: Tuple ):
lowerCamelCase__ : str = self.get_dummy_components()
lowerCamelCase__ : List[str] = self.pipeline_class(**UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
lowerCamelCase__ : List[Any] = 10.0
lowerCamelCase__ : Any = 4
lowerCamelCase__ : Union[str, Any] = self.get_dummy_inputs(UpperCamelCase__ )
lowerCamelCase__ : List[str] = steps
lowerCamelCase__ : Optional[int] = scale
lowerCamelCase__ : Union[str, Any] = pipe(**UpperCamelCase__ )[0]
lowerCamelCase__ : Any = self.get_dummy_inputs(UpperCamelCase__ )
lowerCamelCase__ : Any = steps
lowerCamelCase__ : Union[str, Any] = scale
lowerCamelCase__ : Tuple = pipe(**UpperCamelCase__ , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
lowerCamelCase__ : str = self.get_dummy_inputs(UpperCamelCase__ )
lowerCamelCase__ : Tuple = steps
lowerCamelCase__ : int = scale
lowerCamelCase__ : List[str] = pipe(**UpperCamelCase__ , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
lowerCamelCase__ : Optional[Any] = self.get_dummy_inputs(UpperCamelCase__ )
lowerCamelCase__ : Optional[int] = steps
lowerCamelCase__ : Optional[Any] = scale
lowerCamelCase__ : Optional[Any] = pipe(**UpperCamelCase__ , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
assert np.sum(np.abs(output_a - output_a ) ) > 1e-3
def lowerCamelCase_ ( self: str ):
return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowerCamelCase_ ( self: int ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3 )
def lowerCamelCase_ ( self: int ):
self._test_inference_batch_single_identical(expected_max_diff=2e-3 )
def lowerCamelCase_ ( self: Optional[Any] ):
lowerCamelCase__ : Optional[Any] = self.get_dummy_components()
lowerCamelCase__ : str = self.pipeline_class(**UpperCamelCase__ )
pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(UpperCamelCase__ )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _lowercase ( unittest.TestCase ):
def lowerCamelCase_ ( self: List[str] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase_ ( self: Any ):
lowerCamelCase__ : Optional[int] = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
lowerCamelCase__ : Tuple = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=UpperCamelCase__ , controlnet=UpperCamelCase__ )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
lowerCamelCase__ : List[Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
lowerCamelCase__ : List[Any] = """evil space-punk bird"""
lowerCamelCase__ : List[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((512, 512) )
lowerCamelCase__ : Union[str, Any] = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((512, 512) )
lowerCamelCase__ : Optional[int] = pipe(
UpperCamelCase__ , UpperCamelCase__ , control_image=UpperCamelCase__ , generator=UpperCamelCase__ , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
lowerCamelCase__ : Optional[int] = output.images[0]
assert image.shape == (512, 512, 3)
lowerCamelCase__ : Optional[int] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9e-2
| 41
|
'''simple docstring'''
from collections.abc import Sequence
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> float:
return sum(c * (x**i) for i, c in enumerate(UpperCamelCase ) )
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase ) -> float:
lowerCamelCase__ : str = 0.0
for coeff in reversed(UpperCamelCase ):
lowerCamelCase__ : Optional[int] = result * x + coeff
return result
if __name__ == "__main__":
_A : Any =(0.0, 0.0, 5.0, 9.3, 7.0)
_A : Optional[Any] =10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 41
| 1
|
import argparse
import os
import re
import packaging.version
_lowerCamelCase = '''examples/'''
_lowerCamelCase = {
'''examples''': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), '''check_min_version("VERSION")\n'''),
'''init''': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '''__version__ = "VERSION"\n'''),
'''setup''': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'''\1version="VERSION",'''),
'''doc''': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), '''release = "VERSION"\n'''),
}
_lowerCamelCase = {
'''init''': '''src/diffusers/__init__.py''',
'''setup''': '''setup.py''',
}
_lowerCamelCase = '''README.md'''
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any , __UpperCamelCase : int , __UpperCamelCase : Any ) -> List[str]:
with open(a__ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCase_ = f.read()
UpperCAmelCase_ = REPLACE_PATTERNS[pattern]
UpperCAmelCase_ = replace.replace('''VERSION''' , a__ )
UpperCAmelCase_ = re_pattern.sub(a__ , a__ )
with open(a__ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.write(a__ )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Any ) -> Any:
for folder, directories, fnames in os.walk(a__ ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove('''research_projects''' )
if "legacy" in directories:
directories.remove('''legacy''' )
for fname in fnames:
if fname.endswith('''.py''' ):
update_version_in_file(os.path.join(a__ , a__ ) , a__ , pattern='''examples''' )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : Tuple , __UpperCamelCase : str=False ) -> Optional[Any]:
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(a__ , a__ , a__ )
if not patch:
update_version_in_examples(a__ )
def SCREAMING_SNAKE_CASE ( ) -> int:
UpperCAmelCase_ = '''🤗 Transformers currently provides the following architectures'''
UpperCAmelCase_ = '''1. Want to contribute a new model?'''
with open(a__ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCase_ = f.readlines()
# Find the start of the list.
UpperCAmelCase_ = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
UpperCAmelCase_ = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith('''1.''' ):
UpperCAmelCase_ = lines[index].replace(
'''https://huggingface.co/docs/diffusers/main/model_doc''' , '''https://huggingface.co/docs/diffusers/model_doc''' , )
index += 1
with open(a__ , '''w''' , encoding='''utf-8''' , newline='''\n''' ) as f:
f.writelines(a__ )
def SCREAMING_SNAKE_CASE ( ) -> Any:
with open(REPLACE_FILES['''init'''] , '''r''' ) as f:
UpperCAmelCase_ = f.read()
UpperCAmelCase_ = REPLACE_PATTERNS['''init'''][0].search(a__ ).groups()[0]
return packaging.version.parse(a__ )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int=False ) -> Dict:
UpperCAmelCase_ = get_version()
if patch and default_version.is_devrelease:
raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!''' )
if default_version.is_devrelease:
UpperCAmelCase_ = default_version.base_version
elif patch:
UpperCAmelCase_ = f'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
UpperCAmelCase_ = f'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
UpperCAmelCase_ = input(f'Which version are you releasing? [{default_version}]' )
if len(a__ ) == 0:
UpperCAmelCase_ = default_version
print(f'Updating version to {version}.' )
global_version_update(a__ , patch=a__ )
def SCREAMING_SNAKE_CASE ( ) -> Any:
UpperCAmelCase_ = get_version()
UpperCAmelCase_ = f'{current_version.major}.{current_version.minor + 1}.0.dev0'
UpperCAmelCase_ = current_version.base_version
# Check with the user we got that right.
UpperCAmelCase_ = input(f'Which version are we developing now? [{dev_version}]' )
if len(a__ ) == 0:
UpperCAmelCase_ = dev_version
print(f'Updating version to {version}.' )
global_version_update(a__ )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser()
parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
_lowerCamelCase = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print('Nothing to do after a patch :-)')
else:
post_release_work()
| 369
|
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : float , __UpperCamelCase : float ) -> float:
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 177
| 0
|
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 233
|
lowerCamelCase : Dict = '''0.21.0'''
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 233
| 1
|
from PIL import Image
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->Image:
"""simple docstring"""
lowercase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_UpperCamelCase ) -> int:
return int(128 + factor * (c - 128) )
return img.point(_A )
if __name__ == "__main__":
# Load image
with Image.open('''image_data/lena.jpg''') as img:
# Change contrast to 170
__a = change_contrast(img, 1_70)
cont_img.save('''image_data/lena_high_contrast.png''', format='''png''')
| 355
|
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( _UpperCamelCase ) ->Tuple:
"""simple docstring"""
lowercase : List[str] = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(_UpperCamelCase, _UpperCamelCase )
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase , lowercase : str = emb.weight.shape
lowercase : Optional[int] = nn.Linear(_UpperCamelCase, _UpperCamelCase, bias=_UpperCamelCase )
lowercase : Any = emb.weight.data
return lin_layer
def __lowercase ( _UpperCamelCase ) ->List[str]:
"""simple docstring"""
lowercase : Optional[int] = torch.load(_UpperCamelCase, map_location='''cpu''' )
lowercase : List[str] = mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
lowercase : int = mam_aaa['''model''']
remove_ignore_keys_(_UpperCamelCase )
lowercase : Any = state_dict['''encoder.embed_tokens.weight'''].shape[0]
lowercase : Dict = MaMaaaConfig(
vocab_size=_UpperCamelCase, max_position_embeddings=1024, encoder_layers=args.encoder_layers, decoder_layers=args.decoder_layers, encoder_attention_heads=args.encoder_attention_heads, decoder_attention_heads=args.decoder_attention_heads, encoder_ffn_dim=args.encoder_ffn_embed_dim, decoder_ffn_dim=args.decoder_ffn_embed_dim, d_model=args.encoder_embed_dim, encoder_layerdrop=args.encoder_layerdrop, decoder_layerdrop=args.decoder_layerdrop, dropout=args.dropout, attention_dropout=args.attention_dropout, activation_dropout=args.activation_dropout, activation_function='''relu''', )
lowercase : Union[str, Any] = state_dict['''decoder.embed_tokens.weight''']
lowercase : Dict = MaMaaaForConditionalGeneration(_UpperCamelCase )
model.model.load_state_dict(_UpperCamelCase, strict=_UpperCamelCase )
lowercase : Dict = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
__a = argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''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.''')
__a = parser.parse_args()
__a = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 173
| 0
|
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase )-> int:
'''simple docstring'''
UpperCAmelCase : Optional[int] =1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase : Any =n - k
# Calculate C(n,k)
for i in range(__lowerCAmelCase ):
result *= n - i
result //= i + 1
return result
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
return binomial_coefficient(2 * node_count , __lowerCAmelCase ) // (node_count + 1)
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
if n < 0:
raise ValueError('''factorial() not defined for negative values''' )
UpperCAmelCase : List[Any] =1
for i in range(1 , n + 1 ):
result *= i
return result
def lowerCAmelCase_ ( __lowerCAmelCase )-> int:
'''simple docstring'''
return catalan_number(__lowerCAmelCase ) * factorial(__lowerCAmelCase )
if __name__ == "__main__":
__snake_case = int(input('''Enter the number of nodes: ''').strip() or 0)
if node_count <= 0:
raise ValueError('''We need some nodes to work with.''')
print(
f'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
f'binary trees and {catalan_number(node_count)} binary search trees.'
)
| 348
|
import os
from typing import Dict, List, Tuple, TypeVar, Union
__snake_case = TypeVar('''T''')
__snake_case = Union[List[T], Tuple[T, ...]]
__snake_case = Union[T, List[T], Dict[str, T]]
__snake_case = Union[str, bytes, os.PathLike]
| 348
| 1
|
'''simple docstring'''
import math
def __a ( _UpperCamelCase: list , _UpperCamelCase: int = 0 , _UpperCamelCase: int = 0 ) -> list:
"""simple docstring"""
_snake_case = end or len(_UpperCamelCase )
for i in range(_UpperCamelCase , _UpperCamelCase ):
_snake_case = i
_snake_case = array[i]
while temp_index != start and temp_index_value < array[temp_index - 1]:
_snake_case = array[temp_index - 1]
temp_index -= 1
_snake_case = temp_index_value
return array
def __a ( _UpperCamelCase: list , _UpperCamelCase: int , _UpperCamelCase: int ) -> None: # Max Heap
"""simple docstring"""
_snake_case = index
_snake_case = 2 * index + 1 # Left Node
_snake_case = 2 * index + 2 # Right Node
if left_index < heap_size and array[largest] < array[left_index]:
_snake_case = left_index
if right_index < heap_size and array[largest] < array[right_index]:
_snake_case = right_index
if largest != index:
_snake_case , _snake_case = array[largest], array[index]
heapify(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
def __a ( _UpperCamelCase: list ) -> list:
"""simple docstring"""
_snake_case = len(_UpperCamelCase )
for i in range(n // 2 , -1 , -1 ):
heapify(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
for i in range(n - 1 , 0 , -1 ):
_snake_case , _snake_case = array[0], array[i]
heapify(_UpperCamelCase , 0 , _UpperCamelCase )
return array
def __a ( _UpperCamelCase: list , _UpperCamelCase: int , _UpperCamelCase: int , _UpperCamelCase: int ) -> int:
"""simple docstring"""
if (array[first_index] > array[middle_index]) != (
array[first_index] > array[last_index]
):
return array[first_index]
elif (array[middle_index] > array[first_index]) != (
array[middle_index] > array[last_index]
):
return array[middle_index]
else:
return array[last_index]
def __a ( _UpperCamelCase: list , _UpperCamelCase: int , _UpperCamelCase: int , _UpperCamelCase: int ) -> int:
"""simple docstring"""
_snake_case = low
_snake_case = high
while True:
while array[i] < pivot:
i += 1
j -= 1
while pivot < array[j]:
j -= 1
if i >= j:
return i
_snake_case , _snake_case = array[j], array[i]
i += 1
def __a ( _UpperCamelCase: list ) -> list:
"""simple docstring"""
if len(_UpperCamelCase ) == 0:
return array
_snake_case = 2 * math.ceil(math.loga(len(_UpperCamelCase ) ) )
_snake_case = 16
return intro_sort(_UpperCamelCase , 0 , len(_UpperCamelCase ) , _UpperCamelCase , _UpperCamelCase )
def __a ( _UpperCamelCase: list , _UpperCamelCase: int , _UpperCamelCase: int , _UpperCamelCase: int , _UpperCamelCase: int ) -> list:
"""simple docstring"""
while end - start > size_threshold:
if max_depth == 0:
return heap_sort(_UpperCamelCase )
max_depth -= 1
_snake_case = median_of_a(_UpperCamelCase , _UpperCamelCase , start + ((end - start) // 2) + 1 , end - 1 )
_snake_case = partition(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
intro_sort(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
_snake_case = p
return insertion_sort(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase_ : Optional[int] = input('''Enter numbers separated by a comma : ''').strip()
UpperCamelCase_ : List[str] = [float(item) for item in user_input.split(''',''')]
print(sort(unsorted))
| 142
|
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _a ( __lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : Any = ["""image_processor""", """tokenizer"""]
SCREAMING_SNAKE_CASE_ : List[Any] = """BridgeTowerImageProcessor"""
SCREAMING_SNAKE_CASE_ : List[Any] = ("""RobertaTokenizer""", """RobertaTokenizerFast""")
def __init__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> Tuple:
super().__init__(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
def __call__( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = 0 ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,) -> BatchEncoding:
_snake_case = self.tokenizer(
text=_SCREAMING_SNAKE_CASE ,add_special_tokens=_SCREAMING_SNAKE_CASE ,padding=_SCREAMING_SNAKE_CASE ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE ,stride=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_token_type_ids=_SCREAMING_SNAKE_CASE ,return_attention_mask=_SCREAMING_SNAKE_CASE ,return_overflowing_tokens=_SCREAMING_SNAKE_CASE ,return_special_tokens_mask=_SCREAMING_SNAKE_CASE ,return_offsets_mapping=_SCREAMING_SNAKE_CASE ,return_length=_SCREAMING_SNAKE_CASE ,verbose=_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ,)
# add pixel_values + pixel_mask
_snake_case = self.image_processor(
_SCREAMING_SNAKE_CASE ,return_tensors=_SCREAMING_SNAKE_CASE ,do_normalize=_SCREAMING_SNAKE_CASE ,do_center_crop=_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
encoding.update(_SCREAMING_SNAKE_CASE )
return encoding
def _lowercase ( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> List[str]:
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
def _lowercase ( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> List[str]:
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE )
@property
def _lowercase ( self ) -> Any:
_snake_case = self.tokenizer.model_input_names
_snake_case = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 142
| 1
|
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_A : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_A : Union[str, Any] = ''' \"""
Output class for the scheduler\'s step function output.
Args:
prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
denoising loop.
pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
The predicted denoised sample (x_{0}) based on the model output from the current timestep.
`pred_original_sample` can be used to preview progress or for guidance.
\"""
prev_sample: torch.FloatTensor
pred_original_sample: Optional[torch.FloatTensor] = None
'''
class _lowercase ( unittest.TestCase ):
'''simple docstring'''
def a ( self : List[Any] ) -> str:
__lowerCAmelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.diffusers_dir , """schedulers/""" ) )
__lowerCAmelCase = self.diffusers_dir
shutil.copy(
os.path.join(SCREAMING_SNAKE_CASE__ , """src/diffusers/schedulers/scheduling_ddpm.py""" ) , os.path.join(self.diffusers_dir , """schedulers/scheduling_ddpm.py""" ) , )
def a ( self : Union[str, Any] ) -> str:
__lowerCAmelCase = """src/diffusers"""
shutil.rmtree(self.diffusers_dir )
def a ( self : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Tuple=None ) -> Dict:
__lowerCAmelCase = comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
if overwrite_result is not None:
__lowerCAmelCase = comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
__lowerCAmelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
__lowerCAmelCase = black.format_str(SCREAMING_SNAKE_CASE__ , mode=SCREAMING_SNAKE_CASE__ )
__lowerCAmelCase = os.path.join(self.diffusers_dir , """new_code.py""" )
with open(SCREAMING_SNAKE_CASE__ , """w""" , newline="""\n""" ) as f:
f.write(SCREAMING_SNAKE_CASE__ )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(SCREAMING_SNAKE_CASE__ ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=SCREAMING_SNAKE_CASE__ )
with open(SCREAMING_SNAKE_CASE__ , """r""" ) as f:
self.assertTrue(f.read() , SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[Any] ) -> List[str]:
__lowerCAmelCase = check_copies.find_code_in_diffusers("""schedulers.scheduling_ddpm.DDPMSchedulerOutput""" )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def a ( self : Optional[Any] ) -> int:
# Base copy consistency
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , REFERENCE_CODE + """\n""" , )
# With no empty line at the end
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput""" , """DDPMSchedulerOutput""" , SCREAMING_SNAKE_CASE__ , )
# Copy consistency with rename
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , re.sub("""DDPM""" , """Test""" , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with a really long name
__lowerCAmelCase = """TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
self.check_copy_consistency(
f"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}""" , f"""{long_class_name}SchedulerOutput""" , re.sub("""Bert""" , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test""" , """TestSchedulerOutput""" , SCREAMING_SNAKE_CASE__ , overwrite_result=re.sub("""DDPM""" , """Test""" , SCREAMING_SNAKE_CASE__ ) , )
| 229
|
'''simple docstring'''
def UpperCamelCase_ ( snake_case_ : list[int] , snake_case_ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case_ ) == len(snake_case_ ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = equationa
# Calculate the determinants of the matrices
__lowerCAmelCase = aa * ba - aa * ba
__lowerCAmelCase = ca * ba - ca * ba
__lowerCAmelCase = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
__lowerCAmelCase = determinant_x / determinant
__lowerCAmelCase = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 229
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
"Swinv2ForImageClassification",
"Swinv2ForMaskedImageModeling",
"Swinv2Model",
"Swinv2PreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swinva import (
SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
SwinvaForImageClassification,
SwinvaForMaskedImageModeling,
SwinvaModel,
SwinvaPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 370
|
"""simple docstring"""
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
SCREAMING_SNAKE_CASE__ = "true"
def lowerCAmelCase__ ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Dict=8_2 , _UpperCamelCase : int=1_6 ) -> str:
"""simple docstring"""
set_seed(4_2 )
snake_case = RegressionModel()
snake_case = deepcopy(_UpperCamelCase )
snake_case = RegressionDataset(length=_UpperCamelCase )
snake_case = DataLoader(_UpperCamelCase , batch_size=_UpperCamelCase )
model.to(accelerator.device )
snake_case ,snake_case = accelerator.prepare(_UpperCamelCase , _UpperCamelCase )
return model, ddp_model, dataloader
def lowerCAmelCase__ ( _UpperCamelCase : Accelerator , _UpperCamelCase : Optional[Any]=False ) -> List[str]:
"""simple docstring"""
snake_case = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
snake_case = load_dataset('glue' , 'mrpc' , split='validation' )
def tokenize_function(_UpperCamelCase : Optional[Any] ):
snake_case = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=_UpperCamelCase , max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
snake_case = dataset.map(
_UpperCamelCase , batched=_UpperCamelCase , remove_columns=['idx', 'sentence1', 'sentence2'] , )
snake_case = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(_UpperCamelCase : Optional[Any] ):
if use_longest:
return tokenizer.pad(_UpperCamelCase , padding='longest' , return_tensors='pt' )
return tokenizer.pad(_UpperCamelCase , padding='max_length' , max_length=1_2_8 , return_tensors='pt' )
return DataLoader(_UpperCamelCase , shuffle=_UpperCamelCase , collate_fn=_UpperCamelCase , batch_size=1_6 )
def lowerCAmelCase__ ( _UpperCamelCase : str , _UpperCamelCase : Any ) -> List[Any]:
"""simple docstring"""
snake_case = Accelerator(dispatch_batches=_UpperCamelCase , split_batches=_UpperCamelCase )
snake_case = get_dataloader(_UpperCamelCase , not dispatch_batches )
snake_case = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' , return_dict=_UpperCamelCase )
snake_case ,snake_case = accelerator.prepare(_UpperCamelCase , _UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowerCAmelCase__ ( _UpperCamelCase : int , _UpperCamelCase : Any , _UpperCamelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
snake_case = []
for batch in dataloader:
snake_case ,snake_case = batch.values()
with torch.no_grad():
snake_case = model(_UpperCamelCase )
snake_case ,snake_case = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
snake_case ,snake_case = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
snake_case ,snake_case = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def lowerCAmelCase__ ( _UpperCamelCase : Accelerator , _UpperCamelCase : Tuple=8_2 , _UpperCamelCase : Optional[Any]=False , _UpperCamelCase : int=False , _UpperCamelCase : List[str]=1_6 ) -> Optional[Any]:
"""simple docstring"""
snake_case ,snake_case ,snake_case = get_basic_setup(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
snake_case ,snake_case = generate_predictions(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), f"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def lowerCAmelCase__ ( _UpperCamelCase : bool = False , _UpperCamelCase : bool = False ) -> Tuple:
"""simple docstring"""
snake_case = evaluate.load('glue' , 'mrpc' )
snake_case ,snake_case = get_mrpc_setup(_UpperCamelCase , _UpperCamelCase )
# First do baseline
snake_case ,snake_case ,snake_case = setup['no']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
snake_case = model(**_UpperCamelCase )
snake_case = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase , references=batch['labels'] )
snake_case = metric.compute()
# Then do distributed
snake_case ,snake_case ,snake_case = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
snake_case = model(**_UpperCamelCase )
snake_case = outputs.logits.argmax(dim=-1 )
snake_case = batch['labels']
snake_case ,snake_case = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase , references=_UpperCamelCase )
snake_case = 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 lowerCAmelCase__ ( ) -> Tuple:
"""simple docstring"""
snake_case = 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]:
snake_case = 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 , 9_9 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('**Test last batch is not dropped when perfectly divisible**' )
snake_case = Accelerator()
test_torch_metrics(_UpperCamelCase , 5_1_2 )
accelerator.state._reset_state()
def lowerCAmelCase__ ( _UpperCamelCase : Tuple ) -> str:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 149
| 0
|
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __A ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : Tuple = inspect.getfile(accelerate.test_utils )
lowerCAmelCase : List[str] = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_cli.py"] )
lowerCAmelCase : Tuple = ["accelerate", "launch"]
lowerCAmelCase : Union[str, Any] = Path.home() / ".cache/huggingface/accelerate"
lowerCAmelCase : Optional[int] = "default_config.yaml"
lowerCAmelCase : Union[str, Any] = config_folder / config_file
lowerCAmelCase : Union[str, Any] = config_folder / "_default_config.yaml"
lowerCAmelCase : Optional[Any] = Path("tests/test_configs" )
@classmethod
def UpperCAmelCase ( cls : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def UpperCAmelCase ( cls : str ) -> Dict:
"""simple docstring"""
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def UpperCAmelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
lowercase__ : Optional[int] = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] ,env=os.environ.copy() )
def UpperCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
for config in sorted(self.test_config_path.glob('''**/*.yaml''' ) ):
with self.subTest(config_file=_snake_case ):
execute_subprocess_async(
self.base_cmd + ['''--config_file''', str(_snake_case ), self.test_file_path] ,env=os.environ.copy() )
def UpperCAmelCase ( self : Tuple ) -> str:
"""simple docstring"""
execute_subprocess_async(['''accelerate''', '''test'''] ,env=os.environ.copy() )
class __A ( unittest.TestCase ):
'''simple docstring'''
lowerCAmelCase : str = "test-tpu"
lowerCAmelCase : Dict = "us-central1-a"
lowerCAmelCase : List[str] = "ls"
lowerCAmelCase : Any = ["accelerate", "tpu-config"]
lowerCAmelCase : Any = "cd /usr/share"
lowerCAmelCase : Union[str, Any] = "tests/test_samples/test_command_file.sh"
lowerCAmelCase : List[str] = "Running gcloud compute tpus tpu-vm ssh"
def UpperCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
lowercase__ : Dict = run_command(
self.cmd
+ ['''--command''', self.command, '''--tpu_zone''', self.tpu_zone, '''--tpu_name''', self.tpu_name, '''--debug'''] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command''',
self.command,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
lowercase__ : List[str] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--debug'''] ,return_stdout=_snake_case )
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command''', self.command, '''--debug'''] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : Any ) -> int:
"""simple docstring"""
lowercase__ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--command''',
self.command,
'''--command''',
'''echo "Hello World"''',
'''--debug''',
] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
lowercase__ : Optional[Any] = run_command(
self.cmd
+ ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--command_file''', self.command_file, '''--debug'''] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : Any ) -> List[str]:
"""simple docstring"""
lowercase__ : Tuple = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/0_12_0.yaml''',
'''--command_file''',
self.command_file,
'''--tpu_zone''',
self.tpu_zone,
'''--tpu_name''',
self.tpu_name,
'''--debug''',
] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ : int = run_command(
self.cmd + ['''--config_file''', '''tests/test_configs/latest.yaml''', '''--install_accelerate''', '''--debug'''] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all""" ,_snake_case ,)
def UpperCAmelCase ( self : int ) -> List[str]:
"""simple docstring"""
lowercase__ : Dict = run_command(
self.cmd
+ [
'''--config_file''',
'''tests/test_configs/latest.yaml''',
'''--install_accelerate''',
'''--accelerate_version''',
'''12.0.0''',
'''--debug''',
] ,return_stdout=_snake_case ,)
self.assertIn(
f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all""" ,_snake_case ,)
| 16
|
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
__A = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class UpperCAmelCase (_UpperCAmelCase ):
"""simple docstring"""
def __init__( self , **_UpperCAmelCase ):
super().__init__(**_UpperCAmelCase )
requires_backends(self , '''vision''' )
requires_backends(self , '''torch''' )
if self.framework != "pt":
raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )
self.check_model_type(_UpperCAmelCase )
def _snake_case ( self , **_UpperCAmelCase ):
lowercase__: List[Any] = {}
lowercase__: List[Any] = {}
lowercase__: Dict = {}
# preprocess args
if "points_per_batch" in kwargs:
lowercase__: Dict = kwargs['''points_per_batch''']
if "points_per_crop" in kwargs:
lowercase__: Any = kwargs['''points_per_crop''']
if "crops_n_layers" in kwargs:
lowercase__: Union[str, Any] = kwargs['''crops_n_layers''']
if "crop_overlap_ratio" in kwargs:
lowercase__: Optional[Any] = kwargs['''crop_overlap_ratio''']
if "crop_n_points_downscale_factor" in kwargs:
lowercase__: Union[str, Any] = kwargs['''crop_n_points_downscale_factor''']
# postprocess args
if "pred_iou_thresh" in kwargs:
lowercase__: Any = kwargs['''pred_iou_thresh''']
if "stability_score_offset" in kwargs:
lowercase__: Tuple = kwargs['''stability_score_offset''']
if "mask_threshold" in kwargs:
lowercase__: List[str] = kwargs['''mask_threshold''']
if "stability_score_thresh" in kwargs:
lowercase__: str = kwargs['''stability_score_thresh''']
if "crops_nms_thresh" in kwargs:
lowercase__: List[str] = kwargs['''crops_nms_thresh''']
if "output_rle_mask" in kwargs:
lowercase__: Dict = kwargs['''output_rle_mask''']
if "output_bboxes_mask" in kwargs:
lowercase__: int = kwargs['''output_bboxes_mask''']
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self , _UpperCAmelCase , *_UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None , **_UpperCAmelCase ):
return super().__call__(_UpperCAmelCase , *_UpperCAmelCase , num_workers=_UpperCAmelCase , batch_size=_UpperCAmelCase , **_UpperCAmelCase )
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=64 , _UpperCAmelCase = 0 , _UpperCAmelCase = 512 / 1500 , _UpperCAmelCase = 32 , _UpperCAmelCase = 1 , ):
lowercase__: Union[str, Any] = load_image(_UpperCAmelCase )
lowercase__: Dict = self.image_processor.size['''longest_edge''']
lowercase__, lowercase__, lowercase__, lowercase__: Optional[Any] = self.image_processor.generate_crop_boxes(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase__: List[Any] = self.image_processor(images=_UpperCAmelCase , return_tensors='''pt''' )
with self.device_placement():
if self.framework == "pt":
lowercase__: Tuple = self.get_inference_context()
with inference_context():
lowercase__: Optional[Any] = self._ensure_tensor_on_device(_UpperCAmelCase , device=self.device )
lowercase__: Any = self.model.get_image_embeddings(model_inputs.pop('''pixel_values''' ) )
lowercase__: Tuple = image_embeddings
lowercase__: Optional[Any] = grid_points.shape[1]
lowercase__: Tuple = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
'''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
'''To return all points at once, set points_per_batch to None''' )
for i in range(0 , _UpperCAmelCase , _UpperCAmelCase ):
lowercase__: Dict = grid_points[:, i : i + points_per_batch, :, :]
lowercase__: int = input_labels[:, i : i + points_per_batch]
lowercase__: Any = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=0.88 , _UpperCAmelCase=0.95 , _UpperCAmelCase=0 , _UpperCAmelCase=1 , ):
lowercase__: List[Any] = model_inputs.pop('''input_boxes''' )
lowercase__: List[Any] = model_inputs.pop('''is_last''' )
lowercase__: Any = model_inputs.pop('''original_sizes''' ).tolist()
lowercase__: Union[str, Any] = model_inputs.pop('''reshaped_input_sizes''' ).tolist()
lowercase__: List[Any] = self.model(**_UpperCAmelCase )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
lowercase__: int = model_outputs['''pred_masks''']
lowercase__: str = self.image_processor.post_process_masks(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , binarize=_UpperCAmelCase )
lowercase__: str = model_outputs['''iou_scores''']
lowercase__, lowercase__, lowercase__: Optional[int] = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def _snake_case ( self , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=False , _UpperCAmelCase=0.7 , ):
lowercase__: int = []
lowercase__: str = []
lowercase__: List[Any] = []
for model_output in model_outputs:
all_scores.append(model_output.pop('''iou_scores''' ) )
all_masks.extend(model_output.pop('''masks''' ) )
all_boxes.append(model_output.pop('''boxes''' ) )
lowercase__: Any = torch.cat(_UpperCAmelCase )
lowercase__: Dict = torch.cat(_UpperCAmelCase )
lowercase__, lowercase__, lowercase__, lowercase__: Any = self.image_processor.post_process_for_mask_generation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
lowercase__: Union[str, Any] = defaultdict(_UpperCAmelCase )
for output in model_outputs:
for k, v in output.items():
extra[k].append(_UpperCAmelCase )
lowercase__: Any = {}
if output_rle_mask:
lowercase__: Optional[Any] = rle_mask
if output_bboxes_mask:
lowercase__: Optional[int] = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 177
| 0
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
UpperCAmelCase : Tuple = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
UpperCAmelCase : str = {
"vocab_file": {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt",
},
"tokenizer_file": {
"unc-nlp/lxmert-base-uncased": (
"https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"
),
},
}
UpperCAmelCase : Union[str, Any] = {
"unc-nlp/lxmert-base-uncased": 5_12,
}
UpperCAmelCase : Tuple = {
"unc-nlp/lxmert-base-uncased": {"do_lower_case": True},
}
class __lowercase ( a_ ):
"""simple docstring"""
UpperCamelCase : List[str] = VOCAB_FILES_NAMES
UpperCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : List[str] = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : str = LxmertTokenizer
def __init__( self , A=None , A=None , A=True , A="[UNK]" , A="[SEP]" , A="[PAD]" , A="[CLS]" , A="[MASK]" , A=True , A=None , **A , ) -> int:
'''simple docstring'''
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
lowerCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , A ) != do_lower_case
or normalizer_state.get("""strip_accents""" , A ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , A ) != tokenize_chinese_chars
):
lowerCamelCase = getattr(A , normalizer_state.pop("""type""" ) )
lowerCamelCase = do_lower_case
lowerCamelCase = strip_accents
lowerCamelCase = tokenize_chinese_chars
lowerCamelCase = normalizer_class(**A )
lowerCamelCase = do_lower_case
def __A ( self , A , A=None ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = [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 , A , A = None ) -> List[int]:
'''simple docstring'''
lowerCamelCase = [self.sep_token_id]
lowerCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __A ( self , A , A = None ) -> Tuple[str]:
'''simple docstring'''
lowerCamelCase = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 364
|
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
enable_full_determinism()
class __lowercase ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : Union[str, Any] = UNetaDModel
UpperCamelCase : Union[str, Any] = "sample"
@property
def __A ( self ) -> str:
'''simple docstring'''
lowerCamelCase = 4
lowerCamelCase = 3
lowerCamelCase = (32, 32)
lowerCamelCase = floats_tensor((batch_size, num_channels) + sizes ).to(A )
lowerCamelCase = torch.tensor([10] ).to(A )
return {"sample": noise, "timestep": time_step}
@property
def __A ( self ) -> Optional[int]:
'''simple docstring'''
return (3, 32, 32)
@property
def __A ( self ) -> Dict:
'''simple docstring'''
return (3, 32, 32)
def __A ( self ) -> str:
'''simple docstring'''
lowerCamelCase = {
"""block_out_channels""": (32, 64),
"""down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
"""up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
"""attention_head_dim""": 3,
"""out_channels""": 3,
"""in_channels""": 3,
"""layers_per_block""": 2,
"""sample_size""": 32,
}
lowerCamelCase = self.dummy_input
return init_dict, inputs_dict
class __lowercase ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : int = UNetaDModel
UpperCamelCase : Dict = "sample"
@property
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = 4
lowerCamelCase = 4
lowerCamelCase = (32, 32)
lowerCamelCase = floats_tensor((batch_size, num_channels) + sizes ).to(A )
lowerCamelCase = torch.tensor([10] ).to(A )
return {"sample": noise, "timestep": time_step}
@property
def __A ( self ) -> Tuple:
'''simple docstring'''
return (4, 32, 32)
@property
def __A ( self ) -> Dict:
'''simple docstring'''
return (4, 32, 32)
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase = {
"""sample_size""": 32,
"""in_channels""": 4,
"""out_channels""": 4,
"""layers_per_block""": 2,
"""block_out_channels""": (32, 64),
"""attention_head_dim""": 32,
"""down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
"""up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
}
lowerCamelCase = self.dummy_input
return init_dict, inputs_dict
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase , lowerCamelCase = UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" , output_loading_info=A )
self.assertIsNotNone(A )
self.assertEqual(len(loading_info["""missing_keys"""] ) , 0 )
model.to(A )
lowerCamelCase = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != """cuda""" , """This test is supposed to run on GPU""" )
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase , lowerCamelCase = UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" , output_loading_info=A )
model.to(A )
lowerCamelCase = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != """cuda""" , """This test is supposed to run on GPU""" )
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase , lowerCamelCase = UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" , output_loading_info=A )
model_accelerate.to(A )
model_accelerate.eval()
lowerCamelCase = torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
lowerCamelCase = noise.to(A )
lowerCamelCase = torch.tensor([10] * noise.shape[0] ).to(A )
lowerCamelCase = model_accelerate(A , A )["""sample"""]
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
lowerCamelCase , lowerCamelCase = UNetaDModel.from_pretrained(
"""fusing/unet-ldm-dummy-update""" , output_loading_info=A , low_cpu_mem_usage=A )
model_normal_load.to(A )
model_normal_load.eval()
lowerCamelCase = model_normal_load(A , A )["""sample"""]
assert torch_all_close(A , A , rtol=1e-3 )
def __A ( self ) -> Tuple:
'''simple docstring'''
lowerCamelCase = UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" )
model.eval()
model.to(A )
lowerCamelCase = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
lowerCamelCase = noise.to(A )
lowerCamelCase = torch.tensor([10] * noise.shape[0] ).to(A )
with torch.no_grad():
lowerCamelCase = model(A , A ).sample
lowerCamelCase = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
lowerCamelCase = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800] )
# fmt: on
self.assertTrue(torch_all_close(A , A , rtol=1e-3 ) )
class __lowercase ( a_ , a_ , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase : List[str] = UNetaDModel
UpperCamelCase : Optional[int] = "sample"
@property
def __A ( self , A=(32, 32) ) -> List[Any]:
'''simple docstring'''
lowerCamelCase = 4
lowerCamelCase = 3
lowerCamelCase = floats_tensor((batch_size, num_channels) + sizes ).to(A )
lowerCamelCase = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=A )
return {"sample": noise, "timestep": time_step}
@property
def __A ( self ) -> Optional[int]:
'''simple docstring'''
return (3, 32, 32)
@property
def __A ( self ) -> Dict:
'''simple docstring'''
return (3, 32, 32)
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase = {
"""block_out_channels""": [32, 64, 64, 64],
"""in_channels""": 3,
"""layers_per_block""": 1,
"""out_channels""": 3,
"""time_embedding_type""": """fourier""",
"""norm_eps""": 1e-6,
"""mid_block_scale_factor""": math.sqrt(2.0 ),
"""norm_num_groups""": None,
"""down_block_types""": [
"""SkipDownBlock2D""",
"""AttnSkipDownBlock2D""",
"""SkipDownBlock2D""",
"""SkipDownBlock2D""",
],
"""up_block_types""": [
"""SkipUpBlock2D""",
"""SkipUpBlock2D""",
"""AttnSkipUpBlock2D""",
"""SkipUpBlock2D""",
],
}
lowerCamelCase = self.dummy_input
return init_dict, inputs_dict
@slow
def __A ( self ) -> Any:
'''simple docstring'''
lowerCamelCase , lowerCamelCase = UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256""" , output_loading_info=A )
self.assertIsNotNone(A )
self.assertEqual(len(loading_info["""missing_keys"""] ) , 0 )
model.to(A )
lowerCamelCase = self.dummy_input
lowerCamelCase = floats_tensor((4, 3) + (2_56, 2_56) ).to(A )
lowerCamelCase = noise
lowerCamelCase = model(**A )
assert image is not None, "Make sure output is not None"
@slow
def __A ( self ) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase = UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256""" )
model.to(A )
lowerCamelCase = 4
lowerCamelCase = 3
lowerCamelCase = (2_56, 2_56)
lowerCamelCase = torch.ones((batch_size, num_channels) + sizes ).to(A )
lowerCamelCase = torch.tensor(batch_size * [1e-4] ).to(A )
with torch.no_grad():
lowerCamelCase = model(A , A ).sample
lowerCamelCase = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
lowerCamelCase = torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608] )
# fmt: on
self.assertTrue(torch_all_close(A , A , rtol=1e-2 ) )
def __A ( self ) -> Optional[int]:
'''simple docstring'''
lowerCamelCase = UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update""" )
model.to(A )
lowerCamelCase = 4
lowerCamelCase = 3
lowerCamelCase = (32, 32)
lowerCamelCase = torch.ones((batch_size, num_channels) + sizes ).to(A )
lowerCamelCase = torch.tensor(batch_size * [1e-4] ).to(A )
with torch.no_grad():
lowerCamelCase = model(A , A ).sample
lowerCamelCase = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
lowerCamelCase = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256] )
# fmt: on
self.assertTrue(torch_all_close(A , A , rtol=1e-2 ) )
def __A ( self ) -> List[str]:
'''simple docstring'''
pass
| 66
| 0
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = ['image_processor', 'tokenizer']
_SCREAMING_SNAKE_CASE = 'FlavaImageProcessor'
_SCREAMING_SNAKE_CASE = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self , lowercase=None , lowercase=None , **lowercase ) -> str:
lowerCAmelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowercase , )
lowerCAmelCase = kwargs.pop("""feature_extractor""" )
lowerCAmelCase = 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`.""" )
super().__init__(lowercase , lowercase )
lowerCAmelCase = self.image_processor
def __call__( self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = False , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ) -> Dict:
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
lowerCAmelCase = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_token_type_ids=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
if images is not None:
lowerCAmelCase = self.image_processor(
lowercase , return_image_mask=lowercase , return_codebook_pixels=lowercase , return_tensors=lowercase , **lowercase , )
if text is not None and images is not None:
encoding.update(lowercase )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase ) , tensor_type=lowercase )
def _snake_case ( self , *lowercase , **lowercase ) -> Dict:
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def _snake_case ( self , *lowercase , **lowercase ) -> List[Any]:
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def _snake_case ( self ) -> Any:
lowerCAmelCase = self.tokenizer.model_input_names
lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def _snake_case ( self ) -> List[Any]:
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowercase , )
return self.image_processor_class
@property
def _snake_case ( self ) -> int:
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowercase , )
return self.image_processor
| 46
|
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
_UpperCAmelCase = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase = re.compile(r"""([a-z\d])([A-Z])""")
_UpperCAmelCase = re.compile(r"""(?<!_)_(?!_)""")
_UpperCAmelCase = re.compile(r"""(_{2,})""")
_UpperCAmelCase = r"""^\w+(\.\w+)*$"""
_UpperCAmelCase = r"""<>:/\|?*"""
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[int] =_uppercase_uppercase_re.sub(R"""\1_\2""" , lowercase )
SCREAMING_SNAKE_CASE_: str =_lowercase_uppercase_re.sub(R"""\1_\2""" , lowercase )
return name.lower()
def __magic_name__ ( lowercase ):
SCREAMING_SNAKE_CASE_: Optional[int] =_single_underscore_re.split(lowercase )
SCREAMING_SNAKE_CASE_: Any =[_multiple_underscores_re.split(lowercase ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowercase ) if n != """""" )
def __magic_name__ ( lowercase ):
if os.path.basename(lowercase ) != name:
raise ValueError(f'''Should be a dataset name, not a path: {name}''' )
return camelcase_to_snakecase(lowercase )
def __magic_name__ ( lowercase , lowercase ):
if os.path.basename(lowercase ) != name:
raise ValueError(f'''Should be a dataset name, not a path: {name}''' )
if not re.match(_split_re , lowercase ):
raise ValueError(f'''Split name should match \'{_split_re}\'\' but got \'{split}\'.''' )
return f'''{filename_prefix_for_name(lowercase )}-{split}'''
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase=None ):
SCREAMING_SNAKE_CASE_: List[Any] =filename_prefix_for_split(lowercase , lowercase )
if filetype_suffix:
prefix += f'''.{filetype_suffix}'''
SCREAMING_SNAKE_CASE_: Dict =os.path.join(lowercase , lowercase )
return f'''{filepath}*'''
def __magic_name__ ( lowercase , lowercase , lowercase , lowercase=None , lowercase=None ):
SCREAMING_SNAKE_CASE_: List[Any] =filename_prefix_for_split(lowercase , lowercase )
SCREAMING_SNAKE_CASE_: int =os.path.join(lowercase , lowercase )
if shard_lengths:
SCREAMING_SNAKE_CASE_: Any =len(lowercase )
SCREAMING_SNAKE_CASE_: Optional[Any] =[f'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(lowercase )]
if filetype_suffix:
SCREAMING_SNAKE_CASE_: Optional[int] =[filename + f'''.{filetype_suffix}''' for filename in filenames]
return filenames
else:
SCREAMING_SNAKE_CASE_: List[Any] =prefix
if filetype_suffix:
filename += f'''.{filetype_suffix}'''
return [filename]
| 173
| 0
|
"""simple docstring"""
__snake_case = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
"""cookiecutter""": """cookiecutter==1.7.3""",
"""dataclasses""": """dataclasses""",
"""datasets""": """datasets!=2.5.0""",
"""decord""": """decord==0.6.0""",
"""deepspeed""": """deepspeed>=0.9.3""",
"""diffusers""": """diffusers""",
"""dill""": """dill<0.3.5""",
"""evaluate""": """evaluate>=0.2.0""",
"""fairscale""": """fairscale>0.3""",
"""faiss-cpu""": """faiss-cpu""",
"""fastapi""": """fastapi""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1,<=0.7.0""",
"""ftfy""": """ftfy""",
"""fugashi""": """fugashi>=1.0""",
"""GitPython""": """GitPython<3.1.19""",
"""hf-doc-builder""": """hf-doc-builder>=0.3.0""",
"""huggingface-hub""": """huggingface-hub>=0.14.1,<1.0""",
"""importlib_metadata""": """importlib_metadata""",
"""ipadic""": """ipadic>=1.0.0,<2.0""",
"""isort""": """isort>=5.5.4""",
"""jax""": """jax>=0.2.8,!=0.3.2,<=0.4.13""",
"""jaxlib""": """jaxlib>=0.1.65,<=0.4.13""",
"""jieba""": """jieba""",
"""kenlm""": """kenlm""",
"""keras-nlp""": """keras-nlp>=0.3.1""",
"""librosa""": """librosa""",
"""nltk""": """nltk""",
"""natten""": """natten>=0.14.6""",
"""numpy""": """numpy>=1.17""",
"""onnxconverter-common""": """onnxconverter-common""",
"""onnxruntime-tools""": """onnxruntime-tools>=1.4.2""",
"""onnxruntime""": """onnxruntime>=1.4.0""",
"""opencv-python""": """opencv-python""",
"""optuna""": """optuna""",
"""optax""": """optax>=0.0.8,<=0.1.4""",
"""packaging""": """packaging>=20.0""",
"""parameterized""": """parameterized""",
"""phonemizer""": """phonemizer""",
"""protobuf""": """protobuf""",
"""psutil""": """psutil""",
"""pyyaml""": """pyyaml>=5.1""",
"""pydantic""": """pydantic<2""",
"""pytest""": """pytest>=7.2.0""",
"""pytest-timeout""": """pytest-timeout""",
"""pytest-xdist""": """pytest-xdist""",
"""python""": """python>=3.8.0""",
"""ray[tune]""": """ray[tune]""",
"""regex""": """regex!=2019.12.17""",
"""requests""": """requests""",
"""rhoknp""": """rhoknp>=1.1.0,<1.3.1""",
"""rjieba""": """rjieba""",
"""rouge-score""": """rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1""",
"""ruff""": """ruff>=0.0.241,<=0.0.259""",
"""sacrebleu""": """sacrebleu>=1.4.12,<2.0.0""",
"""sacremoses""": """sacremoses""",
"""safetensors""": """safetensors>=0.3.1""",
"""sagemaker""": """sagemaker>=2.31.0""",
"""scikit-learn""": """scikit-learn""",
"""sentencepiece""": """sentencepiece>=0.1.91,!=0.1.92""",
"""sigopt""": """sigopt""",
"""starlette""": """starlette""",
"""sudachipy""": """sudachipy>=0.6.6""",
"""sudachidict_core""": """sudachidict_core>=20220729""",
"""tensorflow-cpu""": """tensorflow-cpu>=2.6,<2.14""",
"""tensorflow""": """tensorflow>=2.6,<2.14""",
"""tensorflow-text""": """tensorflow-text<2.14""",
"""tf2onnx""": """tf2onnx""",
"""timeout-decorator""": """timeout-decorator""",
"""timm""": """timm""",
"""tokenizers""": """tokenizers>=0.11.1,!=0.11.3,<0.14""",
"""torch""": """torch>=1.9,!=1.12.0""",
"""torchaudio""": """torchaudio""",
"""torchvision""": """torchvision""",
"""pyctcdecode""": """pyctcdecode>=0.4.0""",
"""tqdm""": """tqdm>=4.27""",
"""unidic""": """unidic>=1.0.2""",
"""unidic_lite""": """unidic_lite>=1.0.7""",
"""urllib3""": """urllib3<2.0.0""",
"""uvicorn""": """uvicorn""",
}
| 112
|
"""simple docstring"""
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 112
| 1
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Any = logging.get_logger(__name__)
_A : Dict = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : Tuple = "sew"
def __init__( self : int , A : Tuple=3_2 , A : List[str]=7_6_8 , A : List[str]=1_2 , A : Optional[Any]=1_2 , A : Any=3_0_7_2 , A : Tuple=2 , A : Any="gelu" , A : str=0.1 , A : Dict=0.1 , A : str=0.1 , A : List[str]=0.0 , A : List[Any]=0.1 , A : Tuple=0.1 , A : List[str]=0.02 , A : Optional[int]=1e-5 , A : Tuple="group" , A : List[Any]="gelu" , A : Tuple=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , A : Dict=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , A : Optional[Any]=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , A : Optional[Any]=False , A : List[str]=1_2_8 , A : Optional[Any]=1_6 , A : str=True , A : Optional[Any]=0.05 , A : Optional[int]=1_0 , A : Any=2 , A : str=0.0 , A : int=1_0 , A : int=0 , A : int="mean" , A : Any=False , A : Dict=False , A : List[str]=2_5_6 , A : List[str]=0 , A : Tuple=1 , A : Tuple=2 , **A : str , ) ->List[str]:
super().__init__(**A , pad_token_id=A , bos_token_id=A , eos_token_id=A )
lowerCamelCase__ : Dict = hidden_size
lowerCamelCase__ : Union[str, Any] = feat_extract_norm
lowerCamelCase__ : List[Any] = feat_extract_activation
lowerCamelCase__ : Optional[Any] = list(A )
lowerCamelCase__ : str = list(A )
lowerCamelCase__ : Optional[int] = list(A )
lowerCamelCase__ : Union[str, Any] = conv_bias
lowerCamelCase__ : Any = num_conv_pos_embeddings
lowerCamelCase__ : Dict = num_conv_pos_embedding_groups
lowerCamelCase__ : List[str] = len(self.conv_dim )
lowerCamelCase__ : List[str] = num_hidden_layers
lowerCamelCase__ : Optional[int] = intermediate_size
lowerCamelCase__ : int = squeeze_factor
lowerCamelCase__ : Dict = hidden_act
lowerCamelCase__ : Tuple = num_attention_heads
lowerCamelCase__ : int = hidden_dropout
lowerCamelCase__ : Dict = attention_dropout
lowerCamelCase__ : Union[str, Any] = activation_dropout
lowerCamelCase__ : Optional[Any] = feat_proj_dropout
lowerCamelCase__ : Union[str, Any] = final_dropout
lowerCamelCase__ : Tuple = layerdrop
lowerCamelCase__ : Optional[Any] = layer_norm_eps
lowerCamelCase__ : int = initializer_range
lowerCamelCase__ : int = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect.'''
'''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
F"but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)"
F"= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`." )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowerCamelCase__ : Optional[Any] = apply_spec_augment
lowerCamelCase__ : Dict = mask_time_prob
lowerCamelCase__ : List[Any] = mask_time_length
lowerCamelCase__ : List[Any] = mask_time_min_masks
lowerCamelCase__ : Union[str, Any] = mask_feature_prob
lowerCamelCase__ : Any = mask_feature_length
lowerCamelCase__ : str = mask_feature_min_masks
# ctc loss
lowerCamelCase__ : Dict = ctc_loss_reduction
lowerCamelCase__ : Any = ctc_zero_infinity
# sequence classification
lowerCamelCase__ : List[str] = use_weighted_layer_sum
lowerCamelCase__ : Optional[Any] = classifier_proj_size
@property
def __lowerCamelCase ( self : List[str] ) ->Optional[Any]:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 142
|
import datasets
from .evaluate import evaluate
_A : Optional[int] = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n'
_A : int = '\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'
_A : int = '\nComputes SQuAD scores (F1 and EM).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': the text of the answer\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the SQuAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> squad_metric = datasets.load_metric("squad")\n >>> results = squad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __SCREAMING_SNAKE_CASE ( datasets.Metric ):
def __lowerCamelCase ( self : Optional[Any] ) ->List[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) , codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/'''] , reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/'''] , )
def __lowerCamelCase ( self : Dict , A : Tuple , A : Tuple ) ->int:
lowerCamelCase__ : Optional[Any] = {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
lowerCamelCase__ : int = [
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
lowerCamelCase__ : Tuple = evaluate(dataset=A , predictions=A )
return score
| 142
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase_ ( __A ):
def __init__( self : str , *A : Optional[Any] , **A : Dict ):
warnings.warn(
"The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DonutImageProcessor instead." , __lowercase , )
super().__init__(*__lowercase , **__lowercase )
| 359
|
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class UpperCAmelCase_ :
def __init__( self : Tuple , A : str , A : Dict=2 , A : List[Any]=3_2 , A : Optional[Any]=1_6 , A : Tuple=3 , A : Optional[Any]=True , A : List[Any]=True , A : Optional[int]=3_2 , A : Optional[int]=4 , A : Tuple=[0, 1, 2, 3] , A : Optional[int]=4 , A : Tuple=3_7 , A : List[Any]="gelu" , A : List[Any]=0.1 , A : List[str]=0.1 , A : Union[str, Any]=0.02 , A : Optional[int]=3 , A : Optional[Any]=[1, 3_8_4, 2_4, 2_4] , A : Union[str, Any]=True , A : Any=None , ):
_UpperCAmelCase : str = parent
_UpperCAmelCase : int = batch_size
_UpperCAmelCase : List[str] = image_size
_UpperCAmelCase : Tuple = patch_size
_UpperCAmelCase : Any = num_channels
_UpperCAmelCase : List[str] = is_training
_UpperCAmelCase : Optional[Any] = use_labels
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : Dict = num_hidden_layers
_UpperCAmelCase : List[Any] = backbone_out_indices
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : str = hidden_act
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[Any] = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : Tuple = backbone_featmap_shape
_UpperCAmelCase : Optional[Any] = scope
_UpperCAmelCase : Union[str, Any] = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
_UpperCAmelCase : Optional[int] = (image_size // patch_size) ** 2
_UpperCAmelCase : int = num_patches + 1
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Tuple = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : Dict = None
if self.use_labels:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
_UpperCAmelCase : str = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self : str ):
_UpperCAmelCase : Tuple = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
"num_groups": 2,
}
return DPTConfig(
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 , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=A , backbone_featmap_shape=self.backbone_featmap_shape , )
def snake_case_ ( self : Any , A : Optional[Any] , A : str , A : Dict ):
_UpperCAmelCase : List[str] = DPTModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case_ ( self : List[str] , A : str , A : Any , A : List[Any] ):
_UpperCAmelCase : str = self.num_labels
_UpperCAmelCase : Any = DPTForDepthEstimation(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def snake_case_ ( self : List[Any] , A : Any , A : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.num_labels
_UpperCAmelCase : Union[str, Any] = DPTForSemanticSegmentation(A )
model.to(A )
model.eval()
_UpperCAmelCase : int = model(A , labels=A )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = config_and_inputs
_UpperCAmelCase : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : List[str] = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE : Any = (
{
'depth-estimation': DPTForDepthEstimation,
'feature-extraction': DPTModel,
'image-segmentation': DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE : Dict = False
__SCREAMING_SNAKE_CASE : Optional[int] = False
__SCREAMING_SNAKE_CASE : Optional[Any] = False
def snake_case_ ( self : int ):
_UpperCAmelCase : List[str] = DPTModelTester(self )
_UpperCAmelCase : Union[str, Any] = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=3_7 )
def snake_case_ ( self : int ):
self.config_tester.run_common_tests()
@unittest.skip(reason="DPT does not use inputs_embeds" )
def snake_case_ ( self : Union[str, Any] ):
pass
def snake_case_ ( self : Tuple ):
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : str = model_class(A )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_UpperCAmelCase : Dict = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A , nn.Linear ) )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : int = model_class(A )
_UpperCAmelCase : int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : str = [*signature.parameters.keys()]
_UpperCAmelCase : Any = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A )
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*A )
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*A )
def snake_case_ ( self : Optional[int] ):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Optional[int] = True
if model_class in get_values(A ):
continue
_UpperCAmelCase : int = model_class(A )
model.to(A )
model.train()
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : Optional[Any] = model(**A ).loss
loss.backward()
def snake_case_ ( self : Dict ):
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Tuple = False
_UpperCAmelCase : Tuple = True
if model_class in get_values(A ) or not model_class.supports_gradient_checkpointing:
continue
_UpperCAmelCase : int = model_class(A )
model.to(A )
model.gradient_checkpointing_enable()
model.train()
_UpperCAmelCase : str = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : List[str] = model(**A ).loss
loss.backward()
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = _config_zero_init(A )
for model_class in self.all_model_classes:
_UpperCAmelCase : List[Any] = model_class(config=A )
# Skip the check for the backbone
_UpperCAmelCase : Dict = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
_UpperCAmelCase : List[str] = [f'{name}.{key}' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'Parameter {name} of model {model_class} seems not properly initialized' , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def snake_case_ ( self : int ):
pass
@slow
def snake_case_ ( self : Dict ):
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
_UpperCAmelCase : Any = DPTModel.from_pretrained(A )
self.assertIsNotNone(A )
def snake_case_ ( self : Tuple ):
# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : Optional[Any] = "add"
with self.assertRaises(A ):
_UpperCAmelCase : List[Any] = DPTForDepthEstimation(A )
def __snake_case ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : int = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
def snake_case_ ( self : List[str] ):
_UpperCAmelCase : Optional[int] = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
_UpperCAmelCase : Any = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(A )
_UpperCAmelCase : str = prepare_img()
_UpperCAmelCase : Tuple = image_processor(images=A , return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
_UpperCAmelCase : int = model(**A )
_UpperCAmelCase : List[str] = outputs.predicted_depth
# verify the predicted depth
_UpperCAmelCase : int = torch.Size((1, 3_8_4, 3_8_4) )
self.assertEqual(predicted_depth.shape , A )
_UpperCAmelCase : int = torch.tensor(
[[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]] ).to(A )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0 , A , atol=1e-4 ) )
| 202
| 0
|
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class __UpperCamelCase :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=99 , lowerCAmelCase__=64 , lowerCAmelCase__=5 , 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 , ) -> Optional[Any]:
a : Dict = parent
a : List[str] = batch_size
a : Any = seq_length
a : Dict = is_training
a : List[str] = use_input_mask
a : Any = use_token_type_ids
a : List[Any] = use_labels
a : Tuple = vocab_size
a : List[Any] = hidden_size
a : Optional[int] = num_hidden_layers
a : Union[str, Any] = num_attention_heads
a : str = intermediate_size
a : Dict = hidden_act
a : int = hidden_dropout_prob
a : Dict = attention_probs_dropout_prob
a : int = max_position_embeddings
a : Optional[Any] = type_vocab_size
a : Any = type_sequence_label_size
a : List[str] = initializer_range
a : List[Any] = num_labels
a : Optional[int] = num_choices
a : Optional[Any] = scope
a : List[str] = vocab_size - 1
def __a ( self ) -> List[Any]:
a : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a : Tuple = None
if self.use_input_mask:
a : Tuple = random_attention_mask([self.batch_size, self.seq_length] )
a : List[str] = None
if self.use_labels:
a : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a : Any = self.get_config()
return config, input_ids, input_mask, token_labels
def __a ( self ) -> Any:
return GPTNeoXConfig(
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=lowerCAmelCase__ , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def __a ( self ) -> List[Any]:
a, a, a, a : List[Any] = self.prepare_config_and_inputs()
a : int = True
return config, input_ids, input_mask, token_labels
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
a : Dict = GPTNeoXModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : List[Any] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
a : Dict = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
a : str = True
a : Dict = GPTNeoXModel(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[int]:
a : Optional[int] = GPTNeoXForCausalLM(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : Tuple = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
a : Union[str, Any] = self.num_labels
a : Optional[int] = GPTNeoXForQuestionAnswering(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : str = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
a : Union[str, Any] = self.num_labels
a : Union[str, Any] = GPTNeoXForSequenceClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a : Dict = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
a : List[str] = self.num_labels
a : Tuple = GPTNeoXForTokenClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
a : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , labels=lowerCAmelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Union[str, Any]:
a : Any = True
a : Union[str, Any] = GPTNeoXForCausalLM(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
# first forward pass
a : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , use_cache=lowerCAmelCase__ )
a : Tuple = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
a : Tuple = ids_tensor((self.batch_size, 3) , config.vocab_size )
a : Union[str, Any] = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
a : Any = torch.cat([input_ids, next_tokens] , dim=-1 )
a : List[Any] = torch.cat([input_mask, next_mask] , dim=-1 )
a : List[str] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ )
a : Union[str, Any] = output_from_no_past["hidden_states"][0]
a : int = model(
lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , past_key_values=lowerCAmelCase__ , output_hidden_states=lowerCAmelCase__ , )["hidden_states"][0]
# select random slice
a : int = ids_tensor((1,) , output_from_past.shape[-1] ).item()
a : Union[str, Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
a : str = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-3 ) )
def __a ( self ) -> Any:
a : Any = self.prepare_config_and_inputs()
a, a, a, a : int = config_and_inputs
a : Dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( a__ , a__ , a__ , unittest.TestCase ):
lowerCamelCase : List[Any] =(
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowerCamelCase : str =(GPTNeoXForCausalLM,) if is_torch_available() else ()
lowerCamelCase : List[Any] =(
{
"""feature-extraction""": GPTNeoXModel,
"""question-answering""": GPTNeoXForQuestionAnswering,
"""text-classification""": GPTNeoXForSequenceClassification,
"""text-generation""": GPTNeoXForCausalLM,
"""token-classification""": GPTNeoXForTokenClassification,
"""zero-shot""": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase : List[str] =False
lowerCamelCase : Optional[Any] =False
lowerCamelCase : int =False
lowerCamelCase : List[Any] =False
def __a ( self ) -> Optional[int]:
a : Optional[int] = GPTNeoXModelTester(self )
a : Tuple = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=64 , num_attention_heads=8 )
def __a ( self ) -> List[str]:
self.config_tester.run_common_tests()
def __a ( self ) -> List[str]:
a, a, a, a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> Optional[Any]:
a, a, a, a : List[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> Dict:
# This regression test was failing with PyTorch < 1.3
a, a, a, a : str = self.model_tester.prepare_config_and_inputs_for_decoder()
a : Optional[int] = None
self.model_tester.create_and_check_model_as_decoder(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> List[Any]:
a, a, a, a : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def __a ( self ) -> Dict:
a : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCAmelCase__ )
def __a ( self ) -> str:
a : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__ )
def __a ( self ) -> List[str]:
a : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__ )
def __a ( self ) -> int:
a : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__ )
@unittest.skip(reason="Feed forward chunking is not implemented" )
def __a ( self ) -> int:
pass
@parameterized.expand([("linear",), ("dynamic",)] )
def __a ( self , lowerCAmelCase__ ) -> List[Any]:
a, a : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
a : List[str] = ids_tensor([1, 10] , config.vocab_size )
a : List[str] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
a : str = GPTNeoXModel(lowerCAmelCase__ )
original_model.to(lowerCAmelCase__ )
original_model.eval()
a : str = original_model(lowerCAmelCase__ ).last_hidden_state
a : Dict = original_model(lowerCAmelCase__ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
a : int = {"type": scaling_type, "factor": 10.0}
a : Optional[int] = GPTNeoXModel(lowerCAmelCase__ )
scaled_model.to(lowerCAmelCase__ )
scaled_model.eval()
a : Optional[Any] = scaled_model(lowerCAmelCase__ ).last_hidden_state
a : Optional[int] = scaled_model(lowerCAmelCase__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ , atol=1E-5 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def __a ( self ) -> List[Any]:
a : Optional[Any] = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped" )
for checkpointing in [True, False]:
a : int = GPTNeoXForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCAmelCase__ )
a : Any = tokenizer("My favorite food is" , return_tensors="pt" ).to(lowerCAmelCase__ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
a : int = "My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"
a : Optional[Any] = model.generate(**lowerCAmelCase__ , do_sample=lowerCAmelCase__ , max_new_tokens=20 )
a : str = tokenizer.batch_decode(lowerCAmelCase__ )[0]
self.assertEqual(lowerCAmelCase__ , lowerCAmelCase__ )
| 105
|
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
A__: str = logging.get_logger(__name__)
def lowerCAmelCase_ ( A_ ,A_ ,A_):
return [
int(10_00 * (box[0] / width)),
int(10_00 * (box[1] / height)),
int(10_00 * (box[2] / width)),
int(10_00 * (box[3] / height)),
]
def lowerCAmelCase_ ( A_ ,A_ ,A_ = None):
UpperCamelCase__: List[str] = tesseract_config if tesseract_config is not None else ""
# apply OCR
UpperCamelCase__: Optional[int] = to_pil_image(A_)
UpperCamelCase__ , UpperCamelCase__: Tuple = pil_image.size
UpperCamelCase__: List[Any] = pytesseract.image_to_data(A_ ,lang=A_ ,output_type="dict" ,config=A_)
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__: Dict = data["text"], data["left"], data["top"], data["width"], data["height"]
# filter empty words and corresponding coordinates
UpperCamelCase__: List[Any] = [idx for idx, word in enumerate(A_) if not word.strip()]
UpperCamelCase__: Union[str, Any] = [word for idx, word in enumerate(A_) if idx not in irrelevant_indices]
UpperCamelCase__: Dict = [coord for idx, coord in enumerate(A_) if idx not in irrelevant_indices]
UpperCamelCase__: List[Any] = [coord for idx, coord in enumerate(A_) if idx not in irrelevant_indices]
UpperCamelCase__: Optional[int] = [coord for idx, coord in enumerate(A_) if idx not in irrelevant_indices]
UpperCamelCase__: Optional[Any] = [coord for idx, coord in enumerate(A_) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
UpperCamelCase__: List[str] = []
for x, y, w, h in zip(A_ ,A_ ,A_ ,A_):
UpperCamelCase__: str = [x, y, x + w, y + h]
actual_boxes.append(A_)
# finally, normalize the bounding boxes
UpperCamelCase__: Union[str, Any] = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(A_ ,A_ ,A_))
assert len(A_) == len(A_), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class _a ( UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = ["""pixel_values"""]
def __init__( self: int , __lowerCamelCase: bool = True , __lowerCamelCase: Dict[str, int] = None , __lowerCamelCase: PILImageResampling = PILImageResampling.BILINEAR , __lowerCamelCase: bool = True , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional[str] = "" , **__lowerCamelCase: str , ):
'''simple docstring'''
super().__init__(**__lowerCamelCase )
UpperCamelCase__: Optional[Any] = size if size is not None else {"height": 224, "width": 224}
UpperCamelCase__: Dict = get_size_dict(__lowerCamelCase )
UpperCamelCase__: Optional[Any] = do_resize
UpperCamelCase__: Optional[int] = size
UpperCamelCase__: int = resample
UpperCamelCase__: str = apply_ocr
UpperCamelCase__: List[Any] = ocr_lang
UpperCamelCase__: List[Any] = tesseract_config
def UpperCAmelCase_ ( self: int , __lowerCamelCase: np.ndarray , __lowerCamelCase: Dict[str, int] , __lowerCamelCase: PILImageResampling = PILImageResampling.BILINEAR , __lowerCamelCase: Optional[Union[str, ChannelDimension]] = None , **__lowerCamelCase: str , ):
'''simple docstring'''
UpperCamelCase__: int = get_size_dict(__lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
UpperCamelCase__: int = (size["height"], size["width"])
return resize(__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase , data_format=__lowerCamelCase , **__lowerCamelCase )
def UpperCAmelCase_ ( self: Dict , __lowerCamelCase: ImageInput , __lowerCamelCase: bool = None , __lowerCamelCase: Dict[str, int] = None , __lowerCamelCase: PILImageResampling = None , __lowerCamelCase: bool = None , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional[str] = None , __lowerCamelCase: Optional[Union[str, TensorType]] = None , __lowerCamelCase: ChannelDimension = ChannelDimension.FIRST , **__lowerCamelCase: str , ):
'''simple docstring'''
UpperCamelCase__: str = do_resize if do_resize is not None else self.do_resize
UpperCamelCase__: Any = size if size is not None else self.size
UpperCamelCase__: Union[str, Any] = get_size_dict(__lowerCamelCase )
UpperCamelCase__: Tuple = resample if resample is not None else self.resample
UpperCamelCase__: int = apply_ocr if apply_ocr is not None else self.apply_ocr
UpperCamelCase__: Any = ocr_lang if ocr_lang is not None else self.ocr_lang
UpperCamelCase__: Optional[int] = tesseract_config if tesseract_config is not None else self.tesseract_config
UpperCamelCase__: Any = make_list_of_images(__lowerCamelCase )
if not valid_images(__lowerCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
# All transformations expect numpy arrays.
UpperCamelCase__: Union[str, Any] = [to_numpy_array(__lowerCamelCase ) for image in images]
if apply_ocr:
requires_backends(self , "pytesseract" )
UpperCamelCase__: str = []
UpperCamelCase__: Optional[Any] = []
for image in images:
UpperCamelCase__ , UpperCamelCase__: Any = apply_tesseract(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
words_batch.append(__lowerCamelCase )
boxes_batch.append(__lowerCamelCase )
if do_resize:
UpperCamelCase__: List[Any] = [self.resize(image=__lowerCamelCase , size=__lowerCamelCase , resample=__lowerCamelCase ) for image in images]
# flip color channels from RGB to BGR (as Detectron2 requires this)
UpperCamelCase__: List[str] = [flip_channel_order(__lowerCamelCase ) for image in images]
UpperCamelCase__: Optional[Any] = [to_channel_dimension_format(__lowerCamelCase , __lowerCamelCase ) for image in images]
UpperCamelCase__: int = BatchFeature(data={"pixel_values": images} , tensor_type=__lowerCamelCase )
if apply_ocr:
UpperCamelCase__: Dict = words_batch
UpperCamelCase__: Tuple = boxes_batch
return data
| 149
| 0
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _snake_case ( unittest.TestCase):
def A__ ( self : List[Any] ):
lowercase__ = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowercase__ = get_activation("gelu" )
self.assertTrue(torch.allclose(gelu_python(_a ), torch_builtin(_a ) ) )
self.assertFalse(torch.allclose(gelu_python(_a ), gelu_new(_a ) ) )
def A__ ( self : Tuple ):
lowercase__ = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
lowercase__ = get_activation("gelu" )
lowercase__ = get_activation("gelu_10" )
lowercase__ = torch_builtin(_a )
lowercase__ = geluaa(_a )
lowercase__ = torch.where(y_gelu_aa < 10.0, 1, 0 )
self.assertTrue(torch.max(_a ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask, y_gelu_aa * clipped_mask ) )
def A__ ( self : str ):
get_activation("gelu" )
get_activation("gelu_10" )
get_activation("gelu_fast" )
get_activation("gelu_new" )
get_activation("gelu_python" )
get_activation("gelu_pytorch_tanh" )
get_activation("linear" )
get_activation("mish" )
get_activation("quick_gelu" )
get_activation("relu" )
get_activation("sigmoid" )
get_activation("silu" )
get_activation("swish" )
get_activation("tanh" )
with self.assertRaises(_a ):
get_activation("bogus" )
with self.assertRaises(_a ):
get_activation(_a )
def A__ ( self : List[Any] ):
lowercase__ = get_activation("gelu" )
lowercase__ = 1
lowercase__ = get_activation("gelu" )
self.assertEqual(acta.a, 1 )
with self.assertRaises(_a ):
lowercase__ = acta.a
| 363
|
import argparse
import json
import subprocess
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ = []
lowercase__ = (
f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
lowercase__ = subprocess.run(SCREAMING_SNAKE_CASE_ , shell=SCREAMING_SNAKE_CASE_ , stdout=subprocess.PIPE )
lowercase__ = output.stdout.decode("utf-8" )
lowercase__ = json.loads(SCREAMING_SNAKE_CASE_ )
lowercase__ = status["runners"]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(SCREAMING_SNAKE_CASE_ )
# save the result so we can report them on Slack
with open("offline_runners.txt" , "w" ) as fp:
fp.write(json.dumps(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
lowercase__ = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
return values.split("," )
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--target_runners""",
default=None,
type=list_str,
required=True,
help="""Comma-separated list of runners to check status.""",
)
parser.add_argument(
"""--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
)
lowercase_ = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 224
| 0
|
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class a_ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : str = logging.get_logger()
# the current default level is logging.WARNING
SCREAMING_SNAKE_CASE : int = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(_lowerCamelCase )
def __lowerCAmelCase ( self ) ->Union[str, Any]:
SCREAMING_SNAKE_CASE : Any = logging.get_verbosity()
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
SCREAMING_SNAKE_CASE : List[Any] = """Testing 1, 2, 3"""
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(_lowerCamelCase ) as cl:
logger.warning(_lowerCamelCase )
self.assertEqual(cl.out , msg + '''\n''' )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(_lowerCamelCase ) as cl:
logger.warning(_lowerCamelCase )
self.assertEqual(cl.out , '''''' )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(_lowerCamelCase ) as cl:
logger.warning(_lowerCamelCase )
self.assertEqual(cl.out , msg + '''\n''' )
# restore to the original level
logging.set_verbosity(_lowerCamelCase )
@mockenv(TRANSFORMERS_VERBOSITY='''error''' )
def __lowerCAmelCase ( self ) ->Any:
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
SCREAMING_SNAKE_CASE : Any = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
SCREAMING_SNAKE_CASE : List[Any] = os.getenv('''TRANSFORMERS_VERBOSITY''' , _lowerCamelCase )
SCREAMING_SNAKE_CASE : int = logging.log_levels[env_level_str]
SCREAMING_SNAKE_CASE : List[Any] = logging.get_verbosity()
self.assertEqual(
_lowerCamelCase , _lowerCamelCase , F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}""" , )
# restore to the original level
SCREAMING_SNAKE_CASE : Optional[int] = """"""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY='''super-error''' )
def __lowerCAmelCase ( self ) ->Optional[int]:
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
SCREAMING_SNAKE_CASE : Dict = logging.logging.getLogger()
with CaptureLogger(_lowerCamelCase ) as cl:
# this action activates the env var
logging.get_logger('''transformers.models.bart.tokenization_bart''' )
self.assertIn('''Unknown option TRANSFORMERS_VERBOSITY=super-error''' , cl.out )
# no need to restore as nothing was changed
def __lowerCAmelCase ( self ) ->Tuple:
# testing `logger.warning_advice()`
transformers.utils.logging._reset_library_root_logger()
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger('''transformers.models.bart.tokenization_bart''' )
SCREAMING_SNAKE_CASE : Tuple = """Testing 1, 2, 3"""
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''1''' ):
# nothing should be logged as env var disables this method
with CaptureLogger(_lowerCamelCase ) as cl:
logger.warning_advice(_lowerCamelCase )
self.assertEqual(cl.out , '''''' )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='''''' ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(_lowerCamelCase ) as cl:
logger.warning_advice(_lowerCamelCase )
self.assertEqual(cl.out , msg + '''\n''' )
def UpperCAmelCase_( ):
"""simple docstring"""
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 313
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
_A : str = StableDiffusionSAGPipeline
_A : Optional[Any] = TEXT_TO_IMAGE_PARAMS
_A : Any = TEXT_TO_IMAGE_BATCH_PARAMS
_A : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
_A : Tuple = TEXT_TO_IMAGE_IMAGE_PARAMS
_A : List[str] = False
def lowerCAmelCase_ ( self: Optional[Any] ) -> str:
torch.manual_seed(0 )
snake_case_ :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 , )
snake_case_ :Any = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=snake_case , set_alpha_to_one=snake_case , )
torch.manual_seed(0 )
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 )
snake_case_ :Union[str, Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
snake_case_ :Tuple = CLIPTextModel(snake_case )
snake_case_ :str = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
snake_case_ :Dict = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowerCAmelCase_ ( self: List[str] , snake_case: Tuple , snake_case: List[str]=0 ) -> str:
if str(snake_case ).startswith("""mps""" ):
snake_case_ :Tuple = torch.manual_seed(snake_case )
else:
snake_case_ :Optional[int] = torch.Generator(device=snake_case ).manual_seed(snake_case )
snake_case_ :Any = {
"""prompt""": """.""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 1.0,
"""sag_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def lowerCAmelCase_ ( self: Optional[int] ) -> str:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: int ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self: int ) -> List[str]:
snake_case_ :Any = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4""" )
snake_case_ :int = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
snake_case_ :Union[str, Any] = """."""
snake_case_ :str = torch.manual_seed(0 )
snake_case_ :str = sag_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
snake_case_ :List[Any] = output.images
snake_case_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ :List[Any] = np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def lowerCAmelCase_ ( self: Dict ) -> str:
snake_case_ :Tuple = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
snake_case_ :Optional[int] = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
snake_case_ :Tuple = """."""
snake_case_ :Union[str, Any] = torch.manual_seed(0 )
snake_case_ :Tuple = sag_pipe(
[prompt] , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" )
snake_case_ :Optional[int] = output.images
snake_case_ :Optional[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
snake_case_ :Tuple = np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def lowerCAmelCase_ ( self: List[str] ) -> List[str]:
snake_case_ :Optional[int] = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base""" )
snake_case_ :int = sag_pipe.to(snake_case )
sag_pipe.set_progress_bar_config(disable=snake_case )
snake_case_ :Tuple = """."""
snake_case_ :Optional[int] = torch.manual_seed(0 )
snake_case_ :List[str] = sag_pipe(
[prompt] , width=768 , height=512 , generator=snake_case , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="""np""" , )
snake_case_ :Optional[Any] = output.images
assert image.shape == (1, 512, 768, 3)
| 66
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.xglm.modeling_tf_xglm import (
TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXGLMForCausalLM,
TFXGLMModel,
)
@require_tf
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
_SCREAMING_SNAKE_CASE = XGLMConfig
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = """gelu"""
def __init__( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : List[Any]=1_4 , UpperCamelCase__ : int=7 , UpperCamelCase__ : List[Any]=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : str=True , UpperCamelCase__ : List[str]=9_9 , UpperCamelCase__ : str=3_2 , UpperCamelCase__ : Any=2 , UpperCamelCase__ : Optional[int]=4 , UpperCamelCase__ : str=3_7 , UpperCamelCase__ : Tuple="gelu" , UpperCamelCase__ : str=0.1 , UpperCamelCase__ : List[str]=0.1 , UpperCamelCase__ : Union[str, Any]=5_1_2 , UpperCamelCase__ : Optional[Any]=0.0_2 , ):
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_mask
UpperCamelCase = use_labels
UpperCamelCase = vocab_size
UpperCamelCase = d_model
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = ffn_dim
UpperCamelCase = activation_function
UpperCamelCase = activation_dropout
UpperCamelCase = attention_dropout
UpperCamelCase = max_position_embeddings
UpperCamelCase = initializer_range
UpperCamelCase = None
UpperCamelCase = 0
UpperCamelCase = 2
UpperCamelCase = 1
def A ( self : Union[str, Any] ):
"""simple docstring"""
return XGLMConfig.from_pretrained('facebook/xglm-564M' )
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = tf.clip_by_value(
ids_tensor([self.batch_size, self.seq_length] , self.vocab_size ) , clip_value_min=0 , clip_value_max=3 )
UpperCamelCase = None
if self.use_input_mask:
UpperCamelCase = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase = self.get_config()
UpperCamelCase = floats_tensor([self.num_hidden_layers, self.num_attention_heads] , 2 )
return (
config,
input_ids,
input_mask,
head_mask,
)
def A ( self : Union[str, Any] ):
"""simple docstring"""
return XGLMConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , num_layers=self.num_hidden_layers , attention_heads=self.num_attention_heads , ffn_dim=self.ffn_dim , activation_function=self.activation_function , activation_dropout=self.activation_dropout , attention_dropout=self.attention_dropout , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , use_cache=UpperCamelCase__ , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , return_dict=UpperCamelCase__ , )
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {
'input_ids': input_ids,
'head_mask': head_mask,
}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( _a , _a , unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE = (TFXGLMForCausalLM,) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE = (
{"""feature-extraction""": TFXGLMModel, """text-generation""": TFXGLMForCausalLM} if is_tf_available() else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase = TFXGLMModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCamelCase__ , n_embd=3_7 )
def A ( self : Optional[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@slow
def A ( self : List[str] ):
"""simple docstring"""
for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = TFXGLMModel.from_pretrained(UpperCamelCase__ )
self.assertIsNotNone(UpperCamelCase__ )
@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.' )
def A ( self : Dict ):
"""simple docstring"""
super().test_resize_token_embeddings()
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
@slow
def A ( self : Optional[int] , UpperCamelCase__ : Tuple=True ):
"""simple docstring"""
UpperCamelCase = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
UpperCamelCase = tf.convert_to_tensor([[2, 2_6_8, 9_8_6_5]] , dtype=tf.intaa ) # The dog
# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
# fmt: off
UpperCamelCase = [2, 2_6_8, 9_8_6_5, 6_7, 1_1, 1_9_8_8, 5_7_2_5_2, 9_8_6_5, 5, 9_8_4, 6_7, 1_9_8_8, 2_1_3_8_3_8, 1_6_5_8, 5_3, 7_0_4_4_6, 3_3, 6_6_5_7, 2_7_8, 1_5_8_1]
# fmt: on
UpperCamelCase = model.generate(UpperCamelCase__ , do_sample=UpperCamelCase__ , num_beams=1 )
if verify_outputs:
self.assertListEqual(output_ids[0].numpy().tolist() , UpperCamelCase__ )
@slow
def A ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
UpperCamelCase = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
tf.random.set_seed(0 )
UpperCamelCase = tokenizer('Today is a nice day and' , return_tensors='tf' )
UpperCamelCase = tokenized.input_ids
# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
with tf.device(':/CPU:0' ):
UpperCamelCase = model.generate(UpperCamelCase__ , do_sample=UpperCamelCase__ , seed=[7, 0] )
UpperCamelCase = tokenizer.decode(output_ids[0] , skip_special_tokens=UpperCamelCase__ )
UpperCamelCase = (
'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
)
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
@slow
def A ( self : Dict ):
"""simple docstring"""
UpperCamelCase = TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M' )
UpperCamelCase = XGLMTokenizer.from_pretrained('facebook/xglm-564M' )
UpperCamelCase = 'left'
# use different length sentences to test batching
UpperCamelCase = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When',
'Hello, my dog is a little',
]
UpperCamelCase = tokenizer(UpperCamelCase__ , return_tensors='tf' , padding=UpperCamelCase__ )
UpperCamelCase = inputs['input_ids']
UpperCamelCase = model.generate(input_ids=UpperCamelCase__ , attention_mask=inputs['attention_mask'] , max_new_tokens=1_2 )
UpperCamelCase = tokenizer(sentences[0] , return_tensors='tf' ).input_ids
UpperCamelCase = model.generate(input_ids=UpperCamelCase__ , max_new_tokens=1_2 )
UpperCamelCase = tokenizer(sentences[1] , return_tensors='tf' ).input_ids
UpperCamelCase = model.generate(input_ids=UpperCamelCase__ , max_new_tokens=1_2 )
UpperCamelCase = tokenizer.batch_decode(UpperCamelCase__ , skip_special_tokens=UpperCamelCase__ )
UpperCamelCase = tokenizer.decode(output_non_padded[0] , skip_special_tokens=UpperCamelCase__ )
UpperCamelCase = tokenizer.decode(output_padded[0] , skip_special_tokens=UpperCamelCase__ )
UpperCamelCase = [
'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
'left-padding, such as in batched generation. The output for the sequence below should be the same '
'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
'a single',
'Hello, my dog is a little bit of a shy one, but he is very friendly',
]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , [non_padded_sentence, padded_sentence] )
| 249
|
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
@staticmethod
@abstractmethod
def A ( UpperCamelCase__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError()
@abstractmethod
def A ( self : str ):
"""simple docstring"""
raise NotImplementedError()
| 249
| 1
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : Union[str, Any] = {
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json''',
# See all AltCLIP models at https://huggingface.co/models?filter=altclip
}
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : Tuple = '''altclip_text_model'''
def __init__( self : List[Any] , lowerCAmelCase__ : Any=2_5_0_0_0_2 , lowerCAmelCase__ : int=1_0_2_4 , lowerCAmelCase__ : str=2_4 , lowerCAmelCase__ : Union[str, Any]=1_6 , lowerCAmelCase__ : List[str]=4_0_9_6 , lowerCAmelCase__ : Dict="gelu" , lowerCAmelCase__ : List[Any]=0.1 , lowerCAmelCase__ : Union[str, Any]=0.1 , lowerCAmelCase__ : List[str]=5_1_4 , lowerCAmelCase__ : List[str]=1 , lowerCAmelCase__ : List[str]=0.02 , lowerCAmelCase__ : Optional[Any]=0.02 , lowerCAmelCase__ : Optional[Any]=1E-05 , lowerCAmelCase__ : Optional[Any]=1 , lowerCAmelCase__ : List[str]=0 , lowerCAmelCase__ : List[str]=2 , lowerCAmelCase__ : Optional[int]="absolute" , lowerCAmelCase__ : int=True , lowerCAmelCase__ : int=7_6_8 , **lowerCAmelCase__ : int , ):
"""simple docstring"""
super().__init__(pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ , **lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = vocab_size
__SCREAMING_SNAKE_CASE : Dict = hidden_size
__SCREAMING_SNAKE_CASE : List[str] = num_hidden_layers
__SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
__SCREAMING_SNAKE_CASE : List[str] = hidden_act
__SCREAMING_SNAKE_CASE : Any = intermediate_size
__SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
__SCREAMING_SNAKE_CASE : Any = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE : Optional[Any] = max_position_embeddings
__SCREAMING_SNAKE_CASE : List[Any] = type_vocab_size
__SCREAMING_SNAKE_CASE : int = initializer_range
__SCREAMING_SNAKE_CASE : Dict = initializer_factor
__SCREAMING_SNAKE_CASE : Union[str, Any] = layer_norm_eps
__SCREAMING_SNAKE_CASE : Optional[int] = position_embedding_type
__SCREAMING_SNAKE_CASE : int = use_cache
__SCREAMING_SNAKE_CASE : List[str] = project_dim
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : Dict = '''altclip_vision_model'''
def __init__( self : Union[str, Any] , lowerCAmelCase__ : str=7_6_8 , lowerCAmelCase__ : Dict=3_0_7_2 , lowerCAmelCase__ : str=5_1_2 , lowerCAmelCase__ : str=1_2 , lowerCAmelCase__ : Dict=1_2 , lowerCAmelCase__ : List[Any]=3 , lowerCAmelCase__ : int=2_2_4 , lowerCAmelCase__ : Union[str, Any]=3_2 , lowerCAmelCase__ : Any="quick_gelu" , lowerCAmelCase__ : Tuple=1E-5 , lowerCAmelCase__ : List[Any]=0.0 , lowerCAmelCase__ : Tuple=0.02 , lowerCAmelCase__ : List[Any]=1.0 , **lowerCAmelCase__ : List[str] , ):
"""simple docstring"""
super().__init__(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = hidden_size
__SCREAMING_SNAKE_CASE : Optional[Any] = intermediate_size
__SCREAMING_SNAKE_CASE : Any = projection_dim
__SCREAMING_SNAKE_CASE : Optional[Any] = num_hidden_layers
__SCREAMING_SNAKE_CASE : str = num_attention_heads
__SCREAMING_SNAKE_CASE : Dict = num_channels
__SCREAMING_SNAKE_CASE : int = patch_size
__SCREAMING_SNAKE_CASE : int = image_size
__SCREAMING_SNAKE_CASE : List[Any] = initializer_range
__SCREAMING_SNAKE_CASE : Tuple = initializer_factor
__SCREAMING_SNAKE_CASE : Dict = attention_dropout
__SCREAMING_SNAKE_CASE : Any = layer_norm_eps
__SCREAMING_SNAKE_CASE : Dict = hidden_act
@classmethod
def UpperCamelCase__ ( cls : str , lowerCAmelCase__ : Union[str, os.PathLike] , **lowerCAmelCase__ : Optional[int] ):
"""simple docstring"""
cls._set_token_in_kwargs(lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[str] = cls.get_config_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
# get the vision config dict if we are loading from AltCLIPConfig
if config_dict.get("""model_type""" ) == "altclip":
__SCREAMING_SNAKE_CASE : List[str] = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(lowerCAmelCase__ , **lowerCAmelCase__ )
class _UpperCamelCase ( lowerCamelCase__ ):
'''simple docstring'''
_A : Any = '''altclip'''
_A : Union[str, Any] = True
def __init__( self : Any , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : List[str]=None , lowerCAmelCase__ : List[str]=7_6_8 , lowerCAmelCase__ : List[Any]=2.65_92 , **lowerCAmelCase__ : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = kwargs.pop("""text_config_dict""" , lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Any = kwargs.pop("""vision_config_dict""" , lowerCAmelCase__ )
super().__init__(**lowerCAmelCase__ )
# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
if text_config_dict is not None:
if text_config is None:
__SCREAMING_SNAKE_CASE : Optional[int] = {}
# This is the complete result when using `text_config_dict`.
__SCREAMING_SNAKE_CASE : Dict = AltCLIPTextConfig(**lowerCAmelCase__ ).to_dict()
# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
for key, value in _text_config_dict.items():
if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
# If specified in `text_config_dict`
if key in text_config_dict:
__SCREAMING_SNAKE_CASE : Optional[Any] = (
F"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
F"The value `text_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
__SCREAMING_SNAKE_CASE : Any = (
F"`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The "
F"value `text_config[\"{key}\"]` will be overriden."
)
logger.warning(lowerCAmelCase__ )
# Update all values in `text_config` with the ones in `_text_config_dict`.
text_config.update(_text_config_dict )
if vision_config_dict is not None:
if vision_config is None:
__SCREAMING_SNAKE_CASE : Dict = {}
# This is the complete result when using `vision_config_dict`.
__SCREAMING_SNAKE_CASE : List[str] = AltCLIPVisionConfig(**lowerCAmelCase__ ).to_dict()
# convert keys to string instead of integer
if "id2label" in _vision_config_dict:
__SCREAMING_SNAKE_CASE : Any = {
str(lowerCAmelCase__ ): value for key, value in _vision_config_dict["""id2label"""].items()
}
# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
for key, value in _vision_config_dict.items():
if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
# If specified in `vision_config_dict`
if key in vision_config_dict:
__SCREAMING_SNAKE_CASE : Optional[Any] = (
F"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
F"values. The value `vision_config_dict[\"{key}\"]` will be used instead."
)
# If inferred from default argument values (just to be super careful)
else:
__SCREAMING_SNAKE_CASE : Tuple = (
F"`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. "
F"The value `vision_config[\"{key}\"]` will be overriden."
)
logger.warning(lowerCAmelCase__ )
# Update all values in `vision_config` with the ones in `_vision_config_dict`.
vision_config.update(_vision_config_dict )
if text_config is None:
__SCREAMING_SNAKE_CASE : Dict = {}
logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.""" )
if vision_config is None:
__SCREAMING_SNAKE_CASE : List[Any] = {}
logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.""" )
__SCREAMING_SNAKE_CASE : List[Any] = AltCLIPTextConfig(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : Union[str, Any] = AltCLIPVisionConfig(**lowerCAmelCase__ )
__SCREAMING_SNAKE_CASE : List[str] = projection_dim
__SCREAMING_SNAKE_CASE : Tuple = logit_scale_init_value
__SCREAMING_SNAKE_CASE : Union[str, Any] = 1.0
@classmethod
def UpperCamelCase__ ( cls : int , lowerCAmelCase__ : AltCLIPTextConfig , lowerCAmelCase__ : AltCLIPVisionConfig , **lowerCAmelCase__ : Dict ):
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **lowerCAmelCase__ )
def UpperCamelCase__ ( self : Tuple ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = copy.deepcopy(self.__dict__ )
__SCREAMING_SNAKE_CASE : int = self.text_config.to_dict()
__SCREAMING_SNAKE_CASE : List[Any] = self.vision_config.to_dict()
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.__class__.model_type
return output
| 112
|
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list ):
if any(not isinstance(_lowerCamelCase , _lowerCamelCase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(_lowerCamelCase ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(_lowerCamelCase , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 112
| 1
|
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : Any = '▁'
A_ : int = {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}
A_ : Dict = {
'sentencepiece_model_file': 'sentencepiece.bpe.model',
'vocab_file': 'vocab.txt',
}
A_ : Optional[int] = {
'vocab_file': {
'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
},
'sentencepiece_model_file': {
'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
},
}
A_ : int = {
'ernie-m-base': 514,
'ernie-m-large': 514,
}
A_ : int = {
'ernie-m-base': {'do_lower_case': False},
'ernie-m-large': {'do_lower_case': False},
}
class _a (__magic_name__ ):
'''simple docstring'''
UpperCAmelCase__: List[str] = ["input_ids"]
UpperCAmelCase__: str = VOCAB_FILES_NAMES
UpperCAmelCase__: List[Any] = PRETRAINED_INIT_CONFIGURATION
UpperCAmelCase__: Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__: List[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__: Optional[Any] = RESOURCE_FILES_NAMES
def __init__( self , A__ , A__=None , A__=False , A__="utf8" , A__="[UNK]" , A__="[SEP]" , A__="[PAD]" , A__="[CLS]" , A__="[MASK]" , A__ = None , **A__ , ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
A__ : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=A__ , unk_token=A__ , sep_token=A__ , pad_token=A__ , cls_token=A__ , mask_token=A__ , vocab_file=A__ , encoding=A__ , sp_model_kwargs=self.sp_model_kwargs , **A__ , )
A__ : Optional[Any] = do_lower_case
A__ : str = sentencepiece_model_ckpt
A__ : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A__ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
A__ : Tuple = self.load_vocab(filepath=A__ )
else:
A__ : str = {self.sp_model.id_to_piece(A__ ): id for id in range(self.sp_model.get_piece_size() )}
A__ : Dict = {v: k for k, v in self.vocab.items()}
def __A ( self , A__ ):
if text is None:
return None
A__ : List[Any] = self.tokenize(A__ )
A__ : List[Any] = """""", []
for i, ch in enumerate(A__ ):
if ch in self.SP_CHAR_MAPPING:
A__ : List[Any] = self.SP_CHAR_MAPPING.get(A__ )
else:
A__ : List[str] = unicodedata.normalize("""NFKC""" , A__ )
if self.is_whitespace(A__ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(A__ ) )
A__ : Any = normalized_text, [], 0
if self.do_lower_case:
A__ : Tuple = text.lower()
for token in split_tokens:
if token[:1] == "▁":
A__ : List[str] = token[1:]
A__ : int = text[offset:].index(A__ ) + offset
A__ : Dict = start + len(A__ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
A__ : Optional[Any] = end
return token_mapping
@property
def __A ( self ):
return len(self.vocab )
def __A ( self ):
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self ):
A__ : Optional[Any] = self.__dict__.copy()
A__ : List[str] = None
return state
def __setstate__( self , A__ ):
A__ : Any = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
A__ : Optional[int] = {}
A__ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def __A ( self , A__ ):
return "".join((self.SP_CHAR_MAPPING.get(A__ , A__ ) for c in text) )
def __A ( self , A__ , A__=False , A__=64 , A__=0.1 ):
if self.sp_model_kwargs.get("""enable_sampling""" ) is True:
A__ : str = True
if self.sp_model_kwargs.get("""alpha""" ) is not None:
A__ : str = self.sp_model_kwargs.get("""alpha""" )
if self.sp_model_kwargs.get("""nbest_size""" ) is not None:
A__ : Optional[int] = self.sp_model_kwargs.get("""nbest_size""" )
if not enable_sampling:
A__ : Union[str, Any] = self.sp_model.EncodeAsPieces(A__ )
else:
A__ : Dict = self.sp_model.SampleEncodeAsPieces(A__ , A__ , A__ )
A__ : str = []
for pi, piece in enumerate(A__ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(A__ ) and pi != 0:
new_pieces.append(A__ )
continue
else:
continue
A__ : List[str] = 0
for i, chunk in enumerate(A__ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(A__ ) or self.is_punct(A__ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(A__ )
A__ : List[Any] = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
A__ : Any = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
A__ : Any = i
if len(A__ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def __A ( self , A__ ):
A__ : Any = """""".join(A__ ).replace(A__ , """ """ ).strip()
return out_string
def __A ( self , A__ ):
A__ : Any = self.convert_ids_to_tokens(A__ )
A__ : Optional[int] = """""".join(A__ ).replace(A__ , """ """ ).strip()
return out_string
def __A ( self , A__ ):
return self.vocab.get(A__ , self.vocab.get(self.unk_token ) )
def __A ( self , A__ ):
return self.reverse_vocab.get(A__ , self.unk_token )
def __A ( self , A__ , A__=None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
A__ : Dict = [self.cls_token_id]
A__ : List[Any] = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def __A ( self , A__ , A__=None ):
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def __A ( self , A__ , A__=None , A__=False ):
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, 1] + ([0] * len(A__ )) + [1]
return [1] + ([0] * len(A__ )) + [1]
def __A ( self , A__ , A__ = None ):
# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
if token_ids_a is None:
# [CLS] X [SEP]
return (len(A__ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(A__ ) + 1) + [1] * (len(A__ ) + 3)
def __A ( self , A__ ):
if "\u4e00" <= char <= "\u9fff":
return True
return False
def __A ( self , A__ ):
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def __A ( self , A__ ):
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def __A ( self , A__ ):
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(A__ ) == 1:
A__ : Optional[int] = unicodedata.category(A__ )
if cat == "Zs":
return True
return False
def __A ( self , A__ ):
A__ : Optional[int] = {}
with io.open(A__ , """r""" , encoding="""utf-8""" ) as f:
for index, line in enumerate(A__ ):
A__ : Tuple = line.rstrip("""\n""" )
A__ : int = int(A__ )
return token_to_idx
def __A ( self , A__ , A__ = None ):
A__ : List[str] = 0
if os.path.isdir(A__ ):
A__ : Union[str, Any] = os.path.join(
A__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
else:
A__ : str = (filename_prefix + """-""" if filename_prefix else """""") + save_directory
with open(A__ , """w""" , encoding="""utf-8""" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda A__ : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
""" Please check that the vocabulary is not corrupted!""" )
A__ : int = token_index
writer.write(token + """\n""" )
index += 1
A__ : str = os.path.join(A__ , """sentencepiece.bpe.model""" )
with open(A__ , """wb""" ) as fi:
A__ : Dict = self.sp_model.serialized_model_proto()
fi.write(A__ )
return (vocab_file,)
| 371
|
import requests
A_ : List[Any] = 'YOUR API KEY'
def UpperCamelCase (lowercase_: str , lowercase_: str = giphy_api_key ) -> list:
A__ : Dict = """+""".join(query.split() )
A__ : Optional[int] = f"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
A__ : Any = requests.get(lowercase_ ).json()["""data"""]
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 141
| 0
|
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import 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 (
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase , UpperCAmelCase=1_3 , UpperCAmelCase=7 , UpperCAmelCase=True , UpperCAmelCase=True , UpperCAmelCase=False , UpperCAmelCase=True , UpperCAmelCase=9_9 , UpperCAmelCase=6_4 , UpperCAmelCase=5 , UpperCAmelCase=4 , UpperCAmelCase=6_4 , UpperCAmelCase="gelu" , UpperCAmelCase=0.1 , UpperCAmelCase=0.1 , UpperCAmelCase=5_1_2 , UpperCAmelCase=1_6 , UpperCAmelCase=2 , UpperCAmelCase=0.02 , UpperCAmelCase=3 , UpperCAmelCase=4 , UpperCAmelCase=None , ) -> Union[str, Any]:
_lowercase =parent
_lowercase =batch_size
_lowercase =seq_length
_lowercase =is_training
_lowercase =use_input_mask
_lowercase =use_token_type_ids
_lowercase =use_labels
_lowercase =vocab_size
_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 =max_position_embeddings
_lowercase =type_vocab_size
_lowercase =type_sequence_label_size
_lowercase =initializer_range
_lowercase =num_labels
_lowercase =num_choices
_lowercase =scope
def __A (self ) -> Tuple:
return MPNetConfig.from_pretrained('''microsoft/mpnet-base''' )
def __A (self ) -> Dict:
_lowercase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowercase =None
if self.use_input_mask:
_lowercase =random_attention_mask([self.batch_size, self.seq_length] )
_lowercase =None
_lowercase =None
_lowercase =None
if self.use_labels:
_lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowercase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowercase =ids_tensor([self.batch_size] , self.num_choices )
_lowercase =self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __A (self ) -> str:
return MPNetConfig(
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 , initializer_range=self.initializer_range , )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =MPNetModel(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(UpperCAmelCase , UpperCAmelCase )
_lowercase =model(UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[Any]:
_lowercase =MPNetForQuestionAnswering(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(
UpperCAmelCase , attention_mask=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 __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
_lowercase =self.num_labels
_lowercase =MPNetForSequenceClassification(UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Dict:
_lowercase =self.num_choices
_lowercase =MPNetForMultipleChoice(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowercase =input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowercase =model(
UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __A (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> int:
_lowercase =self.num_labels
_lowercase =MPNetForTokenClassification(config=UpperCAmelCase )
model.to(UpperCAmelCase )
model.eval()
_lowercase =model(UpperCAmelCase , attention_mask=UpperCAmelCase , labels=UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __A (self ) -> Tuple:
_lowercase =self.prepare_config_and_inputs()
((_lowercase) , (_lowercase) , (_lowercase) , (_lowercase) , (_lowercase) , (_lowercase)) =config_and_inputs
_lowercase ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase):
SCREAMING_SNAKE_CASE__ = (
(
MPNetForMaskedLM,
MPNetForMultipleChoice,
MPNetForQuestionAnswering,
MPNetForSequenceClassification,
MPNetForTokenClassification,
MPNetModel,
)
if is_torch_available()
else ()
)
SCREAMING_SNAKE_CASE__ = (
{
'''feature-extraction''': MPNetModel,
'''fill-mask''': MPNetForMaskedLM,
'''question-answering''': MPNetForQuestionAnswering,
'''text-classification''': MPNetForSequenceClassification,
'''token-classification''': MPNetForTokenClassification,
'''zero-shot''': MPNetForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE__ = False
SCREAMING_SNAKE_CASE__ = True
def __A (self ) -> Optional[Any]:
_lowercase =MPNetModelTester(self )
_lowercase =ConfigTester(self , config_class=UpperCAmelCase , hidden_size=3_7 )
def __A (self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def __A (self ) -> Dict:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase )
def __A (self ) -> Tuple:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase )
def __A (self ) -> str:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase )
def __A (self ) -> Union[str, Any]:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase )
def __A (self ) -> Optional[Any]:
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase )
@require_torch
class lowerCamelCase__ ( unittest.TestCase):
@slow
def __A (self ) -> str:
_lowercase =MPNetModel.from_pretrained('''microsoft/mpnet-base''' )
_lowercase =torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]] )
_lowercase =model(UpperCAmelCase )[0]
_lowercase =torch.Size((1, 1_1, 7_6_8) )
self.assertEqual(output.shape , UpperCAmelCase )
_lowercase =torch.tensor(
[[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]] )
# compare the actual values for a slice.
self.assertTrue(torch.allclose(output[:, :3, :3] , UpperCAmelCase , atol=1e-4 ) )
| 5
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_A : Optional[int] = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Union[str, Any] = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Dict = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : int = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
_A : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 202
| 0
|
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowerCAmelCase : List[str] = data_utils.TransfoXLTokenizer
lowerCAmelCase : Any = data_utils.TransfoXLCorpus
lowerCAmelCase : Tuple = data_utils
lowerCAmelCase : Optional[Any] = data_utils
def A_( A : Any , A : Dict , A : List[Any] , A : Optional[Any]):
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(A , 'rb') as fp:
UpperCamelCase = pickle.load(A , encoding='latin1')
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
UpperCamelCase = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
print(f'''Save vocabulary to {pytorch_vocab_dump_path}''')
UpperCamelCase = corpus.vocab.__dict__
torch.save(A , A)
UpperCamelCase = corpus.__dict__
corpus_dict_no_vocab.pop('vocab' , A)
UpperCamelCase = pytorch_dump_folder_path + '/' + CORPUS_NAME
print(f'''Save dataset to {pytorch_dataset_dump_path}''')
torch.save(A , A)
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
UpperCamelCase = os.path.abspath(A)
UpperCamelCase = os.path.abspath(A)
print(f'''Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.''')
# Initialise PyTorch model
if transfo_xl_config_file == "":
UpperCamelCase = TransfoXLConfig()
else:
UpperCamelCase = TransfoXLConfig.from_json_file(A)
print(f'''Building PyTorch model from configuration: {config}''')
UpperCamelCase = TransfoXLLMHeadModel(A)
UpperCamelCase = load_tf_weights_in_transfo_xl(A , A , A)
# Save pytorch-model
UpperCamelCase = os.path.join(A , A)
UpperCamelCase = os.path.join(A , A)
print(f'''Save PyTorch model to {os.path.abspath(A)}''')
torch.save(model.state_dict() , A)
print(f'''Save configuration file to {os.path.abspath(A)}''')
with open(A , 'w' , encoding='utf-8') as f:
f.write(config.to_json_string())
if __name__ == "__main__":
lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the folder to store the PyTorch model or dataset/vocab.',
)
parser.add_argument(
'--tf_checkpoint_path',
default='',
type=str,
help='An optional path to a TensorFlow checkpoint path to be converted.',
)
parser.add_argument(
'--transfo_xl_config_file',
default='',
type=str,
help=(
'An optional config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--transfo_xl_dataset_file',
default='',
type=str,
help='An optional dataset file to be converted in a vocabulary.',
)
lowerCAmelCase : Union[str, Any] = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 356
|
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_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 ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class SCREAMING_SNAKE_CASE__ :
def __init__( self , A_ , A_=13 , A_=2 , A_=24 , A_=16 , A_=True , A_=True , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=10 , A_=0.02 , A_=None , A_=2 , A_=2 , )-> int:
'''simple docstring'''
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = patch_size
UpperCamelCase = max_length
UpperCamelCase = num_mel_bins
UpperCamelCase = is_training
UpperCamelCase = use_labels
UpperCamelCase = hidden_size
UpperCamelCase = num_hidden_layers
UpperCamelCase = num_attention_heads
UpperCamelCase = intermediate_size
UpperCamelCase = hidden_act
UpperCamelCase = hidden_dropout_prob
UpperCamelCase = attention_probs_dropout_prob
UpperCamelCase = type_sequence_label_size
UpperCamelCase = initializer_range
UpperCamelCase = scope
UpperCamelCase = frequency_stride
UpperCamelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCamelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
UpperCamelCase = (self.max_length - self.patch_size) // self.time_stride + 1
UpperCamelCase = frequency_out_dimension * time_out_dimension
UpperCamelCase = num_patches + 2
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
UpperCamelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = self.get_config()
return config, input_values, labels
def UpperCAmelCase_ ( self )-> int:
'''simple docstring'''
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A_ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def UpperCAmelCase_ ( self , A_ , A_ , A_ )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = ASTModel(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self )-> Tuple:
'''simple docstring'''
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {'input_values': input_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( snake_case_ , snake_case_ , unittest.TestCase):
lowerCAmelCase_ = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
lowerCAmelCase_ = (
{"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
if is_torch_available()
else {}
)
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
lowerCAmelCase_ = False
def UpperCAmelCase_ ( self , A_ , A_ , A_ , A_ , A_ )-> Dict:
'''simple docstring'''
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def UpperCAmelCase_ ( self )-> Dict:
'''simple docstring'''
UpperCamelCase = ASTModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=A_ , has_text_modality=A_ , hidden_size=37 )
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='AST does not use inputs_embeds' )
def UpperCAmelCase_ ( self )-> List[Any]:
'''simple docstring'''
pass
def UpperCAmelCase_ ( self )-> Any:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCamelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(A_ , nn.Linear ) )
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase = model_class(A_ )
UpperCamelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase = [*signature.parameters.keys()]
UpperCamelCase = ['input_values']
self.assertListEqual(arg_names[:1] , A_ )
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
@slow
def UpperCAmelCase_ ( self )-> Optional[Any]:
'''simple docstring'''
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = ASTModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def A_( ):
UpperCamelCase = hf_hub_download(
repo_id='nielsr/audio-spectogram-transformer-checkpoint' , filename='sample_audio.flac' , repo_type='dataset')
UpperCamelCase , UpperCamelCase = torchaudio.load(A)
return audio, sampling_rate
@require_torch
@require_torchaudio
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase):
@cached_property
def UpperCAmelCase_ ( self )-> str:
'''simple docstring'''
return (
ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593' )
if is_torchaudio_available()
else None
)
@slow
def UpperCAmelCase_ ( self )-> List[str]:
'''simple docstring'''
UpperCamelCase = self.default_feature_extractor
UpperCamelCase = ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593' ).to(A_ )
UpperCamelCase = self.default_feature_extractor
UpperCamelCase , UpperCamelCase = prepare_audio()
UpperCamelCase = audio.squeeze().numpy()
UpperCamelCase = feature_extractor(A_ , sampling_rate=A_ , return_tensors='pt' ).to(A_ )
# forward pass
with torch.no_grad():
UpperCamelCase = model(**A_ )
# verify the logits
UpperCamelCase = torch.Size((1, 527) )
self.assertEqual(outputs.logits.shape , A_ )
UpperCamelCase = torch.tensor([-0.8_760, -7.0_042, -8.6_602] ).to(A_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A_ , atol=1e-4 ) )
| 251
| 0
|
# Copyright 2023 The HuggingFace Inc. 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.
import re
from ..utils import cached_file
# docstyle-ignore
__a = '\nHuman: <<task>>\n\nAssistant: '
__a = 'huggingface-tools/default-prompts'
__a = {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}
def a ( snake_case__: Union[str, Any] , snake_case__: str , snake_case__: Tuple="run" ):
'''simple docstring'''
if prompt_or_repo_id is None:
lowercase_ = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('''\\s''' , snake_case__ ) is not None:
return prompt_or_repo_id
lowercase_ = cached_file(
snake_case__ , PROMPT_FILES[mode] , repo_type='''dataset''' , user_agent={'''agent''': agent_name} )
with open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
return f.read()
| 30
|
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( self : List[str] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[str] ):
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , len(SCREAMING_SNAKE_CASE_ ) )
for a, b in zip(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
self.assertAlmostEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , delta=SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE__ ( self : int ):
lowerCAmelCase_ : int = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def SCREAMING_SNAKE_CASE__ ( self : str ):
lowerCAmelCase_ : Optional[int] = None
ops.enable_eager_execution_internal()
lowerCAmelCase_ : str = tf.config.list_physical_devices('CPU' )
if len(SCREAMING_SNAKE_CASE_ ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
lowerCAmelCase_ : Dict = tf.config.list_logical_devices(device_type='CPU' )
lowerCAmelCase_ : Optional[int] = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
lowerCAmelCase_ : Union[str, Any] = GradientAccumulator()
lowerCAmelCase_ : int = tf.Variable([4.0, 3.0] )
lowerCAmelCase_ ,lowerCAmelCase_ : Optional[int] = create_optimizer(5E-5 , 1_0 , 5 )
lowerCAmelCase_ : Union[str, Any] = tf.Variable([0.0, 0.0] , trainable=SCREAMING_SNAKE_CASE_ )
def accumulate_on_replica(SCREAMING_SNAKE_CASE_ : Optional[int] ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
with strategy.scope():
lowerCAmelCase_ : Tuple = strategy.experimental_local_results(SCREAMING_SNAKE_CASE_ )
local_variables[0].assign(SCREAMING_SNAKE_CASE_ )
local_variables[1].assign(SCREAMING_SNAKE_CASE_ )
strategy.run(SCREAMING_SNAKE_CASE_ , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(SCREAMING_SNAKE_CASE_ )
def _check_local_values(SCREAMING_SNAKE_CASE_ : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
lowerCAmelCase_ : List[Any] = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , SCREAMING_SNAKE_CASE_ , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , SCREAMING_SNAKE_CASE_ , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 224
| 0
|
"""simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
sys.path.append(os.path.join(os.getcwd())) # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score # noqa: E402 # isort:skip
_a = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
transformers_logging.set_verbosity_info()
def __a ( __lowerCamelCase ):
if "token" in model_name_or_path:
return "rag_token"
if "sequence" in model_name_or_path:
return "rag_sequence"
if "bart" in model_name_or_path:
return "bart"
return None
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
return max(metric_fn(__lowerCamelCase, __lowerCamelCase ) for gt in ground_truths )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Optional[int] = [line.strip() for line in open(__lowerCamelCase, "r" ).readlines()]
UpperCAmelCase_ : int = []
if args.gold_data_mode == "qa":
UpperCAmelCase_ : str = pd.read_csv(__lowerCamelCase, sep="\t", header=__lowerCamelCase )
for answer_list in data[1]:
UpperCAmelCase_ : int = ast.literal_eval(__lowerCamelCase )
answers.append(__lowerCamelCase )
else:
UpperCAmelCase_ : Tuple = [line.strip() for line in open(__lowerCamelCase, "r" ).readlines()]
UpperCAmelCase_ : Optional[int] = [[reference] for reference in references]
UpperCAmelCase_ : List[str] = 0
for prediction, ground_truths in zip(__lowerCamelCase, __lowerCamelCase ):
total += 1
em += metric_max_over_ground_truths(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
fa += metric_max_over_ground_truths(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
UpperCAmelCase_ : Optional[int] = 100.0 * em / total
UpperCAmelCase_ : Dict = 100.0 * fa / total
logger.info(f"""F1: {fa:.2f}""" )
logger.info(f"""EM: {em:.2f}""" )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : Any = args.k
UpperCAmelCase_ : str = [line.strip() for line in open(__lowerCamelCase, "r" ).readlines()]
UpperCAmelCase_ : int = [line.strip() for line in open(__lowerCamelCase, "r" ).readlines()]
UpperCAmelCase_ : Union[str, Any] = 0
for hypo, reference in zip(__lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : List[Any] = set(hypo.split("\t" )[:k] )
UpperCAmelCase_ : Optional[Any] = set(reference.split("\t" ) )
total += 1
em += len(hypo_provenance & ref_provenance ) / k
UpperCAmelCase_ : Optional[Any] = 100.0 * em / total
logger.info(f"""Precision@{k}: {em: .2f}""" )
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
def strip_title(__lowerCamelCase ):
if title.startswith("\"" ):
UpperCAmelCase_ : Union[str, Any] = title[1:]
if title.endswith("\"" ):
UpperCAmelCase_ : List[str] = title[:-1]
return title
UpperCAmelCase_ : Optional[Any] = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__lowerCamelCase, return_tensors="pt", padding=__lowerCamelCase, truncation=__lowerCamelCase, )["input_ids"].to(args.device )
UpperCAmelCase_ : Optional[int] = rag_model.rag.question_encoder(__lowerCamelCase )
UpperCAmelCase_ : Dict = question_enc_outputs[0]
UpperCAmelCase_ : str = rag_model.retriever(
__lowerCamelCase, question_enc_pool_output.cpu().detach().to(torch.floataa ).numpy(), prefix=rag_model.rag.generator.config.prefix, n_docs=rag_model.config.n_docs, return_tensors="pt", )
UpperCAmelCase_ : Tuple = rag_model.retriever.index.get_doc_dicts(result.doc_ids )
UpperCAmelCase_ : Tuple = []
for docs in all_docs:
UpperCAmelCase_ : Optional[int] = [strip_title(__lowerCamelCase ) for title in docs["title"]]
provenance_strings.append("\t".join(__lowerCamelCase ) )
return provenance_strings
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
with torch.no_grad():
UpperCAmelCase_ : Any = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
__lowerCamelCase, return_tensors="pt", padding=__lowerCamelCase, truncation=__lowerCamelCase )
UpperCAmelCase_ : Tuple = inputs_dict.input_ids.to(args.device )
UpperCAmelCase_ : Any = inputs_dict.attention_mask.to(args.device )
UpperCAmelCase_ : Any = rag_model.generate( # rag_model overwrites generate
__lowerCamelCase, attention_mask=__lowerCamelCase, num_beams=args.num_beams, min_length=args.min_length, max_length=args.max_length, early_stopping=__lowerCamelCase, num_return_sequences=1, bad_words_ids=[[0, 0]], )
UpperCAmelCase_ : Tuple = rag_model.retriever.generator_tokenizer.batch_decode(__lowerCamelCase, skip_special_tokens=__lowerCamelCase )
if args.print_predictions:
for q, a in zip(__lowerCamelCase, __lowerCamelCase ):
logger.info("Q: {} - A: {}".format(__lowerCamelCase, __lowerCamelCase ) )
return answers
def __a ( ):
UpperCAmelCase_ : Dict = argparse.ArgumentParser()
parser.add_argument(
"--model_type", choices=["rag_sequence", "rag_token", "bart"], type=__lowerCamelCase, help=(
"RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
" model_name_or_path"
), )
parser.add_argument(
"--index_name", default=__lowerCamelCase, choices=["exact", "compressed", "legacy"], type=__lowerCamelCase, help="RAG model retriever type", )
parser.add_argument(
"--index_path", default=__lowerCamelCase, type=__lowerCamelCase, help="Path to the retrieval index", )
parser.add_argument("--n_docs", default=5, type=__lowerCamelCase, help="Number of retrieved docs" )
parser.add_argument(
"--model_name_or_path", default=__lowerCamelCase, type=__lowerCamelCase, required=__lowerCamelCase, help="Path to pretrained checkpoints or model identifier from huggingface.co/models", )
parser.add_argument(
"--eval_mode", choices=["e2e", "retrieval"], default="e2e", type=__lowerCamelCase, help=(
"Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
" precision@k."
), )
parser.add_argument("--k", default=1, type=__lowerCamelCase, help="k for the precision@k calculation" )
parser.add_argument(
"--evaluation_set", default=__lowerCamelCase, type=__lowerCamelCase, required=__lowerCamelCase, help="Path to a file containing evaluation samples", )
parser.add_argument(
"--gold_data_path", default=__lowerCamelCase, type=__lowerCamelCase, required=__lowerCamelCase, help="Path to a tab-separated file with gold samples", )
parser.add_argument(
"--gold_data_mode", default="qa", type=__lowerCamelCase, choices=["qa", "ans"], help=(
"Format of the gold data file"
"qa - a single line in the following format: question [tab] answer_list"
"ans - a single line of the gold file contains the expected answer string"
), )
parser.add_argument(
"--predictions_path", type=__lowerCamelCase, default="predictions.txt", help="Name of the predictions file, to be stored in the checkpoints directory", )
parser.add_argument(
"--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", )
parser.add_argument(
"--eval_batch_size", default=8, type=__lowerCamelCase, help="Batch size per GPU/CPU for evaluation.", )
parser.add_argument(
"--recalculate", help="Recalculate predictions even if the prediction file exists", action="store_true", )
parser.add_argument(
"--num_beams", default=4, type=__lowerCamelCase, help="Number of beams to be used when generating answers", )
parser.add_argument("--min_length", default=1, type=__lowerCamelCase, help="Min length of the generated answers" )
parser.add_argument("--max_length", default=50, type=__lowerCamelCase, help="Max length of the generated answers" )
parser.add_argument(
"--print_predictions", action="store_true", help="If True, prints predictions while evaluating.", )
parser.add_argument(
"--print_docs", action="store_true", help="If True, prints docs retried while generating.", )
UpperCAmelCase_ : List[str] = parser.parse_args()
UpperCAmelCase_ : Tuple = torch.device("cuda" if torch.cuda.is_available() else "cpu" )
return args
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Tuple = {}
if args.model_type is None:
UpperCAmelCase_ : Union[str, Any] = infer_model_type(args.model_name_or_path )
assert args.model_type is not None
if args.model_type.startswith("rag" ):
UpperCAmelCase_ : List[Any] = RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
UpperCAmelCase_ : str = args.n_docs
if args.index_name is not None:
UpperCAmelCase_ : Optional[Any] = args.index_name
if args.index_path is not None:
UpperCAmelCase_ : Dict = args.index_path
else:
UpperCAmelCase_ : Tuple = BartForConditionalGeneration
UpperCAmelCase_ : List[Any] = (
[f.path for f in os.scandir(args.model_name_or_path ) if f.is_dir()]
if args.eval_all_checkpoints
else [args.model_name_or_path]
)
logger.info("Evaluate the following checkpoints: %s", __lowerCamelCase )
UpperCAmelCase_ : Union[str, Any] = get_scores if args.eval_mode == "e2e" else get_precision_at_k
UpperCAmelCase_ : Tuple = evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval
for checkpoint in checkpoints:
if os.path.exists(args.predictions_path ) and (not args.recalculate):
logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path ) )
score_fn(__lowerCamelCase, args.predictions_path, args.gold_data_path )
continue
logger.info("***** Running evaluation for {} *****".format(__lowerCamelCase ) )
logger.info(" Batch size = %d", args.eval_batch_size )
logger.info(" Predictions will be stored under {}".format(args.predictions_path ) )
if args.model_type.startswith("rag" ):
UpperCAmelCase_ : Union[str, Any] = RagRetriever.from_pretrained(__lowerCamelCase, **__lowerCamelCase )
UpperCAmelCase_ : Any = model_class.from_pretrained(__lowerCamelCase, retriever=__lowerCamelCase, **__lowerCamelCase )
model.retriever.init_retrieval()
else:
UpperCAmelCase_ : int = model_class.from_pretrained(__lowerCamelCase, **__lowerCamelCase )
model.to(args.device )
with open(args.evaluation_set, "r" ) as eval_file, open(args.predictions_path, "w" ) as preds_file:
UpperCAmelCase_ : List[Any] = []
for line in tqdm(__lowerCamelCase ):
questions.append(line.strip() )
if len(__lowerCamelCase ) == args.eval_batch_size:
UpperCAmelCase_ : Optional[int] = evaluate_batch_fn(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
preds_file.write("\n".join(__lowerCamelCase ) + "\n" )
preds_file.flush()
UpperCAmelCase_ : List[str] = []
if len(__lowerCamelCase ) > 0:
UpperCAmelCase_ : List[Any] = evaluate_batch_fn(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
preds_file.write("\n".join(__lowerCamelCase ) )
preds_file.flush()
score_fn(__lowerCamelCase, args.predictions_path, args.gold_data_path )
if __name__ == "__main__":
_a = get_args()
main(args)
| 23
|
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = (PNDMScheduler,)
SCREAMING_SNAKE_CASE__ : str = (("""num_inference_steps""", 50),)
def UpperCamelCase__ ( self , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : int = {
"num_train_timesteps": 1000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**lowercase_ )
return config
def UpperCamelCase__ ( self , lowercase_=0 , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = dict(self.forward_default_kwargs )
UpperCAmelCase_ : List[str] = kwargs.pop("num_inference_steps" , lowercase_ )
UpperCAmelCase_ : Union[str, Any] = self.dummy_sample
UpperCAmelCase_ : Dict = 0.1 * sample
UpperCAmelCase_ : Dict = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : List[Any] = self.get_scheduler_config(**lowercase_ )
UpperCAmelCase_ : Dict = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
UpperCAmelCase_ : List[Any] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
UpperCAmelCase_ : Optional[int] = scheduler_class.from_pretrained(lowercase_ )
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals
UpperCAmelCase_ : int = dummy_past_residuals[:]
UpperCAmelCase_ : List[str] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : str = new_scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ : Optional[int] = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Dict = new_scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).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 , lowercase_=0 , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = dict(self.forward_default_kwargs )
UpperCAmelCase_ : str = kwargs.pop("num_inference_steps" , lowercase_ )
UpperCAmelCase_ : Optional[int] = self.dummy_sample
UpperCAmelCase_ : List[str] = 0.1 * sample
UpperCAmelCase_ : Tuple = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : str = self.get_scheduler_config()
UpperCAmelCase_ : Dict = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase_ : List[Any] = dummy_past_residuals[:]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(lowercase_ )
UpperCAmelCase_ : Dict = scheduler_class.from_pretrained(lowercase_ )
# copy over dummy past residuals
new_scheduler.set_timesteps(lowercase_ )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase_ : Optional[Any] = dummy_past_residuals[:]
UpperCAmelCase_ : Union[str, Any] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Dict = new_scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
UpperCAmelCase_ : List[str] = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : int = new_scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ , **lowercase_ ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"
def UpperCamelCase__ ( self , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : str = self.scheduler_classes[0]
UpperCAmelCase_ : Union[str, Any] = self.get_scheduler_config(**lowercase_ )
UpperCAmelCase_ : List[Any] = scheduler_class(**lowercase_ )
UpperCAmelCase_ : Tuple = 10
UpperCAmelCase_ : List[str] = self.dummy_model()
UpperCAmelCase_ : str = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for i, t in enumerate(scheduler.prk_timesteps ):
UpperCAmelCase_ : Tuple = model(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[int] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ ).prev_sample
for i, t in enumerate(scheduler.plms_timesteps ):
UpperCAmelCase_ : Any = model(lowercase_ , lowercase_ )
UpperCAmelCase_ : Optional[Any] = scheduler.step_plms(lowercase_ , lowercase_ , lowercase_ ).prev_sample
return sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : str = dict(self.forward_default_kwargs )
UpperCAmelCase_ : Optional[Any] = kwargs.pop("num_inference_steps" , lowercase_ )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : Any = self.get_scheduler_config()
UpperCAmelCase_ : Tuple = scheduler_class(**lowercase_ )
UpperCAmelCase_ : str = self.dummy_sample
UpperCAmelCase_ : List[Any] = 0.1 * sample
if num_inference_steps is not None and hasattr(lowercase_ , "set_timesteps" ):
scheduler.set_timesteps(lowercase_ )
elif num_inference_steps is not None and not hasattr(lowercase_ , "set_timesteps" ):
UpperCAmelCase_ : List[Any] = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase_ : List[str] = [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
UpperCAmelCase_ : List[str] = dummy_past_residuals[:]
UpperCAmelCase_ : str = scheduler.step_prk(lowercase_ , 0 , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Any = scheduler.step_prk(lowercase_ , 1 , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
UpperCAmelCase_ : Optional[Any] = scheduler.step_plms(lowercase_ , 0 , lowercase_ , **lowercase_ ).prev_sample
UpperCAmelCase_ : Optional[Any] = scheduler.step_plms(lowercase_ , 1 , lowercase_ , **lowercase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for timesteps in [100, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowercase_ )
UpperCAmelCase_ : Optional[int] = self.scheduler_classes[0]
UpperCAmelCase_ : int = self.get_scheduler_config(steps_offset=1 )
UpperCAmelCase_ : Optional[Any] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(10 )
assert torch.equal(
scheduler.timesteps , torch.LongTensor(
[901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1] ) , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for beta_start, beta_end in zip([0.00_01, 0.0_01] , [0.0_02, 0.02] ):
self.check_over_configs(beta_start=lowercase_ , beta_end=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t in [1, 5, 10]:
self.check_over_forward(time_step=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t, num_inference_steps in zip([1, 5, 10] , [10, 50, 100] ):
self.check_over_forward(num_inference_steps=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
# earlier version of set_timesteps() caused an error indexing alpha's with inference steps as power of 3
UpperCAmelCase_ : List[Any] = 27
for scheduler_class in self.scheduler_classes:
UpperCAmelCase_ : List[Any] = self.dummy_sample
UpperCAmelCase_ : Optional[int] = 0.1 * sample
UpperCAmelCase_ : List[str] = self.get_scheduler_config()
UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(lowercase_ )
# before power of 3 fix, would error on first step, so we only need to do two
for i, t in enumerate(scheduler.prk_timesteps[:2] ):
UpperCAmelCase_ : List[str] = scheduler.step_prk(lowercase_ , lowercase_ , lowercase_ ).prev_sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
with self.assertRaises(lowercase_ ):
UpperCAmelCase_ : List[str] = self.scheduler_classes[0]
UpperCAmelCase_ : str = self.get_scheduler_config()
UpperCAmelCase_ : Tuple = scheduler_class(**lowercase_ )
scheduler.step_plms(self.dummy_sample , 1 , self.dummy_sample ).prev_sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[Any] = self.full_loop()
UpperCAmelCase_ : Any = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Dict = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_98.13_18 ) < 1E-2
assert abs(result_mean.item() - 0.25_80 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = self.full_loop(prediction_type="v_prediction" )
UpperCAmelCase_ : str = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 67.39_86 ) < 1E-2
assert abs(result_mean.item() - 0.08_78 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ : Union[str, Any] = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : int = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_30.03_99 ) < 1E-2
assert abs(result_mean.item() - 0.29_95 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ : Tuple = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
UpperCAmelCase_ : int = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_86.94_82 ) < 1E-2
assert abs(result_mean.item() - 0.24_34 ) < 1E-3
| 23
| 1
|
"""simple docstring"""
import sys
a_ = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : Union[str, Any] = 1
for digit in s:
product *= int(__UpperCamelCase )
return product
def __UpperCAmelCase ( __UpperCamelCase = N ):
__lowercase : Any = -sys.maxsize - 1
__lowercase : Union[str, Any] = n[:13]
__lowercase : Tuple = 13
while cur_index < len(__UpperCamelCase ) - 13:
if int(n[cur_index] ) >= int(substr[0] ):
__lowercase : Union[str, Any] = substr[1:] + n[cur_index]
cur_index += 1
else:
__lowercase : List[Any] = max(__UpperCamelCase , str_eval(__UpperCamelCase ) )
__lowercase : List[Any] = n[cur_index : cur_index + 13]
cur_index += 13
return largest_product
if __name__ == "__main__":
print(F"{solution() = }")
| 249
|
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( snake_case , snake_case , unittest.TestCase ):
UpperCamelCase =StableDiffusionDiffEditPipeline
UpperCamelCase =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
UpperCamelCase =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
UpperCamelCase =frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
UpperCamelCase =frozenset([] )
def _lowerCamelCase ( self ) -> str:
torch.manual_seed(0 )
__lowercase : Optional[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 , attention_head_dim=(2, 4) , use_linear_projection=UpperCamelCase_ , )
__lowercase : Optional[Any] = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase_ , set_alpha_to_one=UpperCamelCase_ , )
__lowercase : Optional[int] = DDIMInverseScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase_ , set_alpha_to_zero=UpperCamelCase_ , )
torch.manual_seed(0 )
__lowercase : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
__lowercase : Dict = 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=10_00 , hidden_act='''gelu''' , projection_dim=5_12 , )
__lowercase : Optional[int] = CLIPTextModel(UpperCamelCase_ )
__lowercase : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__lowercase : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''inverse_scheduler''': inverse_scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=0 ) -> Any:
__lowercase : Any = floats_tensor((1, 16, 16) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowercase : Dict = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
if str(UpperCamelCase_ ).startswith('''mps''' ):
__lowercase : List[Any] = torch.manual_seed(UpperCamelCase_ )
else:
__lowercase : Any = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowercase : Any = {
'''prompt''': '''a dog and a newt''',
'''mask_image''': mask,
'''image_latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=0 ) -> int:
__lowercase : int = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowercase : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase : List[Any] = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' )
if str(UpperCamelCase_ ).startswith('''mps''' ):
__lowercase : List[str] = torch.manual_seed(UpperCamelCase_ )
else:
__lowercase : List[Any] = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowercase : int = {
'''image''': image,
'''source_prompt''': '''a cat and a frog''',
'''target_prompt''': '''a dog and a newt''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''num_maps_per_mask''': 2,
'''mask_encode_strength''': 1.0,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCamelCase ( self , UpperCamelCase_ , UpperCamelCase_=0 ) -> Union[str, Any]:
__lowercase : Union[str, Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(UpperCamelCase_ ) ).to(UpperCamelCase_ )
__lowercase : Any = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase : Any = Image.fromarray(np.uinta(UpperCamelCase_ ) ).convert('''RGB''' )
if str(UpperCamelCase_ ).startswith('''mps''' ):
__lowercase : Optional[Any] = torch.manual_seed(UpperCamelCase_ )
else:
__lowercase : int = torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
__lowercase : Optional[int] = {
'''image''': image,
'''prompt''': '''a cat and a frog''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''inpaint_strength''': 1.0,
'''guidance_scale''': 6.0,
'''decode_latents''': True,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCamelCase ( self ) -> Optional[Any]:
if not hasattr(self.pipeline_class , '''_optional_components''' ):
return
__lowercase : Optional[int] = self.get_dummy_components()
__lowercase : List[str] = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
__lowercase : Union[str, Any] = self.get_dummy_inputs(UpperCamelCase_ )
__lowercase : Any = pipe(**UpperCamelCase_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(UpperCamelCase_ )
__lowercase : Tuple = self.pipeline_class.from_pretrained(UpperCamelCase_ )
pipe_loaded.to(UpperCamelCase_ )
pipe_loaded.set_progress_bar_config(disable=UpperCamelCase_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(UpperCamelCase_ , UpperCamelCase_ ) is None , F"""`{optional_component}` did not stay set to None after loading.""" , )
__lowercase : List[Any] = self.get_dummy_inputs(UpperCamelCase_ )
__lowercase : Any = pipe_loaded(**UpperCamelCase_ )[0]
__lowercase : Any = np.abs(output - output_loaded ).max()
self.assertLess(UpperCamelCase_ , 1E-4 )
def _lowerCamelCase ( self ) -> List[Any]:
__lowercase : int = '''cpu'''
__lowercase : Optional[int] = self.get_dummy_components()
__lowercase : Tuple = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : str = self.get_dummy_mask_inputs(UpperCamelCase_ )
__lowercase : int = pipe.generate_mask(**UpperCamelCase_ )
__lowercase : Any = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
__lowercase : List[Any] = np.array([0] * 9 )
__lowercase : str = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase_ , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def _lowerCamelCase ( self ) -> str:
__lowercase : Optional[int] = '''cpu'''
__lowercase : Dict = self.get_dummy_components()
__lowercase : str = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : int = self.get_dummy_inversion_inputs(UpperCamelCase_ )
__lowercase : List[str] = pipe.invert(**UpperCamelCase_ ).images
__lowercase : Any = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__lowercase : Any = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
__lowercase : int = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase_ , 1E-3 )
def _lowerCamelCase ( self ) -> Optional[int]:
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def _lowerCamelCase ( self ) -> str:
__lowercase : Union[str, Any] = '''cpu'''
__lowercase : str = self.get_dummy_components()
__lowercase : Any = {'''beta_start''': 0.0_0_0_8_5, '''beta_end''': 0.0_1_2, '''beta_schedule''': '''scaled_linear'''}
__lowercase : str = DPMSolverMultistepScheduler(**UpperCamelCase_ )
__lowercase : List[str] = DPMSolverMultistepInverseScheduler(**UpperCamelCase_ )
__lowercase : int = self.pipeline_class(**UpperCamelCase_ )
pipe.to(UpperCamelCase_ )
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : str = self.get_dummy_inversion_inputs(UpperCamelCase_ )
__lowercase : str = pipe.invert(**UpperCamelCase_ ).images
__lowercase : Any = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
__lowercase : Union[str, Any] = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
__lowercase : Tuple = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCamelCase_ , 1E-3 )
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
def _lowerCamelCase ( self ) -> Union[str, Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _lowerCamelCase ( cls ) -> Optional[Any]:
__lowercase : List[str] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png''' )
__lowercase : Optional[int] = raw_image.convert('''RGB''' ).resize((7_68, 7_68) )
__lowercase : Any = raw_image
def _lowerCamelCase ( self ) -> Optional[int]:
__lowercase : str = torch.manual_seed(0 )
__lowercase : Optional[int] = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=UpperCamelCase_ , torch_dtype=torch.floataa )
__lowercase : List[str] = DDIMScheduler.from_config(pipe.scheduler.config )
__lowercase : Dict = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : Tuple = '''a bowl of fruit'''
__lowercase : int = '''a bowl of pears'''
__lowercase : str = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCamelCase_ , target_prompt=UpperCamelCase_ , generator=UpperCamelCase_ , )
__lowercase : Dict = pipe.invert(
prompt=UpperCamelCase_ , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCamelCase_ ).latents
__lowercase : Optional[int] = pipe(
prompt=UpperCamelCase_ , mask_image=UpperCamelCase_ , image_latents=UpperCamelCase_ , generator=UpperCamelCase_ , negative_prompt=UpperCamelCase_ , inpaint_strength=0.7 , output_type='''numpy''' , ).images[0]
__lowercase : int = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
def _lowerCamelCase ( self ) -> Tuple:
__lowercase : Union[str, Any] = torch.manual_seed(0 )
__lowercase : Dict = StableDiffusionDiffEditPipeline.from_pretrained(
'''stabilityai/stable-diffusion-2-1''' , safety_checker=UpperCamelCase_ , torch_dtype=torch.floataa )
__lowercase : int = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__lowercase : str = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=UpperCamelCase_ )
__lowercase : List[str] = '''a bowl of fruit'''
__lowercase : Union[str, Any] = '''a bowl of pears'''
__lowercase : int = pipe.generate_mask(
image=self.raw_image , source_prompt=UpperCamelCase_ , target_prompt=UpperCamelCase_ , generator=UpperCamelCase_ , )
__lowercase : List[Any] = pipe.invert(
prompt=UpperCamelCase_ , image=self.raw_image , inpaint_strength=0.7 , generator=UpperCamelCase_ , num_inference_steps=25 , ).latents
__lowercase : Optional[int] = pipe(
prompt=UpperCamelCase_ , mask_image=UpperCamelCase_ , image_latents=UpperCamelCase_ , generator=UpperCamelCase_ , negative_prompt=UpperCamelCase_ , inpaint_strength=0.7 , num_inference_steps=25 , output_type='''numpy''' , ).images[0]
__lowercase : Union[str, Any] = (
np.array(
load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/diffedit/pears.png''' ).resize((7_68, 7_68) ) )
/ 2_55
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 249
| 1
|
"""simple docstring"""
def _snake_case ( UpperCamelCase : list ):
UpperCAmelCase : Any = len(UpperCamelCase )
for i in range(1 , UpperCamelCase ):
UpperCAmelCase : Dict = collection[i]
UpperCAmelCase : Optional[int] = 0
UpperCAmelCase : List[Any] = i - 1
while low <= high:
UpperCAmelCase : List[Any] = (low + high) // 2
if val < collection[mid]:
UpperCAmelCase : Union[str, Any] = mid - 1
else:
UpperCAmelCase : Union[str, Any] = mid + 1
for j in range(UpperCamelCase , UpperCamelCase , -1 ):
UpperCAmelCase : Any = collection[j - 1]
UpperCAmelCase : Any = val
return collection
if __name__ == "__main__":
A: Optional[int] = input("Enter numbers separated by a comma:\n").strip()
A: List[str] = [int(item) for item in user_input.split(",")]
print(binary_insertion_sort(unsorted))
| 357
|
"""simple docstring"""
def _snake_case ( UpperCamelCase : Tuple , UpperCamelCase : Tuple , UpperCamelCase : Tuple=False ):
if isinstance(UpperCamelCase , UpperCamelCase ) and isinstance(UpperCamelCase , UpperCamelCase ):
UpperCAmelCase : List[str] = len(set_a.intersection(UpperCamelCase ) )
if alternative_union:
UpperCAmelCase : int = len(UpperCamelCase ) + len(UpperCamelCase )
else:
UpperCAmelCase : Any = len(set_a.union(UpperCamelCase ) )
return intersection / union
if isinstance(UpperCamelCase , (list, tuple) ) and isinstance(UpperCamelCase , (list, tuple) ):
UpperCAmelCase : Any = [element for element in set_a if element in set_b]
if alternative_union:
UpperCAmelCase : Optional[int] = len(UpperCamelCase ) + len(UpperCamelCase )
return len(UpperCamelCase ) / union
else:
UpperCAmelCase : Tuple = set_a + [element for element in set_b if element not in set_a]
return len(UpperCamelCase ) / len(UpperCamelCase )
return len(UpperCamelCase ) / len(UpperCamelCase )
return None
if __name__ == "__main__":
A: List[Any] = {"a", "b", "c", "d", "e"}
A: List[Any] = {"c", "d", "e", "f", "h", "i"}
print(jaccard_similarity(set_a, set_b))
| 76
| 0
|
'''simple docstring'''
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transformers.models.bert.modeling_flax_bert import FlaxBertModel
from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel
@require_flax
class a__ ( unittest.TestCase ):
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
for model_name in ["bert-base-cased", "bert-large-uncased"]:
with self.subTest(__lowercase ):
_lowercase : Tuple = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
_lowercase : Tuple = FlaxAutoModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
for model_name in ["roberta-base", "roberta-large"]:
with self.subTest(__lowercase ):
_lowercase : Optional[Any] = AutoConfig.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
_lowercase : List[str] = FlaxAutoModel.from_pretrained(__lowercase )
self.assertIsNotNone(__lowercase )
self.assertIsInstance(__lowercase , __lowercase )
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
for model_name in ["bert-base-cased", "bert-large-uncased"]:
_lowercase : Dict = AutoTokenizer.from_pretrained(__lowercase )
_lowercase : List[Any] = FlaxBertModel.from_pretrained(__lowercase )
_lowercase : str = tokenizer("Do you support jax jitted function?" , return_tensors=TensorType.JAX )
@jax.jit
def eval(**_UpperCamelCase ):
return model(**__lowercase )
eval(**__lowercase ).block_until_ready()
@slow
def _lowerCamelCase ( self ):
"""simple docstring"""
for model_name in ["roberta-base", "roberta-large"]:
_lowercase : Optional[Any] = AutoTokenizer.from_pretrained(__lowercase )
_lowercase : Any = FlaxRobertaModel.from_pretrained(__lowercase )
_lowercase : str = tokenizer("Do you support jax jitted function?" , return_tensors=TensorType.JAX )
@jax.jit
def eval(**_UpperCamelCase ):
return model(**__lowercase )
eval(**__lowercase ).block_until_ready()
def _lowerCamelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
__lowercase , "bert-base is not a local folder and is not a valid model identifier" ):
_lowercase : Union[str, Any] = FlaxAutoModel.from_pretrained("bert-base" )
def _lowerCamelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
__lowercase , R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
_lowercase : Any = FlaxAutoModel.from_pretrained(__lowercase , revision="aaaaaa" )
def _lowerCamelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(
__lowercase , "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack" , ):
_lowercase : List[Any] = FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model" )
def _lowerCamelCase ( self ):
"""simple docstring"""
with self.assertRaisesRegex(__lowercase , "Use `from_pt=True` to load this model" ):
_lowercase : List[str] = FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only" )
| 250
|
'''simple docstring'''
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class lowerCAmelCase ( A ):
lowerCAmelCase_ = "data2vec-audio"
def __init__( self : Tuple , __lowercase : Optional[int]=32 , __lowercase : List[str]=768 , __lowercase : List[str]=12 , __lowercase : str=12 , __lowercase : Tuple=3072 , __lowercase : Any="gelu" , __lowercase : Dict=0.1 , __lowercase : Any=0.1 , __lowercase : Tuple=0.1 , __lowercase : List[str]=0.0 , __lowercase : List[Any]=0.1 , __lowercase : str=0.1 , __lowercase : Optional[int]=0.0_2 , __lowercase : Dict=1E-5 , __lowercase : Any="gelu" , __lowercase : Dict=(512, 512, 512, 512, 512, 512, 512) , __lowercase : str=(5, 2, 2, 2, 2, 2, 2) , __lowercase : List[Any]=(10, 3, 3, 3, 3, 2, 2) , __lowercase : Dict=False , __lowercase : int=16 , __lowercase : Any=19 , __lowercase : Tuple=5 , __lowercase : Optional[Any]=0.0_5 , __lowercase : Optional[int]=10 , __lowercase : int=2 , __lowercase : Optional[Any]=0.0 , __lowercase : Tuple=10 , __lowercase : Union[str, Any]=0 , __lowercase : Optional[int]="sum" , __lowercase : str=False , __lowercase : Union[str, Any]=False , __lowercase : Any=256 , __lowercase : str=(512, 512, 512, 512, 1500) , __lowercase : Union[str, Any]=(5, 3, 3, 1, 1) , __lowercase : List[Any]=(1, 2, 3, 1, 1) , __lowercase : Any=512 , __lowercase : int=0 , __lowercase : Union[str, Any]=1 , __lowercase : Optional[int]=2 , __lowercase : Any=False , __lowercase : Optional[int]=3 , __lowercase : Optional[Any]=2 , __lowercase : Any=3 , __lowercase : Tuple=None , **__lowercase : Optional[Any] , ):
"""simple docstring"""
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase )
__lowercase =hidden_size
__lowercase =feat_extract_activation
__lowercase =list(__lowercase )
__lowercase =list(__lowercase )
__lowercase =list(__lowercase )
__lowercase =conv_bias
__lowercase =num_conv_pos_embeddings
__lowercase =num_conv_pos_embedding_groups
__lowercase =conv_pos_kernel_size
__lowercase =len(self.conv_dim )
__lowercase =num_hidden_layers
__lowercase =intermediate_size
__lowercase =hidden_act
__lowercase =num_attention_heads
__lowercase =hidden_dropout
__lowercase =attention_dropout
__lowercase =activation_dropout
__lowercase =feat_proj_dropout
__lowercase =final_dropout
__lowercase =layerdrop
__lowercase =layer_norm_eps
__lowercase =initializer_range
__lowercase =vocab_size
__lowercase =use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowercase =mask_time_prob
__lowercase =mask_time_length
__lowercase =mask_time_min_masks
__lowercase =mask_feature_prob
__lowercase =mask_feature_length
__lowercase =mask_feature_min_masks
# ctc loss
__lowercase =ctc_loss_reduction
__lowercase =ctc_zero_infinity
# adapter
__lowercase =add_adapter
__lowercase =adapter_kernel_size
__lowercase =adapter_stride
__lowercase =num_adapter_layers
__lowercase =output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowercase =classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowercase =list(__lowercase )
__lowercase =list(__lowercase )
__lowercase =list(__lowercase )
__lowercase =xvector_output_dim
@property
def snake_case ( self : Optional[Any] ):
"""simple docstring"""
return math.prod(self.conv_stride )
| 141
| 0
|
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class __UpperCamelCase ( unittest.TestCase ):
def __init__(self : List[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any=1_3 , __SCREAMING_SNAKE_CASE : List[str]=7 , __SCREAMING_SNAKE_CASE : Dict=True , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Union[str, Any]=True , __SCREAMING_SNAKE_CASE : int=True , __SCREAMING_SNAKE_CASE : Union[str, Any]=9_9 , __SCREAMING_SNAKE_CASE : int=3_2 , __SCREAMING_SNAKE_CASE : Optional[int]=5 , __SCREAMING_SNAKE_CASE : Optional[int]=4 , __SCREAMING_SNAKE_CASE : Any=3_7 , __SCREAMING_SNAKE_CASE : List[Any]="gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , __SCREAMING_SNAKE_CASE : List[str]=5_1_2 , __SCREAMING_SNAKE_CASE : Optional[Any]=1_6 , __SCREAMING_SNAKE_CASE : Union[str, Any]=2 , __SCREAMING_SNAKE_CASE : Tuple=0.0_2 , __SCREAMING_SNAKE_CASE : Tuple=4 , ):
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 SCREAMING_SNAKE_CASE__ (self : 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 = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=__SCREAMING_SNAKE_CASE , )
return config, input_ids, attention_mask
def SCREAMING_SNAKE_CASE__ (self : str):
A = self.prepare_config_and_inputs()
A , A , A = config_and_inputs
A = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class __UpperCamelCase ( _A , unittest.TestCase ):
SCREAMING_SNAKE_CASE = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def SCREAMING_SNAKE_CASE__ (self : List[Any]):
A = FlaxDistilBertModelTester(self)
@slow
def SCREAMING_SNAKE_CASE__ (self : Any):
for model_class_name in self.all_model_classes:
A = model_class_name.from_pretrained("distilbert-base-uncased")
A = model(np.ones((1, 1)))
self.assertIsNotNone(__SCREAMING_SNAKE_CASE)
@require_flax
class __UpperCamelCase ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE__ (self : Dict):
A = FlaxDistilBertModel.from_pretrained("distilbert-base-uncased")
A = np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]])
A = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
A = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE)[0]
A = (1, 1_1, 7_6_8)
self.assertEqual(output.shape , __SCREAMING_SNAKE_CASE)
A = np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]])
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , __SCREAMING_SNAKE_CASE , atol=1E-4))
| 369
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Union[str, Any] = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
),
# See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}
class __UpperCamelCase ( _A ):
SCREAMING_SNAKE_CASE = "speech_to_text_2"
SCREAMING_SNAKE_CASE = ["past_key_values"]
SCREAMING_SNAKE_CASE = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}
def __init__(self : Any , __SCREAMING_SNAKE_CASE : List[str]=1_0_0_0_0 , __SCREAMING_SNAKE_CASE : Dict=6 , __SCREAMING_SNAKE_CASE : Dict=2_0_4_8 , __SCREAMING_SNAKE_CASE : str=4 , __SCREAMING_SNAKE_CASE : int=0.0 , __SCREAMING_SNAKE_CASE : Tuple=True , __SCREAMING_SNAKE_CASE : List[Any]="relu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=2_5_6 , __SCREAMING_SNAKE_CASE : int=0.1 , __SCREAMING_SNAKE_CASE : List[str]=0.0 , __SCREAMING_SNAKE_CASE : Optional[Any]=0.0 , __SCREAMING_SNAKE_CASE : Tuple=0.0_2 , __SCREAMING_SNAKE_CASE : int=2 , __SCREAMING_SNAKE_CASE : List[Any]=True , __SCREAMING_SNAKE_CASE : Any=1 , __SCREAMING_SNAKE_CASE : List[str]=0 , __SCREAMING_SNAKE_CASE : Optional[Any]=2 , __SCREAMING_SNAKE_CASE : int=1_0_2_4 , **__SCREAMING_SNAKE_CASE : str , ):
A = vocab_size
A = d_model
A = decoder_ffn_dim
A = decoder_layers
A = decoder_attention_heads
A = dropout
A = attention_dropout
A = activation_dropout
A = activation_function
A = init_std
A = decoder_layerdrop
A = use_cache
A = decoder_layers
A = scale_embedding # scale factor will be sqrt(d_model) if True
A = max_target_positions
super().__init__(
pad_token_id=__SCREAMING_SNAKE_CASE , bos_token_id=__SCREAMING_SNAKE_CASE , eos_token_id=__SCREAMING_SNAKE_CASE , decoder_start_token_id=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE , )
| 57
| 0
|
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( __UpperCamelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCAmelCase_ : List[Any] = 0.00
lowerCAmelCase_ : Tuple = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase_ : Optional[Any] = f'''Resistor at index {index} has a negative or zero value!'''
raise ValueError(__UpperCamelCase )
first_sum += 1 / float(__UpperCamelCase )
index += 1
return 1 / first_sum
def __lowerCamelCase ( __UpperCamelCase ) -> str:
"""simple docstring"""
lowerCAmelCase_ : Dict = 0.00
lowerCAmelCase_ : Union[str, Any] = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
lowerCAmelCase_ : Optional[int] = f'''Resistor at index {index} has a negative value!'''
raise ValueError(__UpperCamelCase )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 241
|
'''simple docstring'''
from itertools import product
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = sides_number
SCREAMING_SNAKE_CASE : str = max_face_number * dice_number
SCREAMING_SNAKE_CASE : Tuple = [0] * (max_total + 1)
SCREAMING_SNAKE_CASE : Dict = 1
SCREAMING_SNAKE_CASE : int = range(__UpperCamelCase ,max_face_number + 1 )
for dice_numbers in product(__UpperCamelCase ,repeat=__UpperCamelCase ):
SCREAMING_SNAKE_CASE : Any = sum(__UpperCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = total_frequency_distribution(
sides_number=4 ,dice_number=9 )
SCREAMING_SNAKE_CASE : str = total_frequency_distribution(
sides_number=6 ,dice_number=6 )
SCREAMING_SNAKE_CASE : Dict = 0
SCREAMING_SNAKE_CASE : List[Any] = 9
SCREAMING_SNAKE_CASE : Dict = 4 * 9
SCREAMING_SNAKE_CASE : str = 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] )
SCREAMING_SNAKE_CASE : Optional[Any] = (4**9) * (6**6)
SCREAMING_SNAKE_CASE : int = peter_wins_count / total_games_number
SCREAMING_SNAKE_CASE : Optional[Any] = round(__UpperCamelCase ,ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F"""{solution() = }""")
| 251
| 0
|
'''simple docstring'''
from typing import List
from .keymap import KEYMAP, get_character
def __UpperCamelCase ( UpperCAmelCase ):
def decorator(UpperCAmelCase ):
lowercase__ : Dict = getattr(UpperCAmelCase , '''handle_key''' , [] )
handle += [key]
setattr(UpperCAmelCase , '''handle_key''' , UpperCAmelCase )
return func
return decorator
def __UpperCamelCase ( *UpperCAmelCase ):
def decorator(UpperCAmelCase ):
lowercase__ : Union[str, Any] = getattr(UpperCAmelCase , '''handle_key''' , [] )
handle += keys
setattr(UpperCAmelCase , '''handle_key''' , UpperCAmelCase )
return func
return decorator
class UpperCAmelCase ( a__ ):
'''simple docstring'''
def __new__( cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Any:
lowercase__ : List[Any] = super().__new__(cls , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
if not hasattr(__lowerCAmelCase , '''key_handler''' ):
setattr(__lowerCAmelCase , '''key_handler''' , {} )
setattr(__lowerCAmelCase , '''handle_input''' , KeyHandler.handle_input )
for value in attrs.values():
lowercase__ : Optional[Any] = getattr(__lowerCAmelCase , '''handle_key''' , [] )
for key in handled_keys:
lowercase__ : Tuple = value
return new_cls
@staticmethod
def _lowerCAmelCase( cls ) -> Tuple:
lowercase__ : Dict = get_character()
if char != KEYMAP["undefined"]:
lowercase__ : Tuple = ord(__lowerCAmelCase )
lowercase__ : str = cls.key_handler.get(__lowerCAmelCase )
if handler:
lowercase__ : str = char
return handler(cls )
else:
return None
def __UpperCamelCase ( cls ):
return KeyHandler(cls.__name__ , cls.__bases__ , cls.__dict__.copy() )
| 214
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase ( a__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = DDIMPipeline
SCREAMING_SNAKE_CASE = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
SCREAMING_SNAKE_CASE = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"latents",
"callback",
"callback_steps",
}
SCREAMING_SNAKE_CASE = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
SCREAMING_SNAKE_CASE = False
def _lowerCAmelCase( self ) -> Union[str, Any]:
torch.manual_seed(0 )
lowercase__ : Optional[int] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
lowercase__ : Any = DDIMScheduler()
lowercase__ : Union[str, Any] = {'''unet''': unet, '''scheduler''': scheduler}
return components
def _lowerCAmelCase( self , __lowerCAmelCase , __lowerCAmelCase=0 ) -> int:
if str(__lowerCAmelCase ).startswith('''mps''' ):
lowercase__ : Optional[Any] = torch.manual_seed(__lowerCAmelCase )
else:
lowercase__ : int = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
lowercase__ : List[Any] = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def _lowerCAmelCase( self ) -> Optional[Any]:
lowercase__ : int = '''cpu'''
lowercase__ : Optional[Any] = self.get_dummy_components()
lowercase__ : str = self.pipeline_class(**__lowerCAmelCase )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : str = self.get_dummy_inputs(__lowerCAmelCase )
lowercase__ : str = pipe(**__lowerCAmelCase ).images
lowercase__ : List[str] = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
lowercase__ : Optional[int] = np.array(
[1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04] )
lowercase__ : Optional[Any] = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__lowerCAmelCase , 1E-3 )
def _lowerCAmelCase( self ) -> str:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def _lowerCAmelCase( self ) -> List[str]:
super().test_save_load_local(expected_max_difference=3E-3 )
def _lowerCAmelCase( self ) -> Tuple:
super().test_save_load_optional_components(expected_max_difference=3E-3 )
def _lowerCAmelCase( self ) -> int:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def _lowerCAmelCase( self ) -> List[Any]:
lowercase__ : List[str] = '''google/ddpm-cifar10-32'''
lowercase__ : List[Any] = UNetaDModel.from_pretrained(__lowerCAmelCase )
lowercase__ : str = DDIMScheduler()
lowercase__ : List[Any] = DDIMPipeline(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
ddim.to(__lowerCAmelCase )
ddim.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Dict = torch.manual_seed(0 )
lowercase__ : Optional[int] = ddim(generator=__lowerCAmelCase , eta=0.0 , output_type='''numpy''' ).images
lowercase__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase__ : Optional[int] = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _lowerCAmelCase( self ) -> Union[str, Any]:
lowercase__ : Optional[Any] = '''google/ddpm-ema-bedroom-256'''
lowercase__ : Tuple = UNetaDModel.from_pretrained(__lowerCAmelCase )
lowercase__ : Union[str, Any] = DDIMScheduler.from_pretrained(__lowerCAmelCase )
lowercase__ : List[Any] = DDIMPipeline(unet=__lowerCAmelCase , scheduler=__lowerCAmelCase )
ddpm.to(__lowerCAmelCase )
ddpm.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase__ : Union[str, Any] = torch.manual_seed(0 )
lowercase__ : Optional[Any] = ddpm(generator=__lowerCAmelCase , output_type='''numpy''' ).images
lowercase__ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase__ : Union[str, Any] = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 214
| 1
|
'''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCamelCase__: Any = logging.get_logger(__name__)
UpperCamelCase__: int = OrderedDict(
[
# Base model mapping
("albert", "FlaxAlbertModel"),
("bart", "FlaxBartModel"),
("beit", "FlaxBeitModel"),
("bert", "FlaxBertModel"),
("big_bird", "FlaxBigBirdModel"),
("blenderbot", "FlaxBlenderbotModel"),
("blenderbot-small", "FlaxBlenderbotSmallModel"),
("clip", "FlaxCLIPModel"),
("distilbert", "FlaxDistilBertModel"),
("electra", "FlaxElectraModel"),
("gpt-sw3", "FlaxGPT2Model"),
("gpt2", "FlaxGPT2Model"),
("gpt_neo", "FlaxGPTNeoModel"),
("gptj", "FlaxGPTJModel"),
("longt5", "FlaxLongT5Model"),
("marian", "FlaxMarianModel"),
("mbart", "FlaxMBartModel"),
("mt5", "FlaxMT5Model"),
("opt", "FlaxOPTModel"),
("pegasus", "FlaxPegasusModel"),
("regnet", "FlaxRegNetModel"),
("resnet", "FlaxResNetModel"),
("roberta", "FlaxRobertaModel"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormModel"),
("roformer", "FlaxRoFormerModel"),
("t5", "FlaxT5Model"),
("vision-text-dual-encoder", "FlaxVisionTextDualEncoderModel"),
("vit", "FlaxViTModel"),
("wav2vec2", "FlaxWav2Vec2Model"),
("whisper", "FlaxWhisperModel"),
("xglm", "FlaxXGLMModel"),
("xlm-roberta", "FlaxXLMRobertaModel"),
]
)
UpperCamelCase__: Any = OrderedDict(
[
# Model for pre-training mapping
("albert", "FlaxAlbertForPreTraining"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForPreTraining"),
("big_bird", "FlaxBigBirdForPreTraining"),
("electra", "FlaxElectraForPreTraining"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("t5", "FlaxT5ForConditionalGeneration"),
("wav2vec2", "FlaxWav2Vec2ForPreTraining"),
("whisper", "FlaxWhisperForConditionalGeneration"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCamelCase__: Dict = OrderedDict(
[
# Model for Masked LM mapping
("albert", "FlaxAlbertForMaskedLM"),
("bart", "FlaxBartForConditionalGeneration"),
("bert", "FlaxBertForMaskedLM"),
("big_bird", "FlaxBigBirdForMaskedLM"),
("distilbert", "FlaxDistilBertForMaskedLM"),
("electra", "FlaxElectraForMaskedLM"),
("mbart", "FlaxMBartForConditionalGeneration"),
("roberta", "FlaxRobertaForMaskedLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMaskedLM"),
("roformer", "FlaxRoFormerForMaskedLM"),
("xlm-roberta", "FlaxXLMRobertaForMaskedLM"),
]
)
UpperCamelCase__: int = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("bart", "FlaxBartForConditionalGeneration"),
("blenderbot", "FlaxBlenderbotForConditionalGeneration"),
("blenderbot-small", "FlaxBlenderbotSmallForConditionalGeneration"),
("encoder-decoder", "FlaxEncoderDecoderModel"),
("longt5", "FlaxLongT5ForConditionalGeneration"),
("marian", "FlaxMarianMTModel"),
("mbart", "FlaxMBartForConditionalGeneration"),
("mt5", "FlaxMT5ForConditionalGeneration"),
("pegasus", "FlaxPegasusForConditionalGeneration"),
("t5", "FlaxT5ForConditionalGeneration"),
]
)
UpperCamelCase__: int = OrderedDict(
[
# Model for Image-classsification
("beit", "FlaxBeitForImageClassification"),
("regnet", "FlaxRegNetForImageClassification"),
("resnet", "FlaxResNetForImageClassification"),
("vit", "FlaxViTForImageClassification"),
]
)
UpperCamelCase__: Tuple = OrderedDict(
[
("vision-encoder-decoder", "FlaxVisionEncoderDecoderModel"),
]
)
UpperCamelCase__: Union[str, Any] = OrderedDict(
[
# Model for Causal LM mapping
("bart", "FlaxBartForCausalLM"),
("bert", "FlaxBertForCausalLM"),
("big_bird", "FlaxBigBirdForCausalLM"),
("electra", "FlaxElectraForCausalLM"),
("gpt-sw3", "FlaxGPT2LMHeadModel"),
("gpt2", "FlaxGPT2LMHeadModel"),
("gpt_neo", "FlaxGPTNeoForCausalLM"),
("gptj", "FlaxGPTJForCausalLM"),
("opt", "FlaxOPTForCausalLM"),
("roberta", "FlaxRobertaForCausalLM"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForCausalLM"),
("xglm", "FlaxXGLMForCausalLM"),
("xlm-roberta", "FlaxXLMRobertaForCausalLM"),
]
)
UpperCamelCase__: Optional[Any] = OrderedDict(
[
# Model for Sequence Classification mapping
("albert", "FlaxAlbertForSequenceClassification"),
("bart", "FlaxBartForSequenceClassification"),
("bert", "FlaxBertForSequenceClassification"),
("big_bird", "FlaxBigBirdForSequenceClassification"),
("distilbert", "FlaxDistilBertForSequenceClassification"),
("electra", "FlaxElectraForSequenceClassification"),
("mbart", "FlaxMBartForSequenceClassification"),
("roberta", "FlaxRobertaForSequenceClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForSequenceClassification"),
("roformer", "FlaxRoFormerForSequenceClassification"),
("xlm-roberta", "FlaxXLMRobertaForSequenceClassification"),
]
)
UpperCamelCase__: Optional[int] = OrderedDict(
[
# Model for Question Answering mapping
("albert", "FlaxAlbertForQuestionAnswering"),
("bart", "FlaxBartForQuestionAnswering"),
("bert", "FlaxBertForQuestionAnswering"),
("big_bird", "FlaxBigBirdForQuestionAnswering"),
("distilbert", "FlaxDistilBertForQuestionAnswering"),
("electra", "FlaxElectraForQuestionAnswering"),
("mbart", "FlaxMBartForQuestionAnswering"),
("roberta", "FlaxRobertaForQuestionAnswering"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForQuestionAnswering"),
("roformer", "FlaxRoFormerForQuestionAnswering"),
("xlm-roberta", "FlaxXLMRobertaForQuestionAnswering"),
]
)
UpperCamelCase__: List[Any] = OrderedDict(
[
# Model for Token Classification mapping
("albert", "FlaxAlbertForTokenClassification"),
("bert", "FlaxBertForTokenClassification"),
("big_bird", "FlaxBigBirdForTokenClassification"),
("distilbert", "FlaxDistilBertForTokenClassification"),
("electra", "FlaxElectraForTokenClassification"),
("roberta", "FlaxRobertaForTokenClassification"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForTokenClassification"),
("roformer", "FlaxRoFormerForTokenClassification"),
("xlm-roberta", "FlaxXLMRobertaForTokenClassification"),
]
)
UpperCamelCase__: Tuple = OrderedDict(
[
# Model for Multiple Choice mapping
("albert", "FlaxAlbertForMultipleChoice"),
("bert", "FlaxBertForMultipleChoice"),
("big_bird", "FlaxBigBirdForMultipleChoice"),
("distilbert", "FlaxDistilBertForMultipleChoice"),
("electra", "FlaxElectraForMultipleChoice"),
("roberta", "FlaxRobertaForMultipleChoice"),
("roberta-prelayernorm", "FlaxRobertaPreLayerNormForMultipleChoice"),
("roformer", "FlaxRoFormerForMultipleChoice"),
("xlm-roberta", "FlaxXLMRobertaForMultipleChoice"),
]
)
UpperCamelCase__: Dict = OrderedDict(
[
("bert", "FlaxBertForNextSentencePrediction"),
]
)
UpperCamelCase__: str = OrderedDict(
[
("speech-encoder-decoder", "FlaxSpeechEncoderDecoderModel"),
("whisper", "FlaxWhisperForConditionalGeneration"),
]
)
UpperCamelCase__: Any = OrderedDict(
[
("whisper", "FlaxWhisperForAudioClassification"),
]
)
UpperCamelCase__: Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
UpperCamelCase__: Union[str, Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
UpperCamelCase__: List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
UpperCamelCase__: List[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
UpperCamelCase__: Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase__: str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
UpperCamelCase__: List[str] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
UpperCamelCase__: Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase__: str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
UpperCamelCase__: Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
UpperCamelCase__: Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
UpperCamelCase__: List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
UpperCamelCase__: Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
UpperCamelCase__: Optional[int] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_MAPPING
UpperCamelCase__: List[str] = auto_class_update(FlaxAutoModel)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_PRETRAINING_MAPPING
UpperCamelCase__: str = auto_class_update(FlaxAutoModelForPreTraining, head_doc="pretraining")
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
UpperCamelCase__: Dict = auto_class_update(FlaxAutoModelForCausalLM, head_doc="causal language modeling")
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_MASKED_LM_MAPPING
UpperCamelCase__: List[str] = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="masked language modeling")
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
UpperCamelCase__: Union[str, Any] = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="sequence-to-sequence language modeling", checkpoint_for_example="t5-base"
)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
UpperCamelCase__: str = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="sequence classification"
)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
UpperCamelCase__: Tuple = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="question answering")
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
UpperCamelCase__: Union[str, Any] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="token classification"
)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
UpperCamelCase__: List[str] = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="multiple choice")
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
UpperCamelCase__: Tuple = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="next sentence prediction"
)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCamelCase__: Tuple = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="image classification"
)
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
UpperCamelCase__: int = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="vision-to-text modeling")
class SCREAMING_SNAKE_CASE( _BaseAutoModelClass ):
"""simple docstring"""
lowerCamelCase__ = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
UpperCamelCase__: Union[str, Any] = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="sequence-to-sequence speech-to-text modeling"
)
| 23
|
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
UpperCamelCase__: int = logging.get_logger(__name__)
UpperCamelCase__: Dict = {
"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",
"adapter_layer": "encoder.layers.*.adapter_layer",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
"pooling_layer.linear": "projector",
"pooling_layer.projection": "classifier",
}
UpperCamelCase__: Optional[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
"projector",
"classifier",
]
def snake_case_ ( _lowerCAmelCase : str ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = {}
with open(_lowerCAmelCase , '''r''' ) as file:
for line_number, line in enumerate(_lowerCAmelCase ):
UpperCAmelCase : List[str] = line.strip()
if line:
UpperCAmelCase : str = line.split()
UpperCAmelCase : Union[str, Any] = line_number
UpperCAmelCase : List[Any] = words[0]
UpperCAmelCase : Union[str, Any] = value
return result
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : str ) -> int:
for attribute in key.split('''.''' ):
UpperCAmelCase : Dict = getattr(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Dict = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowerCAmelCase ):
UpperCAmelCase : Any = PARAM_MAPPING[full_name.split('''.''' )[-1]]
UpperCAmelCase : Dict = '''param'''
if weight_type is not None and weight_type != "param":
UpperCAmelCase : Optional[int] = getattr(_lowerCAmelCase , _lowerCAmelCase ).shape
elif weight_type is not None and weight_type == "param":
UpperCAmelCase : List[Any] = hf_pointer
for attribute in hf_param_name.split('''.''' ):
UpperCAmelCase : Optional[Any] = getattr(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : int = shape_pointer.shape
# let's reduce dimension
UpperCAmelCase : Union[str, Any] = value[0]
else:
UpperCAmelCase : List[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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 : int = value
elif weight_type == "weight_g":
UpperCAmelCase : str = value
elif weight_type == "weight_v":
UpperCAmelCase : Dict = value
elif weight_type == "bias":
UpperCAmelCase : str = value
elif weight_type == "param":
for attribute in hf_param_name.split('''.''' ):
UpperCAmelCase : int = getattr(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Optional[int] = value
else:
UpperCAmelCase : Tuple = value
logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> List[Any]:
UpperCAmelCase : List[str] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowerCAmelCase ):
UpperCAmelCase : List[str] = PARAM_MAPPING[full_name.split('''.''' )[-1]]
UpperCAmelCase : Any = '''param'''
if weight_type is not None and weight_type != "param":
UpperCAmelCase : Optional[int] = '''.'''.join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
UpperCAmelCase : Optional[int] = '''.'''.join([key, hf_param_name] )
else:
UpperCAmelCase : List[Any] = key
UpperCAmelCase : Tuple = value if '''lm_head''' in full_key else value[0]
UpperCamelCase__: Tuple = {
"W_a": "linear_1.weight",
"W_b": "linear_2.weight",
"b_a": "linear_1.bias",
"b_b": "linear_2.bias",
"ln_W": "norm.weight",
"ln_b": "norm.bias",
}
def snake_case_ ( _lowerCAmelCase : str , _lowerCAmelCase : List[str] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Optional[Any]=None ) -> int:
UpperCAmelCase : List[Any] = False
for key, mapped_key in MAPPING.items():
UpperCAmelCase : int = '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
UpperCAmelCase : Optional[Any] = True
if "*" in mapped_key:
UpperCAmelCase : Tuple = name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
UpperCAmelCase : List[Any] = mapped_key.replace('''*''' , _lowerCAmelCase )
if "weight_g" in name:
UpperCAmelCase : str = '''weight_g'''
elif "weight_v" in name:
UpperCAmelCase : int = '''weight_v'''
elif "bias" in name:
UpperCAmelCase : int = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCAmelCase : List[str] = '''weight'''
else:
UpperCAmelCase : Dict = None
if hf_dict is not None:
rename_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
set_recursively(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return is_used
return is_used
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Any:
UpperCAmelCase : Dict = []
UpperCAmelCase : Dict = fairseq_model.state_dict()
UpperCAmelCase : Union[str, Any] = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase : Dict = False
if "conv_layers" in name:
load_conv_layer(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , hf_model.config.feat_extract_norm == '''group''' , )
UpperCAmelCase : Any = True
else:
UpperCAmelCase : Optional[Any] = load_wavaveca_layer(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not is_used:
unused_weights.append(_lowerCAmelCase )
logger.warning(f"""Unused weights: {unused_weights}""" )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
UpperCAmelCase : Any = full_name.split('''conv_layers.''' )[-1]
UpperCAmelCase : Optional[int] = name.split('''.''' )
UpperCAmelCase : Tuple = int(items[0] )
UpperCAmelCase : Tuple = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
UpperCAmelCase : Tuple = value
logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
UpperCAmelCase : 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
UpperCAmelCase : Union[str, Any] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"""{full_name} has size {value.shape}, but"""
f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
UpperCAmelCase : List[str] = value
logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowerCAmelCase )
@torch.no_grad()
def snake_case_ ( _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : int=True , _lowerCAmelCase : Optional[int]=False ) -> Dict:
if config_path is not None:
UpperCAmelCase : List[str] = WavaVecaConfig.from_pretrained(_lowerCAmelCase )
else:
UpperCAmelCase : List[Any] = WavaVecaConfig()
if is_seq_class:
UpperCAmelCase : Optional[Any] = read_txt_into_dict(_lowerCAmelCase )
UpperCAmelCase : Optional[int] = idalabel
UpperCAmelCase : Optional[Any] = WavaVecaForSequenceClassification(_lowerCAmelCase )
UpperCAmelCase : Dict = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , )
feature_extractor.save_pretrained(_lowerCAmelCase )
elif is_finetuned:
if dict_path:
UpperCAmelCase : Dict = Dictionary.load(_lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCAmelCase : Any = target_dict.pad_index
UpperCAmelCase : Tuple = target_dict.bos_index
UpperCAmelCase : Optional[int] = target_dict.eos_index
UpperCAmelCase : Union[str, Any] = len(target_dict.symbols )
UpperCAmelCase : Dict = os.path.join(_lowerCAmelCase , '''vocab.json''' )
if not os.path.isdir(_lowerCAmelCase ):
logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_lowerCAmelCase ) )
return
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
UpperCAmelCase : List[Any] = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCAmelCase : List[str] = 0
UpperCAmelCase : List[str] = 1
with open(_lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as vocab_handle:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
UpperCAmelCase : Optional[int] = WavaVecaCTCTokenizer(
_lowerCAmelCase , 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=_lowerCAmelCase , )
UpperCAmelCase : int = True if config.feat_extract_norm == '''layer''' else False
UpperCAmelCase : int = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=_lowerCAmelCase , return_attention_mask=_lowerCAmelCase , )
UpperCAmelCase : str = WavaVecaProcessor(feature_extractor=_lowerCAmelCase , tokenizer=_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
UpperCAmelCase : Union[str, Any] = WavaVecaForCTC(_lowerCAmelCase )
else:
UpperCAmelCase : Dict = WavaVecaForPreTraining(_lowerCAmelCase )
if is_finetuned or is_seq_class:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
else:
UpperCAmelCase : Optional[Any] = argparse.Namespace(task='''audio_pretraining''' )
UpperCAmelCase : List[Any] = fairseq.tasks.setup_task(_lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : int = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=_lowerCAmelCase )
UpperCAmelCase : Optional[int] = model[0].eval()
recursively_load_weights(_lowerCAmelCase , _lowerCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase__: Dict = 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"
)
parser.add_argument(
"--is_seq_class",
action="store_true",
help="Whether the model to convert is a fine-tuned sequence classification model or not",
)
UpperCamelCase__: Any = parser.parse_args()
UpperCamelCase__: int = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 23
| 1
|
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 __SCREAMING_SNAKE_CASE :
'''simple docstring'''
_a = BlenderbotConfig
_a = {}
_a = 'gelu'
def __init__( self : str, lowerCamelCase : str, lowerCamelCase : str=13, lowerCamelCase : Union[str, Any]=7, lowerCamelCase : List[str]=True, lowerCamelCase : Union[str, Any]=False, lowerCamelCase : str=99, lowerCamelCase : Tuple=32, lowerCamelCase : Dict=2, lowerCamelCase : Any=4, lowerCamelCase : Union[str, Any]=37, lowerCamelCase : int=0.1, lowerCamelCase : str=0.1, lowerCamelCase : List[Any]=20, lowerCamelCase : str=2, lowerCamelCase : List[Any]=1, lowerCamelCase : str=0, )-> List[str]:
lowerCamelCase__ : int =parent
lowerCamelCase__ : Optional[Any] =batch_size
lowerCamelCase__ : List[Any] =seq_length
lowerCamelCase__ : Optional[Any] =is_training
lowerCamelCase__ : Union[str, Any] =use_labels
lowerCamelCase__ : Optional[Any] =vocab_size
lowerCamelCase__ : Any =hidden_size
lowerCamelCase__ : Union[str, Any] =num_hidden_layers
lowerCamelCase__ : List[Any] =num_attention_heads
lowerCamelCase__ : Tuple =intermediate_size
lowerCamelCase__ : Optional[Any] =hidden_dropout_prob
lowerCamelCase__ : Union[str, Any] =attention_probs_dropout_prob
lowerCamelCase__ : str =max_position_embeddings
lowerCamelCase__ : str =eos_token_id
lowerCamelCase__ : Union[str, Any] =pad_token_id
lowerCamelCase__ : Optional[int] =bos_token_id
def snake_case ( self : str )-> Optional[int]:
lowerCamelCase__ : Optional[int] =ids_tensor([self.batch_size, self.seq_length - 1], self.vocab_size )
lowerCamelCase__ : Union[str, Any] =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ), 1 )
lowerCamelCase__ : Tuple =tf.concat([input_ids, eos_tensor], axis=1 )
lowerCamelCase__ : List[str] =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Tuple =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, )
lowerCamelCase__ : Optional[int] =prepare_blenderbot_inputs_dict(lowerCamelCase, lowerCamelCase, lowerCamelCase )
return config, inputs_dict
def snake_case ( self : List[str], lowerCamelCase : Any, lowerCamelCase : Tuple )-> Union[str, Any]:
lowerCamelCase__ : str =TFBlenderbotModel(config=lowerCamelCase ).get_decoder()
lowerCamelCase__ : Dict =inputs_dict['''input_ids''']
lowerCamelCase__ : Tuple =input_ids[:1, :]
lowerCamelCase__ : Optional[int] =inputs_dict['''attention_mask'''][:1, :]
lowerCamelCase__ : Dict =inputs_dict['''head_mask''']
lowerCamelCase__ : Tuple =1
# first forward pass
lowerCamelCase__ : Optional[Any] =model(lowerCamelCase, attention_mask=lowerCamelCase, head_mask=lowerCamelCase, use_cache=lowerCamelCase )
lowerCamelCase__ : List[Any] =outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowerCamelCase__ : List[str] =ids_tensor((self.batch_size, 3), config.vocab_size )
lowerCamelCase__ : int =tf.cast(ids_tensor((self.batch_size, 3), 2 ), tf.inta )
# append to next input_ids and
lowerCamelCase__ : List[Any] =tf.concat([input_ids, next_tokens], axis=-1 )
lowerCamelCase__ : Optional[int] =tf.concat([attention_mask, next_attn_mask], axis=-1 )
lowerCamelCase__ : Dict =model(lowerCamelCase, attention_mask=lowerCamelCase )[0]
lowerCamelCase__ : Optional[Any] =model(lowerCamelCase, attention_mask=lowerCamelCase, past_key_values=lowerCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1], output_from_past.shape[1] )
# select random slice
lowerCamelCase__ : Tuple =int(ids_tensor((1,), output_from_past.shape[-1] ) )
lowerCamelCase__ : Optional[Any] =output_from_no_past[:, -3:, random_slice_idx]
lowerCamelCase__ : int =output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(lowerCamelCase, lowerCamelCase, rtol=1E-3 )
def snake_case__ ( __lowerCamelCase : List[str] , __lowerCamelCase : Optional[int] , __lowerCamelCase : List[str] , __lowerCamelCase : str=None , __lowerCamelCase : str=None , __lowerCamelCase : int=None , __lowerCamelCase : Tuple=None , __lowerCamelCase : Dict=None , ):
"""simple docstring"""
if attention_mask is None:
lowerCamelCase__ : Union[str, Any] =tf.cast(tf.math.not_equal(__lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowerCamelCase__ : Dict =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:
lowerCamelCase__ : Any =tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowerCamelCase__ : Optional[int] =tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowerCamelCase__ : 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 __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
_a = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
_a = (
{
'conversational': TFBlenderbotForConditionalGeneration,
'feature-extraction': TFBlenderbotModel,
'summarization': TFBlenderbotForConditionalGeneration,
'text2text-generation': TFBlenderbotForConditionalGeneration,
'translation': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
_a = True
_a = False
_a = False
def snake_case ( self : Optional[Any] )-> Tuple:
lowerCamelCase__ : Tuple =TFBlenderbotModelTester(self )
lowerCamelCase__ : Optional[int] =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : str )-> List[str]:
self.config_tester.run_common_tests()
def snake_case ( self : str )-> Union[str, Any]:
lowerCamelCase__ : Union[str, Any] =self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase )
@require_tokenizers
@require_tf
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
_a = ['My friends are cool but they eat too many carbs.']
_a = 'facebook/blenderbot-400M-distill'
@cached_property
def snake_case ( self : List[str] )-> List[Any]:
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def snake_case ( self : Any )-> Dict:
lowerCamelCase__ : List[str] =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def snake_case ( self : Optional[int] )-> Union[str, Any]:
lowerCamelCase__ : Any =self.tokenizer(self.src_text, return_tensors='''tf''' )
lowerCamelCase__ : int =self.model.generate(
model_inputs.input_ids, )
lowerCamelCase__ : List[Any] =self.tokenizer.batch_decode(generated_ids.numpy(), skip_special_tokens=lowerCamelCase )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 371
|
"""simple docstring"""
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
if "model" in orig_key:
lowerCamelCase__ : Optional[int] =orig_key.replace('''model.''' , '''''' )
if "norm1" in orig_key:
lowerCamelCase__ : Union[str, Any] =orig_key.replace('''norm1''' , '''attention.output.LayerNorm''' )
if "norm2" in orig_key:
lowerCamelCase__ : List[Any] =orig_key.replace('''norm2''' , '''output.LayerNorm''' )
if "norm" in orig_key:
lowerCamelCase__ : List[str] =orig_key.replace('''norm''' , '''LayerNorm''' )
if "transformer" in orig_key:
lowerCamelCase__ : str =orig_key.split('''.''' )[0].split('''_''' )[-1]
lowerCamelCase__ : Dict =orig_key.replace(f'''transformer_{layer_num}''' , f'''encoder.layer.{layer_num}''' )
if "mha.attn" in orig_key:
lowerCamelCase__ : Union[str, Any] =orig_key.replace('''mha.attn''' , '''attention.self''' )
if "mha" in orig_key:
lowerCamelCase__ : str =orig_key.replace('''mha''' , '''attention''' )
if "W_q" in orig_key:
lowerCamelCase__ : Union[str, Any] =orig_key.replace('''W_q''' , '''self.query''' )
if "W_k" in orig_key:
lowerCamelCase__ : Optional[int] =orig_key.replace('''W_k''' , '''self.key''' )
if "W_v" in orig_key:
lowerCamelCase__ : List[str] =orig_key.replace('''W_v''' , '''self.value''' )
if "ff1" in orig_key:
lowerCamelCase__ : Dict =orig_key.replace('''ff1''' , '''intermediate.dense''' )
if "ff2" in orig_key:
lowerCamelCase__ : Union[str, Any] =orig_key.replace('''ff2''' , '''output.dense''' )
if "ff" in orig_key:
lowerCamelCase__ : str =orig_key.replace('''ff''' , '''output.dense''' )
if "mlm_class" in orig_key:
lowerCamelCase__ : Tuple =orig_key.replace('''mlm.mlm_class''' , '''cls.predictions.decoder''' )
if "mlm" in orig_key:
lowerCamelCase__ : Optional[int] =orig_key.replace('''mlm''' , '''cls.predictions.transform''' )
if "cls" not in orig_key:
lowerCamelCase__ : Optional[int] ='''yoso.''' + orig_key
return orig_key
def snake_case__ ( __lowerCamelCase : List[str] , __lowerCamelCase : Any ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowerCamelCase__ : Optional[Any] =orig_state_dict.pop(__lowerCamelCase )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
lowerCamelCase__ : List[str] =val
lowerCamelCase__ : Optional[int] =orig_state_dict['''cls.predictions.decoder.bias''']
lowerCamelCase__ : str =torch.arange(__lowerCamelCase ).expand((1, -1) ) + 2
return orig_state_dict
def snake_case__ ( __lowerCamelCase : str , __lowerCamelCase : Tuple , __lowerCamelCase : Tuple ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] =torch.load(__lowerCamelCase , map_location='''cpu''' )['''model_state_dict''']
lowerCamelCase__ : List[Any] =YosoConfig.from_json_file(__lowerCamelCase )
lowerCamelCase__ : List[str] =YosoForMaskedLM(__lowerCamelCase )
lowerCamelCase__ : Tuple =convert_checkpoint_helper(config.max_position_embeddings , __lowerCamelCase )
print(model.load_state_dict(__lowerCamelCase ) )
model.eval()
model.save_pretrained(__lowerCamelCase )
print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''' )
if __name__ == "__main__":
_lowercase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for YOSO model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_lowercase : Optional[Any] = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 272
| 0
|
"""simple docstring"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowercase_ = re.compile(r"^(?P<major>\d+)" r"\.(?P<minor>\d+)" r"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class __lowerCAmelCase :
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = 4_2
__UpperCAmelCase : Union[str, Any] = None
__UpperCAmelCase : List[str] = None
__UpperCAmelCase : Optional[int] = None
__UpperCAmelCase : int = None
def __UpperCAmelCase ( self ):
__a = _str_to_version_tuple(self.version_str )
def __repr__( self ):
return f'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''
@property
def __UpperCAmelCase ( self ):
return self.major, self.minor, self.patch
def __UpperCAmelCase ( self , _a ):
if isinstance(_a , _a ):
return Version(_a )
elif isinstance(_a , _a ):
return other
raise TypeError(f'''{other} (type {type(_a )}) cannot be compared to version.''' )
def __eq__( self , _a ):
try:
__a = self._validate_operand(_a )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self , _a ):
__a = self._validate_operand(_a )
return self.tuple < other.tuple
def __hash__( self ):
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def __UpperCAmelCase ( cls , _a ):
__a = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def __UpperCAmelCase ( self ):
return self.version_str
def lowercase ( lowerCAmelCase__ : Dict ) -> str:
__a = _VERSION_REG.match(_a )
if not res:
raise ValueError(f'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.''' )
return tuple(int(_a ) for v in [res.group('''major''' ), res.group('''minor''' ), res.group('''patch''' )] )
def lowercase ( lowerCAmelCase__ : List[str] ) -> List[Any]:
return ".".join(str(_a ) for v in version_tuple )
| 45
|
a_ = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def lowerCamelCase__ ( _a , _a , _a):
SCREAMING_SNAKE_CASE : int = set()
# keep track of all the paths to be checked
SCREAMING_SNAKE_CASE : int = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
SCREAMING_SNAKE_CASE : Optional[int] = queue.pop(0)
# get the last node from the path
SCREAMING_SNAKE_CASE : Union[str, Any] = path[-1]
if node not in explored:
SCREAMING_SNAKE_CASE : List[str] = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
SCREAMING_SNAKE_CASE : List[Any] = list(_a)
new_path.append(_a)
queue.append(_a)
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(_a)
# in case there's no path between the 2 nodes
return []
def lowerCamelCase__ ( _a , _a , _a):
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
SCREAMING_SNAKE_CASE : str = [start]
SCREAMING_SNAKE_CASE : Optional[Any] = set(_a)
# Keep tab on distances from `start` node.
SCREAMING_SNAKE_CASE : Union[str, Any] = {start: 0, target: -1}
while queue:
SCREAMING_SNAKE_CASE : Optional[int] = queue.pop(0)
if node == target:
SCREAMING_SNAKE_CASE : Union[str, Any] = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node])
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(_a)
queue.append(_a)
SCREAMING_SNAKE_CASE : Optional[Any] = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, 'G', 'D')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, 'G', 'D')) # returns 4
| 76
| 0
|
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCamelCase_ : List[Any] = datasets.utils.logging.get_logger(__name__)
class _a ( folder_based_builder.FolderBasedBuilderConfig ):
SCREAMING_SNAKE_CASE_ : bool = None
SCREAMING_SNAKE_CASE_ : bool = None
class _a ( folder_based_builder.FolderBasedBuilder ):
SCREAMING_SNAKE_CASE_ : List[str] = datasets.Audio()
SCREAMING_SNAKE_CASE_ : Any = """audio"""
SCREAMING_SNAKE_CASE_ : List[Any] = AudioFolderConfig
SCREAMING_SNAKE_CASE_ : List[str] # definition at the bottom of the script
SCREAMING_SNAKE_CASE_ : str = AudioClassification(audio_column="""audio""" , label_column="""label""" )
UpperCamelCase_ : Union[str, Any] = [
'''.aiff''',
'''.au''',
'''.avr''',
'''.caf''',
'''.flac''',
'''.htk''',
'''.svx''',
'''.mat4''',
'''.mat5''',
'''.mpc2k''',
'''.ogg''',
'''.paf''',
'''.pvf''',
'''.raw''',
'''.rf64''',
'''.sd2''',
'''.sds''',
'''.ircam''',
'''.voc''',
'''.w64''',
'''.wav''',
'''.nist''',
'''.wavex''',
'''.wve''',
'''.xi''',
'''.mp3''',
'''.opus''',
]
UpperCamelCase_ : str = AUDIO_EXTENSIONS
| 142
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __a ( _UpperCamelCase: Tuple ) -> Union[str, Any]:
"""simple docstring"""
_snake_case = os.path.join(args.tf_model_dir , "parameters.json" )
_snake_case = json.loads(open(_UpperCamelCase ).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" ):
_snake_case = args.output + ".pt"
_snake_case = OrderedDict()
with tf.device("/CPU:0" ):
_snake_case = tf.train.load_checkpoint(args.tf_model_dir )
_snake_case = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_snake_case = reader.get_tensor(_UpperCamelCase ).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" ):
_snake_case = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
_snake_case = 8
_snake_case = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_snake_case = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/moe" ):
_snake_case = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
_snake_case = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
_snake_case = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/softmlp/kernel" ):
_snake_case = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
_snake_case = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
_snake_case = key_name[-9:-7]
for i in range(16 ):
_snake_case = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
_snake_case = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/mlp" ):
_snake_case = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
_snake_case = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
_snake_case = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/p1/bias" ):
_snake_case = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/p2/kernel" ):
_snake_case = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
_snake_case = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/p2/bias" ):
_snake_case = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/ln" ):
_snake_case = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_snake_case = "model.blocks.%d.feed_forward.norm.bias" % player
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/g" ):
_snake_case = "model.blocks.%d.feed_forward.norm.weight" % player
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/att" ):
_snake_case = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
_snake_case = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_snake_case = state[:, 0, :, :]
_snake_case = state[:, 1, :, :]
_snake_case = state[:, 2, :, :]
_snake_case = (
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
_snake_case = (
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
_snake_case = (
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
_snake_case = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
_snake_case = torch.tensor(_UpperCamelCase )
_snake_case = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
_snake_case = torch.tensor(_UpperCamelCase )
_snake_case = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/o/kernel" ):
_snake_case = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
_snake_case = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/an" ):
_snake_case = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_snake_case = "model.blocks.%d.self_attn.norm.bias" % player
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.endswith("/g" ):
_snake_case = "model.blocks.%d.self_attn.norm.weight" % player
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
_snake_case = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
_snake_case = "model.%s.weight" % nlayer
_snake_case = vnp.copy() # same in embedded
_snake_case = torch.tensor(_UpperCamelCase )
if key_name.startswith("model/wte" ):
_snake_case = "lm_head.weight"
_snake_case = vnp.copy() # same in embedded
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name.startswith("model/wob" ):
_snake_case = "final_logits_bias"
_snake_case = vnp.copy() # same in embedded
_snake_case = state.reshape((1, -1) )
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name == "model/dense/kernel":
_snake_case = "model.last_project.weight"
_snake_case = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_snake_case = torch.tensor(_UpperCamelCase )
elif key_name == "model/dense_1/bias":
_snake_case = "model.last_project.bias"
_snake_case = vnp.copy() # same because it is one dimensional
_snake_case = torch.tensor(_UpperCamelCase )
torch.save(_UpperCamelCase , args.output )
if __name__ == "__main__":
UpperCamelCase_ : 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''')
UpperCamelCase_ : Any = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 142
| 1
|
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
_snake_case = logging.get_logger(__name__)
@add_end_docstrings(lowerCAmelCase__ )
class lowercase ( lowerCAmelCase__ ):
def __init__( self , *_a , **_a ) -> List[str]:
super().__init__(*__a , **__a )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING )
def a__ ( self , _a=None , _a=None , _a=None ) -> List[str]:
_A : int = {}
_A : Optional[Any] = {}
if prompt is not None:
_A : Tuple = prompt
if generate_kwargs is not None:
_A : int = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
_A : Optional[int] = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
"""'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
""" please use only one""" )
_A : Tuple = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _a , **_a ) -> Dict:
return super().__call__(__a , **__a )
def a__ ( self , _a , _a=None ) -> Tuple:
_A : List[str] = load_image(__a )
if prompt is not None:
if not isinstance(__a , __a ):
raise ValueError(
F'''Received an invalid text input, got - {type(__a )} - but expected a single string. '''
"""Note also that one single text can be provided for conditional image to text generation.""" )
_A : List[str] = self.model.config.model_type
if model_type == "git":
_A : List[Any] = self.image_processor(images=__a , return_tensors=self.framework )
_A : List[Any] = self.tokenizer(text=__a , add_special_tokens=__a ).input_ids
_A : str = [self.tokenizer.cls_token_id] + input_ids
_A : Optional[Any] = torch.tensor(__a ).unsqueeze(0 )
model_inputs.update({"""input_ids""": input_ids} )
elif model_type == "pix2struct":
_A : int = self.image_processor(images=__a , header_text=__a , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
_A : Optional[Any] = self.image_processor(images=__a , return_tensors=self.framework )
_A : Union[str, Any] = self.tokenizer(__a , return_tensors=self.framework )
model_inputs.update(__a )
else:
raise ValueError(F'''Model type {model_type} does not support conditional text generation''' )
else:
_A : int = self.image_processor(images=__a , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
_A : Any = None
return model_inputs
def a__ ( self , _a , _a=None ) -> int:
# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
if (
"input_ids" in model_inputs
and isinstance(model_inputs["""input_ids"""] , __a )
and all(x is None for x in model_inputs["""input_ids"""] )
):
_A : Any = None
if generate_kwargs is None:
_A : Union[str, Any] = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
_A : Any = model_inputs.pop(self.model.main_input_name )
_A : int = self.model.generate(__a , **__a , **__a )
return model_outputs
def a__ ( self , _a ) -> List[Any]:
_A : Optional[int] = []
for output_ids in model_outputs:
_A : Dict = {
"""generated_text""": self.tokenizer.decode(
__a , skip_special_tokens=__a , )
}
records.append(__a )
return records
| 26
|
"""simple docstring"""
import numpy
# List of input, output pairs
A : Any = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, Any] = [2, 4, 1, 5]
A : int = len(train_data)
A : Dict = 0.009
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase="train" ):
'''simple docstring'''
return calculate_hypothesis_value(_UpperCamelCase , _UpperCamelCase ) - output(
_UpperCamelCase , _UpperCamelCase )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 0
for i in range(len(_UpperCamelCase ) - 1 ):
hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
hyp_val += parameter_vector[0]
return hyp_val
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if data_set == "train":
return train_data[example_no][1]
elif data_set == "test":
return test_data[example_no][1]
return None
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if data_set == "train":
return _hypothesis_value(train_data[example_no][0] )
elif data_set == "test":
return _hypothesis_value(test_data[example_no][0] )
return None
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase=m ):
'''simple docstring'''
__lowerCAmelCase = 0
for i in range(_UpperCamelCase ):
if index == -1:
summation_value += _error(_UpperCamelCase )
else:
summation_value += _error(_UpperCamelCase ) * train_data[i][0][index]
return summation_value
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = summation_of_cost_derivative(_UpperCamelCase , _UpperCamelCase ) / m
return cost_derivative_value
def _lowerCamelCase ( ):
'''simple docstring'''
global parameter_vector
# Tune these values to set a tolerance value for predicted output
__lowerCAmelCase = 0.00_00_02
__lowerCAmelCase = 0
__lowerCAmelCase = 0
while True:
j += 1
__lowerCAmelCase = [0, 0, 0, 0]
for i in range(0 , len(_UpperCamelCase ) ):
__lowerCAmelCase = get_cost_derivative(i - 1 )
__lowerCAmelCase = (
parameter_vector[i] - LEARNING_RATE * cost_derivative
)
if numpy.allclose(
_UpperCamelCase , _UpperCamelCase , atol=_UpperCamelCase , rtol=_UpperCamelCase , ):
break
__lowerCAmelCase = temp_parameter_vector
print(("Number of iterations:", j) )
def _lowerCamelCase ( ):
'''simple docstring'''
for i in range(len(_UpperCamelCase ) ):
print(("Actual output value:", output(_UpperCamelCase , "test" )) )
print(("Hypothesis output:", calculate_hypothesis_value(_UpperCamelCase , "test" )) )
if __name__ == "__main__":
run_gradient_descent()
print("\nTesting gradient descent for a linear hypothesis function.\n")
test_gradient_descent()
| 57
| 0
|
"""simple docstring"""
import re
import string
import numpy as np
import datasets
SCREAMING_SNAKE_CASE__ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
SCREAMING_SNAKE_CASE__ = "\nArgs:\n predictions: List of predicted texts.\n references: List of reference texts.\n regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n ignore when calculating the exact matches. Note: these regexes are removed\n from the input data before the changes based on the options below (e.g. ignore_case,\n ignore_punctuation, ignore_numbers) are applied.\n ignore_case: Boolean, defaults to False. If true, turns everything\n to lowercase so that capitalization differences are ignored.\n ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\n ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n comparing predictions and references.\nReturns:\n exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 25.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 50.0\n\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n >>> print(round(results[\"exact_match\"], 1))\n 75.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n >>> print(round(results[\"exact_match\"], 1))\n 100.0\n\n >>> exact_match = datasets.load_metric(\"exact_match\")\n >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n >>> results = exact_match.compute(references=refs, predictions=preds)\n >>> print(round(results[\"exact_match\"], 1))\n 33.3\n\n"
SCREAMING_SNAKE_CASE__ = "\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , reference_urls=[] , )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=False , lowerCAmelCase=False , lowerCAmelCase=False , ):
"""simple docstring"""
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
snake_case = np.array([re.sub(lowerCAmelCase , '' , lowerCAmelCase ) for x in predictions] )
snake_case = np.array([re.sub(lowerCAmelCase , '' , lowerCAmelCase ) for x in references] )
else:
snake_case = np.asarray(lowerCAmelCase )
snake_case = np.asarray(lowerCAmelCase )
if ignore_case:
snake_case = np.char.lower(lowerCAmelCase )
snake_case = np.char.lower(lowerCAmelCase )
if ignore_punctuation:
snake_case = string.punctuation.maketrans('' , '' , string.punctuation )
snake_case = np.char.translate(lowerCAmelCase , table=lowerCAmelCase )
snake_case = np.char.translate(lowerCAmelCase , table=lowerCAmelCase )
if ignore_numbers:
snake_case = string.digits.maketrans('' , '' , string.digits )
snake_case = np.char.translate(lowerCAmelCase , table=lowerCAmelCase )
snake_case = np.char.translate(lowerCAmelCase , table=lowerCAmelCase )
snake_case = predictions == references
return {"exact_match": np.mean(lowerCAmelCase ) * 1_00}
| 149
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 149
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ = {
'''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = ['''ConvNextFeatureExtractor''']
snake_case_ = ['''ConvNextImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ConvNextForImageClassification''',
'''ConvNextModel''',
'''ConvNextPreTrainedModel''',
'''ConvNextBackbone''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ = [
'''TFConvNextForImageClassification''',
'''TFConvNextModel''',
'''TFConvNextPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 214
|
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
snake_case_ = logging.getLogger(__name__)
snake_case_ = 50 # max width of layer names
snake_case_ = 70 # max width of quantizer names
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Tuple ):
'''simple docstring'''
lowercase__ : str = parser.add_argument_group('quant_trainer arguments' )
group.add_argument('--wprec' , type=SCREAMING_SNAKE_CASE_ , default=8 , help='weight precision' )
group.add_argument('--aprec' , type=SCREAMING_SNAKE_CASE_ , default=8 , help='activation precision' )
group.add_argument('--quant-per-tensor' , action='store_true' , help='per tensor weight scaling' )
group.add_argument('--quant-disable' , action='store_true' , help='disable all quantizers' )
group.add_argument('--quant-disable-embeddings' , action='store_true' , help='disable all embeddings quantizers' )
group.add_argument('--quant-disable-keyword' , type=SCREAMING_SNAKE_CASE_ , nargs='+' , help='disable quantizers by keyword' )
group.add_argument('--quant-disable-layer-module' , type=SCREAMING_SNAKE_CASE_ , help='disable quantizers by keyword under layer.' )
group.add_argument('--quant-enable-layer-module' , type=SCREAMING_SNAKE_CASE_ , help='enable quantizers by keyword under layer' )
group.add_argument('--calibrator' , default='max' , help='which quantization range calibrator to use' )
group.add_argument('--percentile' , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help='percentile for PercentileCalibrator' )
group.add_argument('--fuse-qkv' , action='store_true' , help='use the same scale factor for qkv' )
group.add_argument('--clip-gelu' , metavar='N' , type=SCREAMING_SNAKE_CASE_ , help='clip gelu output maximum value to N' )
group.add_argument(
'--recalibrate-weights' , action='store_true' , help=(
'recalibrate weight amaxes by taking the max of the weights.'
' amaxes will be computed with the current quantization granularity (axis).'
) , )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
if args.calibrator == "max":
lowercase__ : Optional[int] = 'max'
elif args.calibrator == "percentile":
if args.percentile is None:
raise ValueError('Specify --percentile when using percentile calibrator' )
lowercase__ : Optional[Any] = 'histogram'
elif args.calibrator == "mse":
lowercase__ : int = 'histogram'
else:
raise ValueError(f"""Invalid calibrator {args.calibrator}""" )
lowercase__ : Any = QuantDescriptor(num_bits=args.aprec , calib_method=SCREAMING_SNAKE_CASE_ )
lowercase__ : List[Any] = QuantDescriptor(num_bits=args.wprec , axis=(None if args.quant_per_tensor else (0,)) )
quant_nn.QuantLinear.set_default_quant_desc_input(SCREAMING_SNAKE_CASE_ )
quant_nn.QuantLinear.set_default_quant_desc_weight(SCREAMING_SNAKE_CASE_ )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Union[str, Any]=False , SCREAMING_SNAKE_CASE_ : Any=False ):
'''simple docstring'''
logger.info('Configuring Model for Quantization' )
logger.info(f"""using quantization package {pytorch_quantization.__file__}""" )
if not calib:
if args.quant_disable_embeddings:
set_quantizer_by_name(SCREAMING_SNAKE_CASE_ , ['embeddings'] , which='weight' , _disabled=SCREAMING_SNAKE_CASE_ )
if args.quant_disable:
set_quantizer_by_name(SCREAMING_SNAKE_CASE_ , [''] , _disabled=SCREAMING_SNAKE_CASE_ )
if args.quant_disable_keyword:
set_quantizer_by_name(SCREAMING_SNAKE_CASE_ , args.quant_disable_keyword , _disabled=SCREAMING_SNAKE_CASE_ )
if args.quant_disable_layer_module:
set_quantizer_by_name(SCREAMING_SNAKE_CASE_ , [R'layer.\d+.' + args.quant_disable_layer_module] , _disabled=SCREAMING_SNAKE_CASE_ )
if args.quant_enable_layer_module:
set_quantizer_by_name(SCREAMING_SNAKE_CASE_ , [R'layer.\d+.' + args.quant_enable_layer_module] , _disabled=SCREAMING_SNAKE_CASE_ )
if args.recalibrate_weights:
recalibrate_weights(SCREAMING_SNAKE_CASE_ )
if args.fuse_qkv:
fuse_qkv(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if args.clip_gelu:
clip_gelu(SCREAMING_SNAKE_CASE_ , args.clip_gelu )
# if args.local_rank in [-1, 0] and not calib:
print_quant_summary(SCREAMING_SNAKE_CASE_ )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Dict ):
'''simple docstring'''
logger.info('Enabling Calibration' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
logger.info(f"""{name:80}: {module}""" )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
'''simple docstring'''
logger.info('Loading calibrated amax' )
for name, module in model.named_modules():
if name.endswith('_quantizer' ):
if module._calibrator is not None:
if isinstance(module._calibrator , calib.MaxCalibrator ):
module.load_calib_amax()
else:
module.load_calib_amax('percentile' , percentile=args.percentile )
module.enable_quant()
module.disable_calib()
else:
module.enable()
model.cuda()
print_quant_summary(SCREAMING_SNAKE_CASE_ )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
'''simple docstring'''
def fusea(SCREAMING_SNAKE_CASE_ : Any , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
for mod in [qq, qk, qv]:
if not hasattr(SCREAMING_SNAKE_CASE_ , '_amax' ):
print(' WARNING: NO AMAX BUFFER' )
return
lowercase__ : List[Any] = qq._amax.detach().item()
lowercase__ : Optional[int] = qk._amax.detach().item()
lowercase__ : List[str] = qv._amax.detach().item()
lowercase__ : Tuple = max(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
qq._amax.fill_(SCREAMING_SNAKE_CASE_ )
qk._amax.fill_(SCREAMING_SNAKE_CASE_ )
qv._amax.fill_(SCREAMING_SNAKE_CASE_ )
logger.info(f""" q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}""" )
for name, mod in model.named_modules():
if name.endswith('.attention.self' ):
logger.info(f"""FUSE_QKV: {name:{name_width}}""" )
fusea(mod.matmul_q_input_quantizer , mod.matmul_k_input_quantizer , mod.matmul_v_input_quantizer )
if args.quant_per_tensor:
fusea(mod.query._weight_quantizer , mod.key._weight_quantizer , mod.value._weight_quantizer )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
for name, mod in model.named_modules():
if name.endswith('.output.dense' ) and not name.endswith('attention.output.dense' ):
lowercase__ : Any = mod._input_quantizer._amax.data.detach().item()
mod._input_quantizer._amax.data.detach().clamp_(max=SCREAMING_SNAKE_CASE_ )
lowercase__ : List[str] = mod._input_quantizer._amax.data.detach().item()
logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}""" )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(SCREAMING_SNAKE_CASE_ , '_weight_quantizer' ) and mod._weight_quantizer.axis is not None:
lowercase__ : Union[str, Any] = mod.weight.shape[0]
lowercase__ : str = mod._weight_quantizer._amax.detach()
lowercase__ : Any = torch.ones(SCREAMING_SNAKE_CASE_ , dtype=amax.dtype , device=amax.device ) * amax
print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}""" )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(SCREAMING_SNAKE_CASE_ , '_weight_quantizer' ):
if not hasattr(mod.weight_quantizer , '_amax' ):
print('RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER' )
continue
# determine which axes to reduce across
# e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
lowercase__ : Dict = set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis )
lowercase__ : Union[str, Any] = set(range(len(mod.weight.size() ) ) ) - axis_set
lowercase__ : str = pytorch_quantization.utils.reduce_amax(mod.weight , axis=SCREAMING_SNAKE_CASE_ , keepdims=SCREAMING_SNAKE_CASE_ ).detach()
logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}""" )
lowercase__ : Union[str, Any] = amax
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Optional[Any]=25 , SCREAMING_SNAKE_CASE_ : Any=180 , SCREAMING_SNAKE_CASE_ : Optional[int]=None ):
'''simple docstring'''
if ignore is None:
lowercase__ : Tuple = []
elif not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ : str = [ignore]
lowercase__ : Optional[Any] = 0
for name, mod in model.named_modules():
if not hasattr(SCREAMING_SNAKE_CASE_ , 'weight' ):
continue
lowercase__ : Optional[Any] = max(SCREAMING_SNAKE_CASE_ , len(SCREAMING_SNAKE_CASE_ ) )
for name, mod in model.named_modules():
lowercase__ : Dict = getattr(SCREAMING_SNAKE_CASE_ , '_input_quantizer' , SCREAMING_SNAKE_CASE_ )
lowercase__ : Optional[Any] = getattr(SCREAMING_SNAKE_CASE_ , '_weight_quantizer' , SCREAMING_SNAKE_CASE_ )
if not hasattr(SCREAMING_SNAKE_CASE_ , 'weight' ):
continue
if type(SCREAMING_SNAKE_CASE_ ) in ignore:
continue
if [True for s in ignore if type(SCREAMING_SNAKE_CASE_ ) is str and s in name]:
continue
lowercase__ : Optional[int] = f"""Act:{input_q.extra_repr()}"""
lowercase__ : Dict = f"""Wgt:{weight_q.extra_repr()}"""
lowercase__ : Tuple = f"""{name:{name_width}} {act_str} {wgt_str}"""
if len(SCREAMING_SNAKE_CASE_ ) <= line_width:
logger.info(SCREAMING_SNAKE_CASE_ )
else:
logger.info(f"""{name:{name_width}} {act_str}""" )
logger.info(f"""{" ":{name_width}} {wgt_str}""" )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : int ):
'''simple docstring'''
lowercase__ : List[str] = 0
for name, mod in model.named_modules():
if isinstance(SCREAMING_SNAKE_CASE_ , pytorch_quantization.nn.TensorQuantizer ):
print(f"""{name:80} {mod}""" )
count += 1
print(f"""{count} TensorQuantizers found in model""" )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[int] ):
'''simple docstring'''
lowercase__ : Dict = getattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if quantizer_mod is not None:
assert hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
logger.warning(f"""{name} has no {quantizer}""" )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : List[Any]="both" , **SCREAMING_SNAKE_CASE_ : Tuple ):
'''simple docstring'''
lowercase__ : Optional[int] = f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
for k, v in kwargs.items():
s += f""" {k}={v}"""
if which in ["input", "both"]:
set_quantizer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , '_input_quantizer' , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
if which in ["weight", "both"]:
set_quantizer(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , '_weight_quantizer' , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(SCREAMING_SNAKE_CASE_ )
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Optional[Any] , SCREAMING_SNAKE_CASE_ : Dict , **SCREAMING_SNAKE_CASE_ : List[str] ):
'''simple docstring'''
for name, mod in model.named_modules():
if hasattr(SCREAMING_SNAKE_CASE_ , '_input_quantizer' ) or hasattr(SCREAMING_SNAKE_CASE_ , '_weight_quantizer' ):
for n in names:
if re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
set_quantizers(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
elif name.endswith('_quantizer' ):
for n in names:
if re.search(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
lowercase__ : Any = f"""Warning: changing {name:{name_width}}"""
for k, v in kwargs.items():
s += f""" {k}={v}"""
setattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
logger.info(SCREAMING_SNAKE_CASE_ )
| 214
| 1
|
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
lowerCamelCase__ = logging.get_logger(__name__)
@dataclass
class A__ :
lowercase = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
lowercase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
lowercase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = self.task_name.lower()
class A__ ( __magic_name__ ):
lowercase = 'train'
lowercase = 'dev'
lowercase = 'test'
class A__ ( __magic_name__ ):
lowercase = 42
lowercase = 42
lowercase = 42
def __init__( self : Optional[Any] , a : GlueDataTrainingArguments , a : PreTrainedTokenizerBase , a : Optional[int] = None , a : Union[str, Split] = Split.train , a : Optional[str] = None , ):
'''simple docstring'''
warnings.warn(
'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py' , a , )
lowerCAmelCase__ : int = args
lowerCAmelCase__ : int = glue_processors[args.task_name]()
lowerCAmelCase__ : List[Any] = glue_output_modes[args.task_name]
if isinstance(a , a ):
try:
lowerCAmelCase__ : Optional[Any] = Split[mode]
except KeyError:
raise KeyError('mode is not a valid split name' )
# Load data features from cache or dataset file
lowerCAmelCase__ : str = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}''' , )
lowerCAmelCase__ : List[Any] = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = label_list[2], label_list[1]
lowerCAmelCase__ : Dict = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowerCAmelCase__ : Any = cached_features_file + '.lock'
with FileLock(a ):
if os.path.exists(a ) and not args.overwrite_cache:
lowerCAmelCase__ : Dict = time.time()
lowerCAmelCase__ : str = torch.load(a )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
else:
logger.info(f'''Creating features from dataset file at {args.data_dir}''' )
if mode == Split.dev:
lowerCAmelCase__ : Union[str, Any] = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
lowerCAmelCase__ : Tuple = self.processor.get_test_examples(args.data_dir )
else:
lowerCAmelCase__ : str = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
lowerCAmelCase__ : Union[str, Any] = examples[:limit_length]
lowerCAmelCase__ : List[Any] = glue_convert_examples_to_features(
a , a , max_length=args.max_seq_length , label_list=a , output_mode=self.output_mode , )
lowerCAmelCase__ : int = time.time()
torch.save(self.features , a )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self : List[str] ):
'''simple docstring'''
return len(self.features )
def __getitem__( self : Union[str, Any] , a : int ):
'''simple docstring'''
return self.features[i]
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return self.label_list
| 307
|
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lowerCamelCase__ = """python tqdm regex requests packaging filelock numpy tokenizers""".split()
if sys.version_info < (3, 7):
pkgs_to_check_at_runtime.append("""dataclasses""")
if sys.version_info < (3, 8):
pkgs_to_check_at_runtime.append("""importlib_metadata""")
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(F"""can't find {pkg} in {deps.keys()}, check dependency_versions_table.py""")
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=None ) -> int:
require_version(deps[pkg] , SCREAMING_SNAKE_CASE_ )
| 307
| 1
|
"""simple docstring"""
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__A = "Run commands across TPU VMs for initial setup before running `accelerate launch`."
def a__ ( __SCREAMING_SNAKE_CASE=None ) -> Any:
if subparsers is not None:
__lowerCAmelCase: Union[str, Any] = subparsers.add_parser("tpu-config" , description=_description )
else:
__lowerCAmelCase: List[str] = argparse.ArgumentParser("Accelerate tpu-config command" , description=_description )
# Core arguments
__lowerCAmelCase: Dict = parser.add_argument_group(
"Config Arguments" , "Arguments that can be configured through `accelerate config`." )
config_args.add_argument(
"--config_file" , type=_A , default=_A , help="Path to the config file to use for accelerate." , )
config_args.add_argument(
"--tpu_name" , default=_A , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." , )
config_args.add_argument(
"--tpu_zone" , default=_A , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." , )
__lowerCAmelCase: Optional[Any] = parser.add_argument_group("TPU Arguments" , "Arguments for options ran inside the TPU." )
pod_args.add_argument(
"--use_alpha" , action="store_true" , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." , )
pod_args.add_argument(
"--command_file" , default=_A , help="The path to the file containing the commands to run on the pod on startup." , )
pod_args.add_argument(
"--command" , action="append" , nargs="+" , help="A command to run on the pod. Can be passed multiple times." , )
pod_args.add_argument(
"--install_accelerate" , action="store_true" , help="Whether to install accelerate on the pod. Defaults to False." , )
pod_args.add_argument(
"--accelerate_version" , default="latest" , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub." , )
pod_args.add_argument(
"--debug" , action="store_true" , help="If set, will print the command that would be run instead of running it." )
if subparsers is not None:
parser.set_defaults(func=_A )
return parser
def a__ ( __SCREAMING_SNAKE_CASE ) -> int:
__lowerCAmelCase: List[str] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_A ):
__lowerCAmelCase: Optional[int] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
__lowerCAmelCase: int = defaults.command_file
if not args.command and defaults.commands is not None:
__lowerCAmelCase: Tuple = defaults.commands
if not args.tpu_name:
__lowerCAmelCase: List[str] = defaults.tpu_name
if not args.tpu_zone:
__lowerCAmelCase: List[str] = defaults.tpu_zone
if args.accelerate_version == "dev":
__lowerCAmelCase: int = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
__lowerCAmelCase: Any = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , _A ):
__lowerCAmelCase: Optional[int] = F"accelerate=={args.accelerate_version}"
if not args.command_file and not args.command:
raise ValueError("You must specify either a command file or a command to run on the pod." )
if args.command_file:
with open(args.command_file , "r" ) as f:
__lowerCAmelCase: Any = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , _A ):
__lowerCAmelCase: Dict = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
__lowerCAmelCase: List[Any] = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [F"pip install {args.accelerate_version}"]
new_cmd += args.command
__lowerCAmelCase: Optional[Any] = "; ".join(_A )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
__lowerCAmelCase: List[str] = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"Running {' '.join(_A )}" )
return
subprocess.run(_A )
print("Successfully setup pod." )
def a__ ( ) -> Dict:
__lowerCAmelCase: str = tpu_command_parser()
__lowerCAmelCase: Optional[int] = parser.parse_args()
tpu_command_launcher(_A )
| 217
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'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def snake_case__ ( _A: Union[str, Any] , _A: Tuple , _A: Any=1e-12 ) -> str:
'''simple docstring'''
lowerCAmelCase = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(_A , axis=1 ) , a_min=_A ) ).T
lowerCAmelCase = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(_A , axis=1 ) , a_min=_A ) ).T
return jnp.matmul(_A , norm_emb_a.T )
class a__( nn.Module ):
'''simple docstring'''
UpperCAmelCase_ : CLIPConfig
UpperCAmelCase_ : jnp.dtype = jnp.floataa
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = FlaxCLIPVisionModule(self.config.vision_config)
lowerCAmelCase = nn.Dense(self.config.projection_dim , use_bias=__lowerCAmelCase , dtype=self.dtype)
lowerCAmelCase = self.param("""concept_embeds""" , jax.nn.initializers.ones , (17, self.config.projection_dim))
lowerCAmelCase = self.param(
"""special_care_embeds""" , jax.nn.initializers.ones , (3, self.config.projection_dim))
lowerCAmelCase = self.param("""concept_embeds_weights""" , jax.nn.initializers.ones , (17,))
lowerCAmelCase = self.param("""special_care_embeds_weights""" , jax.nn.initializers.ones , (3,))
def __call__( self , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = self.vision_model(__lowerCAmelCase)[1]
lowerCAmelCase = self.visual_projection(__lowerCAmelCase)
lowerCAmelCase = jax_cosine_distance(__lowerCAmelCase , self.special_care_embeds)
lowerCAmelCase = jax_cosine_distance(__lowerCAmelCase , self.concept_embeds)
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
lowerCAmelCase = 0.0
lowerCAmelCase = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
lowerCAmelCase = jnp.round(__lowerCAmelCase , 3)
lowerCAmelCase = jnp.any(special_scores > 0 , axis=1 , keepdims=__lowerCAmelCase)
# Use a lower threshold if an image has any special care concept
lowerCAmelCase = is_special_care * 0.01
lowerCAmelCase = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
lowerCAmelCase = jnp.round(__lowerCAmelCase , 3)
lowerCAmelCase = jnp.any(concept_scores > 0 , axis=1)
return has_nsfw_concepts
class a__( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase_ : int = CLIPConfig
UpperCAmelCase_ : Any = '''clip_input'''
UpperCAmelCase_ : List[str] = FlaxStableDiffusionSafetyCheckerModule
def __init__( self , __lowerCAmelCase , __lowerCAmelCase = None , __lowerCAmelCase = 0 , __lowerCAmelCase = jnp.floataa , __lowerCAmelCase = True , **__lowerCAmelCase , ):
"""simple docstring"""
if input_shape is None:
lowerCAmelCase = (1, 224, 224, 3)
lowerCAmelCase = self.module_class(config=__lowerCAmelCase , dtype=__lowerCAmelCase , **__lowerCAmelCase)
super().__init__(__lowerCAmelCase , __lowerCAmelCase , input_shape=__lowerCAmelCase , seed=__lowerCAmelCase , dtype=__lowerCAmelCase , _do_init=_do_init)
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = None):
"""simple docstring"""
lowerCAmelCase = jax.random.normal(__lowerCAmelCase , __lowerCAmelCase)
lowerCAmelCase , lowerCAmelCase = jax.random.split(__lowerCAmelCase)
lowerCAmelCase = {"""params""": params_rng, """dropout""": dropout_rng}
lowerCAmelCase = self.module.init(__lowerCAmelCase , __lowerCAmelCase)["""params"""]
return random_params
def __call__( self , __lowerCAmelCase , __lowerCAmelCase = None , ):
"""simple docstring"""
lowerCAmelCase = jnp.transpose(__lowerCAmelCase , (0, 2, 3, 1))
return self.module.apply(
{"""params""": params or self.params} , jnp.array(__lowerCAmelCase , dtype=jnp.floataa) , rngs={} , )
| 272
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = 'mra'
def __init__( self , __lowerCamelCase=5_0_2_6_5 , __lowerCamelCase=7_6_8 , __lowerCamelCase=1_2 , __lowerCamelCase=1_2 , __lowerCamelCase=3_0_7_2 , __lowerCamelCase="gelu" , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=5_1_2 , __lowerCamelCase=1 , __lowerCamelCase=0.02 , __lowerCamelCase=1E-5 , __lowerCamelCase="absolute" , __lowerCamelCase=4 , __lowerCamelCase="full" , __lowerCamelCase=0 , __lowerCamelCase=0 , __lowerCamelCase=1 , __lowerCamelCase=0 , __lowerCamelCase=2 , **__lowerCamelCase , ) -> int:
super().__init__(pad_token_id=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
_SCREAMING_SNAKE_CASE : Dict = max_position_embeddings
_SCREAMING_SNAKE_CASE : str = hidden_size
_SCREAMING_SNAKE_CASE : str = num_hidden_layers
_SCREAMING_SNAKE_CASE : Optional[int] = num_attention_heads
_SCREAMING_SNAKE_CASE : Dict = intermediate_size
_SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
_SCREAMING_SNAKE_CASE : Any = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : Optional[int] = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : Optional[Any] = initializer_range
_SCREAMING_SNAKE_CASE : int = type_vocab_size
_SCREAMING_SNAKE_CASE : Any = layer_norm_eps
_SCREAMING_SNAKE_CASE : int = position_embedding_type
_SCREAMING_SNAKE_CASE : str = block_per_row
_SCREAMING_SNAKE_CASE : str = approx_mode
_SCREAMING_SNAKE_CASE : Union[str, Any] = initial_prior_first_n_blocks
_SCREAMING_SNAKE_CASE : List[Any] = initial_prior_diagonal_n_blocks
| 325
|
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={'vocab_file': 'spiece.model'}
UpperCamelCase__ ={
'vocab_file': {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
}
}
UpperCamelCase__ ={
'albert-base-v1': 512,
'albert-large-v1': 512,
'albert-xlarge-v1': 512,
'albert-xxlarge-v1': 512,
'albert-base-v2': 512,
'albert-large-v2': 512,
'albert-xlarge-v2': 512,
'albert-xxlarge-v2': 512,
}
UpperCamelCase__ ='▁'
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = VOCAB_FILES_NAMES
__snake_case = PRETRAINED_VOCAB_FILES_MAP
__snake_case = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , __lowerCamelCase , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=False , __lowerCamelCase="[CLS]" , __lowerCamelCase="[SEP]" , __lowerCamelCase="<unk>" , __lowerCamelCase="[SEP]" , __lowerCamelCase="<pad>" , __lowerCamelCase="[CLS]" , __lowerCamelCase="[MASK]" , __lowerCamelCase = None , **__lowerCamelCase , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_SCREAMING_SNAKE_CASE : List[Any] = (
AddedToken(__lowerCamelCase , lstrip=__lowerCamelCase , rstrip=__lowerCamelCase , normalized=__lowerCamelCase )
if isinstance(__lowerCamelCase , __lowerCamelCase )
else mask_token
)
_SCREAMING_SNAKE_CASE : Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=__lowerCamelCase , remove_space=__lowerCamelCase , keep_accents=__lowerCamelCase , bos_token=__lowerCamelCase , eos_token=__lowerCamelCase , unk_token=__lowerCamelCase , sep_token=__lowerCamelCase , pad_token=__lowerCamelCase , cls_token=__lowerCamelCase , mask_token=__lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCamelCase , )
_SCREAMING_SNAKE_CASE : Dict = do_lower_case
_SCREAMING_SNAKE_CASE : List[Any] = remove_space
_SCREAMING_SNAKE_CASE : str = keep_accents
_SCREAMING_SNAKE_CASE : Optional[int] = vocab_file
_SCREAMING_SNAKE_CASE : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__lowerCamelCase )
@property
def UpperCamelCase_ ( self ) -> Optional[Any]:
return len(self.sp_model )
def UpperCamelCase_ ( self ) -> Optional[Any]:
_SCREAMING_SNAKE_CASE : Any = {self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[Any]:
_SCREAMING_SNAKE_CASE : str = self.__dict__.copy()
_SCREAMING_SNAKE_CASE : Optional[Any] = None
return state
def __setstate__( self , __lowerCamelCase ) -> Tuple:
_SCREAMING_SNAKE_CASE : Optional[Any] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_SCREAMING_SNAKE_CASE : Optional[int] = {}
_SCREAMING_SNAKE_CASE : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Optional[int]:
if self.remove_space:
_SCREAMING_SNAKE_CASE : List[str] = " ".join(inputs.strip().split() )
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = inputs
_SCREAMING_SNAKE_CASE : str = outputs.replace("``" , "\"" ).replace("''" , "\"" )
if not self.keep_accents:
_SCREAMING_SNAKE_CASE : str = unicodedata.normalize("NFKD" , __lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[Any] = "".join([c for c in outputs if not unicodedata.combining(__lowerCamelCase )] )
if self.do_lower_case:
_SCREAMING_SNAKE_CASE : Dict = outputs.lower()
return outputs
def UpperCamelCase_ ( self , __lowerCamelCase ) -> List[str]:
_SCREAMING_SNAKE_CASE : int = self.preprocess_text(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : str = self.sp_model.encode(__lowerCamelCase , out_type=__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Any = []
for piece in pieces:
if len(__lowerCamelCase ) > 1 and piece[-1] == str("," ) and piece[-2].isdigit():
_SCREAMING_SNAKE_CASE : Dict = self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowerCamelCase , "" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_SCREAMING_SNAKE_CASE : Union[str, Any] = cur_pieces[1:]
else:
_SCREAMING_SNAKE_CASE : Tuple = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(__lowerCamelCase )
else:
new_pieces.append(__lowerCamelCase )
return new_pieces
def UpperCamelCase_ ( self , __lowerCamelCase ) -> List[Any]:
return self.sp_model.PieceToId(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase ) -> str:
return self.sp_model.IdToPiece(__lowerCamelCase )
def UpperCamelCase_ ( self , __lowerCamelCase ) -> Dict:
_SCREAMING_SNAKE_CASE : Dict = []
_SCREAMING_SNAKE_CASE : List[str] = ""
_SCREAMING_SNAKE_CASE : List[Any] = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__lowerCamelCase ) + token
_SCREAMING_SNAKE_CASE : str = True
_SCREAMING_SNAKE_CASE : Optional[Any] = []
else:
current_sub_tokens.append(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : Optional[int] = False
out_string += self.sp_model.decode(__lowerCamelCase )
return out_string.strip()
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None ) -> List[int]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = [self.sep_token_id]
_SCREAMING_SNAKE_CASE : int = [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 UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None , __lowerCamelCase = False ) -> List[int]:
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 not None:
return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]
return [1] + ([0] * len(__lowerCamelCase )) + [1]
def UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None ) -> List[int]:
_SCREAMING_SNAKE_CASE : Dict = [self.sep_token_id]
_SCREAMING_SNAKE_CASE : Tuple = [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 UpperCamelCase_ ( self , __lowerCamelCase , __lowerCamelCase = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCamelCase ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_SCREAMING_SNAKE_CASE : List[Any] = os.path.join(
__lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCamelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCamelCase , "wb" ) as fi:
_SCREAMING_SNAKE_CASE : Optional[int] = self.sp_model.serialized_model_proto()
fi.write(__lowerCamelCase )
return (out_vocab_file,)
| 325
| 1
|
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends
if is_vision_available():
import PIL
# soft dependency
if is_pytesseract_available():
import pytesseract
_A : Optional[Any] = logging.get_logger(__name__)
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> List[str]:
"""simple docstring"""
return [
int(1000 * (box[0] / width) ),
int(1000 * (box[1] / height) ),
int(1000 * (box[2] / width) ),
int(1000 * (box[3] / height) ),
]
def _a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__ : Tuple = to_pil_image(UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ : Any = pil_image.size
lowerCamelCase__ : int = pytesseract.image_to_data(UpperCAmelCase , lang=UpperCAmelCase , output_type='''dict''' , config=UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Dict = data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']
# filter empty words and corresponding coordinates
lowerCamelCase__ : int = [idx for idx, word in enumerate(UpperCAmelCase ) if not word.strip()]
lowerCamelCase__ : Optional[Any] = [word for idx, word in enumerate(UpperCAmelCase ) if idx not in irrelevant_indices]
lowerCamelCase__ : List[Any] = [coord for idx, coord in enumerate(UpperCAmelCase ) if idx not in irrelevant_indices]
lowerCamelCase__ : Any = [coord for idx, coord in enumerate(UpperCAmelCase ) if idx not in irrelevant_indices]
lowerCamelCase__ : List[str] = [coord for idx, coord in enumerate(UpperCAmelCase ) if idx not in irrelevant_indices]
lowerCamelCase__ : List[Any] = [coord for idx, coord in enumerate(UpperCAmelCase ) if idx not in irrelevant_indices]
# turn coordinates into (left, top, left+width, top+height) format
lowerCamelCase__ : str = []
for x, y, w, h in zip(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
lowerCamelCase__ : Tuple = [x, y, x + w, y + h]
actual_boxes.append(UpperCAmelCase )
# finally, normalize the bounding boxes
lowerCamelCase__ : str = []
for box in actual_boxes:
normalized_boxes.append(normalize_box(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) )
assert len(UpperCAmelCase ) == len(UpperCAmelCase ), "Not as many words as there are bounding boxes"
return words, normalized_boxes
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ ):
_UpperCAmelCase : int = ["pixel_values"]
def __init__( self : int , A : bool = True , A : Dict[str, int] = None , A : PILImageResampling = PILImageResampling.BILINEAR , A : bool = True , A : float = 1 / 2_5_5 , A : bool = True , A : Union[float, Iterable[float]] = None , A : Union[float, Iterable[float]] = None , A : bool = True , A : Optional[str] = None , A : Optional[str] = "" , **A : Tuple , ) ->None:
super().__init__(**A )
lowerCamelCase__ : Optional[int] = size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
lowerCamelCase__ : List[str] = get_size_dict(A )
lowerCamelCase__ : Any = do_resize
lowerCamelCase__ : List[str] = size
lowerCamelCase__ : int = resample
lowerCamelCase__ : int = do_rescale
lowerCamelCase__ : Union[str, Any] = rescale_value
lowerCamelCase__ : str = do_normalize
lowerCamelCase__ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowerCamelCase__ : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
lowerCamelCase__ : Optional[int] = apply_ocr
lowerCamelCase__ : List[Any] = ocr_lang
lowerCamelCase__ : Any = tesseract_config
def __lowerCamelCase ( self : Dict , A : np.ndarray , A : Dict[str, int] , A : PILImageResampling = PILImageResampling.BILINEAR , A : Optional[Union[str, ChannelDimension]] = None , **A : int , ) ->np.ndarray:
lowerCamelCase__ : str = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
lowerCamelCase__ : Tuple = (size['''height'''], size['''width'''])
return resize(A , size=A , resample=A , data_format=A , **A )
def __lowerCamelCase ( self : Any , A : np.ndarray , A : Union[int, float] , A : Optional[Union[str, ChannelDimension]] = None , **A : Tuple , ) ->np.ndarray:
return rescale(A , scale=A , data_format=A , **A )
def __lowerCamelCase ( self : List[Any] , A : np.ndarray , A : Union[float, Iterable[float]] , A : Union[float, Iterable[float]] , A : Optional[Union[str, ChannelDimension]] = None , **A : Any , ) ->np.ndarray:
return normalize(A , mean=A , std=A , data_format=A , **A )
def __lowerCamelCase ( self : List[str] , A : ImageInput , A : bool = None , A : Dict[str, int] = None , A : Optional[Any]=None , A : bool = None , A : float = None , A : bool = None , A : Union[float, Iterable[float]] = None , A : Union[float, Iterable[float]] = None , A : bool = None , A : Optional[str] = None , A : Optional[str] = None , A : Optional[Union[str, TensorType]] = None , A : ChannelDimension = ChannelDimension.FIRST , **A : Tuple , ) ->PIL.Image.Image:
lowerCamelCase__ : Any = do_resize if do_resize is not None else self.do_resize
lowerCamelCase__ : Optional[int] = size if size is not None else self.size
lowerCamelCase__ : Dict = get_size_dict(A )
lowerCamelCase__ : Dict = resample if resample is not None else self.resample
lowerCamelCase__ : Dict = do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase__ : Any = rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase__ : Any = do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase__ : List[Any] = image_mean if image_mean is not None else self.image_mean
lowerCamelCase__ : str = image_std if image_std is not None else self.image_std
lowerCamelCase__ : List[Any] = apply_ocr if apply_ocr is not None else self.apply_ocr
lowerCamelCase__ : Dict = ocr_lang if ocr_lang is not None else self.ocr_lang
lowerCamelCase__ : List[Any] = tesseract_config if tesseract_config is not None else self.tesseract_config
lowerCamelCase__ : Tuple = make_list_of_images(A )
if not valid_images(A ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.''' )
# All transformations expect numpy arrays.
lowerCamelCase__ : Union[str, Any] = [to_numpy_array(A ) for image in images]
# Tesseract OCR to get words + normalized bounding boxes
if apply_ocr:
requires_backends(self , '''pytesseract''' )
lowerCamelCase__ : Union[str, Any] = []
lowerCamelCase__ : List[Any] = []
for image in images:
lowerCamelCase__ , lowerCamelCase__ : List[str] = apply_tesseract(A , A , A )
words_batch.append(A )
boxes_batch.append(A )
if do_resize:
lowerCamelCase__ : Any = [self.resize(image=A , size=A , resample=A ) for image in images]
if do_rescale:
lowerCamelCase__ : Optional[int] = [self.rescale(image=A , scale=A ) for image in images]
if do_normalize:
lowerCamelCase__ : Union[str, Any] = [self.normalize(image=A , mean=A , std=A ) for image in images]
lowerCamelCase__ : Union[str, Any] = [to_channel_dimension_format(A , A ) for image in images]
lowerCamelCase__ : Dict = BatchFeature(data={'''pixel_values''': images} , tensor_type=A )
if apply_ocr:
lowerCamelCase__ : Union[str, Any] = words_batch
lowerCamelCase__ : int = boxes_batch
return data
| 142
|
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def _a ( UpperCAmelCase ) -> Dict:
"""simple docstring"""
lowerCamelCase__ : Dict = [
'''encoder.version''',
'''decoder.version''',
'''model.encoder.version''',
'''model.decoder.version''',
'''decoder.output_projection.weight''',
'''_float_tensor''',
'''encoder.embed_positions._float_tensor''',
'''decoder.embed_positions._float_tensor''',
]
for k in ignore_keys:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def _a ( UpperCAmelCase ) -> Any:
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] = list(s_dict.keys() )
for key in keys:
if "transformer_layers" in key:
lowerCamelCase__ : Any = s_dict.pop(UpperCAmelCase )
elif "subsample" in key:
lowerCamelCase__ : Any = s_dict.pop(UpperCAmelCase )
def _a ( UpperCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = emb.weight.shape
lowerCamelCase__ : str = nn.Linear(UpperCAmelCase , UpperCAmelCase , bias=UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = emb.weight.data
return lin_layer
def _a ( UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase__ : List[Any] = torch.load(UpperCAmelCase , map_location='''cpu''' )
lowerCamelCase__ : List[Any] = mam_aaa['''args''']
lowerCamelCase__ : Dict = mam_aaa['''model''']
lowerCamelCase__ : Optional[Any] = state_dict['''decoder.output_projection.weight''']
remove_ignore_keys_(UpperCAmelCase )
rename_keys(UpperCAmelCase )
lowerCamelCase__ : Tuple = state_dict['''decoder.embed_tokens.weight'''].shape[0]
lowerCamelCase__ : Tuple = args.share_decoder_input_output_embed
lowerCamelCase__ : Dict = [int(UpperCAmelCase ) for i in args.conv_kernel_sizes.split(''',''' )]
lowerCamelCase__ : str = SpeechaTextConfig(
vocab_size=UpperCAmelCase , max_source_positions=args.max_source_positions , max_target_positions=args.max_target_positions , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='''relu''' , num_conv_layers=len(UpperCAmelCase ) , conv_channels=args.conv_channels , conv_kernel_sizes=UpperCAmelCase , input_feat_per_channel=args.input_feat_per_channel , input_channels=args.input_channels , tie_word_embeddings=UpperCAmelCase , num_beams=5 , max_length=200 , use_cache=UpperCAmelCase , decoder_start_token_id=2 , early_stopping=UpperCAmelCase , )
lowerCamelCase__ : Optional[int] = SpeechaTextForConditionalGeneration(UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ : Dict = model.model.load_state_dict(UpperCAmelCase , strict=UpperCAmelCase )
if len(UpperCAmelCase ) > 0 and not set(UpperCAmelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f" but all the following weights are missing {missing}" )
if tie_embeds:
lowerCamelCase__ : List[str] = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowerCamelCase__ : Tuple = lm_head_weights
model.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_A : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
_A : str = parser.parse_args()
convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
| 142
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_UpperCamelCase = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"""RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ResNetForImageClassification""",
"""ResNetModel""",
"""ResNetPreTrainedModel""",
"""ResNetBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"""TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFResNetForImageClassification""",
"""TFResNetModel""",
"""TFResNetPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCamelCase = [
"""FlaxResNetForImageClassification""",
"""FlaxResNetModel""",
"""FlaxResNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
_UpperCamelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 234
|
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger("""transformers.models.speecht5""")
_UpperCamelCase = {
"""speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
"""speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
"""speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
"""speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
_UpperCamelCase = {
"""text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
"""text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
_UpperCamelCase = {
"""speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
"""speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
"""speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
"""speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
"""speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
_UpperCamelCase = {
"""speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
"""speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
"""speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
"""speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
"""speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
"""speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
"""speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
"""speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
"""speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
_UpperCamelCase = {
"""text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
_UpperCamelCase = {
"""text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
_UpperCamelCase = {
"""encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
"""encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
"""encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
"""encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
"""encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
"""encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
"""encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
"""encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
"""encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
_UpperCamelCase = {
"""decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
"""decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
"""decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
"""decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
"""decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
"""decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
"""decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
"""decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
"""decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
"""decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
"""decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
"""decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
"""decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
_UpperCamelCase = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
_UpperCamelCase = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCamelCase = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
_UpperCamelCase = []
_UpperCamelCase = [
"""encoder.version""",
"""encoder.layers.*.norm_k.weight""",
"""encoder.layers.*.norm_k.bias""",
"""decoder.version""",
"""decoder.layers.*.norm_k.weight""",
"""decoder.layers.*.norm_k.bias""",
"""decoder.pos_emb.pe_k""",
"""speech_encoder_prenet.embed_positions._float_tensor""",
"""text_decoder_prenet.embed_positions._float_tensor""",
]
_UpperCamelCase = IGNORE_KEYS + [
"""encoder.proj""",
"""text_encoder_prenet.*""",
"""speech_decoder_prenet.*""",
"""speech_decoder_postnet.*""",
]
_UpperCamelCase = IGNORE_KEYS + [
"""encoder.proj""",
"""speech_encoder_prenet.*""",
"""text_decoder_prenet.*""",
"""text_decoder_postnet.*""",
]
_UpperCamelCase = IGNORE_KEYS + [
"""encoder.proj""",
"""text_encoder_prenet.*""",
"""text_decoder_prenet.*""",
"""text_decoder_postnet.*""",
]
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
for attribute in key.split(""".""" ):
UpperCAmelCase = getattr(_snake_case , _snake_case )
if weight_type is not None:
UpperCAmelCase = getattr(_snake_case , _snake_case ).shape
else:
UpperCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
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 = value
elif weight_type == "weight_g":
UpperCAmelCase = value
elif weight_type == "weight_v":
UpperCAmelCase = value
elif weight_type == "bias":
UpperCAmelCase = value
elif weight_type == "running_mean":
UpperCAmelCase = value
elif weight_type == "running_var":
UpperCAmelCase = value
elif weight_type == "num_batches_tracked":
UpperCAmelCase = value
else:
UpperCAmelCase = value
logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.''' )
def _a ( _snake_case , _snake_case ):
"""simple docstring"""
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
UpperCAmelCase , UpperCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
if task == "s2t":
UpperCAmelCase = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase = MAPPING_S2T
UpperCAmelCase = IGNORE_KEYS_S2T
elif task == "t2s":
UpperCAmelCase = None
UpperCAmelCase = MAPPING_T2S
UpperCAmelCase = IGNORE_KEYS_T2S
elif task == "s2s":
UpperCAmelCase = hf_model.speechta.encoder.prenet.feature_encoder
UpperCAmelCase = MAPPING_S2S
UpperCAmelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F'''Unsupported task: {task}''' )
for name, value in fairseq_dict.items():
if should_ignore(_snake_case , _snake_case ):
logger.info(F'''{name} was ignored''' )
continue
UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == """group""" , )
UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
UpperCAmelCase , UpperCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
UpperCAmelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
UpperCAmelCase = True
if "*" in mapped_key:
UpperCAmelCase = name.split(_snake_case )[0].split(""".""" )[-2]
UpperCAmelCase = mapped_key.replace("""*""" , _snake_case )
if "weight_g" in name:
UpperCAmelCase = """weight_g"""
elif "weight_v" in name:
UpperCAmelCase = """weight_v"""
elif "bias" in name:
UpperCAmelCase = """bias"""
elif "weight" in name:
UpperCAmelCase = """weight"""
elif "running_mean" in name:
UpperCAmelCase = """running_mean"""
elif "running_var" in name:
UpperCAmelCase = """running_var"""
elif "num_batches_tracked" in name:
UpperCAmelCase = """num_batches_tracked"""
else:
UpperCAmelCase = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = full_name.split("""conv_layers.""" )[-1]
UpperCAmelCase = name.split(""".""" )
UpperCAmelCase = int(items[0] )
UpperCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
UpperCAmelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
UpperCAmelCase = 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:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
@torch.no_grad()
def _a ( _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=None , _snake_case=None , ):
"""simple docstring"""
if config_path is not None:
UpperCAmelCase = SpeechTaConfig.from_pretrained(_snake_case )
else:
UpperCAmelCase = SpeechTaConfig()
if task == "s2t":
UpperCAmelCase = config.max_text_positions
UpperCAmelCase = SpeechTaForSpeechToText(_snake_case )
elif task == "t2s":
UpperCAmelCase = 1876
UpperCAmelCase = 600
UpperCAmelCase = config.max_speech_positions
UpperCAmelCase = SpeechTaForTextToSpeech(_snake_case )
elif task == "s2s":
UpperCAmelCase = 1876
UpperCAmelCase = config.max_speech_positions
UpperCAmelCase = SpeechTaForSpeechToSpeech(_snake_case )
else:
raise ValueError(F'''Unknown task name: {task}''' )
if vocab_path:
UpperCAmelCase = SpeechTaTokenizer(_snake_case , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
UpperCAmelCase = AddedToken("""<mask>""" , lstrip=_snake_case , rstrip=_snake_case )
UpperCAmelCase = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
UpperCAmelCase = SpeechTaFeatureExtractor()
UpperCAmelCase = SpeechTaProcessor(tokenizer=_snake_case , feature_extractor=_snake_case )
processor.save_pretrained(_snake_case )
UpperCAmelCase = torch.load(_snake_case )
recursively_load_weights(fairseq_checkpoint["""model"""] , _snake_case , _snake_case )
model.save_pretrained(_snake_case )
if repo_id:
print("""Pushing to the hub...""" )
processor.push_to_hub(_snake_case )
model.push_to_hub(_snake_case )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
parser.add_argument(
"""--task""",
default="""s2t""",
type=str,
help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
)
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_UpperCamelCase = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 234
| 1
|
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def lowerCAmelCase_ ( A_):
UpperCamelCase__: str = []
for line in lines:
UpperCamelCase__: Dict = re.sub(R"#.*" ,"" ,A_) # remove comments
if line:
filtered_lines.append(A_)
UpperCamelCase__: int = "\n".join(A_)
# Make a hash from all this code
UpperCamelCase__: List[Any] = full_str.encode("utf-8")
return shaaaa(A_).hexdigest()
# get importable module names and hash for caching
A__: List[Any] = {
'''csv''': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'''json''': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'''pandas''': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'''parquet''': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'''arrow''': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'''text''': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'''imagefolder''': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'''audiofolder''': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
A__: Optional[Any] = {
'''.csv''': ('''csv''', {}),
'''.tsv''': ('''csv''', {'''sep''': '''\t'''}),
'''.json''': ('''json''', {}),
'''.jsonl''': ('''json''', {}),
'''.parquet''': ('''parquet''', {}),
'''.arrow''': ('''arrow''', {}),
'''.txt''': ('''text''', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''imagefolder''', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('''audiofolder''', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
A__: Tuple = {'''imagefolder''', '''audiofolder'''}
# Used to filter data files based on extensions given a module name
A__: Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('''.zip''')
_MODULE_TO_EXTENSIONS["audiofolder"].append('''.zip''')
| 149
|
from torch import nn
def lowerCAmelCase_ ( A_):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"Unsupported activation function: {act_fn}")
| 149
| 1
|
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
def A(__a: Dict ):
lowerCAmelCase_ = r"\w+[.]\d+"
lowerCAmelCase_ = re.findall(__a , __a )
for pat in pats:
lowerCAmelCase_ = key.replace(__a , "_".join(pat.split("." ) ) )
return key
def A(__a: str , __a: Tuple , __a: List[Any] ):
lowerCAmelCase_ = pt_tuple_key[:-1] + ("scale",)
if (
any("norm" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
lowerCAmelCase_ = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
lowerCAmelCase_ = pt_tuple_key[:-1] + ("scale",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
lowerCAmelCase_ = pt_tuple_key[:-1] + ("embedding",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
lowerCAmelCase_ = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
lowerCAmelCase_ = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
lowerCAmelCase_ = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight":
lowerCAmelCase_ = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
lowerCAmelCase_ = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
lowerCAmelCase_ = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A(__a: Dict , __a: Any , __a: List[Any]=42 ):
# Step 1: Convert pytorch tensor to numpy
lowerCAmelCase_ = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
lowerCAmelCase_ = flax_model.init_weights(PRNGKey(__a ) )
lowerCAmelCase_ = flatten_dict(__a )
lowerCAmelCase_ = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
lowerCAmelCase_ = rename_key(__a )
lowerCAmelCase_ = tuple(renamed_pt_key.split("." ) )
# Correctly rename weight parameters
lowerCAmelCase_ , lowerCAmelCase_ = rename_key_and_reshape_tensor(__a , __a , __a )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# also add unexpected weight so that warning is thrown
lowerCAmelCase_ = jnp.asarray(__a )
return unflatten_dict(__a )
| 351
|
import string
from math import logaa
def A(__a: str , __a: str ):
lowerCAmelCase_ = document.translate(
str.maketrans("" , "" , string.punctuation ) ).replace("\n" , "" )
lowerCAmelCase_ = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def A(__a: str , __a: str ):
lowerCAmelCase_ = corpus.lower().translate(
str.maketrans("" , "" , string.punctuation ) ) # strip all punctuation and replace it with ''
lowerCAmelCase_ = corpus_without_punctuation.split("\n" )
lowerCAmelCase_ = term.lower()
return (len([doc for doc in docs if term in doc] ), len(__a ))
def A(__a: int , __a: int , __a: List[Any]=False ):
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ) , 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ) , 3 )
def A(__a: int , __a: int ):
return round(tf * idf , 3 )
| 22
| 0
|
def a_ ( _A = 1000 ) -> int:
"""simple docstring"""
return sum(e for e in range(3 , _A ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 307
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxPriorConfig""",
"""JukeboxVQVAEConfig""",
],
"""tokenization_jukebox""": ["""JukeboxTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Tuple = [
"""JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""JukeboxModel""",
"""JukeboxPreTrainedModel""",
"""JukeboxVQVAE""",
"""JukeboxPrior""",
]
if TYPE_CHECKING:
from .configuration_jukebox import (
JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
JukeboxConfig,
JukeboxPriorConfig,
JukeboxVQVAEConfig,
)
from .tokenization_jukebox import JukeboxTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_jukebox import (
JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
JukeboxModel,
JukeboxPreTrainedModel,
JukeboxPrior,
JukeboxVQVAE,
)
else:
import sys
__UpperCamelCase : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 307
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
a : Tuple = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
a : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 150
|
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
a : List[str] = {
"""return_dict""": False,
"""output_hidden_states""": True,
"""output_attentions""": True,
"""torchscript""": True,
"""torch_dtype""": """float16""",
"""use_bfloat16""": True,
"""tf_legacy_loss""": True,
"""pruned_heads""": {"""a""": 1},
"""tie_word_embeddings""": False,
"""is_decoder""": True,
"""cross_attention_hidden_size""": 128,
"""add_cross_attention""": True,
"""tie_encoder_decoder""": True,
"""max_length""": 50,
"""min_length""": 3,
"""do_sample""": True,
"""early_stopping""": True,
"""num_beams""": 3,
"""num_beam_groups""": 3,
"""diversity_penalty""": 0.5,
"""temperature""": 2.0,
"""top_k""": 10,
"""top_p""": 0.7,
"""typical_p""": 0.2,
"""repetition_penalty""": 0.8,
"""length_penalty""": 0.8,
"""no_repeat_ngram_size""": 5,
"""encoder_no_repeat_ngram_size""": 5,
"""bad_words_ids""": [1, 2, 3],
"""num_return_sequences""": 3,
"""chunk_size_feed_forward""": 5,
"""output_scores""": True,
"""return_dict_in_generate""": True,
"""forced_bos_token_id""": 2,
"""forced_eos_token_id""": 3,
"""remove_invalid_values""": True,
"""architectures""": ["""BertModel"""],
"""finetuning_task""": """translation""",
"""id2label""": {0: """label"""},
"""label2id""": {"""label""": """0"""},
"""tokenizer_class""": """BertTokenizerFast""",
"""prefix""": """prefix""",
"""bos_token_id""": 6,
"""pad_token_id""": 7,
"""eos_token_id""": 8,
"""sep_token_id""": 9,
"""decoder_start_token_id""": 10,
"""exponential_decay_length_penalty""": (5, 1.01),
"""suppress_tokens""": [0, 1],
"""begin_suppress_tokens""": 2,
"""task_specific_params""": {"""translation""": """some_params"""},
"""problem_type""": """regression""",
}
@is_staging_test
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
@classmethod
def UpperCAmelCase_ ( cls ):
'''simple docstring'''
lowercase__ : Union[str, Any]= TOKEN
HfFolder.save_token(snake_case__ )
@classmethod
def UpperCAmelCase_ ( cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="test-config" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-config-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-config" )
except HTTPError:
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("test-config" , use_auth_token=self._token )
lowercase__ : List[Any]= BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case__ , getattr(snake_case__ , snake_case__ ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-config" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(snake_case__ , repo_id="test-config" , push_to_hub=snake_case__ , use_auth_token=self._token )
lowercase__ : int= BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case__ , getattr(snake_case__ , snake_case__ ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
config.push_to_hub("valid_org/test-config-org" , use_auth_token=self._token )
lowercase__ : Optional[Any]= BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case__ , getattr(snake_case__ , snake_case__ ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-config-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
snake_case__ , repo_id="valid_org/test-config-org" , push_to_hub=snake_case__ , use_auth_token=self._token )
lowercase__ : Any= BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(snake_case__ , getattr(snake_case__ , snake_case__ ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
CustomConfig.register_for_auto_class()
lowercase__ : Union[str, Any]= CustomConfig(attribute=42 )
config.push_to_hub("test-dynamic-config" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"AutoConfig": "custom_configuration.CustomConfig"} )
lowercase__ : List[str]= AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=snake_case__ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , "CustomConfig" )
self.assertEqual(new_config.attribute , 42 )
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
lowercase__ : str= c.n_embd + 1 # int
lowercase__ : Tuple= c.resid_pdrop + 1.0 # float
lowercase__ : Union[str, Any]= not c.scale_attn_weights # bool
lowercase__ : Optional[Any]= c.summary_type + "foo" # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(snake_case__ , c.n_embd , "mismatch for key: n_embd" )
self.assertEqual(snake_case__ , c.resid_pdrop , "mismatch for key: resid_pdrop" )
self.assertEqual(snake_case__ , c.scale_attn_weights , "mismatch for key: scale_attn_weights" )
self.assertEqual(snake_case__ , c.summary_type , "mismatch for key: summary_type" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= PretrainedConfig()
lowercase__ : List[str]= [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
snake_case__ , ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"] )
lowercase__ : Tuple= [key for key, value in config_common_kwargs.items() if value == getattr(snake_case__ , snake_case__ )]
if len(snake_case__ ) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
F''' {', '.join(snake_case__ )}.''' )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
with self.assertRaises(snake_case__ ):
# config is in subfolder, the following should not work without specifying the subfolder
lowercase__ : Optional[int]= BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" )
lowercase__ : Optional[Any]= BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" , subfolder="bert" )
self.assertIsNotNone(snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# A mock response for an HTTP head request to emulate server down
lowercase__ : str= mock.Mock()
lowercase__ : Optional[Any]= 500
lowercase__ : Any= {}
lowercase__ : Tuple= HTTPError
lowercase__ : List[Any]= {}
# Download this model to make sure it's in the cache.
lowercase__ : Any= BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=snake_case__ ) as mock_head:
lowercase__ : Any= BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# This test is for deprecated behavior and can be removed in v5
lowercase__ : Optional[Any]= BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= AutoConfig.from_pretrained("bert-base-cased" )
lowercase__ : Optional[int]= ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(snake_case__ )
lowercase__ : List[Any]= 2
json.dump(configuration.to_dict() , open(os.path.join(snake_case__ , "config.4.0.0.json" ) , "w" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
lowercase__ : int= AutoConfig.from_pretrained(snake_case__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
lowercase__ : Optional[int]= ["config.42.0.0.json"]
lowercase__ : int= 768
configuration.save_pretrained(snake_case__ )
shutil.move(os.path.join(snake_case__ , "config.4.0.0.json" ) , os.path.join(snake_case__ , "config.42.0.0.json" ) )
lowercase__ : Optional[Any]= AutoConfig.from_pretrained(snake_case__ )
self.assertEqual(new_configuration.hidden_size , 768 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
lowercase__ : Optional[Any]= "hf-internal-testing/test-two-configs"
import transformers as new_transformers
lowercase__ : Optional[Any]= "v4.0.0"
lowercase__, lowercase__ : str= new_transformers.models.auto.AutoConfig.from_pretrained(
snake_case__ , return_unused_kwargs=snake_case__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(snake_case__ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
lowercase__ : Dict= "v3.0.0"
lowercase__ : Tuple= old_transformers.models.auto.AutoConfig.from_pretrained(snake_case__ )
self.assertEqual(old_configuration.hidden_size , 768 )
| 150
| 1
|
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[Any] ) -> Tuple:
__lowercase = [0] * len(SCREAMING_SNAKE_CASE )
__lowercase = []
__lowercase = [1] * len(SCREAMING_SNAKE_CASE )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(SCREAMING_SNAKE_CASE ) ):
if indegree[i] == 0:
queue.append(SCREAMING_SNAKE_CASE )
while queue:
__lowercase = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
__lowercase = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(SCREAMING_SNAKE_CASE )
print(max(SCREAMING_SNAKE_CASE ) )
# Adjacency list of Graph
SCREAMING_SNAKE_CASE__ = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 325
|
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")
class A__ :
def __init__( self : Tuple , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : List[str] , _UpperCAmelCase : bool = True , _UpperCAmelCase : bool = False ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = scheduler
__lowercase = optimizers if isinstance(_UpperCAmelCase , (list, tuple) ) else [optimizers]
__lowercase = split_batches
__lowercase = step_with_optimizer
__lowercase = GradientState()
def a__ ( self : Optional[int] , *_UpperCAmelCase : int , **_UpperCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
__lowercase = AcceleratorState().num_processes
for _ in range(_UpperCAmelCase ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
else:
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
def a__ ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return self.scheduler.get_last_lr()
def a__ ( self : List[str] ) -> Tuple:
"""simple docstring"""
return self.scheduler.state_dict()
def a__ ( self : Optional[int] , _UpperCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
self.scheduler.load_state_dict(_UpperCAmelCase )
def a__ ( self : Dict ) -> int:
"""simple docstring"""
return self.scheduler.get_lr()
def a__ ( self : Union[str, Any] , *_UpperCAmelCase : Union[str, Any] , **_UpperCAmelCase : List[str] ) -> Any:
"""simple docstring"""
return self.scheduler.print_lr(*_UpperCAmelCase , **_UpperCAmelCase )
| 325
| 1
|
import string
from math import logaa
def _UpperCamelCase ( snake_case__, snake_case__ ) -> int:
__UpperCAmelCase : Optional[Any] = document.translate(
str.maketrans("", "", string.punctuation ) ).replace("\n", "" )
__UpperCAmelCase : Any = document_without_punctuation.split(" " ) # word tokenization
return len([word for word in tokenize_document if word.lower() == term.lower()] )
def _UpperCamelCase ( snake_case__, snake_case__ ) -> tuple[int, int]:
__UpperCAmelCase : int = corpus.lower().translate(
str.maketrans("", "", string.punctuation ) ) # strip all punctuation and replace it with ''
__UpperCAmelCase : List[str] = corpus_without_punctuation.split("\n" )
__UpperCAmelCase : Dict = term.lower()
return (len([doc for doc in docs if term in doc] ), len(a__ ))
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__=False ) -> float:
if smoothing:
if n == 0:
raise ValueError("log10(0) is undefined." )
return round(1 + logaa(n / (1 + df) ), 3 )
if df == 0:
raise ZeroDivisionError("df must be > 0" )
elif n == 0:
raise ValueError("log10(0) is undefined." )
return round(logaa(n / df ), 3 )
def _UpperCamelCase ( snake_case__, snake_case__ ) -> float:
return round(tf * idf, 3 )
| 360
|
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class _snake_case ( _lowercase ):
lowerCamelCase__: Any = ["image_processor", "tokenizer"]
lowerCamelCase__: Optional[Any] = "BlipImageProcessor"
lowerCamelCase__: Optional[int] = "AutoTokenizer"
def __init__( self: List[str] , __lowerCamelCase: str , __lowerCamelCase: List[str] , __lowerCamelCase: Optional[Any] ) -> Dict:
super().__init__(__lowerCamelCase , __lowerCamelCase )
# add QFormer tokenizer
__UpperCAmelCase : Dict = qformer_tokenizer
def __call__( self: Any , __lowerCamelCase: ImageInput = None , __lowerCamelCase: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __lowerCamelCase: bool = True , __lowerCamelCase: Union[bool, str, PaddingStrategy] = False , __lowerCamelCase: Union[bool, str, TruncationStrategy] = None , __lowerCamelCase: Optional[int] = None , __lowerCamelCase: int = 0 , __lowerCamelCase: Optional[int] = None , __lowerCamelCase: Optional[bool] = None , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = False , __lowerCamelCase: bool = True , __lowerCamelCase: Optional[Union[str, TensorType]] = None , **__lowerCamelCase: Dict , ) -> BatchFeature:
if images is None and text is None:
raise ValueError("You have to specify at least images or text." )
__UpperCAmelCase : str = BatchFeature()
if text is not None:
__UpperCAmelCase : Any = self.tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
encoding.update(__lowerCamelCase )
__UpperCAmelCase : Dict = self.qformer_tokenizer(
text=__lowerCamelCase , add_special_tokens=__lowerCamelCase , padding=__lowerCamelCase , truncation=__lowerCamelCase , max_length=__lowerCamelCase , stride=__lowerCamelCase , pad_to_multiple_of=__lowerCamelCase , return_attention_mask=__lowerCamelCase , return_overflowing_tokens=__lowerCamelCase , return_special_tokens_mask=__lowerCamelCase , return_offsets_mapping=__lowerCamelCase , return_token_type_ids=__lowerCamelCase , return_length=__lowerCamelCase , verbose=__lowerCamelCase , return_tensors=__lowerCamelCase , **__lowerCamelCase , )
__UpperCAmelCase : int = qformer_text_encoding.pop("input_ids" )
__UpperCAmelCase : Optional[int] = qformer_text_encoding.pop("attention_mask" )
if images is not None:
__UpperCAmelCase : Union[str, Any] = self.image_processor(__lowerCamelCase , return_tensors=__lowerCamelCase )
encoding.update(__lowerCamelCase )
return encoding
def _lowerCamelCase ( self: Any , *__lowerCamelCase: Any , **__lowerCamelCase: Any ) -> Optional[Any]:
return self.tokenizer.batch_decode(*__lowerCamelCase , **__lowerCamelCase )
def _lowerCamelCase ( self: Tuple , *__lowerCamelCase: Any , **__lowerCamelCase: Dict ) -> Tuple:
return self.tokenizer.decode(*__lowerCamelCase , **__lowerCamelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _lowerCamelCase ( self: List[str] ) -> Tuple:
__UpperCAmelCase : str = self.tokenizer.model_input_names
__UpperCAmelCase : Dict = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _lowerCamelCase ( self: Union[str, Any] , __lowerCamelCase: Union[str, Any] , **__lowerCamelCase: Optional[Any] ) -> str:
if os.path.isfile(__lowerCamelCase ):
raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file''' )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
__UpperCAmelCase : List[str] = os.path.join(__lowerCamelCase , "qformer_tokenizer" )
self.qformer_tokenizer.save_pretrained(__lowerCamelCase )
return super().save_pretrained(__lowerCamelCase , **__lowerCamelCase )
@classmethod
def _lowerCamelCase ( cls: Tuple , __lowerCamelCase: Tuple , **__lowerCamelCase: Optional[int] ) -> Union[str, Any]:
__UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained(__lowerCamelCase , subfolder="qformer_tokenizer" )
__UpperCAmelCase : List[Any] = cls._get_arguments_from_pretrained(__lowerCamelCase , **__lowerCamelCase )
args.append(__lowerCamelCase )
return cls(*__lowerCamelCase )
| 342
| 0
|
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class lowerCAmelCase__ :
@staticmethod
def lowerCAmelCase__ ( *lowerCamelCase__ : Optional[int] , **lowerCamelCase__ : List[Any] ) ->Dict:
'''simple docstring'''
pass
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : Optional[Any] = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def __lowerCAmelCase (__lowerCAmelCase ):
_UpperCAmelCase : Optional[int] = np.array(__lowerCAmelCase )
_UpperCAmelCase : Tuple = npimg.shape
return {"hash": hashimage(__lowerCAmelCase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class lowerCAmelCase__ ( unittest.TestCase ):
lowerCAmelCase : List[str] = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
lowerCAmelCase : int = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def lowerCAmelCase__ ( self : Optional[int] , lowerCamelCase__ : Dict , lowerCamelCase__ : Any , lowerCamelCase__ : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = MaskGenerationPipeline(model=lowerCamelCase__ , image_processor=lowerCamelCase__ )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCAmelCase__ ( self : Union[str, Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : Any ) ->List[Any]:
'''simple docstring'''
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def lowerCAmelCase__ ( self : Any ) ->Any:
'''simple docstring'''
pass
@slow
@require_torch
def lowerCAmelCase__ ( self : str ) ->List[str]:
'''simple docstring'''
_UpperCAmelCase : str = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
_UpperCAmelCase : List[str] = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=2_56 )
# Shortening by hashing
_UpperCAmelCase : List[str] = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__ ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(lowerCamelCase__ , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0_4_4_4},
{"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1},
{"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
{"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
{"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
{"mask": {"hash": "e2d0b7a0b7", "shape": (4_80, 6_40)}, "scores": 0.9_9_6_7},
{"mask": {"hash": "453c7844bd", "shape": (4_80, 6_40)}, "scores": 0.9_9_3},
{"mask": {"hash": "3d44f2926d", "shape": (4_80, 6_40)}, "scores": 0.9_9_0_9},
{"mask": {"hash": "64033ddc3f", "shape": (4_80, 6_40)}, "scores": 0.9_8_7_9},
{"mask": {"hash": "801064ff79", "shape": (4_80, 6_40)}, "scores": 0.9_8_3_4},
{"mask": {"hash": "6172f276ef", "shape": (4_80, 6_40)}, "scores": 0.9_7_1_6},
{"mask": {"hash": "b49e60e084", "shape": (4_80, 6_40)}, "scores": 0.9_6_1_2},
{"mask": {"hash": "a811e775fd", "shape": (4_80, 6_40)}, "scores": 0.9_5_9_9},
{"mask": {"hash": "a6a8ebcf4b", "shape": (4_80, 6_40)}, "scores": 0.9_5_5_2},
{"mask": {"hash": "9d8257e080", "shape": (4_80, 6_40)}, "scores": 0.9_5_3_2},
{"mask": {"hash": "32de6454a8", "shape": (4_80, 6_40)}, "scores": 0.9_5_1_6},
{"mask": {"hash": "af3d4af2c8", "shape": (4_80, 6_40)}, "scores": 0.9_4_9_9},
{"mask": {"hash": "3c6db475fb", "shape": (4_80, 6_40)}, "scores": 0.9_4_8_3},
{"mask": {"hash": "c290813fb9", "shape": (4_80, 6_40)}, "scores": 0.9_4_6_4},
{"mask": {"hash": "b6f0b8f606", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
{"mask": {"hash": "92ce16bfdf", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
{"mask": {"hash": "c749b25868", "shape": (4_80, 6_40)}, "scores": 0.9_4_0_8},
{"mask": {"hash": "efb6cab859", "shape": (4_80, 6_40)}, "scores": 0.9_3_3_5},
{"mask": {"hash": "1ff2eafb30", "shape": (4_80, 6_40)}, "scores": 0.9_3_2_6},
{"mask": {"hash": "788b798e24", "shape": (4_80, 6_40)}, "scores": 0.9_2_6_2},
{"mask": {"hash": "abea804f0e", "shape": (4_80, 6_40)}, "scores": 0.8_9_9_9},
{"mask": {"hash": "7b9e8ddb73", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_6},
{"mask": {"hash": "cd24047c8a", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_4},
{"mask": {"hash": "6943e6bcbd", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_3},
{"mask": {"hash": "b5f47c9191", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def lowerCAmelCase__ ( self : Any ) ->List[Any]:
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] = "facebook/sam-vit-huge"
_UpperCAmelCase : int = pipeline("mask-generation" , model=lowerCamelCase__ )
_UpperCAmelCase : Optional[Any] = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
_UpperCAmelCase : Tuple = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(lowerCamelCase__ ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(lowerCamelCase__ , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0_4_4_4},
{"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1_0},
{"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
{"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
{"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
] , )
| 234
|
'''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 234
| 1
|
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''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''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def _snake_case ( A , A , A , A , A ) -> Tuple:
for attribute in key.split('''.''' ):
lowerCAmelCase__ = getattr(A , A )
if weight_type is not None:
lowerCAmelCase__ = getattr(A , A ).shape
else:
lowerCAmelCase__ = 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":
lowerCAmelCase__ = value
elif weight_type == "weight_g":
lowerCAmelCase__ = value
elif weight_type == "weight_v":
lowerCAmelCase__ = value
elif weight_type == "bias":
lowerCAmelCase__ = value
else:
lowerCAmelCase__ = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def _snake_case ( A , A ) -> List[str]:
lowerCAmelCase__ = []
lowerCAmelCase__ = fairseq_model.state_dict()
lowerCAmelCase__ = hf_model.feature_extractor
lowerCAmelCase__ = hf_model.adapter
for name, value in fairseq_dict.items():
lowerCAmelCase__ = False
if "conv_layers" in name:
load_conv_layer(
A , A , A , A , hf_model.config.feat_extract_norm == '''group''' , )
lowerCAmelCase__ = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(A , A , A , A )
lowerCAmelCase__ = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
lowerCAmelCase__ = True
if "*" in mapped_key:
lowerCAmelCase__ = name.split(A )[0].split('''.''' )[-2]
lowerCAmelCase__ = mapped_key.replace('''*''' , A )
if "weight_g" in name:
lowerCAmelCase__ = '''weight_g'''
elif "weight_v" in name:
lowerCAmelCase__ = '''weight_v'''
elif "bias" in name:
lowerCAmelCase__ = '''bias'''
elif "weight" in name:
lowerCAmelCase__ = '''weight'''
else:
lowerCAmelCase__ = None
set_recursively(A , A , A , A , A )
continue
if not is_used:
unused_weights.append(A )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _snake_case ( A , A , A , A , A ) -> int:
lowerCAmelCase__ = full_name.split('''conv_layers.''' )[-1]
lowerCAmelCase__ = name.split('''.''' )
lowerCAmelCase__ = int(items[0] )
lowerCAmelCase__ = 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."""
)
lowerCAmelCase__ = 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."""
)
lowerCAmelCase__ = 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."
)
lowerCAmelCase__ = 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."""
)
lowerCAmelCase__ = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(A )
def _snake_case ( A , A , A , A ) -> Tuple:
lowerCAmelCase__ = full_name.split('''adaptor.''' )[-1]
lowerCAmelCase__ = name.split('''.''' )
if items[1].isdigit():
lowerCAmelCase__ = int(items[1] )
else:
lowerCAmelCase__ = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
lowerCAmelCase__ = value
logger.info(F"""Adapter proj layer norm bias was initialized from {full_name}.""" )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), F"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
lowerCAmelCase__ = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), F"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
lowerCAmelCase__ = value
logger.info(F"""Adapter proj layer bias was initialized from {full_name}.""" )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), F"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
lowerCAmelCase__ = value
logger.info(F"""Adapter proj layer weight was initialized from {full_name}.""" )
elif isinstance(A , A ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
lowerCAmelCase__ = value
logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}.""" )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), F"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
lowerCAmelCase__ = value
logger.info(F"""Adapter layer {layer_id} bias was initialized from {full_name}.""" )
else:
unused_weights.append(A )
def _snake_case ( A ) -> Any:
lowerCAmelCase__ , lowerCAmelCase__ = emb.weight.shape
lowerCAmelCase__ = nn.Linear(A , A , bias=A )
lowerCAmelCase__ = emb.weight.data
return lin_layer
@torch.no_grad()
def _snake_case ( A , A , A , A , A , A , A , A , A , A , A , ) -> str:
lowerCAmelCase__ = WavaVecaConfig.from_pretrained(
A , add_adapter=A , adapter_stride=A , adapter_kernel_size=A , use_auth_token=A , output_hidden_size=A , )
lowerCAmelCase__ = MBartConfig.from_pretrained(A )
# load model
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
} , )
lowerCAmelCase__ = model[0].eval()
# load feature extractor
lowerCAmelCase__ = WavaVecaFeatureExtractor.from_pretrained(A , use_auth_token=A )
# set weights for wav2vec2 encoder
lowerCAmelCase__ = WavaVecaModel(A )
recursively_load_weights_wavaveca(model.encoder , A )
# load decoder weights
lowerCAmelCase__ = MBartForCausalLM(A )
lowerCAmelCase__ , lowerCAmelCase__ = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=A )
logger.warning(F"""The following keys are missing when loading the decoder weights: {missing_keys}""" )
logger.warning(F"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}""" )
lowerCAmelCase__ = SpeechEncoderDecoderModel(encoder=A , decoder=A )
lowerCAmelCase__ = False
lowerCAmelCase__ = MBartaaTokenizer(A )
tokenizer.save_pretrained(A )
lowerCAmelCase__ = hf_wavavec.config.to_dict()
lowerCAmelCase__ = tokenizer.pad_token_id
lowerCAmelCase__ = tokenizer.bos_token_id
lowerCAmelCase__ = tokenizer.eos_token_id
lowerCAmelCase__ = '''mbart50'''
lowerCAmelCase__ = '''wav2vec2'''
lowerCAmelCase__ = tokenizer.eos_token_id
lowerCAmelCase__ = 250004
lowerCAmelCase__ = tokenizer.eos_token_id
lowerCAmelCase__ = SpeechEncoderDecoderConfig.from_dict(A )
hf_wavavec.save_pretrained(A )
feature_extractor.save_pretrained(A )
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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-xls-r-1b''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/mbart-large-50-one-to-many-mmt''',
type=str,
help='''Path to hf decoder checkpoint config''',
)
parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
parser.add_argument('''--encoder_output_dim''', default=1_024, type=int, help='''encoder output dim''')
parser.add_argument('''--start_token_id''', default=250_004, type=int, help='''`decoder_start_token_id` of model config''')
__UpperCAmelCase = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
)
| 228
|
'''simple docstring'''
def _snake_case ( A = 10 ) -> str:
if not isinstance(A , A ) or n < 0:
raise ValueError('''Invalid input''' )
lowerCAmelCase__ = 10**n
lowerCAmelCase__ = 28433 * (pow(2 , 7830457 , A )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f"""{solution(10) = }""")
| 228
| 1
|
"""simple docstring"""
def lowercase__( __SCREAMING_SNAKE_CASE : int ):
if not isinstance(__lowercase , __lowercase ):
lowercase_ : Any = F'''Input value of [number={number}] must be an integer'''
raise TypeError(__lowercase )
if number < 0:
return False
lowercase_ : Optional[Any] = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 213
|
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '''
'''library. You can have a look at this example script for pointers: '''
'''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''
)
def UpperCAmelCase_ ( __lowercase : Any , __lowercase : Tuple ) -> int:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
return (preds == labels).mean()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : str ) -> Optional[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = simple_accuracy(__lowercase , __lowercase )
_UpperCAmelCase = fa_score(y_true=__lowercase , y_pred=__lowercase )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[int] , __lowercase : List[str] ) -> List[Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
_UpperCAmelCase = pearsonr(__lowercase , __lowercase )[0]
_UpperCAmelCase = spearmanr(__lowercase , __lowercase )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : str ) -> Tuple:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
assert len(__lowercase ) == len(__lowercase ), f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(__lowercase , __lowercase )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mrpc":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "sts-b":
return pearson_and_spearman(__lowercase , __lowercase )
elif task_name == "qqp":
return acc_and_fa(__lowercase , __lowercase )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "qnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "rte":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "wnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
elif task_name == "hans":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
def UpperCAmelCase_ ( __lowercase : List[Any] , __lowercase : Dict , __lowercase : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(__lowercase , __lowercase )
requires_backends(__lowercase , "sklearn" )
if len(__lowercase ) != len(__lowercase ):
raise ValueError(f'Predictions and labels have mismatched lengths {len(__lowercase )} and {len(__lowercase )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(__lowercase , __lowercase )}
else:
raise KeyError(__lowercase )
| 22
| 0
|
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class _UpperCamelCase ( _a ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] ="""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""
def snake_case ( self , __a=0 ):
__lowerCAmelCase = floats_tensor((1, 3, 1_28, 1_28) , rng=random.Random(snake_case_ ) )
__lowerCAmelCase = np.random.RandomState(snake_case_ )
__lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""strength""": 0.7_5,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**snake_case_ ).images
__lowerCAmelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 1_28, 1_28, 3)
__lowerCAmelCase = np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**snake_case_ ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
__lowerCAmelCase = np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
# warmup pass to apply optimizations
__lowerCAmelCase = pipe(**self.get_dummy_inputs() )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**snake_case_ ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
__lowerCAmelCase = np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**snake_case_ ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
__lowerCAmelCase = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**snake_case_ ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
__lowerCAmelCase = np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def snake_case ( self ):
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint , provider="CPUExecutionProvider" )
__lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = self.get_dummy_inputs()
__lowerCAmelCase = pipe(**snake_case_ ).images
__lowerCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 1_28, 1_28, 3)
__lowerCAmelCase = np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def snake_case ( self ):
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def snake_case ( self ):
__lowerCAmelCase = ort.SessionOptions()
__lowerCAmelCase = False
return options
def snake_case ( self ):
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
__lowerCAmelCase = init_image.resize((7_68, 5_12) )
# using the PNDM scheduler by default
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , revision="onnx" , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = """A fantasy landscape, trending on artstation"""
__lowerCAmelCase = np.random.RandomState(0 )
__lowerCAmelCase = pipe(
prompt=snake_case_ , image=snake_case_ , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=10 , generator=snake_case_ , output_type="np" , )
__lowerCAmelCase = output.images
__lowerCAmelCase = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
__lowerCAmelCase = np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def snake_case ( self ):
__lowerCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
__lowerCAmelCase = init_image.resize((7_68, 5_12) )
__lowerCAmelCase = LMSDiscreteScheduler.from_pretrained(
"runwayml/stable-diffusion-v1-5" , subfolder="scheduler" , revision="onnx" )
__lowerCAmelCase = OnnxStableDiffusionImgaImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" , revision="onnx" , scheduler=snake_case_ , safety_checker=snake_case_ , feature_extractor=snake_case_ , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=snake_case_ )
__lowerCAmelCase = """A fantasy landscape, trending on artstation"""
__lowerCAmelCase = np.random.RandomState(0 )
__lowerCAmelCase = pipe(
prompt=snake_case_ , image=snake_case_ , strength=0.7_5 , guidance_scale=7.5 , num_inference_steps=20 , generator=snake_case_ , output_type="np" , )
__lowerCAmelCase = output.images
__lowerCAmelCase = images[0, 2_55:2_58, 3_83:3_86, -1]
assert images.shape == (1, 5_12, 7_68, 3)
__lowerCAmelCase = np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 371
|
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, 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
A : Optional[int] = 1e-4
if is_torch_available():
import torch
from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder
@require_torch
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a=16 , __a=13 , __a=7 , __a=14 , __a=10 , __a=19 , __a=5 , __a=4 , __a=True , __a=16 , __a=2 , __a=4 , __a=4 , __a="gelu" , __a=0.1 , __a=0.1 , __a=[1, 2, 3, 4, 5] , __a=25 , __a=5 , ):
__lowerCAmelCase = d_model
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = prediction_length
__lowerCAmelCase = context_length
__lowerCAmelCase = cardinality
__lowerCAmelCase = num_time_features
__lowerCAmelCase = lags_sequence
__lowerCAmelCase = embedding_dimension
__lowerCAmelCase = is_training
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = context_length
__lowerCAmelCase = prediction_length + label_length
__lowerCAmelCase = label_length
__lowerCAmelCase = moving_average
__lowerCAmelCase = autocorrelation_factor
def snake_case ( self ):
return AutoformerConfig(
d_model=self.d_model , 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 , prediction_length=self.prediction_length , context_length=self.context_length , label_length=self.label_length , lags_sequence=self.lags_sequence , num_time_features=self.num_time_features , num_static_categorical_features=1 , cardinality=[self.cardinality] , embedding_dimension=[self.embedding_dimension] , moving_average=self.moving_average , )
def snake_case ( self , __a ):
__lowerCAmelCase = config.context_length + max(config.lags_sequence )
__lowerCAmelCase = ids_tensor([self.batch_size, 1] , config.cardinality[0] )
__lowerCAmelCase = floats_tensor([self.batch_size, _past_length, config.num_time_features] )
__lowerCAmelCase = floats_tensor([self.batch_size, _past_length] )
__lowerCAmelCase = floats_tensor([self.batch_size, _past_length] ) > 0.5
# decoder inputs
__lowerCAmelCase = floats_tensor([self.batch_size, config.prediction_length, config.num_time_features] )
__lowerCAmelCase = floats_tensor([self.batch_size, config.prediction_length] )
__lowerCAmelCase = {
"past_values": past_values,
"static_categorical_features": static_categorical_features,
"past_time_features": past_time_features,
"past_observed_mask": past_observed_mask,
"future_time_features": future_time_features,
"future_values": future_values,
}
return inputs_dict
def snake_case ( self ):
__lowerCAmelCase = self.get_config()
__lowerCAmelCase = self.prepare_autoformer_inputs_dict(__a )
return config, inputs_dict
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def snake_case ( self , __a , __a ):
__lowerCAmelCase = AutoformerModel(config=__a ).to(__a ).eval()
__lowerCAmelCase = model(**__a )
__lowerCAmelCase = outputs.encoder_last_hidden_state
__lowerCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase = model.get_encoder()
encoder.save_pretrained(__a )
__lowerCAmelCase = AutoformerEncoder.from_pretrained(__a ).to(__a )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = model.create_network_inputs(**__a )
__lowerCAmelCase , __lowerCAmelCase = model.decomposition_layer(transformer_inputs[:, : config.context_length, ...] )
__lowerCAmelCase = torch.cat(
(transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) , dim=-1 , )
__lowerCAmelCase = encoder(inputs_embeds=__a )[0]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
__lowerCAmelCase = (
torch.mean(transformer_inputs[:, : config.context_length, ...] , dim=1 )
.unsqueeze(1 )
.repeat(1 , config.prediction_length , 1 )
)
__lowerCAmelCase = torch.zeros(
[transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] , device=enc_input.device , )
__lowerCAmelCase = torch.cat(
(
torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
__lowerCAmelCase = torch.cat(
(
torch.cat((trend_input[:, -config.label_length :, ...], mean) , dim=1 ),
feature[:, config.context_length - config.label_length :, ...],
) , dim=-1 , )
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCAmelCase = model.get_decoder()
decoder.save_pretrained(__a )
__lowerCAmelCase = AutoformerDecoder.from_pretrained(__a ).to(__a )
__lowerCAmelCase = decoder(
trend=__a , inputs_embeds=__a , encoder_hidden_states=__a , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class _UpperCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : List[Any] =(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
__UpperCAmelCase : List[Any] =(AutoformerForPrediction,) if is_torch_available() else ()
__UpperCAmelCase : Tuple ={"""feature-extraction""": AutoformerModel} if is_torch_available() else {}
__UpperCAmelCase : Tuple =False
__UpperCAmelCase : Any =False
__UpperCAmelCase : Dict =False
__UpperCAmelCase : Union[str, Any] =False
__UpperCAmelCase : Union[str, Any] =False
__UpperCAmelCase : Optional[Any] =False
def snake_case ( self ):
__lowerCAmelCase = AutoformerModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=__a , has_text_modality=__a )
def snake_case ( self ):
self.config_tester.run_common_tests()
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(__a )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a )
__lowerCAmelCase , __lowerCAmelCase = model_class.from_pretrained(__a , output_loading_info=__a )
self.assertEqual(info["missing_keys"] , [] )
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__a )
@unittest.skip(reason="Model has no tokens embeddings" )
def snake_case ( self ):
pass
def snake_case ( self ):
__lowerCAmelCase = inspect.signature(getattr(__a , "forward" ) )
# The main input is the name of the argument after `self`
__lowerCAmelCase = list(model_signature.parameters.keys() )[1]
self.assertEqual(AutoformerModel.main_input_name , __a )
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(__a )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = [
"past_values",
"past_time_features",
"past_observed_mask",
"static_categorical_features",
"static_real_features",
"future_values",
"future_time_features",
]
if model.__class__.__name__ in ["AutoformerForPrediction"]:
expected_arg_names.append("future_observed_mask" )
expected_arg_names.extend(
[
"decoder_attention_mask",
"head_mask",
"decoder_head_mask",
"cross_attn_head_mask",
"encoder_outputs",
"past_key_values",
"output_hidden_states",
"output_attentions",
"use_cache",
"return_dict",
] )
self.assertListEqual(arg_names[: len(__a )] , __a )
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = True
__lowerCAmelCase = getattr(self.model_tester , "seq_length" , __a )
__lowerCAmelCase = getattr(self.model_tester , "decoder_seq_length" , __a )
__lowerCAmelCase = getattr(self.model_tester , "encoder_seq_length" , __a )
__lowerCAmelCase = getattr(self.model_tester , "d_model" , __a )
__lowerCAmelCase = getattr(self.model_tester , "num_attention_heads" , __a )
__lowerCAmelCase = d_model // num_attention_heads
for model_class in self.all_model_classes:
__lowerCAmelCase = True
__lowerCAmelCase = False
__lowerCAmelCase = True
__lowerCAmelCase = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(__a , __a ) )
__lowerCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__lowerCAmelCase = True
__lowerCAmelCase = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(__a , __a ) )
__lowerCAmelCase = outputs.encoder_attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
__lowerCAmelCase = len(__a )
__lowerCAmelCase = 7
if "last_hidden_state" in outputs:
correct_outlen += 1
if "trend" in outputs:
correct_outlen += 1
if "past_key_values" in outputs:
correct_outlen += 1 # past_key_values have been returned
if "loss" in outputs:
correct_outlen += 1
if "params" in outputs:
correct_outlen += 1
self.assertEqual(__a , __a )
# decoder attentions
__lowerCAmelCase = outputs.decoder_attentions
self.assertIsInstance(__a , (list, tuple) )
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# cross attentions
__lowerCAmelCase = outputs.cross_attentions
self.assertIsInstance(__a , (list, tuple) )
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(cross_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, decoder_seq_length, dim] , )
# Check attention is always last and order is fine
__lowerCAmelCase = True
__lowerCAmelCase = True
__lowerCAmelCase = model_class(__a )
model.to(__a )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(__a , __a ) )
self.assertEqual(out_len + 2 , len(__a ) )
__lowerCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, dim] , )
@is_flaky()
def snake_case ( self ):
super().test_retain_grad_hidden_states_attentions()
def _lowerCamelCase ( _UpperCamelCase="train-batch.pt" ):
'''simple docstring'''
__lowerCAmelCase = hf_hub_download(repo_id="hf-internal-testing/tourism-monthly-batch" , filename=_UpperCamelCase , repo_type="dataset" )
__lowerCAmelCase = torch.load(_UpperCamelCase , map_location=_UpperCamelCase )
return batch
@require_torch
@slow
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self ):
__lowerCAmelCase = AutoformerModel.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a )
__lowerCAmelCase = prepare_batch()
with torch.no_grad():
__lowerCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , future_values=batch["future_values"] , future_time_features=batch["future_time_features"] , )[0]
__lowerCAmelCase = torch.Size(
(64, model.config.prediction_length + model.config.label_length, model.config.feature_size) )
self.assertEqual(output.shape , __a )
__lowerCAmelCase = torch.tensor(
[[0.3_5_9_3, -1.3_3_9_8, 0.6_3_3_0], [0.2_2_7_9, 1.5_3_9_6, -0.1_7_9_2], [0.0_4_5_0, 1.3_2_2_5, -0.2_3_3_5]] , device=__a )
self.assertTrue(torch.allclose(output[0, :3, :3] , __a , atol=__a ) )
def snake_case ( self ):
__lowerCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a )
__lowerCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
__lowerCAmelCase = model(
past_values=batch["past_values"] , past_time_features=batch["past_time_features"] , past_observed_mask=batch["past_observed_mask"] , static_categorical_features=batch["static_categorical_features"] , ).encoder_last_hidden_state
__lowerCAmelCase = torch.Size((64, model.config.context_length, model.config.d_model) )
self.assertEqual(output.shape , __a )
__lowerCAmelCase = torch.tensor(
[[-0.0_7_3_4, -0.9_0_3_6, 0.8_3_5_8], [4.7_1_8_6, 2.4_1_1_3, 1.9_5_8_1], [1.7_9_5_3, 2.3_5_5_8, 1.2_9_7_0]] , device=__a )
self.assertTrue(torch.allclose(output[0, :3, :3] , __a , atol=__a ) )
def snake_case ( self ):
__lowerCAmelCase = AutoformerForPrediction.from_pretrained("huggingface/autoformer-tourism-monthly" ).to(__a )
__lowerCAmelCase = prepare_batch("val-batch.pt" )
with torch.no_grad():
__lowerCAmelCase = model.generate(
static_categorical_features=batch["static_categorical_features"] , past_time_features=batch["past_time_features"] , past_values=batch["past_values"] , future_time_features=batch["future_time_features"] , past_observed_mask=batch["past_observed_mask"] , )
__lowerCAmelCase = torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length) )
self.assertEqual(outputs.sequences.shape , __a )
__lowerCAmelCase = torch.tensor([3_1_3_0.6_7_6_3, 4_0_5_6.5_2_9_3, 7_0_5_3.0_7_8_6] , device=__a )
__lowerCAmelCase = outputs.sequences.mean(dim=1 )
self.assertTrue(torch.allclose(mean_prediction[0, -3:] , __a , rtol=1e-1 ) )
| 259
| 0
|
"""simple docstring"""
import math
def lowerCAmelCase__ ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_6_0:
raise ValueError('In Malus Law, the angle is in the range 0-360 degrees' )
# handling of values out of allowed range
return initial_intensity * (math.cos(math.radians(_UpperCamelCase ) ) ** 2)
if __name__ == "__main__":
import doctest
doctest.testmod(name="malus_law")
| 150
|
"""simple docstring"""
def lowerCAmelCase__ ( _UpperCamelCase : list[int] ) -> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(_UpperCamelCase , (list, tuple) ) or not all(
isinstance(_UpperCamelCase , _UpperCamelCase ) for number in numbers ):
raise ValueError('numbers must be an iterable of integers' )
snake_case = snake_case = snake_case = numbers[0]
for i in range(1 , len(_UpperCamelCase ) ):
# update the maximum and minimum subarray products
snake_case = numbers[i]
if number < 0:
snake_case ,snake_case = min_till_now, max_till_now
snake_case = max(_UpperCamelCase , max_till_now * number )
snake_case = min(_UpperCamelCase , min_till_now * number )
# update the maximum product found till now
snake_case = max(_UpperCamelCase , _UpperCamelCase )
return max_prod
| 150
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class __A ( unittest.TestCase ):
def lowercase__ ( self : Optional[int] ):
lowerCAmelCase : Tuple = tempfile.mkdtemp()
# fmt: off
lowerCAmelCase : List[Any] = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
# fmt: on
lowerCAmelCase : str = dict(zip(UpperCAmelCase_ , range(len(UpperCAmelCase_ ) ) ) )
lowerCAmelCase : Optional[Any] = ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
lowerCAmelCase : Tuple = {'unk_token': '<unk>'}
lowerCAmelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
lowerCAmelCase : List[str] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(UpperCAmelCase_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(UpperCAmelCase_ ) )
lowerCAmelCase : Dict = {
'do_resize': True,
'size': 20,
'do_center_crop': True,
'crop_size': 18,
'do_normalize': True,
'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
}
lowerCAmelCase : Union[str, Any] = os.path.join(self.tmpdirname , UpperCAmelCase_ )
with open(self.image_processor_file , 'w' , encoding='utf-8' ) as fp:
json.dump(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : Any , **UpperCAmelCase_ : Dict ):
return CLIPTokenizer.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def lowercase__ ( self : Tuple , **UpperCAmelCase_ : str ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def lowercase__ ( self : Optional[int] , **UpperCAmelCase_ : Optional[int] ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **UpperCAmelCase_ )
def lowercase__ ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self : List[str] ):
lowerCAmelCase : str = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
lowerCAmelCase : List[Any] = [Image.fromarray(np.moveaxis(UpperCAmelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase__ ( self : Any ):
lowerCAmelCase : List[str] = self.get_tokenizer()
lowerCAmelCase : List[str] = self.get_rust_tokenizer()
lowerCAmelCase : Optional[int] = self.get_image_processor()
lowerCAmelCase : Optional[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
lowerCAmelCase : int = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
lowerCAmelCase : Dict = CLIPProcessor.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 , UpperCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer , UpperCAmelCase_ )
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 , UpperCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor , UpperCAmelCase_ )
def lowercase__ ( self : Tuple ):
lowerCAmelCase : Any = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
lowerCAmelCase : Tuple = self.get_tokenizer(bos_token='(BOS)' , eos_token='(EOS)' )
lowerCAmelCase : Union[str, Any] = self.get_image_processor(do_normalize=UpperCAmelCase_ , padding_value=1.0 )
lowerCAmelCase : Dict = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token='(BOS)' , eos_token='(EOS)' , do_normalize=UpperCAmelCase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , UpperCAmelCase_ )
def lowercase__ ( self : List[str] ):
lowerCAmelCase : Any = self.get_image_processor()
lowerCAmelCase : Union[str, Any] = self.get_tokenizer()
lowerCAmelCase : str = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
lowerCAmelCase : Dict = self.prepare_image_inputs()
lowerCAmelCase : List[str] = image_processor(UpperCAmelCase_ , return_tensors='np' )
lowerCAmelCase : int = processor(images=UpperCAmelCase_ , return_tensors='np' )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : Union[str, Any] = self.get_image_processor()
lowerCAmelCase : Union[str, Any] = self.get_tokenizer()
lowerCAmelCase : Dict = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
lowerCAmelCase : Optional[int] = 'lower newer'
lowerCAmelCase : List[str] = processor(text=UpperCAmelCase_ )
lowerCAmelCase : Union[str, Any] = tokenizer(UpperCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase__ ( self : Optional[Any] ):
lowerCAmelCase : Tuple = self.get_image_processor()
lowerCAmelCase : Dict = self.get_tokenizer()
lowerCAmelCase : List[str] = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
lowerCAmelCase : Optional[Any] = 'lower newer'
lowerCAmelCase : Optional[int] = self.prepare_image_inputs()
lowerCAmelCase : Union[str, Any] = processor(text=UpperCAmelCase_ , images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , ['input_ids', 'attention_mask', 'pixel_values'] )
# test if it raises when no input is passed
with pytest.raises(UpperCAmelCase_ ):
processor()
def lowercase__ ( self : List[str] ):
lowerCAmelCase : Optional[Any] = self.get_image_processor()
lowerCAmelCase : str = self.get_tokenizer()
lowerCAmelCase : Union[str, Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
lowerCAmelCase : List[str] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
lowerCAmelCase : Any = processor.batch_decode(UpperCAmelCase_ )
lowerCAmelCase : List[Any] = tokenizer.batch_decode(UpperCAmelCase_ )
self.assertListEqual(UpperCAmelCase_ , UpperCAmelCase_ )
def lowercase__ ( self : Union[str, Any] ):
lowerCAmelCase : List[Any] = self.get_image_processor()
lowerCAmelCase : Dict = self.get_tokenizer()
lowerCAmelCase : List[Any] = CLIPProcessor(tokenizer=UpperCAmelCase_ , image_processor=UpperCAmelCase_ )
lowerCAmelCase : Dict = 'lower newer'
lowerCAmelCase : Tuple = self.prepare_image_inputs()
lowerCAmelCase : List[str] = processor(text=UpperCAmelCase_ , images=UpperCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 323
|
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A : List[Any] = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
'''AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AutoformerForPrediction''',
'''AutoformerModel''',
'''AutoformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
__A : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 323
| 1
|
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ : Tuple = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : Union[str, Any] = "vit"
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Dict=7_6_8 , UpperCAmelCase__ : Tuple=1_2 , UpperCAmelCase__ : str=1_2 , UpperCAmelCase__ : Dict=3_0_7_2 , UpperCAmelCase__ : List[Any]="gelu" , UpperCAmelCase__ : Optional[int]=0.0 , UpperCAmelCase__ : int=0.0 , UpperCAmelCase__ : Optional[Any]=0.02 , UpperCAmelCase__ : str=1E-12 , UpperCAmelCase__ : List[str]=2_2_4 , UpperCAmelCase__ : List[str]=1_6 , UpperCAmelCase__ : Union[str, Any]=3 , UpperCAmelCase__ : List[str]=True , UpperCAmelCase__ : Optional[int]=1_6 , **UpperCAmelCase__ : Optional[Any] , ) -> List[Any]:
super().__init__(**UpperCAmelCase__ )
__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_act
__SCREAMING_SNAKE_CASE = hidden_dropout_prob
__SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
__SCREAMING_SNAKE_CASE = initializer_range
__SCREAMING_SNAKE_CASE = layer_norm_eps
__SCREAMING_SNAKE_CASE = image_size
__SCREAMING_SNAKE_CASE = patch_size
__SCREAMING_SNAKE_CASE = num_channels
__SCREAMING_SNAKE_CASE = qkv_bias
__SCREAMING_SNAKE_CASE = encoder_stride
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : List[Any] = version.parse("1.11")
@property
def UpperCAmelCase_ ( self : Optional[Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCAmelCase_ ( self : Any ) -> float:
return 1E-4
| 54
|
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_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 (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class snake_case__ :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=10 , lowerCAmelCase__=3 , lowerCAmelCase__=2 , lowerCAmelCase__=2 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=32 , lowerCAmelCase__=5 , lowerCAmelCase__=4 , lowerCAmelCase__=37 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=10 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__="divided_space_time" , lowerCAmelCase__=None , ) -> List[str]:
__magic_name__ : int = parent
__magic_name__ : Tuple = batch_size
__magic_name__ : int = image_size
__magic_name__ : str = num_channels
__magic_name__ : Dict = patch_size
__magic_name__ : Tuple = num_frames
__magic_name__ : List[Any] = is_training
__magic_name__ : List[Any] = use_labels
__magic_name__ : Dict = hidden_size
__magic_name__ : List[Any] = num_hidden_layers
__magic_name__ : str = num_attention_heads
__magic_name__ : List[Any] = intermediate_size
__magic_name__ : Dict = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Union[str, Any] = attention_probs_dropout_prob
__magic_name__ : Tuple = attention_type
__magic_name__ : List[str] = initializer_range
__magic_name__ : Optional[Any] = scope
__magic_name__ : Tuple = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
__magic_name__ : str = (image_size // patch_size) ** 2
__magic_name__ : Any = (num_frames) * self.num_patches_per_frame + 1
def __magic_name__ ( self ) -> Dict:
__magic_name__ : Optional[Any] = floats_tensor(
[self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size] )
__magic_name__ : str = None
if self.use_labels:
__magic_name__ : List[Any] = ids_tensor([self.batch_size] , self.num_labels )
__magic_name__ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def __magic_name__ ( self ) -> str:
__magic_name__ : Dict = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
__magic_name__ : Optional[Any] = self.num_labels
return config
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Optional[Any]:
__magic_name__ : List[Any] = TimesformerModel(config=lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : Optional[Any] = model(lowerCAmelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> Any:
__magic_name__ : int = TimesformerForVideoClassification(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
__magic_name__ : List[Any] = model(lowerCAmelCase__ )
# verify the logits shape
__magic_name__ : List[Any] = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , lowerCAmelCase__ )
def __magic_name__ ( self ) -> Any:
__magic_name__ : Union[str, Any] = self.prepare_config_and_inputs()
__magic_name__ ,__magic_name__ ,__magic_name__ : Tuple = config_and_inputs
__magic_name__ : Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class snake_case__ ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
lowercase__ : Tuple = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
lowercase__ : Union[str, Any] = (
{'''feature-extraction''': TimesformerModel, '''video-classification''': TimesformerForVideoClassification}
if is_torch_available()
else {}
)
lowercase__ : int = False
lowercase__ : str = False
lowercase__ : Tuple = False
lowercase__ : Any = False
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : List[Any] = TimesformerModelTester(self )
__magic_name__ : List[str] = ConfigTester(
self , config_class=lowerCAmelCase__ , has_text_modality=lowerCAmelCase__ , hidden_size=37 )
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=False ) -> List[str]:
__magic_name__ : List[str] = copy.deepcopy(lowerCAmelCase__ )
if return_labels:
if model_class in get_values(lowerCAmelCase__ ):
__magic_name__ : Union[str, Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCAmelCase__ )
return inputs_dict
def __magic_name__ ( self ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def __magic_name__ ( self ) -> str:
pass
def __magic_name__ ( self ) -> Optional[int]:
__magic_name__ ,__magic_name__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : List[Any] = model_class(lowerCAmelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__magic_name__ : Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCAmelCase__ , nn.Linear ) )
def __magic_name__ ( self ) -> Optional[Any]:
__magic_name__ ,__magic_name__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Dict = model_class(lowerCAmelCase__ )
__magic_name__ : Optional[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ : Optional[int] = [*signature.parameters.keys()]
__magic_name__ : Any = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCAmelCase__ )
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase__ )
def __magic_name__ ( self ) -> Union[str, Any]:
__magic_name__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase__ )
@slow
def __magic_name__ ( self ) -> Optional[int]:
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : List[str] = TimesformerModel.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
def __magic_name__ ( self ) -> List[Any]:
if not self.has_attentions:
pass
else:
__magic_name__ ,__magic_name__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ : Optional[int] = True
for model_class in self.all_model_classes:
__magic_name__ : Tuple = self.model_tester.seq_length
__magic_name__ : int = self.model_tester.num_frames
__magic_name__ : Any = True
__magic_name__ : Tuple = False
__magic_name__ : Optional[int] = True
__magic_name__ : str = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__magic_name__ : List[str] = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
__magic_name__ : Optional[Any] = True
__magic_name__ : Optional[Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : Optional[int] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__magic_name__ : int = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
__magic_name__ : Union[str, Any] = len(lowerCAmelCase__ )
# Check attention is always last and order is fine
__magic_name__ : str = True
__magic_name__ : Optional[Any] = True
__magic_name__ : int = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : List[str] = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
self.assertEqual(out_len + 1 , len(lowerCAmelCase__ ) )
__magic_name__ : Union[str, Any] = outputs.attentions
self.assertEqual(len(lowerCAmelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def __magic_name__ ( self ) -> Any:
def check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
__magic_name__ : Union[str, Any] = model_class(lowerCAmelCase__ )
model.to(lowerCAmelCase__ )
model.eval()
with torch.no_grad():
__magic_name__ : int = model(**self._prepare_for_class(lowerCAmelCase__ , lowerCAmelCase__ ) )
__magic_name__ : Optional[Any] = outputs.hidden_states
__magic_name__ : str = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCAmelCase__ ) , lowerCAmelCase__ )
__magic_name__ : str = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
__magic_name__ ,__magic_name__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ : Optional[Any] = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__magic_name__ : Union[str, Any] = True
check_hidden_states_output(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCamelCase ( ):
"""simple docstring"""
__magic_name__ : List[Any] = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""", filename="""eating_spaghetti.npy""", repo_type="""dataset""" )
__magic_name__ : List[str] = np.load(_A )
return list(_A )
@require_torch
@require_vision
class snake_case__ ( unittest.TestCase ):
@cached_property
def __magic_name__ ( self ) -> Optional[Any]:
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def __magic_name__ ( self ) -> List[Any]:
__magic_name__ : Dict = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
lowerCAmelCase__ )
__magic_name__ : str = self.default_image_processor
__magic_name__ : Any = prepare_video()
__magic_name__ : Dict = image_processor(video[:8] , return_tensors="""pt""" ).to(lowerCAmelCase__ )
# forward pass
with torch.no_grad():
__magic_name__ : int = model(**lowerCAmelCase__ )
# verify the logits
__magic_name__ : Optional[int] = torch.Size((1, 4_00) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(lowerCAmelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCAmelCase__ , atol=1e-4 ) )
| 342
| 0
|
def snake_case () -> int:
return 1
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) + two_pence(UpperCAmelCase__ )
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else ten_pence(x - 1_0 ) + five_pence(UpperCAmelCase__ )
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else twenty_pence(x - 2_0 ) + ten_pence(UpperCAmelCase__ )
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else fifty_pence(x - 5_0 ) + twenty_pence(UpperCAmelCase__ )
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else one_pound(x - 1_0_0 ) + fifty_pence(UpperCAmelCase__ )
def snake_case (UpperCAmelCase__ ) -> int:
return 0 if x < 0 else two_pound(x - 2_0_0 ) + one_pound(UpperCAmelCase__ )
def snake_case (UpperCAmelCase__ = 2_0_0 ) -> int:
return two_pound(UpperCAmelCase__ )
if __name__ == "__main__":
print(solution(int(input().strip())))
| 292
|
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def snake_case (UpperCAmelCase__ ) -> tuple:
return (data["data"], data["target"])
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> np.ndarray:
UpperCamelCase_: Dict = XGBRegressor(verbosity=0 , random_state=4_2 )
xgb.fit(UpperCAmelCase__ , UpperCAmelCase__ )
# Predict target for test data
UpperCamelCase_: int = xgb.predict(UpperCAmelCase__ )
UpperCamelCase_: Any = predictions.reshape(len(UpperCAmelCase__ ) , 1 )
return predictions
def snake_case () -> None:
UpperCamelCase_: Union[str, Any] = fetch_california_housing()
UpperCamelCase_ ,UpperCamelCase_: Tuple = data_handling(UpperCAmelCase__ )
UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_: Optional[Any] = train_test_split(
UpperCAmelCase__ , UpperCAmelCase__ , test_size=0.25 , random_state=1 )
UpperCamelCase_: Union[str, Any] = xgboost(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Error printing
print(F'''Mean Absolute Error : {mean_absolute_error(UpperCAmelCase__ , UpperCAmelCase__ )}''' )
print(F'''Mean Square Error : {mean_squared_error(UpperCAmelCase__ , UpperCAmelCase__ )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 292
| 1
|
def __A ( __lowerCamelCase = 100 ) -> int:
a = n * (n + 1) * (2 * n + 1) / 6
a = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'{solution() = }')
| 228
|
def __A ( __lowerCamelCase ) -> int:
a = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
a = hex_num[0] == """-"""
if is_negative:
a = hex_num[1:]
try:
a = int(__lowerCamelCase , 16 )
except ValueError:
raise ValueError("""Invalid value was passed to the function""" )
a = """"""
while int_num > 0:
a = str(int_num % 2 ) + bin_str
int_num >>= 1
return int(("""-""" + bin_str) if is_negative else bin_str )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 228
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class lowerCAmelCase_ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase : Union[str, Any] = """pegasus"""
_lowerCAmelCase : List[str] = ["""past_key_values"""]
_lowerCAmelCase : int = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self , lowerCAmelCase=5_02_65 , lowerCAmelCase=10_24 , lowerCAmelCase=12 , lowerCAmelCase=40_96 , lowerCAmelCase=16 , lowerCAmelCase=12 , lowerCAmelCase=40_96 , lowerCAmelCase=16 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase="gelu" , lowerCAmelCase=10_24 , lowerCAmelCase=0.1 , lowerCAmelCase=0.0 , lowerCAmelCase=0.0 , lowerCAmelCase=0.02 , lowerCAmelCase=0 , lowerCAmelCase=False , lowerCAmelCase=0 , lowerCAmelCase=1 , lowerCAmelCase=1 , **lowerCAmelCase , ):
"""simple docstring"""
snake_case = vocab_size
snake_case = max_position_embeddings
snake_case = d_model
snake_case = encoder_ffn_dim
snake_case = encoder_layers
snake_case = encoder_attention_heads
snake_case = decoder_ffn_dim
snake_case = decoder_layers
snake_case = decoder_attention_heads
snake_case = dropout
snake_case = attention_dropout
snake_case = activation_dropout
snake_case = activation_function
snake_case = init_std
snake_case = encoder_layerdrop
snake_case = decoder_layerdrop
snake_case = use_cache
snake_case = encoder_layers
snake_case = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , is_encoder_decoder=lowerCAmelCase , decoder_start_token_id=lowerCAmelCase , forced_eos_token_id=lowerCAmelCase , **lowerCAmelCase , )
@property
def snake_case ( self ):
"""simple docstring"""
return self.encoder_attention_heads
@property
def snake_case ( self ):
"""simple docstring"""
return self.d_model
| 356
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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 tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=99 , lowerCAmelCase=32 , lowerCAmelCase=2 , lowerCAmelCase=4 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=5_12 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=False , lowerCAmelCase=True , lowerCAmelCase="None" , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=None , ):
"""simple docstring"""
snake_case = parent
snake_case = batch_size
snake_case = seq_length
snake_case = is_training
snake_case = use_input_mask
snake_case = use_token_type_ids
snake_case = use_labels
snake_case = vocab_size
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = max_position_embeddings
snake_case = type_vocab_size
snake_case = type_sequence_label_size
snake_case = initializer_range
snake_case = num_labels
snake_case = num_choices
snake_case = relative_attention
snake_case = position_biased_input
snake_case = pos_att_type
snake_case = scope
def snake_case ( self ):
"""simple docstring"""
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case = None
if self.use_input_mask:
snake_case = random_attention_mask([self.batch_size, self.seq_length] )
snake_case = None
if self.use_token_type_ids:
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case = None
snake_case = None
snake_case = None
if self.use_labels:
snake_case = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case = DebertaVaConfig(
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 , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=lowerCAmelCase , )
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 ):
"""simple docstring"""
snake_case = TFDebertaVaModel(config=lowerCAmelCase )
snake_case = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
snake_case = [input_ids, input_mask]
snake_case = model(lowerCAmelCase )
snake_case = model(lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
snake_case = TFDebertaVaForMaskedLM(config=lowerCAmelCase )
snake_case = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case = 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 ):
"""simple docstring"""
snake_case = self.num_labels
snake_case = TFDebertaVaForSequenceClassification(config=lowerCAmelCase )
snake_case = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case = 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 ):
"""simple docstring"""
snake_case = self.num_labels
snake_case = TFDebertaVaForTokenClassification(config=lowerCAmelCase )
snake_case = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case = 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 ):
"""simple docstring"""
snake_case = TFDebertaVaForQuestionAnswering(config=lowerCAmelCase )
snake_case = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
snake_case = 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 ):
"""simple docstring"""
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,(
snake_case
) ,
) = config_and_inputs
snake_case = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase_ ( lowerCAmelCase , lowerCAmelCase , unittest.TestCase ):
"""simple docstring"""
_lowerCAmelCase : Optional[int] = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
_lowerCAmelCase : Any = (
{
"""feature-extraction""": TFDebertaVaModel,
"""fill-mask""": TFDebertaVaForMaskedLM,
"""question-answering""": TFDebertaVaForQuestionAnswering,
"""text-classification""": TFDebertaVaForSequenceClassification,
"""token-classification""": TFDebertaVaForTokenClassification,
"""zero-shot""": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCAmelCase : Optional[Any] = False
_lowerCAmelCase : List[Any] = False
def snake_case ( self ):
"""simple docstring"""
snake_case = TFDebertaVaModelTester(self )
snake_case = ConfigTester(self , config_class=lowerCAmelCase , hidden_size=37 )
def snake_case ( self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )
def snake_case ( self ):
"""simple docstring"""
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )
@slow
def snake_case ( self ):
"""simple docstring"""
snake_case = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
self.assertIsNotNone(lowerCAmelCase )
@require_tf
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
@unittest.skip(reason='Model not available yet' )
def snake_case ( self ):
"""simple docstring"""
pass
@slow
def snake_case ( self ):
"""simple docstring"""
snake_case = TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge' )
snake_case = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
snake_case = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case = model(lowerCAmelCase , attention_mask=lowerCAmelCase )[0]
snake_case = tf.constant(
[[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , lowerCAmelCase , atol=1E-4 )
| 149
| 0
|
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def A ( _lowerCamelCase="" ):
'''simple docstring'''
_lowerCAmelCase : Tuple = tempfile.mkdtemp()
return os.path.join(_lowerCamelCase , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.rand(12, dtype=torch.floataa) - 0.5
_lowerCAmelCase : List[str] = AgentAudio(__a)
_lowerCAmelCase : int = str(agent_type.to_string())
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(__a, agent_type.to_raw(), atol=1E-4))
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(__a))
# Ensure that the file contains the same value as the original tensor
_lowerCAmelCase , _lowerCAmelCase : List[str] = sf.read(__a)
self.assertTrue(torch.allclose(__a, torch.tensor(__a), atol=1E-4))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = torch.rand(12, dtype=torch.floataa) - 0.5
_lowerCAmelCase : Optional[int] = get_new_path(suffix=".wav")
sf.write(__a, __a, 1_6000)
_lowerCAmelCase : Any = AgentAudio(__a)
self.assertTrue(torch.allclose(__a, agent_type.to_raw(), atol=1E-4))
self.assertEqual(agent_type.to_string(), __a)
@require_vision
@require_torch
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = torch.randint(0, 256, (64, 64, 3))
_lowerCAmelCase : str = AgentImage(__a)
_lowerCAmelCase : Optional[Any] = str(agent_type.to_string())
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(__a, agent_type._tensor, atol=1E-4))
self.assertIsInstance(agent_type.to_raw(), Image.Image)
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(__a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png"
_lowerCAmelCase : Tuple = Image.open(__a)
_lowerCAmelCase : Optional[Any] = AgentImage(__a)
self.assertTrue(path.samefile(agent_type.to_string()))
self.assertTrue(image == agent_type.to_raw())
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(__a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = Path(get_tests_dir("fixtures/tests_samples/COCO")) / "000000039769.png"
_lowerCAmelCase : Optional[Any] = Image.open(__a)
_lowerCAmelCase : Any = AgentImage(__a)
self.assertFalse(path.samefile(agent_type.to_string()))
self.assertTrue(image == agent_type.to_raw())
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(__a))
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = "Hey!"
_lowerCAmelCase : Any = AgentText(__a)
self.assertEqual(__a, agent_type.to_string())
self.assertEqual(__a, agent_type.to_raw())
self.assertEqual(__a, __a)
| 36
|
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] ):
UpperCamelCase :Tuple = len(SCREAMING_SNAKE_CASE__ )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCamelCase :Dict = 0
print(SCREAMING_SNAKE_CASE__ , end=''',''' )
# Consider rest of the activities
for j in range(SCREAMING_SNAKE_CASE__ ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(SCREAMING_SNAKE_CASE__ , end=''',''' )
UpperCamelCase :List[str] = j
if __name__ == "__main__":
import doctest
doctest.testmod()
__snake_case = [1, 3, 0, 5, 8, 5]
__snake_case = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 259
| 0
|
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
def count_of_possible_combinations(_A ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(_A )
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
def count_of_possible_combinations_with_dp_array(
_A , _A ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
SCREAMING_SNAKE_CASE__ = sum(
count_of_possible_combinations_with_dp_array(target - item , _A )
for item in array )
SCREAMING_SNAKE_CASE__ = answer
return answer
SCREAMING_SNAKE_CASE__ = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(_A , _A )
def UpperCAmelCase_ ( _A , _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = [0] * (target + 1)
SCREAMING_SNAKE_CASE__ = 1
for i in range(1 , target + 1 ):
for j in range(_A ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
_SCREAMING_SNAKE_CASE : Union[str, Any] = 3
_SCREAMING_SNAKE_CASE : Any = 5
_SCREAMING_SNAKE_CASE : Optional[Any] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 353
|
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_SCREAMING_SNAKE_CASE : str = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': 1000,
'''block_out_channels''': [32, 64],
'''attention_head_dim''': 8,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
_SCREAMING_SNAKE_CASE : Dict = {
'''sample_size''': 64,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 3,
'''num_class_embeds''': 1000,
'''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''scale_shift''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
_SCREAMING_SNAKE_CASE : int = {
'''sample_size''': 256,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'''num_class_embeds''': None,
'''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
'''attention_head_dim''': 64,
'''down_block_types''': [
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''ResnetDownsampleBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
'''AttnDownBlock2D''',
],
'''up_block_types''': [
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''AttnUpBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
'''ResnetUpsampleBlock2D''',
],
'''resnet_time_scale_shift''': '''default''',
'''upsample_type''': '''resnet''',
'''downsample_type''': '''resnet''',
}
_SCREAMING_SNAKE_CASE : int = {
'''num_train_timesteps''': 40,
'''sigma_min''': 0.0_0_2,
'''sigma_max''': 8_0.0,
}
_SCREAMING_SNAKE_CASE : str = {
'''num_train_timesteps''': 201,
'''sigma_min''': 0.0_0_2,
'''sigma_max''': 8_0.0,
}
_SCREAMING_SNAKE_CASE : Tuple = {
'''num_train_timesteps''': 151,
'''sigma_min''': 0.0_0_2,
'''sigma_max''': 8_0.0,
}
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
if isinstance(_A , _A ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError('''boolean value expected''' )
def UpperCAmelCase_ ( _A , _A , _A , _A , _A=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.in_layers.0.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.in_layers.0.bias''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.in_layers.2.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.in_layers.2.bias''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.out_layers.0.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.out_layers.0.bias''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.out_layers.3.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.out_layers.3.bias''']
if has_skip:
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.skip_connection.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.skip_connection.bias''']
return new_checkpoint
def UpperCAmelCase_ ( _A , _A , _A , _A , _A=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3 , dim=0 )
SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3 , dim=0 )
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.norm.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.norm.bias''']
SCREAMING_SNAKE_CASE__ = weight_q.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ = bias_q.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ = weight_k.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ = bias_k.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ = weight_v.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ = bias_v.squeeze(-1 ).squeeze(-1 )
SCREAMING_SNAKE_CASE__ = (
checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1 ).squeeze(-1 )
)
SCREAMING_SNAKE_CASE__ = checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def UpperCAmelCase_ ( _A , _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = torch.load(_A , map_location='''cpu''' )
SCREAMING_SNAKE_CASE__ = {}
SCREAMING_SNAKE_CASE__ = checkpoint['''time_embed.0.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint['''time_embed.0.bias''']
SCREAMING_SNAKE_CASE__ = checkpoint['''time_embed.2.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint['''time_embed.2.bias''']
if unet_config["num_class_embeds"] is not None:
SCREAMING_SNAKE_CASE__ = checkpoint['''label_emb.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint['''input_blocks.0.0.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint['''input_blocks.0.0.bias''']
SCREAMING_SNAKE_CASE__ = unet_config['''down_block_types''']
SCREAMING_SNAKE_CASE__ = unet_config['''layers_per_block''']
SCREAMING_SNAKE_CASE__ = unet_config['''attention_head_dim''']
SCREAMING_SNAKE_CASE__ = unet_config['''block_out_channels''']
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = channels_list[0]
for i, layer_type in enumerate(_A ):
SCREAMING_SNAKE_CASE__ = channels_list[i]
SCREAMING_SNAKE_CASE__ = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(_A ):
SCREAMING_SNAKE_CASE__ = F'''down_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ = F'''input_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ = True if j == 0 and downsample_block_has_skip else False
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A , has_skip=_A )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(_A ):
SCREAMING_SNAKE_CASE__ = F'''down_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ = F'''input_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ = True if j == 0 and downsample_block_has_skip else False
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A , has_skip=_A )
SCREAMING_SNAKE_CASE__ = F'''down_blocks.{i}.attentions.{j}'''
SCREAMING_SNAKE_CASE__ = F'''input_blocks.{current_layer}.1'''
SCREAMING_SNAKE_CASE__ = convert_attention(
_A , _A , _A , _A , _A )
current_layer += 1
if i != len(_A ) - 1:
SCREAMING_SNAKE_CASE__ = F'''down_blocks.{i}.downsamplers.0'''
SCREAMING_SNAKE_CASE__ = F'''input_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A )
current_layer += 1
SCREAMING_SNAKE_CASE__ = current_channels
# hardcoded the mid-block for now
SCREAMING_SNAKE_CASE__ = '''mid_block.resnets.0'''
SCREAMING_SNAKE_CASE__ = '''middle_block.0'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A )
SCREAMING_SNAKE_CASE__ = '''mid_block.attentions.0'''
SCREAMING_SNAKE_CASE__ = '''middle_block.1'''
SCREAMING_SNAKE_CASE__ = convert_attention(_A , _A , _A , _A , _A )
SCREAMING_SNAKE_CASE__ = '''mid_block.resnets.1'''
SCREAMING_SNAKE_CASE__ = '''middle_block.2'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A )
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = unet_config['''up_block_types''']
for i, layer_type in enumerate(_A ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
SCREAMING_SNAKE_CASE__ = F'''up_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ = F'''output_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A , has_skip=_A )
current_layer += 1
if i != len(_A ) - 1:
SCREAMING_SNAKE_CASE__ = F'''up_blocks.{i}.upsamplers.0'''
SCREAMING_SNAKE_CASE__ = F'''output_blocks.{current_layer-1}.1'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
SCREAMING_SNAKE_CASE__ = F'''up_blocks.{i}.resnets.{j}'''
SCREAMING_SNAKE_CASE__ = F'''output_blocks.{current_layer}.0'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A , has_skip=_A )
SCREAMING_SNAKE_CASE__ = F'''up_blocks.{i}.attentions.{j}'''
SCREAMING_SNAKE_CASE__ = F'''output_blocks.{current_layer}.1'''
SCREAMING_SNAKE_CASE__ = convert_attention(
_A , _A , _A , _A , _A )
current_layer += 1
if i != len(_A ) - 1:
SCREAMING_SNAKE_CASE__ = F'''up_blocks.{i}.upsamplers.0'''
SCREAMING_SNAKE_CASE__ = F'''output_blocks.{current_layer-1}.2'''
SCREAMING_SNAKE_CASE__ = convert_resnet(_A , _A , _A , _A )
SCREAMING_SNAKE_CASE__ = checkpoint['''out.0.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint['''out.0.bias''']
SCREAMING_SNAKE_CASE__ = checkpoint['''out.2.weight''']
SCREAMING_SNAKE_CASE__ = checkpoint['''out.2.bias''']
return new_checkpoint
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
parser.add_argument(
'''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
)
parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')
_SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
_SCREAMING_SNAKE_CASE : List[str] = strabool(args.class_cond)
_SCREAMING_SNAKE_CASE : int = os.path.basename(args.unet_path)
print(F"Checkpoint: {ckpt_name}")
# Get U-Net config
if "imagenet64" in ckpt_name:
_SCREAMING_SNAKE_CASE : Optional[Any] = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_SCREAMING_SNAKE_CASE : int = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
_SCREAMING_SNAKE_CASE : Union[str, Any] = TEST_UNET_CONFIG
else:
raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")
if not args.class_cond:
_SCREAMING_SNAKE_CASE : Union[str, Any] = None
_SCREAMING_SNAKE_CASE : int = con_pt_to_diffuser(args.unet_path, unet_config)
_SCREAMING_SNAKE_CASE : Optional[int] = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
_SCREAMING_SNAKE_CASE : Optional[Any] = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
_SCREAMING_SNAKE_CASE : Any = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
_SCREAMING_SNAKE_CASE : int = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F"Checkpoint type {ckpt_name} is not currently supported.")
_SCREAMING_SNAKE_CASE : int = CMStochasticIterativeScheduler(**scheduler_config)
_SCREAMING_SNAKE_CASE : str = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 218
| 0
|
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = SwinaSRConfig()
if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE : Any = 4
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
SCREAMING_SNAKE_CASE : Any = 4
SCREAMING_SNAKE_CASE : Dict = 48
SCREAMING_SNAKE_CASE : Tuple = """pixelshuffle_aux"""
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE : Union[str, Any] = [6, 6, 6, 6]
SCREAMING_SNAKE_CASE : Any = 60
SCREAMING_SNAKE_CASE : Tuple = [6, 6, 6, 6]
SCREAMING_SNAKE_CASE : int = """pixelshuffledirect"""
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE : Tuple = 4
SCREAMING_SNAKE_CASE : Dict = """nearest+conv"""
elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
SCREAMING_SNAKE_CASE : Optional[Any] = 1
SCREAMING_SNAKE_CASE : Optional[Any] = 1
SCREAMING_SNAKE_CASE : Optional[int] = 1_26
SCREAMING_SNAKE_CASE : Dict = 7
SCREAMING_SNAKE_CASE : int = 255.0
SCREAMING_SNAKE_CASE : Optional[Any] = """"""
return config
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if "patch_embed.proj" in name and "layers" not in name:
SCREAMING_SNAKE_CASE : List[str] = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("""patch_embed.norm""" , """embeddings.patch_embeddings.layernorm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE : Any = name.replace("""layers""" , """encoder.stages""" )
if "residual_group.blocks" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""residual_group.blocks""" , """layers""" )
if "attn.proj" in name:
SCREAMING_SNAKE_CASE : List[str] = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE : int = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE : List[Any] = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE : Dict = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE : Union[str, Any] = name.replace("""mlp.fc2""" , """output.dense""" )
if "q_bias" in name:
SCREAMING_SNAKE_CASE : Optional[int] = name.replace("""q_bias""" , """query.bias""" )
if "k_bias" in name:
SCREAMING_SNAKE_CASE : Any = name.replace("""k_bias""" , """key.bias""" )
if "v_bias" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""v_bias""" , """value.bias""" )
if "cpb_mlp" in name:
SCREAMING_SNAKE_CASE : List[str] = name.replace("""cpb_mlp""" , """continuous_position_bias_mlp""" )
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""patch_embed.proj""" , """patch_embed.projection""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE : Optional[int] = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE : Optional[int] = """layernorm.bias"""
if "conv_first" in name:
SCREAMING_SNAKE_CASE : int = name.replace("""conv_first""" , """first_convolution""" )
if (
"upsample" in name
or "conv_before_upsample" in name
or "conv_bicubic" in name
or "conv_up" in name
or "conv_hr" in name
or "conv_last" in name
or "aux" in name
):
# heads
if "conv_last" in name:
SCREAMING_SNAKE_CASE : Dict = name.replace("""conv_last""" , """final_convolution""" )
if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
if "conv_before_upsample.0" in name:
SCREAMING_SNAKE_CASE : Dict = name.replace("""conv_before_upsample.0""" , """conv_before_upsample""" )
if "upsample.0" in name:
SCREAMING_SNAKE_CASE : Any = name.replace("""upsample.0""" , """upsample.convolution_0""" )
if "upsample.2" in name:
SCREAMING_SNAKE_CASE : Dict = name.replace("""upsample.2""" , """upsample.convolution_1""" )
SCREAMING_SNAKE_CASE : List[Any] = """upsample.""" + name
elif config.upsampler == "pixelshuffledirect":
SCREAMING_SNAKE_CASE : Optional[int] = name.replace("""upsample.0.weight""" , """upsample.conv.weight""" )
SCREAMING_SNAKE_CASE : Optional[Any] = name.replace("""upsample.0.bias""" , """upsample.conv.bias""" )
else:
pass
else:
SCREAMING_SNAKE_CASE : int = """swin2sr.""" + name
return name
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE : List[str] = orig_state_dict.pop(lowerCamelCase_ )
if "qkv" in key:
SCREAMING_SNAKE_CASE : Any = key.split(""".""" )
SCREAMING_SNAKE_CASE : Optional[int] = int(key_split[1] )
SCREAMING_SNAKE_CASE : Any = int(key_split[4] )
SCREAMING_SNAKE_CASE : Dict = config.embed_dim
if "weight" in key:
SCREAMING_SNAKE_CASE : List[Any] = val[:dim, :]
SCREAMING_SNAKE_CASE : int = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE : Tuple = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE : List[Any] = val[:dim]
SCREAMING_SNAKE_CASE : Any = val[dim : dim * 2]
SCREAMING_SNAKE_CASE : Any = val[-dim:]
pass
else:
SCREAMING_SNAKE_CASE : Optional[int] = val
return orig_state_dict
def __A ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = get_config(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = SwinaSRForImageSuperResolution(lowerCamelCase_ )
model.eval()
SCREAMING_SNAKE_CASE : Any = torch.hub.load_state_dict_from_url(lowerCamelCase_ , map_location="""cpu""" )
SCREAMING_SNAKE_CASE : Tuple = convert_state_dict(lowerCamelCase_ , lowerCamelCase_ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = model.load_state_dict(lowerCamelCase_ , strict=lowerCamelCase_ )
if len(lowerCamelCase_ ) > 0:
raise ValueError("""Missing keys when converting: {}""".format(lowerCamelCase_ ) )
for key in unexpected_keys:
if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
raise ValueError(f'''Unexpected key {key} in state_dict''' )
# verify values
SCREAMING_SNAKE_CASE : Optional[Any] = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
SCREAMING_SNAKE_CASE : Optional[Any] = Image.open(requests.get(lowerCamelCase_ , stream=lowerCamelCase_ ).raw ).convert("""RGB""" )
SCREAMING_SNAKE_CASE : Dict = SwinaSRImageProcessor()
# pixel_values = processor(image, return_tensors="pt").pixel_values
SCREAMING_SNAKE_CASE : str = 1_26 if """Jpeg""" in checkpoint_url else 2_56
SCREAMING_SNAKE_CASE : Union[str, Any] = Compose(
[
Resize((image_size, image_size) ),
ToTensor(),
Normalize(mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] ),
] )
SCREAMING_SNAKE_CASE : List[str] = transforms(lowerCamelCase_ ).unsqueeze(0 )
if config.num_channels == 1:
SCREAMING_SNAKE_CASE : List[str] = pixel_values[:, 0, :, :].unsqueeze(1 )
SCREAMING_SNAKE_CASE : List[Any] = model(lowerCamelCase_ )
# assert values
if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE : Tuple = torch.Size([1, 3, 5_12, 5_12] )
SCREAMING_SNAKE_CASE : int = torch.tensor(
[[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]] )
elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
SCREAMING_SNAKE_CASE : Tuple = torch.Size([1, 3, 10_24, 10_24] )
SCREAMING_SNAKE_CASE : List[str] = torch.tensor(
[[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]] )
elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
# TODO values didn't match exactly here
SCREAMING_SNAKE_CASE : List[str] = torch.Size([1, 3, 10_24, 10_24] )
SCREAMING_SNAKE_CASE : int = torch.tensor(
[[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]] )
elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
SCREAMING_SNAKE_CASE : List[str] = torch.Size([1, 3, 5_12, 5_12] )
SCREAMING_SNAKE_CASE : Dict = torch.tensor(
[[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]] )
elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
SCREAMING_SNAKE_CASE : Dict = torch.Size([1, 3, 10_24, 10_24] )
SCREAMING_SNAKE_CASE : int = torch.tensor(
[[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]] )
assert (
outputs.reconstruction.shape == expected_shape
), f'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
assert torch.allclose(outputs.reconstruction[0, 0, :3, :3] , lowerCamelCase_ , atol=1E-3 )
print("""Looks ok!""" )
SCREAMING_SNAKE_CASE : Any = {
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
"""swin2SR-classical-sr-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
"""swin2SR-classical-sr-x4-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
"""swin2SR-compressed-sr-x4-48"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
"""swin2SR-lightweight-x2-64"""
),
"""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
"""swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
),
}
SCREAMING_SNAKE_CASE : Tuple = url_to_name[checkpoint_url]
if pytorch_dump_folder_path is not None:
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowerCamelCase_ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(lowerCamelCase_ )
if push_to_hub:
model.push_to_hub(f'''caidas/{model_name}''' )
processor.push_to_hub(f'''caidas/{model_name}''' )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""",
type=str,
help="""URL of the original Swin2SR checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the converted model to the hub.""")
__UpperCAmelCase = parser.parse_args()
convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 323
|
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""google/efficientnet-b7""": """https://huggingface.co/google/efficientnet-b7/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''efficientnet'''
def __init__( self : Tuple , lowerCamelCase_ : int = 3 , lowerCamelCase_ : int = 6_00 , lowerCamelCase_ : float = 2.0 , lowerCamelCase_ : float = 3.1 , lowerCamelCase_ : int = 8 , lowerCamelCase_ : List[int] = [3, 3, 5, 3, 5, 5, 3] , lowerCamelCase_ : List[int] = [32, 16, 24, 40, 80, 1_12, 1_92] , lowerCamelCase_ : List[int] = [16, 24, 40, 80, 1_12, 1_92, 3_20] , lowerCamelCase_ : List[int] = [] , lowerCamelCase_ : List[int] = [1, 2, 2, 2, 1, 2, 1] , lowerCamelCase_ : List[int] = [1, 2, 2, 3, 3, 4, 1] , lowerCamelCase_ : List[int] = [1, 6, 6, 6, 6, 6, 6] , lowerCamelCase_ : float = 0.25 , lowerCamelCase_ : str = "swish" , lowerCamelCase_ : int = 25_60 , lowerCamelCase_ : str = "mean" , lowerCamelCase_ : float = 0.02 , lowerCamelCase_ : float = 0.001 , lowerCamelCase_ : float = 0.99 , lowerCamelCase_ : float = 0.5 , lowerCamelCase_ : float = 0.2 , **lowerCamelCase_ : int , ):
'''simple docstring'''
super().__init__(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE : int = image_size
SCREAMING_SNAKE_CASE : int = width_coefficient
SCREAMING_SNAKE_CASE : List[str] = depth_coefficient
SCREAMING_SNAKE_CASE : Optional[Any] = depth_divisor
SCREAMING_SNAKE_CASE : List[str] = kernel_sizes
SCREAMING_SNAKE_CASE : Dict = in_channels
SCREAMING_SNAKE_CASE : List[str] = out_channels
SCREAMING_SNAKE_CASE : Any = depthwise_padding
SCREAMING_SNAKE_CASE : Dict = strides
SCREAMING_SNAKE_CASE : Optional[Any] = num_block_repeats
SCREAMING_SNAKE_CASE : Any = expand_ratios
SCREAMING_SNAKE_CASE : Union[str, Any] = squeeze_expansion_ratio
SCREAMING_SNAKE_CASE : List[str] = hidden_act
SCREAMING_SNAKE_CASE : Dict = hidden_dim
SCREAMING_SNAKE_CASE : List[str] = pooling_type
SCREAMING_SNAKE_CASE : List[Any] = initializer_range
SCREAMING_SNAKE_CASE : Any = batch_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = batch_norm_momentum
SCREAMING_SNAKE_CASE : Dict = dropout_rate
SCREAMING_SNAKE_CASE : int = drop_connect_rate
SCREAMING_SNAKE_CASE : Optional[Any] = sum(lowerCamelCase_ ) * 4
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = version.parse('''1.11''' )
@property
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCamelCase_ ( self : Dict ):
'''simple docstring'''
return 1e-5
| 323
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
A_ : str = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Union[str, Any] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
A_ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 368
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] = logging.get_logger(__name__)
A_ : Union[str, Any] = {
"""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 lowercase ( _lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = """roformer"""
def __init__( self ,a_=50_000 ,a_=None ,a_=768 ,a_=12 ,a_=12 ,a_=3_072 ,a_="gelu" ,a_=0.1 ,a_=0.1 ,a_=1_536 ,a_=2 ,a_=0.02 ,a_=1E-1_2 ,a_=0 ,a_=False ,a_=True ,**a_ ,) -> Tuple:
super().__init__(pad_token_id=a_ ,**a_ )
_UpperCAmelCase : List[Any] = vocab_size
_UpperCAmelCase : str = hidden_size if embedding_size is None else embedding_size
_UpperCAmelCase : List[Any] = hidden_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : Any = type_vocab_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[int] = rotary_value
_UpperCAmelCase : Any = use_cache
class lowercase ( _lowerCamelCase ):
"""simple docstring"""
@property
def _snake_case ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCAmelCase : Optional[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
_UpperCAmelCase : List[Any] = {0: """batch""", 1: """sequence"""}
_UpperCAmelCase : Tuple = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
("""token_type_ids""", dynamic_axis),
] )
| 349
| 0
|
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : List[Any] ) -> str:
'''simple docstring'''
_UpperCAmelCase = R'\w+[.]\d+'
_UpperCAmelCase = re.findall(snake_case__ , snake_case__ )
for pat in pats:
_UpperCAmelCase = key.replace(snake_case__ , '_'.join(pat.split('.' ) ) )
return key
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any ) -> Tuple:
'''simple docstring'''
_UpperCAmelCase = pt_tuple_key[:-1] + ('scale',)
if (
any('norm' in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
_UpperCAmelCase = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
_UpperCAmelCase = pt_tuple_key[:-1] + ('scale',)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
_UpperCAmelCase = pt_tuple_key[:-1] + ('embedding',)
return renamed_pt_tuple_key, pt_tensor
# conv layer
_UpperCAmelCase = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
_UpperCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_UpperCAmelCase = pt_tuple_key[:-1] + ('kernel',)
if pt_tuple_key[-1] == "weight":
_UpperCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_UpperCAmelCase = pt_tuple_key[:-1] + ('weight',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_UpperCAmelCase = pt_tuple_key[:-1] + ('bias',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int] , _UpperCAmelCase : Optional[int]=42 ) -> Union[str, Any]:
'''simple docstring'''
# Step 1: Convert pytorch tensor to numpy
_UpperCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
_UpperCAmelCase = flax_model.init_weights(PRNGKey(snake_case__ ) )
_UpperCAmelCase = flatten_dict(snake_case__ )
_UpperCAmelCase = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_UpperCAmelCase = rename_key(snake_case__ )
_UpperCAmelCase = tuple(renamed_pt_key.split('.' ) )
# Correctly rename weight parameters
_UpperCAmelCase , _UpperCAmelCase = rename_key_and_reshape_tensor(snake_case__ , snake_case__ , snake_case__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# also add unexpected weight so that warning is thrown
_UpperCAmelCase = jnp.asarray(snake_case__ )
return unflatten_dict(snake_case__ )
| 339
|
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__="attention" ) -> List[Any]:
lowerCamelCase = lowerCamelCase = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :] )
lowerCamelCase = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
lowerCamelCase = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :] )
lowerCamelCase = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
lowerCamelCase = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :] )
lowerCamelCase = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
lowerCamelCase = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :] )
lowerCamelCase = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__=False ) -> List[str]:
if split_mlp_wi:
lowerCamelCase = params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
lowerCamelCase = params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
lowerCamelCase = (wi_a, wi_a)
else:
lowerCamelCase = params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]
lowerCamelCase = params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
return wi, wo
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Tuple:
return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]
def a__ ( snake_case__ , *, snake_case__ , snake_case__ , snake_case__ = False ) -> Dict:
lowerCamelCase = traverse_util.flatten_dict(variables["""target"""] )
lowerCamelCase = {"""/""".join(snake_case__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCamelCase = """encoder/encoder/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , snake_case__ )
lowerCamelCase = collections.OrderedDict()
# Shared embeddings.
lowerCamelCase = old["""token_embedder/embedding"""]
# Encoder.
for i in range(snake_case__ ):
# Block i, layer 0 (Self Attention).
lowerCamelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = tax_attention_lookup(snake_case__ , snake_case__ , """encoder""" , """attention""" )
lowerCamelCase = layer_norm
lowerCamelCase = k.T
lowerCamelCase = o.T
lowerCamelCase = q.T
lowerCamelCase = v.T
# Block i, layer 1 (MLP).
lowerCamelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCamelCase , lowerCamelCase = tax_mlp_lookup(snake_case__ , snake_case__ , """encoder""" , snake_case__ )
lowerCamelCase = layer_norm
if split_mlp_wi:
lowerCamelCase = wi[0].T
lowerCamelCase = wi[1].T
else:
lowerCamelCase = wi.T
lowerCamelCase = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
lowerCamelCase = tax_relpos_bias_lookup(
snake_case__ , snake_case__ , """encoder""" ).T
lowerCamelCase = old["""encoder/encoder_norm/scale"""]
if not scalable_attention:
lowerCamelCase = tax_relpos_bias_lookup(
snake_case__ , 0 , """encoder""" ).T
lowerCamelCase = tax_relpos_bias_lookup(
snake_case__ , 0 , """decoder""" ).T
if not is_encoder_only:
# Decoder.
for i in range(snake_case__ ):
# Block i, layer 0 (Self Attention).
lowerCamelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = tax_attention_lookup(snake_case__ , snake_case__ , """decoder""" , """self_attention""" )
lowerCamelCase = layer_norm
lowerCamelCase = k.T
lowerCamelCase = o.T
lowerCamelCase = q.T
lowerCamelCase = v.T
# Block i, layer 1 (Cross Attention).
lowerCamelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = tax_attention_lookup(snake_case__ , snake_case__ , """decoder""" , """encoder_decoder_attention""" )
lowerCamelCase = layer_norm
lowerCamelCase = k.T
lowerCamelCase = o.T
lowerCamelCase = q.T
lowerCamelCase = v.T
# Block i, layer 2 (MLP).
lowerCamelCase = tax_layer_norm_lookup(snake_case__ , snake_case__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCamelCase , lowerCamelCase = tax_mlp_lookup(snake_case__ , snake_case__ , """decoder""" , snake_case__ )
lowerCamelCase = layer_norm
if split_mlp_wi:
lowerCamelCase = wi[0].T
lowerCamelCase = wi[1].T
else:
lowerCamelCase = wi.T
lowerCamelCase = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
lowerCamelCase = tax_relpos_bias_lookup(snake_case__ , snake_case__ , """decoder""" ).T
lowerCamelCase = old["""decoder/decoder_norm/scale"""]
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCamelCase = old["""decoder/logits_dense/kernel"""].T
return new
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCamelCase = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCamelCase = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCamelCase = state_dict["""shared.weight"""]
return state_dict
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
lowerCamelCase = checkpoints.load_tax_checkpoint(snake_case__ )
lowerCamelCase = convert_tax_to_pytorch(
snake_case__ , num_layers=config.num_layers , is_encoder_only=snake_case__ , scalable_attention=snake_case__ )
lowerCamelCase = make_state_dict(snake_case__ , snake_case__ )
model.load_state_dict(snake_case__ , strict=snake_case__ )
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False , snake_case__ = False , ) -> str:
lowerCamelCase = MTaConfig.from_json_file(snake_case__ )
print(F'Building PyTorch model from configuration: {config}' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCamelCase = UMTaEncoderModel(snake_case__ )
else:
lowerCamelCase = UMTaForConditionalGeneration(snake_case__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(snake_case__ )
# Verify that we can load the checkpoint.
model.from_pretrained(snake_case__ )
print("""Done""" )
if __name__ == "__main__":
lowerCAmelCase : Optional[int] = argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
# Required parameters
parser.add_argument(
"""--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
)
parser.add_argument(
"""--config_file""",
default=None,
type=str,
required=True,
help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
)
parser.add_argument(
"""--scalable_attention""",
action="""store_true""",
help="""Whether the model uses scaled attention (umt5 model)""",
default=False,
)
lowerCAmelCase : int = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 291
| 0
|
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
__UpperCAmelCase : int = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 48000,
"sample_size": 65536,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 48000,
"sample_size": 65536,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 48000,
"sample_size": 131072,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 16000,
"sample_size": 65536,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 16000,
"sample_size": 65536,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 16000,
"sample_size": 65536,
},
}
def a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Optional[Any] ):
"""simple docstring"""
return torch.atana(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) / math.pi * 2
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : str = torch.sin(t * math.pi / 2 ) ** 2
UpperCamelCase : List[str] = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
class UpperCAmelCase_ ( _a):
'''simple docstring'''
pass
class UpperCAmelCase_ ( nn.Module):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
super().__init__()
UpperCamelCase : Optional[int] = DiffusionAttnUnetaD(__SCREAMING_SNAKE_CASE , n_attn_layers=4 )
UpperCamelCase : Union[str, Any] = deepcopy(self.diffusion )
UpperCamelCase : Union[str, Any] = torch.quasirandom.SobolEngine(1 , scramble=__SCREAMING_SNAKE_CASE )
def a ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
UpperCamelCase : str = MODELS_MAP[model_name]['''url''']
os.system(F"""wget {url} ./""" )
return F"""./{model_name}.ckpt"""
__UpperCAmelCase : Optional[int] = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
__UpperCAmelCase : int = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
__UpperCAmelCase : Tuple = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
__UpperCAmelCase : List[str] = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
__UpperCAmelCase : str = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
__UpperCAmelCase : Tuple = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def a ( SCREAMING_SNAKE_CASE_ : Optional[Any] ):
"""simple docstring"""
if name.startswith('''skip''' ):
return name.replace('''skip''' , RES_CONV_MAP['''skip'''] )
# name has to be of format main.{digit}
if not name.startswith('''main.''' ):
raise ValueError(F"""ResConvBlock error with {name}""" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def a ( SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
for key, value in ATTN_MAP.items():
if name.startswith(SCREAMING_SNAKE_CASE_ ) and not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
elif name.startswith(SCREAMING_SNAKE_CASE_ ):
return [name.replace(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for v in value]
raise ValueError(F"""Attn error with {name}""" )
def a ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str]=1_3 ):
"""simple docstring"""
UpperCamelCase : Optional[int] = input_string
if string.split('''.''' )[0] == "timestep_embed":
return string.replace('''timestep_embed''' , '''time_proj''' )
UpperCamelCase : Any = 0
if string.startswith('''net.3.''' ):
depth += 1
UpperCamelCase : int = string[6:]
elif string.startswith('''net.''' ):
UpperCamelCase : str = string[4:]
while string.startswith('''main.7.''' ):
depth += 1
UpperCamelCase : str = string[7:]
if string.startswith('''main.''' ):
UpperCamelCase : str = string[5:]
# mid block
if string[:2].isdigit():
UpperCamelCase : Optional[int] = string[:2]
UpperCamelCase : List[str] = string[2:]
else:
UpperCamelCase : List[str] = string[0]
UpperCamelCase : List[str] = string[1:]
if depth == max_depth:
UpperCamelCase : Optional[Any] = MID_NUM_TO_LAYER[layer_num]
UpperCamelCase : str = '''mid_block'''
elif depth > 0 and int(SCREAMING_SNAKE_CASE_ ) < 7:
UpperCamelCase : List[Any] = DOWN_NUM_TO_LAYER[layer_num]
UpperCamelCase : List[str] = F"""down_blocks.{depth}"""
elif depth > 0 and int(SCREAMING_SNAKE_CASE_ ) > 7:
UpperCamelCase : Dict = UP_NUM_TO_LAYER[layer_num]
UpperCamelCase : List[str] = F"""up_blocks.{max_depth - depth - 1}"""
elif depth == 0:
UpperCamelCase : str = DEPTH_0_TO_LAYER[layer_num]
UpperCamelCase : Any = F"""up_blocks.{max_depth - 1}""" if int(SCREAMING_SNAKE_CASE_ ) > 3 else '''down_blocks.0'''
if not string_left.startswith('''.''' ):
raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}.""" )
UpperCamelCase : Optional[Any] = string_left[1:]
if "resnets" in new_layer:
UpperCamelCase : Tuple = convert_resconv_naming(SCREAMING_SNAKE_CASE_ )
elif "attentions" in new_layer:
UpperCamelCase : Tuple = convert_attn_naming(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = new_string_left
if not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = prefix + '''.''' + new_layer + '''.''' + string_left
else:
UpperCamelCase : Tuple = [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
return new_string
def a ( SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
UpperCamelCase : Dict = {}
for k, v in state_dict.items():
if k.endswith('''kernel''' ):
# up- and downsample layers, don't have trainable weights
continue
UpperCamelCase : Tuple = rename(SCREAMING_SNAKE_CASE_ )
# check if we need to transform from Conv => Linear for attention
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : str = transform_conv_attns(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : int = v
return new_state_dict
def a ( SCREAMING_SNAKE_CASE_ : List[str] , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : Dict ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE_ ) == 1:
if len(v.shape ) == 3:
# weight
UpperCamelCase : Optional[Any] = v[:, :, 0]
else:
# bias
UpperCamelCase : Tuple = v
else:
# qkv matrices
UpperCamelCase : List[str] = v.shape[0]
UpperCamelCase : Optional[Any] = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
UpperCamelCase : Tuple = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
UpperCamelCase : Tuple = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def a ( SCREAMING_SNAKE_CASE_ : Any ):
"""simple docstring"""
UpperCamelCase : Tuple = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
UpperCamelCase : Any = args.model_path.split('''/''' )[-1].split('''.''' )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
UpperCamelCase : Optional[int] = download(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = MODELS_MAP[model_name]['''sample_rate''']
UpperCamelCase : List[str] = MODELS_MAP[model_name]['''sample_size''']
UpperCamelCase : List[Any] = Object()
UpperCamelCase : Optional[int] = sample_size
UpperCamelCase : Optional[Any] = sample_rate
UpperCamelCase : Optional[int] = 0
UpperCamelCase : List[Any] = UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_ , sample_rate=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Union[str, Any] = diffusers_model.state_dict()
UpperCamelCase : Union[str, Any] = DiffusionUncond(SCREAMING_SNAKE_CASE_ )
orig_model.load_state_dict(torch.load(args.model_path , map_location=SCREAMING_SNAKE_CASE_ )['''state_dict'''] )
UpperCamelCase : Optional[int] = orig_model.diffusion_ema.eval()
UpperCamelCase : Any = orig_model.state_dict()
UpperCamelCase : Any = rename_orig_weights(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
UpperCamelCase : Tuple = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(SCREAMING_SNAKE_CASE_ ) == 0, F"""Problem with {renamed_minus_diffusers}"""
assert all(k.endswith('''kernel''' ) for k in list(SCREAMING_SNAKE_CASE_ ) ), F"""Problem with {diffusers_minus_renamed}"""
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
if key == "time_proj.weight":
UpperCamelCase : Union[str, Any] = value.squeeze()
UpperCamelCase : Optional[Any] = value
diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = 1_0_0
UpperCamelCase : Dict = 3_3
UpperCamelCase : Optional[int] = IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : List[str] = torch.manual_seed(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = torch.randn([1, 2, config.sample_size] , generator=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Dict = torch.linspace(1 , 0 , steps + 1 , device=SCREAMING_SNAKE_CASE_ )[:-1]
UpperCamelCase : str = get_crash_schedule(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : int = DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_ , scheduler=SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Any = torch.manual_seed(3_3 )
UpperCamelCase : Optional[Any] = pipe(num_inference_steps=SCREAMING_SNAKE_CASE_ , generator=SCREAMING_SNAKE_CASE_ ).audios
UpperCamelCase : Optional[Any] = sampling.iplms_sample(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , {} )
UpperCamelCase : str = generated.clamp(-1 , 1 )
UpperCamelCase : str = (generated - audio).abs().sum()
UpperCamelCase : int = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print('''Diff sum''' , SCREAMING_SNAKE_CASE_ )
print('''Diff max''' , SCREAMING_SNAKE_CASE_ )
assert diff_max < 1E-3, F"""Diff max: {diff_max} is too much :-/"""
print(F"""Conversion for {model_name} successful!""" )
if __name__ == "__main__":
__UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
parser.add_argument(
"--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
)
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
__UpperCAmelCase : int = parser.parse_args()
main(args)
| 315
|
import collections
import os
import re
from pathlib import Path
__UpperCAmelCase : List[str] = "src/transformers"
# Matches is_xxx_available()
__UpperCAmelCase : int = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
__UpperCAmelCase : Optional[int] = re.compile(r"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__UpperCAmelCase : List[Any] = re.compile(r"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
__UpperCAmelCase : List[Any] = re.compile(r"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
__UpperCAmelCase : str = re.compile(r"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__UpperCAmelCase : Union[str, Any] = re.compile(r"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma: "MyModel",
__UpperCAmelCase : Dict = re.compile(r"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only: ["foo", "bar"],
__UpperCAmelCase : str = re.compile(r"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
__UpperCAmelCase : str = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
__UpperCAmelCase : Any = re.compile(r"^\s*try:")
# Catches a line with else:
__UpperCAmelCase : List[Any] = re.compile(r"^\s*else:")
def a ( SCREAMING_SNAKE_CASE_ : Dict ):
"""simple docstring"""
if _re_test_backend.search(SCREAMING_SNAKE_CASE_ ) is None:
return None
UpperCamelCase : Union[str, Any] = [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE_ )]
backends.sort()
return "_and_".join(SCREAMING_SNAKE_CASE_ )
def a ( SCREAMING_SNAKE_CASE_ : Tuple ):
"""simple docstring"""
with open(SCREAMING_SNAKE_CASE_ , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCamelCase : Tuple = f.readlines()
UpperCamelCase : Tuple = 0
while line_index < len(SCREAMING_SNAKE_CASE_ ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(SCREAMING_SNAKE_CASE_ ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCamelCase : List[Any] = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
UpperCamelCase : Optional[int] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Optional[int] = _re_one_line_import_struct.search(SCREAMING_SNAKE_CASE_ ).groups()[0]
UpperCamelCase : str = re.findall(R'''\[([^\]]+)\]''' , SCREAMING_SNAKE_CASE_ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
UpperCamelCase : List[Any] = _re_import_struct_key_value.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
UpperCamelCase : List[str] = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
UpperCamelCase : Dict = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
UpperCamelCase : Dict = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCamelCase : Optional[Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCamelCase : str = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
UpperCamelCase : str = lines[line_index]
if _re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_import_struct_add_one.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ) is not None:
UpperCamelCase : Union[str, Any] = _re_import_struct_add_many.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(''', ''' )
UpperCamelCase : List[Any] = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ) is not None:
UpperCamelCase : str = _re_between_brackets.search(SCREAMING_SNAKE_CASE_ ).groups()[0].split(''', ''' )
UpperCamelCase : Dict = [obj[1:-1] for obj in imports if len(SCREAMING_SNAKE_CASE_ ) > 0]
objects.extend(SCREAMING_SNAKE_CASE_ )
elif _re_quote_object.search(SCREAMING_SNAKE_CASE_ ) is not None:
objects.append(_re_quote_object.search(SCREAMING_SNAKE_CASE_ ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 1_2 + '''"''' ):
objects.append(line[1_3:-3] )
line_index += 1
UpperCamelCase : Tuple = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCamelCase : int = []
while (
line_index < len(SCREAMING_SNAKE_CASE_ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
UpperCamelCase : Tuple = lines[line_index]
UpperCamelCase : Any = _re_import.search(SCREAMING_SNAKE_CASE_ )
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
UpperCamelCase : Any = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(SCREAMING_SNAKE_CASE_ ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCamelCase : Optional[Any] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCamelCase : Dict = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCamelCase : Dict = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
UpperCamelCase : Optional[Any] = lines[line_index]
UpperCamelCase : str = _re_import.search(SCREAMING_SNAKE_CASE_ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 1_2 ):
objects.append(line[1_2:-2] )
line_index += 1
UpperCamelCase : str = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def a ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ):
"""simple docstring"""
def find_duplicates(SCREAMING_SNAKE_CASE_ : Any ):
return [k for k, v in collections.Counter(SCREAMING_SNAKE_CASE_ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
UpperCamelCase : Dict = []
for key in import_dict_objects.keys():
UpperCamelCase : Union[str, Any] = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}""" )
UpperCamelCase : Dict = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}""" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
UpperCamelCase : List[str] = '''base imports''' if key == '''none''' else F"""{key} backend"""
errors.append(F"""Differences for {name}:""" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F""" {a} in TYPE_HINT but not in _import_structure.""" )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F""" {a} in _import_structure but not in TYPE_HINT.""" )
return errors
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = []
for root, _, files in os.walk(SCREAMING_SNAKE_CASE_ ):
if "__init__.py" in files:
UpperCamelCase : int = os.path.join(SCREAMING_SNAKE_CASE_ , '''__init__.py''' )
UpperCamelCase : Optional[int] = parse_init(SCREAMING_SNAKE_CASE_ )
if objects is not None:
UpperCamelCase : str = analyze_results(*SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : List[Any] = F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
failures.append('''\n'''.join(SCREAMING_SNAKE_CASE_ ) )
if len(SCREAMING_SNAKE_CASE_ ) > 0:
raise ValueError('''\n\n'''.join(SCREAMING_SNAKE_CASE_ ) )
def a ( ):
"""simple docstring"""
UpperCamelCase : Dict = []
for path, directories, files in os.walk(SCREAMING_SNAKE_CASE_ ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(SCREAMING_SNAKE_CASE_ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(SCREAMING_SNAKE_CASE_ ) / folder).glob('''*.py''' ) ) ) == 0:
continue
UpperCamelCase : List[str] = str((Path(SCREAMING_SNAKE_CASE_ ) / folder).relative_to(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : str = short_path.replace(os.path.sep , '''.''' )
submodules.append(SCREAMING_SNAKE_CASE_ )
for fname in files:
if fname == "__init__.py":
continue
UpperCamelCase : Tuple = str((Path(SCREAMING_SNAKE_CASE_ ) / fname).relative_to(SCREAMING_SNAKE_CASE_ ) )
UpperCamelCase : int = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(SCREAMING_SNAKE_CASE_ )
return submodules
__UpperCAmelCase : Optional[int] = [
"convert_pytorch_checkpoint_to_tf2",
"modeling_flax_pytorch_utils",
"models.esm.openfold_utils",
]
def a ( ):
"""simple docstring"""
from transformers.utils import direct_transformers_import
UpperCamelCase : Tuple = direct_transformers_import(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(SCREAMING_SNAKE_CASE_ , '''__init__.py''' ) , '''r''' ) as f:
UpperCamelCase : List[Any] = f.read()
import_structure_keys.update(set(re.findall(R'''import_structure\[\"([^\"]*)\"\]''' , SCREAMING_SNAKE_CASE_ ) ) )
UpperCamelCase : Union[str, Any] = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(SCREAMING_SNAKE_CASE_ ) > 0:
UpperCamelCase : str = '''\n'''.join(F"""- {module}""" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed in the main init of Transformers:\n'''
F"""{list_of_modules}\n"""
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 315
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
# Initialise PyTorch model
snake_case_ = MobileBertConfig.from_json_file(A_ )
print(f'Building PyTorch model from configuration: {config}' )
snake_case_ = MobileBertForPreTraining(A_ )
# Load weights from tf checkpoint
snake_case_ = load_tf_weights_in_mobilebert(A_ , A_ , A_ )
# Save pytorch-model
print(f'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , A_ )
if __name__ == "__main__":
__UpperCamelCase = 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(
'''--mobilebert_config_file''',
default=None,
type=str,
required=True,
help=(
'''The config json file corresponding to the pre-trained MobileBERT 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.'''
)
__UpperCamelCase = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 69
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Any = logging.get_logger(__name__)
A__: List[str] = {
'''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class _a ( UpperCamelCase__):
"""simple docstring"""
UpperCamelCase__ = """pegasus"""
UpperCamelCase__ = ["""past_key_values"""]
UpperCamelCase__ = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self: List[str] , __lowerCamelCase: Dict=5_0265 , __lowerCamelCase: int=1024 , __lowerCamelCase: Dict=12 , __lowerCamelCase: Dict=4096 , __lowerCamelCase: str=16 , __lowerCamelCase: List[Any]=12 , __lowerCamelCase: int=4096 , __lowerCamelCase: Optional[Any]=16 , __lowerCamelCase: Union[str, Any]=0.0 , __lowerCamelCase: int=0.0 , __lowerCamelCase: List[str]=True , __lowerCamelCase: Union[str, Any]=True , __lowerCamelCase: List[Any]="gelu" , __lowerCamelCase: List[Any]=1024 , __lowerCamelCase: int=0.1 , __lowerCamelCase: Union[str, Any]=0.0 , __lowerCamelCase: Union[str, Any]=0.0 , __lowerCamelCase: Any=0.02 , __lowerCamelCase: Union[str, Any]=0 , __lowerCamelCase: str=False , __lowerCamelCase: Optional[int]=0 , __lowerCamelCase: Optional[Any]=1 , __lowerCamelCase: Optional[int]=1 , **__lowerCamelCase: Union[str, Any] , ):
'''simple docstring'''
UpperCamelCase__: Union[str, Any] = vocab_size
UpperCamelCase__: List[Any] = max_position_embeddings
UpperCamelCase__: Tuple = d_model
UpperCamelCase__: str = encoder_ffn_dim
UpperCamelCase__: Optional[int] = encoder_layers
UpperCamelCase__: List[Any] = encoder_attention_heads
UpperCamelCase__: Tuple = decoder_ffn_dim
UpperCamelCase__: int = decoder_layers
UpperCamelCase__: List[str] = decoder_attention_heads
UpperCamelCase__: int = dropout
UpperCamelCase__: List[str] = attention_dropout
UpperCamelCase__: Tuple = activation_dropout
UpperCamelCase__: Optional[int] = activation_function
UpperCamelCase__: Dict = init_std
UpperCamelCase__: Optional[Any] = encoder_layerdrop
UpperCamelCase__: Any = decoder_layerdrop
UpperCamelCase__: Optional[int] = use_cache
UpperCamelCase__: Optional[int] = encoder_layers
UpperCamelCase__: List[Any] = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , is_encoder_decoder=__lowerCamelCase , decoder_start_token_id=__lowerCamelCase , forced_eos_token_id=__lowerCamelCase , **__lowerCamelCase , )
@property
def UpperCAmelCase_ ( self: Any ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase_ ( self: Tuple ):
'''simple docstring'''
return self.d_model
| 149
| 0
|
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_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 MobileNetVaForImageClassification, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __magic_name__ ( __UpperCAmelCase ):
def __snake_case ( self : Optional[int] ):
'''simple docstring'''
lowercase :List[str] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(snake_case__ , '''tf_padding''' ) )
self.parent.assertTrue(hasattr(snake_case__ , '''depth_multiplier''' ) )
class __magic_name__ :
def __init__( self : Any , snake_case__ : List[Any] , snake_case__ : Union[str, Any]=1_3 , snake_case__ : List[Any]=3 , snake_case__ : List[Any]=3_2 , snake_case__ : Union[str, Any]=0.25 , snake_case__ : Optional[int]=8 , snake_case__ : Tuple=True , snake_case__ : Dict=1_0_2_4 , snake_case__ : Optional[Any]=3_2 , snake_case__ : Tuple="relu6" , snake_case__ : Union[str, Any]=0.1 , snake_case__ : List[str]=0.02 , snake_case__ : Optional[int]=True , snake_case__ : Optional[Any]=True , snake_case__ : List[str]=1_0 , snake_case__ : Optional[int]=None , ):
'''simple docstring'''
lowercase :int = parent
lowercase :Dict = batch_size
lowercase :int = num_channels
lowercase :Any = image_size
lowercase :int = depth_multiplier
lowercase :List[str] = min_depth
lowercase :Union[str, Any] = tf_padding
lowercase :Tuple = int(last_hidden_size * depth_multiplier )
lowercase :Optional[Any] = output_stride
lowercase :Optional[Any] = hidden_act
lowercase :Any = classifier_dropout_prob
lowercase :Union[str, Any] = use_labels
lowercase :List[Any] = is_training
lowercase :int = num_labels
lowercase :Optional[Any] = initializer_range
lowercase :Tuple = scope
def __snake_case ( self : List[str] ):
'''simple docstring'''
lowercase :List[str] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase :List[Any] = None
lowercase :Dict = None
if self.use_labels:
lowercase :Optional[Any] = ids_tensor([self.batch_size] , self.num_labels )
lowercase :Dict = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase :Union[str, Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def __snake_case ( self : int ):
'''simple docstring'''
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , min_depth=self.min_depth , tf_padding=self.tf_padding , hidden_act=self.hidden_act , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def __snake_case ( self : str , snake_case__ : Optional[Any] , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : Tuple ):
'''simple docstring'''
lowercase :Tuple = MobileNetVaModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase :Optional[int] = model(snake_case__ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __snake_case ( self : Optional[Any] , snake_case__ : Tuple , snake_case__ : List[Any] , snake_case__ : List[Any] , snake_case__ : Any ):
'''simple docstring'''
lowercase :List[str] = self.num_labels
lowercase :Union[str, Any] = MobileNetVaForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase :List[str] = model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __snake_case ( self : List[str] ):
'''simple docstring'''
lowercase :Optional[Any] = self.prepare_config_and_inputs()
lowercase , lowercase , lowercase , lowercase :Optional[Any] = config_and_inputs
lowercase :int = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
__A : Optional[Any] = (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
__A : Dict = (
{"feature-extraction": MobileNetVaModel, "image-classification": MobileNetVaForImageClassification}
if is_torch_available()
else {}
)
__A : Tuple = False
__A : Optional[int] = False
__A : List[Any] = False
__A : Tuple = False
def __snake_case ( self : int ):
'''simple docstring'''
lowercase :List[str] = MobileNetVaModelTester(self )
lowercase :Optional[int] = MobileNetVaConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ )
def __snake_case ( self : Optional[int] ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileNetV1 does not use inputs_embeds''' )
def __snake_case ( self : Optional[int] ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileNetV1 does not support input and output embeddings''' )
def __snake_case ( self : Dict ):
'''simple docstring'''
pass
@unittest.skip(reason='''MobileNetV1 does not output attentions''' )
def __snake_case ( self : int ):
'''simple docstring'''
pass
def __snake_case ( self : Tuple ):
'''simple docstring'''
lowercase , lowercase :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase :str = model_class(snake_case__ )
lowercase :str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase :Tuple = [*signature.parameters.keys()]
lowercase :int = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , snake_case__ )
def __snake_case ( self : Union[str, Any] ):
'''simple docstring'''
lowercase :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def __snake_case ( self : str ):
'''simple docstring'''
def check_hidden_states_output(snake_case__ : Any , snake_case__ : Union[str, Any] , snake_case__ : int ):
lowercase :Tuple = model_class(snake_case__ )
model.to(snake_case__ )
model.eval()
with torch.no_grad():
lowercase :Union[str, Any] = model(**self._prepare_for_class(snake_case__ , snake_case__ ) )
lowercase :List[Any] = outputs.hidden_states
lowercase :Tuple = 2_6
self.assertEqual(len(snake_case__ ) , snake_case__ )
lowercase , lowercase :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase :Tuple = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase :Any = True
check_hidden_states_output(snake_case__ , snake_case__ , snake_case__ )
def __snake_case ( self : List[str] ):
'''simple docstring'''
lowercase :Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
@slow
def __snake_case ( self : Tuple ):
'''simple docstring'''
for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase :Optional[Any] = MobileNetVaModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def lowerCamelCase () -> Tuple:
lowercase :List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
return image
@require_torch
@require_vision
class __magic_name__ ( unittest.TestCase ):
@cached_property
def __snake_case ( self : int ):
'''simple docstring'''
return (
MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v1_1.0_224''' ) if is_vision_available() else None
)
@slow
def __snake_case ( self : Dict ):
'''simple docstring'''
lowercase :Union[str, Any] = MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v1_1.0_224''' ).to(snake_case__ )
lowercase :int = self.default_image_processor
lowercase :Optional[Any] = prepare_img()
lowercase :Optional[int] = image_processor(images=snake_case__ , return_tensors='''pt''' ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowercase :Optional[int] = model(**snake_case__ )
# verify the logits
lowercase :List[str] = torch.Size((1, 1_0_0_1) )
self.assertEqual(outputs.logits.shape , snake_case__ )
lowercase :List[Any] = torch.tensor([-4.17_39, -1.12_33, 3.12_05] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case__ , atol=1e-4 ) )
| 172
|
"""simple docstring"""
import io
import os
import unicodedata
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''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}
UpperCAmelCase = {
'''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
'''vocab_file''': '''vocab.txt''',
}
UpperCAmelCase = {
'''vocab_file''': {
'''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
'''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
},
'''sentencepiece_model_file''': {
'''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
'''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
},
}
UpperCAmelCase = {
'''ernie-m-base''': 514,
'''ernie-m-large''': 514,
}
UpperCAmelCase = {
'''ernie-m-base''': {'''do_lower_case''': False},
'''ernie-m-large''': {'''do_lower_case''': False},
}
class __magic_name__ ( __UpperCAmelCase ):
__A : List[str] = ["input_ids"]
__A : Optional[Any] = VOCAB_FILES_NAMES
__A : str = PRETRAINED_INIT_CONFIGURATION
__A : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A : List[str] = PRETRAINED_VOCAB_FILES_MAP
__A : List[str] = RESOURCE_FILES_NAMES
def __init__( self : Dict , snake_case__ : List[Any] , snake_case__ : List[Any]=None , snake_case__ : int=False , snake_case__ : Optional[int]="utf8" , snake_case__ : List[str]="[UNK]" , snake_case__ : Tuple="[SEP]" , snake_case__ : List[Any]="[PAD]" , snake_case__ : Dict="[CLS]" , snake_case__ : Dict="[MASK]" , snake_case__ : Optional[Dict[str, Any]] = None , **snake_case__ : str , ):
'''simple docstring'''
lowercase :Any = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , vocab_file=snake_case__ , encoding=snake_case__ , sp_model_kwargs=self.sp_model_kwargs , **snake_case__ , )
lowercase :Dict = do_lower_case
lowercase :str = sentencepiece_model_ckpt
lowercase :Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(snake_case__ )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase :Tuple = self.load_vocab(filepath=snake_case__ )
else:
lowercase :str = {self.sp_model.id_to_piece(snake_case__ ): id for id in range(self.sp_model.get_piece_size() )}
lowercase :Any = {v: k for k, v in self.vocab.items()}
def __snake_case ( self : List[str] , snake_case__ : str ):
'''simple docstring'''
if text is None:
return None
lowercase :List[Any] = self.tokenize(snake_case__ )
lowercase , lowercase :List[str] = '''''', []
for i, ch in enumerate(snake_case__ ):
if ch in self.SP_CHAR_MAPPING:
lowercase :Optional[int] = self.SP_CHAR_MAPPING.get(snake_case__ )
else:
lowercase :Optional[int] = unicodedata.normalize('''NFKC''' , snake_case__ )
if self.is_whitespace(snake_case__ ):
continue
normalized_text += ch
char_mapping.extend([i] * len(snake_case__ ) )
lowercase , lowercase , lowercase :int = normalized_text, [], 0
if self.do_lower_case:
lowercase :Any = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase :Tuple = token[1:]
lowercase :List[str] = text[offset:].index(snake_case__ ) + offset
lowercase :Tuple = start + len(snake_case__ )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase :int = end
return token_mapping
@property
def __snake_case ( self : List[Any] ):
'''simple docstring'''
return len(self.vocab )
def __snake_case ( self : Optional[Any] ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self : Optional[int] ):
'''simple docstring'''
lowercase :Any = self.__dict__.copy()
lowercase :Optional[int] = None
return state
def __setstate__( self : Tuple , snake_case__ : Dict ):
'''simple docstring'''
lowercase :Optional[int] = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
lowercase :Dict = {}
lowercase :List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def __snake_case ( self : int , snake_case__ : List[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(snake_case__ , snake_case__ ) for c in text) )
def __snake_case ( self : List[str] , snake_case__ : Optional[int] , snake_case__ : int=False , snake_case__ : Dict=6_4 , snake_case__ : Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get('''enable_sampling''' ) is True:
lowercase :Any = True
if self.sp_model_kwargs.get('''alpha''' ) is not None:
lowercase :Any = self.sp_model_kwargs.get('''alpha''' )
if self.sp_model_kwargs.get('''nbest_size''' ) is not None:
lowercase :Optional[Any] = self.sp_model_kwargs.get('''nbest_size''' )
if not enable_sampling:
lowercase :Any = self.sp_model.EncodeAsPieces(snake_case__ )
else:
lowercase :List[Any] = self.sp_model.SampleEncodeAsPieces(snake_case__ , snake_case__ , snake_case__ )
lowercase :str = []
for pi, piece in enumerate(snake_case__ ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(snake_case__ ) and pi != 0:
new_pieces.append(snake_case__ )
continue
else:
continue
lowercase :int = 0
for i, chunk in enumerate(snake_case__ ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(snake_case__ ) or self.is_punct(snake_case__ ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(snake_case__ )
lowercase :Optional[int] = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase :str = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase :Dict = i
if len(snake_case__ ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def __snake_case ( self : Dict , snake_case__ : str ):
'''simple docstring'''
lowercase :int = ''''''.join(snake_case__ ).replace(snake_case__ , ''' ''' ).strip()
return out_string
def __snake_case ( self : int , snake_case__ : str ):
'''simple docstring'''
lowercase :Tuple = self.convert_ids_to_tokens(snake_case__ )
lowercase :Any = ''''''.join(snake_case__ ).replace(snake_case__ , ''' ''' ).strip()
return out_string
def __snake_case ( self : int , snake_case__ : Union[str, Any] ):
'''simple docstring'''
return self.vocab.get(snake_case__ , self.vocab.get(self.unk_token ) )
def __snake_case ( self : List[Any] , snake_case__ : List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(snake_case__ , self.unk_token )
def __snake_case ( self : Union[str, Any] , snake_case__ : List[Any] , snake_case__ : Any=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase :int = [self.cls_token_id]
lowercase :str = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def __snake_case ( self : Any , snake_case__ : Dict , snake_case__ : str=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def __snake_case ( self : List[Any] , snake_case__ : Optional[int] , snake_case__ : Any=None , snake_case__ : Optional[int]=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(snake_case__ )) + [1, 1] + ([0] * len(snake_case__ )) + [1]
return [1] + ([0] * len(snake_case__ )) + [1]
def __snake_case ( self : List[Any] , snake_case__ : List[int] , snake_case__ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(snake_case__ ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(snake_case__ ) + 1) + [1] * (len(snake_case__ ) + 3)
def __snake_case ( self : List[Any] , snake_case__ : Any ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def __snake_case ( self : List[str] , snake_case__ : Any ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def __snake_case ( self : List[str] , snake_case__ : Union[str, Any] ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def __snake_case ( self : Optional[int] , snake_case__ : List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(snake_case__ ) == 1:
lowercase :str = unicodedata.category(snake_case__ )
if cat == "Zs":
return True
return False
def __snake_case ( self : str , snake_case__ : Any ):
'''simple docstring'''
lowercase :Dict = {}
with io.open(snake_case__ , '''r''' , encoding='''utf-8''' ) as f:
for index, line in enumerate(snake_case__ ):
lowercase :Dict = line.rstrip('''\n''' )
lowercase :str = int(snake_case__ )
return token_to_idx
def __snake_case ( self : Dict , snake_case__ : str , snake_case__ : Optional[str] = None ):
'''simple docstring'''
lowercase :Optional[int] = 0
if os.path.isdir(snake_case__ ):
lowercase :str = os.path.join(
snake_case__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
else:
lowercase :Any = (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
with open(snake_case__ , '''w''' , encoding='''utf-8''' ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda snake_case__ : kv[1] ):
if index != token_index:
logger.warning(
f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
''' Please check that the vocabulary is not corrupted!''' )
lowercase :Optional[int] = token_index
writer.write(token + '''\n''' )
index += 1
lowercase :int = os.path.join(snake_case__ , '''sentencepiece.bpe.model''' )
with open(snake_case__ , '''wb''' ) as fi:
lowercase :Tuple = self.sp_model.serialized_model_proto()
fi.write(snake_case__ )
return (vocab_file,)
| 172
| 1
|
"""simple docstring"""
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
lowerCAmelCase__ = threading.Lock()
lowerCAmelCase__ = None
lowerCAmelCase__ = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
lowerCAmelCase__ = logging.WARNING
lowerCAmelCase__ = True
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Dict = os.getenv("TRANSFORMERS_VERBOSITY" , SCREAMING_SNAKE_CASE )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def a__ ( ):
'''simple docstring'''
return __name__.split("." )[0]
def a__ ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def a__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
lowerCAmelCase : int = logging.StreamHandler() # Set sys.stderr as stream.
lowerCAmelCase : Optional[int] = sys.stderr.flush
# Apply our default configuration to the library root logger.
lowerCAmelCase : Optional[Any] = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
lowerCAmelCase : List[Any] = False
def a__ ( ):
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
lowerCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
lowerCAmelCase : Optional[Any] = None
def a__ ( ):
'''simple docstring'''
return log_levels
def a__ ( SCREAMING_SNAKE_CASE : Optional[str] = None ):
'''simple docstring'''
if name is None:
lowerCAmelCase : List[str] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def a__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
return set_verbosity(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def a__ ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def a__ ( SCREAMING_SNAKE_CASE : logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(SCREAMING_SNAKE_CASE )
def a__ ( SCREAMING_SNAKE_CASE : logging.Handler ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
_configure_library_root_logger()
lowerCAmelCase : Optional[Any] = False
def a__ ( ):
'''simple docstring'''
_configure_library_root_logger()
lowerCAmelCase : List[Any] = True
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Tuple = _get_library_root_logger().handlers
for handler in handlers:
lowerCAmelCase : Optional[Any] = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" )
handler.setFormatter(SCREAMING_SNAKE_CASE )
def a__ ( ):
'''simple docstring'''
lowerCAmelCase : Dict = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(SCREAMING_SNAKE_CASE )
def a__ ( self : int , *SCREAMING_SNAKE_CASE : str , **SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
lowerCAmelCase : Union[str, Any] = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , SCREAMING_SNAKE_CASE )
if no_advisory_warnings:
return
self.warning(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = warning_advice
@functools.lru_cache(SCREAMING_SNAKE_CASE )
def a__ ( self : str , *SCREAMING_SNAKE_CASE : Dict , **SCREAMING_SNAKE_CASE : Optional[int] ):
'''simple docstring'''
self.warning(*SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )
lowerCAmelCase__ = warning_once
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self , *snake_case__ , **snake_case__ ): # pylint: disable=unused-argument
"""simple docstring"""
lowerCAmelCase : Dict = args[0] if args else None
def __iter__( self ):
"""simple docstring"""
return iter(self._iterator )
def __getattr__( self , snake_case__ ):
"""simple docstring"""
def empty_fn(*snake_case__ , **snake_case__ ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ):
"""simple docstring"""
return self
def __exit__( self , snake_case__ , snake_case__ , snake_case__ ):
"""simple docstring"""
return
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __call__( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm(*snake_case__ , **snake_case__ )
else:
return EmptyTqdm(*snake_case__ , **snake_case__ )
def lowercase__ ( self , *snake_case__ , **snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Optional[int] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*snake_case__ , **snake_case__ )
def lowercase__ ( self ):
"""simple docstring"""
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
lowerCAmelCase__ = _tqdm_cls()
def a__ ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def a__ ( ):
'''simple docstring'''
global _tqdm_active
lowerCAmelCase : Optional[Any] = True
hf_hub_utils.enable_progress_bars()
def a__ ( ):
'''simple docstring'''
global _tqdm_active
lowerCAmelCase : Tuple = False
hf_hub_utils.disable_progress_bars()
| 108
|
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_lowerCAmelCase : str = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
def __init__( self , *__snake_case , **__snake_case ) -> None:
'''simple docstring'''
warnings.warn(
'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use CLIPImageProcessor instead.' , __snake_case , )
super().__init__(*__snake_case , **__snake_case )
| 218
| 0
|
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( a ) -> Optional[int]:
# getting number of pixels in the image
_A: Any = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(a ):
for j in range(a ):
_A: int = [2_55, 2_55, 2_55] - img[i][j]
return img
if __name__ == "__main__":
# read original image
UpperCAmelCase__ : Union[str, Any] = imread('image_data/lena.jpg', 1)
# convert to its negative
UpperCAmelCase__ : str = convert_to_negative(img)
# show result image
imshow('negative of original image', img)
waitKey(0)
destroyAllWindows()
| 354
|
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : List[str]=3 , lowerCAmelCase_ : List[str]=3_2 , lowerCAmelCase_ : Union[str, Any]=3 , lowerCAmelCase_ : int=1_0 , lowerCAmelCase_ : Tuple=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : Optional[Any]=[1, 1, 2, 1] , lowerCAmelCase_ : Optional[Any]=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[Any]="relu" , lowerCAmelCase_ : int=3 , lowerCAmelCase_ : List[Any]=None , ):
"""simple docstring"""
_A: str = parent
_A: List[Any] = batch_size
_A: Optional[int] = image_size
_A: Dict = num_channels
_A: str = embeddings_size
_A: Any = hidden_sizes
_A: Dict = depths
_A: Any = is_training
_A: int = use_labels
_A: Tuple = hidden_act
_A: int = num_labels
_A: int = scope
_A: str = len(lowerCAmelCase_ )
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A: Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A: Union[str, Any] = self.get_config()
return config, pixel_values
def __magic_name__ ( self : str ):
"""simple docstring"""
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , image_size=self.image_size , )
def __magic_name__ ( self : str , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : str ):
"""simple docstring"""
_A: str = FlaxRegNetModel(config=lowerCAmelCase_ )
_A: Optional[int] = model(lowerCAmelCase_ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def __magic_name__ ( self : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple ):
"""simple docstring"""
_A: Union[str, Any] = self.num_labels
_A: Union[str, Any] = FlaxRegNetForImageClassification(config=lowerCAmelCase_ )
_A: str = model(lowerCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A: str = self.prepare_config_and_inputs()
_A , _A: Optional[int] = config_and_inputs
_A: Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Tuple = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
__UpperCamelCase : Union[str, Any] = False
__UpperCamelCase : List[Any] = False
__UpperCamelCase : int = False
def __magic_name__ ( self : int ):
"""simple docstring"""
_A: int = FlaxRegNetModelTester(self )
_A: Union[str, Any] = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ )
def __magic_name__ ( self : str ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def __magic_name__ ( self : int ):
"""simple docstring"""
return
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
_A: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase_ )
def __magic_name__ ( self : Tuple ):
"""simple docstring"""
_A: Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_ )
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def __magic_name__ ( self : str ):
"""simple docstring"""
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def __magic_name__ ( self : Optional[int] ):
"""simple docstring"""
pass
def __magic_name__ ( self : List[Any] ):
"""simple docstring"""
_A , _A: int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A: Union[str, Any] = model_class(lowerCAmelCase_ )
_A: Any = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A: Any = [*signature.parameters.keys()]
_A: Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def __magic_name__ ( self : str ):
"""simple docstring"""
def check_hidden_states_output(lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase_ : Tuple ):
_A: int = model_class(lowerCAmelCase_ )
_A: List[str] = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
_A: str = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_A: Tuple = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_ ) , expected_num_stages + 1 )
_A , _A: List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A: Optional[Any] = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A: int = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def __magic_name__ ( self : Dict ):
"""simple docstring"""
_A , _A: str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_A: int = self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ )
_A: Union[str, Any] = model_class(lowerCAmelCase_ )
@jax.jit
def model_jitted(lowerCAmelCase_ : Optional[Any] , **lowerCAmelCase_ : Optional[Any] ):
return model(pixel_values=lowerCAmelCase_ , **lowerCAmelCase_ )
with self.subTest('''JIT Enabled''' ):
_A: str = model_jitted(**lowerCAmelCase_ ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
_A: List[Any] = 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 )
def lowerCamelCase__ ( ) -> Tuple:
_A: List[str] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_flax
class UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __magic_name__ ( self : Union[str, Any] ):
"""simple docstring"""
return AutoImageProcessor.from_pretrained('''facebook/regnet-y-040''' ) if is_vision_available() else None
@slow
def __magic_name__ ( self : List[str] ):
"""simple docstring"""
_A: List[str] = FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040''' )
_A: str = self.default_image_processor
_A: int = prepare_img()
_A: List[Any] = image_processor(images=lowerCAmelCase_ , return_tensors='''np''' )
_A: str = model(**lowerCAmelCase_ )
# verify the logits
_A: str = (1, 1_0_0_0)
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
_A: Tuple = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] , lowerCAmelCase_ , atol=1e-4 ) )
| 301
| 0
|
snake_case : List[Any] = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 94
|
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
a__ : Any = get_logger(__name__)
class UpperCAmelCase__ :
def __init__( self , lowercase = None ) -> List[str]:
__UpperCamelCase = (
os.path.join(lowercase , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
__UpperCamelCase = Extractor
def __lowerCamelCase ( self , lowercase ) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
__UpperCamelCase = os.path.abspath(lowercase )
return os.path.join(self.extract_dir , hash_url_to_filename(lowercase ) )
def __lowerCamelCase ( self , lowercase , lowercase ) -> bool:
return force_extract or (
not os.path.isfile(lowercase ) and not (os.path.isdir(lowercase ) and os.listdir(lowercase ))
)
def __lowerCamelCase ( self , lowercase , lowercase = False ) -> str:
__UpperCamelCase = self.extractor.infer_extractor_format(lowercase )
if not extractor_format:
return input_path
__UpperCamelCase = self._get_output_path(lowercase )
if self._do_extract(lowercase , lowercase ):
self.extractor.extract(lowercase , lowercase , lowercase )
return output_path
class UpperCAmelCase__ ( UpperCAmelCase_):
@classmethod
@abstractmethod
def __lowerCamelCase ( cls , lowercase , **lowercase ) -> bool:
...
@staticmethod
@abstractmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
...
class UpperCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = []
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> int:
with open(lowercase , """rb""" ) as f:
return f.read(lowercase )
@classmethod
def __lowerCamelCase ( cls , lowercase , lowercase = b"" ) -> bool:
if not magic_number:
__UpperCamelCase = max(len(lowercase ) for cls_magic_number in cls.magic_numbers )
try:
__UpperCamelCase = cls.read_magic_number(lowercase , lowercase )
except OSError:
return False
return any(magic_number.startswith(lowercase ) for cls_magic_number in cls.magic_numbers )
class UpperCAmelCase__ ( UpperCAmelCase_):
@classmethod
def __lowerCamelCase ( cls , lowercase , **lowercase ) -> bool:
return tarfile.is_tarfile(lowercase )
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> str:
def resolved(lowercase ) -> str:
return os.path.realpath(os.path.abspath(lowercase ) )
def badpath(lowercase , lowercase ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(lowercase , lowercase ) ).startswith(lowercase )
def badlink(lowercase , lowercase ) -> bool:
# Links are interpreted relative to the directory containing the link
__UpperCamelCase = resolved(os.path.join(lowercase , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=lowercase )
__UpperCamelCase = resolved(lowercase )
for finfo in members:
if badpath(finfo.name , lowercase ):
logger.error(f"Extraction of {finfo.name} is blocked (illegal path)" )
elif finfo.issym() and badlink(lowercase , lowercase ):
logger.error(f"Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}" )
elif finfo.islnk() and badlink(lowercase , lowercase ):
logger.error(f"Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}" )
else:
yield finfo
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
os.makedirs(lowercase , exist_ok=lowercase )
__UpperCamelCase = tarfile.open(lowercase )
tar_file.extractall(lowercase , members=TarExtractor.safemembers(lowercase , lowercase ) )
tar_file.close()
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''\x1F\x8B''']
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
with gzip.open(lowercase , """rb""" ) as gzip_file:
with open(lowercase , """wb""" ) as extracted_file:
shutil.copyfileobj(lowercase , lowercase )
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [
B'''PK\x03\x04''',
B'''PK\x05\x06''', # empty archive
B'''PK\x07\x08''', # spanned archive
]
@classmethod
def __lowerCamelCase ( cls , lowercase , lowercase = b"" ) -> bool:
if super().is_extractable(lowercase , magic_number=lowercase ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(lowercase , """rb""" ) as fp:
__UpperCamelCase = _EndRecData(lowercase )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
__UpperCamelCase = fp.read(lowercase ) # CD is where we expect it to be
if len(lowercase ) == sizeCentralDir:
__UpperCamelCase = struct.unpack(lowercase , lowercase ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
os.makedirs(lowercase , exist_ok=lowercase )
with zipfile.ZipFile(lowercase , """r""" ) as zip_file:
zip_file.extractall(lowercase )
zip_file.close()
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''\xFD\x37\x7A\x58\x5A\x00''']
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
with lzma.open(lowercase ) as compressed_file:
with open(lowercase , """wb""" ) as extracted_file:
shutil.copyfileobj(lowercase , lowercase )
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''Rar!\x1a\x07\x00''', B'''Rar!\x1a\x07\x01\x00'''] # RAR_ID # RAR5_ID
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(lowercase , exist_ok=lowercase )
__UpperCamelCase = rarfile.RarFile(lowercase )
rf.extractall(lowercase )
rf.close()
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''\x28\xb5\x2F\xFD''']
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
__UpperCamelCase = zstd.ZstdDecompressor()
with open(lowercase , """rb""" ) as ifh, open(lowercase , """wb""" ) as ofh:
dctx.copy_stream(lowercase , lowercase )
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''\x42\x5A\x68''']
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
with bza.open(lowercase , """rb""" ) as compressed_file:
with open(lowercase , """wb""" ) as extracted_file:
shutil.copyfileobj(lowercase , lowercase )
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''\x37\x7A\xBC\xAF\x27\x1C''']
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(lowercase , exist_ok=lowercase )
with pyazr.SevenZipFile(lowercase , """r""" ) as archive:
archive.extractall(lowercase )
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = [B'''\x04\x22\x4D\x18''']
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(lowercase , """rb""" ) as compressed_file:
with open(lowercase , """wb""" ) as extracted_file:
shutil.copyfileobj(lowercase , lowercase )
class UpperCAmelCase__ :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
__SCREAMING_SNAKE_CASE = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def __lowerCamelCase ( cls ) -> Union[str, Any]:
return max(
len(lowercase )
for extractor in cls.extractors.values()
if issubclass(lowercase , lowercase )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def __lowerCamelCase ( lowercase , lowercase ) -> str:
try:
return MagicNumberBaseExtractor.read_magic_number(lowercase , magic_number_length=lowercase )
except OSError:
return b""
@classmethod
def __lowerCamelCase ( cls , lowercase , lowercase = False ) -> bool:
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" , category=lowercase , )
__UpperCamelCase = cls.infer_extractor_format(lowercase )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def __lowerCamelCase ( cls , lowercase ) -> str: # <Added version="2.4.0"/>
__UpperCamelCase = cls._get_magic_number_max_length()
__UpperCamelCase = cls._read_magic_number(lowercase , lowercase )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(lowercase , magic_number=lowercase ):
return extractor_format
@classmethod
def __lowerCamelCase ( cls , lowercase , lowercase , lowercase = None , lowercase = "deprecated" , ) -> None:
os.makedirs(os.path.dirname(lowercase ) , exist_ok=lowercase )
# Prevent parallel extractions
__UpperCamelCase = str(Path(lowercase ).with_suffix(""".lock""" ) )
with FileLock(lowercase ):
shutil.rmtree(lowercase , ignore_errors=lowercase )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(lowercase , lowercase ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" , category=lowercase , )
__UpperCamelCase = extractor if extractor != """deprecated""" else extractor_format
else:
__UpperCamelCase = cls.extractors[extractor_format]
return extractor.extract(lowercase , lowercase )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" , category=lowercase , )
for extractor in cls.extractors.values():
if extractor.is_extractable(lowercase ):
return extractor.extract(lowercase , lowercase )
| 349
| 0
|
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
get_from_cache,
http_get,
http_head,
)
_lowercase: Tuple = "\\n Text data.\n Second line of data."
_lowercase: List[Any] = "file"
@pytest.fixture(scope="session" )
def a( A : Optional[int] ) -> Optional[int]:
"""simple docstring"""
a = tmp_path_factory.mktemp("data" ) / (FILE_PATH + ".zstd")
a = bytes(A , "utf-8" )
with zstd.open(A , "wb" ) as f:
f.write(A )
return path
@pytest.fixture
def a( A : List[Any] ) -> Any:
"""simple docstring"""
with open(os.path.join(tmpfs.local_root_dir , A ) , "w" ) as f:
f.write(A )
return FILE_PATH
@pytest.mark.parametrize("compression_format" , ["gzip", "xz", "zstd"] )
def a( A : Tuple , A : Union[str, Any] , A : Tuple , A : int , A : str , A : Tuple ) -> Dict:
"""simple docstring"""
a = {"gzip": gz_file, "xz": xz_file, "zstd": zstd_path}
a = input_paths[compression_format]
a = tmp_path / "cache"
a = DownloadConfig(cache_dir=A , extract_compressed_file=A )
a = cached_path(A , download_config=A )
with open(A ) as f:
a = f.read()
with open(A ) as f:
a = f.read()
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize("default_extracted" , [True, False] )
@pytest.mark.parametrize("default_cache_dir" , [True, False] )
def a( A : Optional[Any] , A : int , A : Union[str, Any] , A : List[Any] , A : Tuple ) -> Optional[Any]:
"""simple docstring"""
a = "custom_cache"
a = "custom_extracted_dir"
a = tmp_path / "custom_extracted_path"
if default_extracted:
a = ("downloads" if default_cache_dir else custom_cache_dir, "extracted")
else:
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_DIR" , A )
monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH" , str(A ) )
a = custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)
a = xz_file
a = (
DownloadConfig(extract_compressed_file=A )
if default_cache_dir
else DownloadConfig(cache_dir=tmp_path / custom_cache_dir , extract_compressed_file=A )
)
a = cached_path(A , download_config=A )
assert Path(A ).parent.parts[-2:] == expected
def a( A : int ) -> Optional[Any]:
"""simple docstring"""
a = str(Path(A ).resolve() )
assert cached_path(A ) == text_file
# relative path
a = str(Path(A ).resolve().relative_to(Path(os.getcwd() ) ) )
assert cached_path(A ) == text_file
def a( A : List[Any] ) -> Dict:
"""simple docstring"""
a = str(tmp_path.resolve() / "__missing_file__.txt" )
with pytest.raises(A ):
cached_path(A )
# relative path
a = "./__missing_file__.txt"
with pytest.raises(A ):
cached_path(A )
def a( A : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
a = get_from_cache(f'''tmp://{tmpfs_file}''' )
with open(A ) as f:
a = f.read()
assert output_file_content == FILE_CONTENT
@patch("datasets.config.HF_DATASETS_OFFLINE" , A )
def a( ) -> Optional[Any]:
"""simple docstring"""
with pytest.raises(A ):
cached_path("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , A )
def a( A : List[str] ) -> int:
"""simple docstring"""
a = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(A ):
http_get("https://huggingface.co" , temp_file=A )
with pytest.raises(A ):
http_head("https://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , A )
def a( A : Tuple ) -> List[Any]:
"""simple docstring"""
a = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(A ):
ftp_get("ftp://huggingface.co" , temp_file=A )
with pytest.raises(A ):
ftp_head("ftp://huggingface.co" )
@patch("datasets.config.HF_DATASETS_OFFLINE" , A )
def a( A : Any ) -> int:
"""simple docstring"""
a = tmp_path_factory.mktemp("data" ) / "file.html"
with pytest.raises(A ):
fsspec_get("s3://huggingface.co" , temp_file=A )
with pytest.raises(A ):
fsspec_head("s3://huggingface.co" )
| 362
|
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowercase: Optional[Any] = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trained on Code},\n author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \\nand Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \\nand Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \\nand Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \\nand Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \\nand Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \\nand Mohammad Bavarian and Clemens Winter and Philippe Tillet \\nand Felipe Petroski Such and Dave Cummings and Matthias Plappert \\nand Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \\nand William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \\nand Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \\nand William Saunders and Christopher Hesse and Andrew N. Carr \\nand Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \\nand Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \\nand Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \\nand Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},\n year={2021},\n eprint={2107.03374},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n"
_lowercase: Union[str, Any] = "\\nThis metric implements the evaluation harness for the HumanEval problem solving dataset\ndescribed in the paper \"Evaluating Large Language Models Trained on Code\"\n(https://arxiv.org/abs/2107.03374).\n"
_lowercase: Union[str, Any] = "\nCalculates how good are predictions given some references, using certain scores\nArgs:\n predictions: list of candidates to evaluate. Each candidates should be a list\n of strings with several code candidates to solve the problem.\n references: a list with a test for each prediction. Each test should evaluate the\n correctness of a code candidate.\n k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])\n num_workers: number of workers used to evaluate the canidate programs (Default: 4).\n timeout:\nReturns:\n pass_at_k: dict with pass rates for each k\n results: dict with granular results of each unittest\nExamples:\n >>> code_eval = datasets.load_metric(\"code_eval\")\n >>> test_cases = [\"assert add(2,3)==5\"]\n >>> candidates = [[\"def add(a,b): return a*b\", \"def add(a, b): return a+b\"]]\n >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])\n >>> print(pass_at_k)\n {'pass@1': 0.5, 'pass@2': 1.0}\n"
_lowercase: List[Any] = "\n################################################################################\n !!!WARNING!!!\n################################################################################\nThe \"code_eval\" metric executes untrusted model-generated code in Python.\nAlthough it is highly unlikely that model-generated code will do something\novertly malicious in response to this test suite, model-generated code may act\ndestructively due to a lack of model capability or alignment.\nUsers are strongly encouraged to sandbox this evaluation suite so that it\ndoes not perform destructive actions on their host or network. For more\ninformation on how OpenAI sandboxes its code, see the paper \"Evaluating Large\nLanguage Models Trained on Code\" (https://arxiv.org/abs/2107.03374).\n\nOnce you have read this disclaimer and taken appropriate precautions,\nset the environment variable HF_ALLOW_CODE_EVAL=\"1\". Within Python you can to this\nwith:\n\n>>> import os\n>>> os.environ[\"HF_ALLOW_CODE_EVAL\"] = \"1\"\n\n################################################################################\\n"
_lowercase: List[Any] = "The MIT License\n\nCopyright (c) OpenAI (https://openai.com)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE."
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Sequence(datasets.Value("string" ) ),
"references": datasets.Value("string" ),
} ) , homepage="https://github.com/openai/human-eval" , codebase_urls=["https://github.com/openai/human-eval"] , reference_urls=["https://github.com/openai/human-eval"] , license=_LICENSE , )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_=[1, 10, 100] , lowerCamelCase_=4 , lowerCamelCase_=3.0 ):
"""simple docstring"""
if os.getenv("HF_ALLOW_CODE_EVAL" , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError("This metric is currently not supported on Windows." )
with ThreadPoolExecutor(max_workers=lowerCamelCase_ ) as executor:
a = []
a = Counter()
a = 0
a = defaultdict(lowerCamelCase_ )
for task_id, (candidates, test_case) in enumerate(zip(lowerCamelCase_ , lowerCamelCase_ ) ):
for candidate in candidates:
a = candidate + "\n" + test_case
a = (test_program, timeout, task_id, completion_id[task_id])
a = executor.submit(lowerCamelCase_ , *lowerCamelCase_ )
futures.append(lowerCamelCase_ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(lowerCamelCase_ ):
a = future.result()
results[result["task_id"]].append((result["completion_id"], result) )
a , a = [], []
for result in results.values():
result.sort()
a = [r[1]["passed"] for r in result]
total.append(len(lowerCamelCase_ ) )
correct.append(sum(lowerCamelCase_ ) )
a = np.array(lowerCamelCase_ )
a = np.array(lowerCamelCase_ )
a = k
a = {F'''pass@{k}''': estimate_pass_at_k(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def a( A : Optional[Any] , A : str , A : Dict ) -> Optional[int]:
"""simple docstring"""
def estimator(A : int , A : int , A : int ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(A , A ):
a = itertools.repeat(A , len(A ) )
else:
assert len(A ) == len(A )
a = iter(A )
return np.array([estimator(int(A ) , int(A ) , A ) for n, c in zip(A , A )] )
| 71
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'''convert''': ['''export''', '''validate_model_outputs'''],
'''features''': ['''FeaturesManager'''],
'''utils''': ['''ParameterFormat''', '''compute_serialized_parameters_size'''],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
a = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 315
|
"""simple docstring"""
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowercase_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
self.assertEqual(x.component(0 ) , 1 )
self.assertEqual(x.component(2 ) , 3 )
_A = Vector()
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([0, 0, 0, 0, 0, 1] )
self.assertEqual(str(_UpperCAmelCase ) , '(0,0,0,0,0,1)' )
def lowerCAmelCase_ ( self : Optional[int] ):
_A = Vector([1, 2, 3, 4] )
self.assertEqual(len(_UpperCAmelCase ) , 4 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2] )
_A = Vector([1, 2, 3, 4, 5] )
_A = Vector([0, 0, 0, 0, 0, 0, 0, 0, 0, 0] )
_A = Vector([1, -1, 1, -1, 2, -3, 4, -5] )
self.assertAlmostEqual(x.euclidean_length() , 2.236 , 3 )
self.assertAlmostEqual(y.euclidean_length() , 7.416 , 3 )
self.assertEqual(z.euclidean_length() , 0 )
self.assertAlmostEqual(w.euclidean_length() , 7.616 , 3 )
def lowerCAmelCase_ ( self : str ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x + y).component(0 ) , 2 )
self.assertEqual((x + y).component(1 ) , 3 )
self.assertEqual((x + y).component(2 ) , 4 )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 1, 1] )
self.assertEqual((x - y).component(0 ) , 0 )
self.assertEqual((x - y).component(1 ) , 1 )
self.assertEqual((x - y).component(2 ) , 2 )
def lowerCAmelCase_ ( self : int ):
_A = Vector([1, 2, 3] )
_A = Vector([2, -1, 4] ) # for test of dot product
_A = Vector([1, -2, -1] )
self.assertEqual(str(x * 3.0 ) , '(3.0,6.0,9.0)' )
self.assertEqual((a * b) , 0 )
def lowerCAmelCase_ ( self : Dict ):
self.assertEqual(str(zero_vector(10 ) ).count('0' ) , 10 )
def lowerCAmelCase_ ( self : Tuple ):
self.assertEqual(str(unit_basis_vector(3 , 1 ) ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 2, 3] )
_A = Vector([1, 0, 1] )
self.assertEqual(str(axpy(2 , _UpperCAmelCase , _UpperCAmelCase ) ) , '(3,4,7)' )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Vector([1, 0, 0, 0, 0, 0] )
_A = x.copy()
self.assertEqual(str(_UpperCAmelCase ) , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Vector([1, 0, 0] )
x.change_component(0 , 0 )
x.change_component(1 , 1 )
self.assertEqual(str(_UpperCAmelCase ) , '(0,1,0)' )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual('|1,2,3|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, -14, -10], [-5, -10, -5], [-2, -1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(minors[x][y] , a.minor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = [[-3, 14, -10], [5, -10, 5], [-2, 1, 0]]
for x in range(a.height() ):
for y in range(a.width() ):
self.assertEqual(cofactors[x][y] , a.cofactor(_UpperCAmelCase , _UpperCAmelCase ) )
def lowerCAmelCase_ ( self : str ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(-5 , a.determinant() )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]] , 3 , 3 )
_A = Vector([1, 2, 3] )
self.assertEqual('(14,32,50)' , str(a * x ) )
self.assertEqual('|2,4,6|\n|8,10,12|\n|14,16,18|\n' , str(a * 2 ) )
def lowerCAmelCase_ ( self : Any ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
a.change_component(0 , 2 , 5 )
self.assertEqual('|1,2,5|\n|2,4,5|\n|6,7,8|\n' , str(_UpperCAmelCase ) )
def lowerCAmelCase_ ( self : List[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
self.assertEqual(7 , a.component(2 , 1 ) , 0.01 )
def lowerCAmelCase_ ( self : Tuple ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|2,4,10|\n|4,8,10|\n|12,14,18|\n' , str(a + b ) )
def lowerCAmelCase_ ( self : Optional[Any] ):
_A = Matrix([[1, 2, 3], [2, 4, 5], [6, 7, 8]] , 3 , 3 )
_A = Matrix([[1, 2, 7], [2, 4, 5], [6, 7, 10]] , 3 , 3 )
self.assertEqual('|0,0,-4|\n|0,0,0|\n|0,0,-2|\n' , str(a - b ) )
def lowerCAmelCase_ ( self : int ):
self.assertEqual(
'|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n|0,0,0,0,0|\n' , str(square_zero_matrix(5 ) ) , )
if __name__ == "__main__":
unittest.main()
| 315
| 1
|
'''simple docstring'''
def a ( lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) != 0 )
def a ( ):
'''simple docstring'''
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 135
|
'''simple docstring'''
def a ( ):
'''simple docstring'''
A_ : Optional[int] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
A_ : Dict = 6
A_ : List[Any] = 1
A_ : Optional[Any] = 19_01
A_ : Tuple = 0
while year < 20_01:
day += 7
if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
A_ : Optional[Any] = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
A_ : str = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
A_ : List[str] = day - days_per_month[month - 2]
if month > 12:
year += 1
A_ : Tuple = 1
if year < 20_01 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 135
| 1
|
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class UpperCamelCase ( lowercase ):
def __init__(self : Optional[int] , _A : pyspark.sql.DataFrame , _A : Optional[NamedSplit] = None , _A : Optional[Features] = None , _A : bool = True , _A : str = None , _A : bool = False , _A : str = None , _A : bool = True , _A : str = "arrow" , **_A : Any , ) -> Optional[int]:
super().__init__(
split=_A , features=_A , cache_dir=_A , keep_in_memory=_A , streaming=_A , **_A , )
__snake_case : Optional[int] = load_from_cache_file
__snake_case : str = file_format
__snake_case : int = Spark(
df=_A , features=_A , cache_dir=_A , working_dir=_A , **_A , )
def _lowercase (self : Any) -> Any:
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split)
__snake_case : str = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=_A , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split)
| 172
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : int= logging.get_logger(__name__)
_a : Optional[Any]= {
"SCUT-DLVCLab/lilt-roberta-en-base": (
"https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
),
}
class UpperCamelCase ( lowercase ):
UpperCAmelCase : List[Any] = """lilt"""
def __init__(self : Dict , _A : Any=3_05_22 , _A : Union[str, Any]=7_68 , _A : Any=12 , _A : Tuple=12 , _A : Optional[int]=30_72 , _A : Tuple="gelu" , _A : str=0.1 , _A : List[Any]=0.1 , _A : Union[str, Any]=5_12 , _A : Any=2 , _A : Tuple=0.02 , _A : List[str]=1E-12 , _A : Optional[int]=0 , _A : Optional[Any]="absolute" , _A : Any=None , _A : List[Any]=4 , _A : Optional[int]=10_24 , **_A : Union[str, Any] , ) -> Tuple:
super().__init__(pad_token_id=_A , **_A)
__snake_case : Optional[int] = vocab_size
__snake_case : List[Any] = hidden_size
__snake_case : Any = num_hidden_layers
__snake_case : Optional[int] = num_attention_heads
__snake_case : Optional[int] = hidden_act
__snake_case : List[str] = intermediate_size
__snake_case : Union[str, Any] = hidden_dropout_prob
__snake_case : Dict = attention_probs_dropout_prob
__snake_case : List[Any] = max_position_embeddings
__snake_case : Dict = type_vocab_size
__snake_case : List[Any] = initializer_range
__snake_case : Optional[Any] = layer_norm_eps
__snake_case : Optional[int] = position_embedding_type
__snake_case : Any = classifier_dropout
__snake_case : Optional[int] = channel_shrink_ratio
__snake_case : Tuple = max_ad_position_embeddings
| 172
| 1
|
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,
)
__snake_case : Any ={
"configuration_xlm_roberta": [
"XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaConfig",
"XLMRobertaOnnxConfig",
],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Optional[int] =["XLMRobertaTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : str =["XLMRobertaTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : List[str] =[
"XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMRobertaForCausalLM",
"XLMRobertaForMaskedLM",
"XLMRobertaForMultipleChoice",
"XLMRobertaForQuestionAnswering",
"XLMRobertaForSequenceClassification",
"XLMRobertaForTokenClassification",
"XLMRobertaModel",
"XLMRobertaPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Union[str, Any] =[
"TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMRobertaForCausalLM",
"TFXLMRobertaForMaskedLM",
"TFXLMRobertaForMultipleChoice",
"TFXLMRobertaForQuestionAnswering",
"TFXLMRobertaForSequenceClassification",
"TFXLMRobertaForTokenClassification",
"TFXLMRobertaModel",
"TFXLMRobertaPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case : Dict =[
"FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
"FlaxXLMRobertaForMaskedLM",
"FlaxXLMRobertaForCausalLM",
"FlaxXLMRobertaForMultipleChoice",
"FlaxXLMRobertaForQuestionAnswering",
"FlaxXLMRobertaForSequenceClassification",
"FlaxXLMRobertaForTokenClassification",
"FlaxXLMRobertaModel",
"FlaxXLMRobertaPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaConfig,
XLMRobertaOnnxConfig,
)
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta import XLMRobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta import (
XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaForCausalLM,
XLMRobertaForMaskedLM,
XLMRobertaForMultipleChoice,
XLMRobertaForQuestionAnswering,
XLMRobertaForSequenceClassification,
XLMRobertaForTokenClassification,
XLMRobertaModel,
XLMRobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm_roberta import (
TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMRobertaForCausalLM,
TFXLMRobertaForMaskedLM,
TFXLMRobertaForMultipleChoice,
TFXLMRobertaForQuestionAnswering,
TFXLMRobertaForSequenceClassification,
TFXLMRobertaForTokenClassification,
TFXLMRobertaModel,
TFXLMRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_xlm_roberta import (
FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaxXLMRobertaForCausalLM,
FlaxXLMRobertaForMaskedLM,
FlaxXLMRobertaForMultipleChoice,
FlaxXLMRobertaForQuestionAnswering,
FlaxXLMRobertaForSequenceClassification,
FlaxXLMRobertaForTokenClassification,
FlaxXLMRobertaModel,
FlaxXLMRobertaPreTrainedModel,
)
else:
import sys
__snake_case : Optional[int] =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 357
|
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__snake_case : Dict =HfArgumentParser(InitializationArguments)
__snake_case : Tuple =parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
__snake_case : Optional[int] =AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case : List[str] ={
'vocab_size': len(tokenizer),
'scale_attn_by_inverse_layer_idx': True,
'reorder_and_upcast_attn': True,
}
# Load model config (GPT-2 large in this case)
__snake_case : List[Any] =AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
__snake_case : int =AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 94
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : str = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = """openai-gpt"""
SCREAMING_SNAKE_CASE_ : Tuple = {
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self : str , __lowerCamelCase : List[str]=4_04_78 , __lowerCamelCase : List[Any]=5_12 , __lowerCamelCase : List[str]=7_68 , __lowerCamelCase : List[str]=12 , __lowerCamelCase : Optional[Any]=12 , __lowerCamelCase : str="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : Any=0.1 , __lowerCamelCase : Dict=0.1 , __lowerCamelCase : Any=1e-5 , __lowerCamelCase : Optional[int]=0.02 , __lowerCamelCase : Optional[int]="cls_index" , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Optional[Any]=True , __lowerCamelCase : Tuple=0.1 , **__lowerCamelCase : Union[str, Any] , ) -> List[str]:
a = vocab_size
a = n_positions
a = n_embd
a = n_layer
a = n_head
a = afn
a = resid_pdrop
a = embd_pdrop
a = attn_pdrop
a = layer_norm_epsilon
a = initializer_range
a = summary_type
a = summary_use_proj
a = summary_activation
a = summary_first_dropout
a = summary_proj_to_labels
super().__init__(**__lowerCamelCase )
| 107
|
"""simple docstring"""
from __future__ import annotations
def lowercase (_lowerCAmelCase , _lowerCAmelCase ):
__lowerCAmelCase = []
create_all_state(1 , _lowerCAmelCase , _lowerCAmelCase , [] , _lowerCAmelCase )
return result
def lowercase (_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
if level == 0:
total_list.append(current_list[:] )
return
for i in range(_lowerCAmelCase , total_number - level + 2 ):
current_list.append(_lowerCAmelCase )
create_all_state(i + 1 , _lowerCAmelCase , level - 1 , _lowerCAmelCase , _lowerCAmelCase )
current_list.pop()
def lowercase (_lowerCAmelCase ):
for i in total_list:
print(*_lowerCAmelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = 4
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = generate_all_combinations(n, k)
print_all_state(total_list)
| 301
| 0
|
'''simple docstring'''
from collections.abc import Sequence
def A_( A : Sequence[float] , A : float):
return sum(c * (x**i) for i, c in enumerate(A))
def A_( A : Sequence[float] , A : float):
UpperCamelCase = 0.0
for coeff in reversed(A):
UpperCamelCase = result * x + coeff
return result
if __name__ == "__main__":
lowerCAmelCase : Any = (0.0, 0.0, 5.0, 9.3, 7.0)
lowerCAmelCase : List[Any] = 10.0
print(evaluate_poly(poly, x))
print(horner(poly, x))
| 359
|
'''simple docstring'''
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
lowerCAmelCase : int = 'bart'
lowerCAmelCase : Union[str, Any] = True
@st.cache(allow_output_mutation=A)
def A_( ):
if LOAD_DENSE_INDEX:
UpperCamelCase = AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased')
UpperCamelCase = AutoModel.from_pretrained('yjernite/retribert-base-uncased').to('cuda:0')
UpperCamelCase = qar_model.eval()
else:
UpperCamelCase , UpperCamelCase = (None, None)
if MODEL_TYPE == "bart":
UpperCamelCase = AutoTokenizer.from_pretrained('yjernite/bart_eli5')
UpperCamelCase = AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5').to('cuda:0')
UpperCamelCase = torch.load('seq2seq_models/eli5_bart_model_blm_2.pth')
sas_model.load_state_dict(save_dict['model'])
UpperCamelCase = sas_model.eval()
else:
UpperCamelCase , UpperCamelCase = make_qa_sas_model(
model_name='t5-small' , from_file='seq2seq_models/eli5_t5_model_1024_4.pth' , device='cuda:0')
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=A)
def A_( ):
if LOAD_DENSE_INDEX:
UpperCamelCase = faiss.StandardGpuResources()
UpperCamelCase = datasets.load_dataset(path='wiki_snippets' , name='wiki40b_en_100_0')['train']
UpperCamelCase = np.memmap(
'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat' , dtype='float32' , mode='r' , shape=(wikiaab_passages.num_rows, 128) , )
UpperCamelCase = faiss.IndexFlatIP(128)
UpperCamelCase = faiss.index_cpu_to_gpu(A , 1 , A)
wikiaab_gpu_index_flat.add(A) # TODO fix for larger GPU
else:
UpperCamelCase , UpperCamelCase = (None, None)
UpperCamelCase = Elasticsearch([{'host': 'localhost', 'port': '9200'}])
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=A)
def A_( ):
UpperCamelCase = datasets.load_dataset('eli5' , name='LFQA_reddit')
UpperCamelCase = elia['train_eli5']
UpperCamelCase = np.memmap(
'eli5_questions_reps.dat' , dtype='float32' , mode='r' , shape=(elia_train.num_rows, 128))
UpperCamelCase = faiss.IndexFlatIP(128)
eli5_train_q_index.add(A)
return (elia_train, eli5_train_q_index)
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : Optional[Any] = load_indexes()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase : int = load_models()
lowerCAmelCase , lowerCAmelCase : Tuple = load_train_data()
def A_( A : Dict , A : str=10):
UpperCamelCase = embed_questions_for_retrieval([question] , A , A)
UpperCamelCase , UpperCamelCase = eli5_train_q_index.search(A , A)
UpperCamelCase = [elia_train[int(A)] for i in I[0]]
return nn_examples
def A_( A : str , A : Optional[int]="wiki40b" , A : List[Any]="dense" , A : Dict=10):
if source == "none":
UpperCamelCase , UpperCamelCase = (' <P> '.join(['' for _ in range(11)]).strip(), [])
else:
if method == "dense":
UpperCamelCase , UpperCamelCase = query_qa_dense_index(
A , A , A , A , A , A)
else:
UpperCamelCase , UpperCamelCase = query_es_index(
A , A , index_name='english_wiki40b_snippets_100w' , n_results=A , )
UpperCamelCase = [
(res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
]
UpperCamelCase = 'question: {} context: {}'.format(A , A)
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda A: None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda A: None),
})
def A_( A : Optional[Any] , A : List[str] , A : str , A : List[Any]=64 , A : List[Any]=256 , A : int=False , A : List[str]=2 , A : Any=0.95 , A : int=0.8):
with torch.no_grad():
UpperCamelCase = qa_sas_generate(
A , A , A , num_answers=1 , num_beams=A , min_len=A , max_len=A , do_sample=A , temp=A , top_p=A , top_k=A , max_input_length=1024 , device='cuda:0' , )[0]
return (answer, support_list)
st.title('Long Form Question Answering with ELI5')
# Start sidebar
lowerCAmelCase : List[str] = '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
lowerCAmelCase : Dict = '\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class="img-container"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
lowerCAmelCase : List[Any] = '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)
lowerCAmelCase : Optional[int] = [
'Answer the question',
'View the retrieved document only',
'View the most similar ELI5 question and answer',
'Show me everything, please!',
]
lowerCAmelCase : List[str] = st.sidebar.checkbox('Demo options')
if demo_options:
lowerCAmelCase : Dict = st.sidebar.selectbox(
'',
action_list,
index=3,
)
lowerCAmelCase : int = action_list.index(action_st)
lowerCAmelCase : Optional[Any] = st.sidebar.selectbox(
'',
['Show full text of passages', 'Show passage section titles'],
index=0,
)
lowerCAmelCase : Any = show_type == 'Show full text of passages'
else:
lowerCAmelCase : Optional[Any] = 3
lowerCAmelCase : Optional[Any] = True
lowerCAmelCase : Optional[Any] = st.sidebar.checkbox('Retrieval options')
if retrieval_options:
lowerCAmelCase : Any = '\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n '
st.sidebar.markdown(retriever_info)
lowerCAmelCase : Optional[Any] = st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
lowerCAmelCase : List[str] = st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
lowerCAmelCase : str = 'wiki40b'
lowerCAmelCase : Tuple = 'dense'
lowerCAmelCase : Union[str, Any] = 'beam'
lowerCAmelCase : Optional[int] = 2
lowerCAmelCase : Any = 64
lowerCAmelCase : int = 2_56
lowerCAmelCase : Optional[Any] = None
lowerCAmelCase : List[str] = None
lowerCAmelCase : List[str] = st.sidebar.checkbox('Generation options')
if generate_options:
lowerCAmelCase : str = '\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n '
st.sidebar.markdown(generate_info)
lowerCAmelCase : str = st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
lowerCAmelCase : Any = st.sidebar.slider(
'Minimum generation length', min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
)
lowerCAmelCase : Any = st.sidebar.slider(
'Maximum generation length', min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
)
if sampled == "beam":
lowerCAmelCase : Union[str, Any] = st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
lowerCAmelCase : Tuple = st.sidebar.slider(
'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
lowerCAmelCase : List[Any] = st.sidebar.slider(
'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
lowerCAmelCase : Tuple = None
# start main text
lowerCAmelCase : Optional[Any] = [
'<MY QUESTION>',
'How do people make chocolate?',
'Why do we get a fever when we are sick?',
'How can different animals perceive different colors?',
'What is natural language processing?',
'What\'s the best way to treat a sunburn?',
'What exactly are vitamins ?',
'How does nuclear energy provide electricity?',
'What\'s the difference between viruses and bacteria?',
'Why are flutes classified as woodwinds when most of them are made out of metal ?',
'Why do people like drinking coffee even though it tastes so bad?',
'What happens when wine ages? How does it make the wine taste better?',
'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
'How does New Zealand have so many large bird predators?',
]
lowerCAmelCase : int = st.selectbox(
'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
lowerCAmelCase : List[str] = st.text_input('Enter your question here:', '')
else:
lowerCAmelCase : Tuple = question_s
if st.button('Show me!'):
if action in [0, 1, 3]:
if index_type == "mixed":
lowerCAmelCase , lowerCAmelCase : Any = make_support(question, source=wiki_source, method='dense', n_results=10)
lowerCAmelCase , lowerCAmelCase : Dict = make_support(question, source=wiki_source, method='sparse', n_results=10)
lowerCAmelCase : List[str] = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
lowerCAmelCase : Optional[Any] = support_list[:10]
lowerCAmelCase : Dict = '<P> ' + ' <P> '.join([res[-1] for res in support_list])
else:
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
lowerCAmelCase , lowerCAmelCase : Union[str, Any] = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == 'sampled'),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('### The model generated answer is:')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
for i, res in enumerate(support_list):
lowerCAmelCase : int = 'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
lowerCAmelCase : Optional[int] = res[1].strip()
if sec_titles == "":
lowerCAmelCase : Dict = '[{}]({})'.format(res[0], wiki_url)
else:
lowerCAmelCase : Dict = sec_titles.split(' & ')
lowerCAmelCase : str = ' & '.join(
['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
)
st.markdown(
'{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
)
if action in [2, 3]:
lowerCAmelCase : Optional[Any] = find_nearest_training(question)
lowerCAmelCase : Optional[int] = nn_train_list[0]
st.markdown(
'--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
)
lowerCAmelCase : List[str] = [
'{}. {}'.format(i + 1, ' \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
if i == 0 or sc > 2
]
st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))
lowerCAmelCase : int = '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 251
| 0
|
from torch import nn
def UpperCAmelCase__ ( lowerCamelCase ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"Unsupported activation function: {act_fn}" )
| 236
|
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
A_ :List[str] = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
A_ :Any = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
A_ :Tuple = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
A_ :List[str] = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
A_ :Tuple = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __A ( datasets.Metric ):
"""simple docstring"""
def __lowercase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string' ) ),
'references': datasets.Value('string' ),
} ) , homepage='https://github.com/openai/human-eval' , codebase_urls=['https://github.com/openai/human-eval'] , reference_urls=['https://github.com/openai/human-eval'] , license=_LICENSE , )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=[1, 10, 100] , lowerCamelCase__=4 , lowerCamelCase__=3.0 ):
"""simple docstring"""
if os.getenv('HF_ALLOW_CODE_EVAL' , 0 ) != "1":
raise ValueError(_WARNING )
if os.name == "nt":
raise NotImplementedError('This metric is currently not supported on Windows.' )
with ThreadPoolExecutor(max_workers=lowerCamelCase__ ) as executor:
__UpperCamelCase : List[str] =[]
__UpperCamelCase : Any =Counter()
__UpperCamelCase : List[Any] =0
__UpperCamelCase : int =defaultdict(lowerCamelCase__ )
for task_id, (candidates, test_case) in enumerate(zip(lowerCamelCase__ , lowerCamelCase__ ) ):
for candidate in candidates:
__UpperCamelCase : str =candidate + '\n' + test_case
__UpperCamelCase : Any =(test_program, timeout, task_id, completion_id[task_id])
__UpperCamelCase : Optional[Any] =executor.submit(lowerCamelCase__ , *lowerCamelCase__ )
futures.append(lowerCamelCase__ )
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(lowerCamelCase__ ):
__UpperCamelCase : str =future.result()
results[result["task_id"]].append((result['completion_id'], result) )
__UpperCamelCase , __UpperCamelCase : int =[], []
for result in results.values():
result.sort()
__UpperCamelCase : str =[r[1]['passed'] for r in result]
total.append(len(lowerCamelCase__ ) )
correct.append(sum(lowerCamelCase__ ) )
__UpperCamelCase : Optional[int] =np.array(lowerCamelCase__ )
__UpperCamelCase : List[str] =np.array(lowerCamelCase__ )
__UpperCamelCase : Union[str, Any] =k
__UpperCamelCase : List[Any] ={f'pass@{k}': estimate_pass_at_k(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def A ( a_ ,a_ ,a_ ) -> Optional[int]:
def estimator(a_ ,a_ ,a_ ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 ,n + 1 ) )
if isinstance(a_ ,a_ ):
__UpperCamelCase : Optional[int] =itertools.repeat(a_ ,len(a_ ) )
else:
assert len(a_ ) == len(a_ )
__UpperCamelCase : List[Any] =iter(a_ )
return np.array([estimator(int(a_ ) ,int(a_ ) ,a_ ) for n, c in zip(a_ ,a_ )] )
| 71
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
def UpperCAmelCase__ ( lowerCAmelCase__ :Dict , lowerCAmelCase__ :Tuple , lowerCAmelCase__ :Optional[Any] ) -> tuple:
'''simple docstring'''
lowercase = namedtuple("""result""" , """name value""" )
if (voltage, current, power).count(0 ) != 1:
raise ValueError("""Only one argument must be 0""" )
elif power < 0:
raise ValueError(
"""Power cannot be negative in any electrical/electronics system""" )
elif voltage == 0:
return result("""voltage""" , power / current )
elif current == 0:
return result("""current""" , power / voltage )
elif power == 0:
return result("""power""" , float(round(abs(voltage * current ) , 2 ) ) )
else:
raise ValueError("""Exactly one argument must be 0""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 369
|
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class _A ( unittest.TestCase ):
def A__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def A__ ( self ):
"""simple docstring"""
lowercase = 1
lowercase = 3
lowercase = (32, 32)
lowercase = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(__lowerCAmelCase )
return image
@property
def A__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowercase = 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 A__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowercase = 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 A__ ( self ):
"""simple docstring"""
torch.manual_seed(0 )
lowercase = 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 , )
return CLIPTextModel(__lowerCAmelCase )
@property
def A__ ( self ):
"""simple docstring"""
def extract(*__lowerCAmelCase , **__lowerCAmelCase ):
class _A :
def __init__( self ):
"""simple docstring"""
lowercase = torch.ones([0] )
def A__ ( self , __lowerCAmelCase ):
"""simple docstring"""
self.pixel_values.to(__lowerCAmelCase )
return self
return Out()
return extract
def A__ ( self ):
"""simple docstring"""
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.dummy_cond_unet
lowercase = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="""scaled_linear""" , clip_sample=__lowerCAmelCase , set_alpha_to_one=__lowerCAmelCase , )
lowercase = self.dummy_vae
lowercase = self.dummy_text_encoder
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
lowercase = StableDiffusionPipeline(
unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , vae=__lowerCAmelCase , text_encoder=__lowerCAmelCase , tokenizer=__lowerCAmelCase , safety_checker=__lowerCAmelCase , feature_extractor=self.dummy_extractor , )
lowercase = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = """A painting of a squirrel eating a burger"""
lowercase = torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
lowercase = sd_pipe([prompt] , generator=__lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
lowercase = output.images
lowercase = torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=__lowerCAmelCase , )[0]
lowercase = image[0, -3:, -3:, -1]
lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A__ ( self ):
"""simple docstring"""
lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
lowercase = self.dummy_cond_unet
lowercase = PNDMScheduler(skip_prk_steps=__lowerCAmelCase )
lowercase = self.dummy_vae
lowercase = self.dummy_text_encoder
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# make sure here that pndm scheduler skips prk
lowercase = StableDiffusionPipeline(
unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , vae=__lowerCAmelCase , text_encoder=__lowerCAmelCase , tokenizer=__lowerCAmelCase , safety_checker=__lowerCAmelCase , feature_extractor=self.dummy_extractor , )
lowercase = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = """A painting of a squirrel eating a burger"""
lowercase = torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
lowercase = sd_pipe([prompt] , generator=__lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" )
lowercase = output.images
lowercase = torch.Generator(device=__lowerCAmelCase ).manual_seed(0 )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=6.0 , num_inference_steps=2 , output_type="""np""" , return_dict=__lowerCAmelCase , )[0]
lowercase = image[0, -3:, -3:, -1]
lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase = np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def A__ ( self ):
"""simple docstring"""
lowercase = StableDiffusionPipeline.from_pretrained(
"""hf-internal-testing/tiny-stable-diffusion-lms-pipe""" , safety_checker=__lowerCAmelCase )
assert isinstance(__lowerCAmelCase , __lowerCAmelCase )
assert isinstance(pipe.scheduler , __lowerCAmelCase )
assert pipe.safety_checker is None
lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__lowerCAmelCase )
lowercase = StableDiffusionPipeline.from_pretrained(__lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
@unittest.skipIf(torch_device != """cuda""" , """This test requires a GPU""" )
def A__ ( self ):
"""simple docstring"""
lowercase = self.dummy_cond_unet
lowercase = PNDMScheduler(skip_prk_steps=__lowerCAmelCase )
lowercase = self.dummy_vae
lowercase = self.dummy_text_encoder
lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
# put models in fp16
lowercase = unet.half()
lowercase = vae.half()
lowercase = bert.half()
# make sure here that pndm scheduler skips prk
lowercase = StableDiffusionPipeline(
unet=__lowerCAmelCase , scheduler=__lowerCAmelCase , vae=__lowerCAmelCase , text_encoder=__lowerCAmelCase , tokenizer=__lowerCAmelCase , safety_checker=__lowerCAmelCase , feature_extractor=self.dummy_extractor , )
lowercase = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = """A painting of a squirrel eating a burger"""
lowercase = sd_pipe([prompt] , num_inference_steps=2 , output_type="""np""" ).images
assert image.shape == (1, 64, 64, 3)
@nightly
@require_torch_gpu
class _A ( unittest.TestCase ):
def A__ ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ ( self ):
"""simple docstring"""
lowercase = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=__lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowercase = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = (
"""portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"""
""" coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"""
""" anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"""
""" children from bahnhof zoo, detailed """
)
lowercase = 40_0366_0346
lowercase = 7
# without safety guidance (sld_guidance_scale = 0)
lowercase = torch.manual_seed(__lowerCAmelCase )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=__lowerCAmelCase , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
lowercase = [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
# without safety guidance (strong configuration)
lowercase = torch.manual_seed(__lowerCAmelCase )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=__lowerCAmelCase , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
lowercase = [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A__ ( self ):
"""simple docstring"""
lowercase = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" , safety_checker=__lowerCAmelCase )
lowercase = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
lowercase = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = """padme amidala taking a bath artwork, safe for work, no nudity"""
lowercase = 27_3497_1755
lowercase = 7
lowercase = torch.manual_seed(__lowerCAmelCase )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=__lowerCAmelCase , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
lowercase = [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
lowercase = torch.manual_seed(__lowerCAmelCase )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=__lowerCAmelCase , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
lowercase = [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def A__ ( self ):
"""simple docstring"""
lowercase = StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
lowercase = sd_pipe.to(__lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase )
lowercase = (
"""the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
""" leyendecker"""
)
lowercase = 10_4435_5234
lowercase = 12
lowercase = torch.manual_seed(__lowerCAmelCase )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=__lowerCAmelCase , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=0 , )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
lowercase = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-7
lowercase = torch.manual_seed(__lowerCAmelCase )
lowercase = sd_pipe(
[prompt] , generator=__lowerCAmelCase , guidance_scale=__lowerCAmelCase , num_inference_steps=50 , output_type="""np""" , width=512 , height=512 , sld_guidance_scale=2000 , sld_warmup_steps=7 , sld_threshold=0.0_2_5 , sld_momentum_scale=0.5 , sld_mom_beta=0.7 , )
lowercase = output.images
lowercase = image[0, -3:, -3:, -1]
lowercase = np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
assert image.shape == (1, 512, 512, 3)
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 32
| 0
|
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class _snake_case :
def __init__( self : Dict ):
__lowerCamelCase : List[Any] = {}
def lowerCamelCase__ ( self : Optional[Any] , UpperCAmelCase : Dict , UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any]=1 ):
if self.graph.get(UpperCAmelCase ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
__lowerCamelCase : str = [[w, v]]
if not self.graph.get(UpperCAmelCase ):
__lowerCamelCase : Optional[int] = []
def lowerCamelCase__ ( self : Optional[Any] ):
return list(self.graph )
def lowerCamelCase__ ( self : Union[str, Any] , UpperCAmelCase : Optional[int] , UpperCAmelCase : Union[str, Any] ):
if self.graph.get(UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(UpperCAmelCase )
def lowerCamelCase__ ( self : str , UpperCAmelCase : Dict=-2 , UpperCAmelCase : Tuple=-1 ):
if s == d:
return []
__lowerCamelCase : Optional[int] = []
__lowerCamelCase : Union[str, Any] = []
if s == -2:
__lowerCamelCase : Optional[int] = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : Optional[int] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : Dict = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : Any = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : List[Any] = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : List[str] = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return visited
def lowerCamelCase__ ( self : int , UpperCAmelCase : Any=-1 ):
if c == -1:
__lowerCamelCase : List[str] = floor(random() * 10000 ) + 10
for i in range(UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__lowerCamelCase : List[str] = floor(random() * c ) + 1
if n != i:
self.add_pair(UpperCAmelCase , UpperCAmelCase , 1 )
def lowerCamelCase__ ( self : Optional[Any] , UpperCAmelCase : Dict=-2 ):
__lowerCamelCase : List[Any] = deque()
__lowerCamelCase : str = []
if s == -2:
__lowerCamelCase : Dict = list(self.graph )[0]
d.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
while d:
__lowerCamelCase : Any = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase__ ( self : str , UpperCAmelCase : Optional[int] ):
__lowerCamelCase : List[Any] = 0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def lowerCamelCase__ ( self : Dict , UpperCAmelCase : List[Any] ):
return len(self.graph[u] )
def lowerCamelCase__ ( self : List[str] , UpperCAmelCase : Dict=-2 ):
__lowerCamelCase : Optional[Any] = []
__lowerCamelCase : str = []
if s == -2:
__lowerCamelCase : str = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : Dict = s
__lowerCamelCase : Optional[Any] = []
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : Any = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : str = node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : Optional[Any] = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : int = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return sorted_nodes
def lowerCamelCase__ ( self : Tuple ):
__lowerCamelCase : Optional[Any] = []
__lowerCamelCase : Optional[int] = []
__lowerCamelCase : int = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : Union[str, Any] = -2
__lowerCamelCase : Dict = []
__lowerCamelCase : List[str] = s
__lowerCamelCase : Optional[int] = False
__lowerCamelCase : List[Any] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : List[str] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase : Optional[int] = len(UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : List[str] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase : str = True
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : Tuple = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : Dict = False
indirect_parents.append(UpperCAmelCase )
__lowerCamelCase : Optional[int] = s
__lowerCamelCase : Any = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return list(UpperCAmelCase )
def lowerCamelCase__ ( self : Optional[int] ):
__lowerCamelCase : Optional[int] = []
__lowerCamelCase : int = []
__lowerCamelCase : str = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : Optional[Any] = -2
__lowerCamelCase : Any = []
__lowerCamelCase : int = s
__lowerCamelCase : str = False
__lowerCamelCase : Optional[int] = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : Optional[int] = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase : Optional[int] = len(UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase : Union[str, Any] = True
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : Dict = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : Optional[Any] = False
indirect_parents.append(UpperCAmelCase )
__lowerCamelCase : List[Any] = s
__lowerCamelCase : Optional[int] = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return False
def lowerCamelCase__ ( self : Optional[Any] , UpperCAmelCase : List[Any]=-2 , UpperCAmelCase : List[str]=-1 ):
__lowerCamelCase : List[str] = time()
self.dfs(UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Dict = time()
return end - begin
def lowerCamelCase__ ( self : Optional[int] , UpperCAmelCase : Dict=-2 ):
__lowerCamelCase : int = time()
self.bfs(UpperCAmelCase )
__lowerCamelCase : Tuple = time()
return end - begin
class _snake_case :
def __init__( self : List[Any] ):
__lowerCamelCase : List[str] = {}
def lowerCamelCase__ ( self : Optional[int] , UpperCAmelCase : Dict , UpperCAmelCase : str , UpperCAmelCase : Optional[int]=1 ):
# check if the u exists
if self.graph.get(UpperCAmelCase ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
__lowerCamelCase : Optional[Any] = [[w, v]]
# add the other way
if self.graph.get(UpperCAmelCase ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
__lowerCamelCase : int = [[w, u]]
def lowerCamelCase__ ( self : List[Any] , UpperCAmelCase : List[Any] , UpperCAmelCase : Tuple ):
if self.graph.get(UpperCAmelCase ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(UpperCAmelCase )
# the other way round
if self.graph.get(UpperCAmelCase ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(UpperCAmelCase )
def lowerCamelCase__ ( self : Tuple , UpperCAmelCase : int=-2 , UpperCAmelCase : List[Any]=-1 ):
if s == d:
return []
__lowerCamelCase : Any = []
__lowerCamelCase : Optional[int] = []
if s == -2:
__lowerCamelCase : Optional[int] = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : List[str] = s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : List[str] = s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(UpperCAmelCase )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : List[Any] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : Optional[Any] = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : Union[str, Any] = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return visited
def lowerCamelCase__ ( self : str , UpperCAmelCase : str=-1 ):
if c == -1:
__lowerCamelCase : List[str] = floor(random() * 10000 ) + 10
for i in range(UpperCAmelCase ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
__lowerCamelCase : int = floor(random() * c ) + 1
if n != i:
self.add_pair(UpperCAmelCase , UpperCAmelCase , 1 )
def lowerCamelCase__ ( self : List[Any] , UpperCAmelCase : List[Any]=-2 ):
__lowerCamelCase : Dict = deque()
__lowerCamelCase : Optional[int] = []
if s == -2:
__lowerCamelCase : Union[str, Any] = list(self.graph )[0]
d.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
while d:
__lowerCamelCase : str = d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def lowerCamelCase__ ( self : Dict , UpperCAmelCase : Any ):
return len(self.graph[u] )
def lowerCamelCase__ ( self : Union[str, Any] ):
__lowerCamelCase : Optional[int] = []
__lowerCamelCase : str = []
__lowerCamelCase : Optional[int] = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : Optional[Any] = -2
__lowerCamelCase : str = []
__lowerCamelCase : Dict = s
__lowerCamelCase : List[str] = False
__lowerCamelCase : int = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase : Any = len(UpperCAmelCase ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : int = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase : Union[str, Any] = True
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : Tuple = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : List[str] = False
indirect_parents.append(UpperCAmelCase )
__lowerCamelCase : str = s
__lowerCamelCase : Dict = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return list(UpperCAmelCase )
def lowerCamelCase__ ( self : Optional[Any] ):
__lowerCamelCase : Any = []
__lowerCamelCase : Union[str, Any] = []
__lowerCamelCase : Optional[Any] = list(self.graph )[0]
stack.append(UpperCAmelCase )
visited.append(UpperCAmelCase )
__lowerCamelCase : Dict = -2
__lowerCamelCase : Dict = []
__lowerCamelCase : List[str] = s
__lowerCamelCase : Dict = False
__lowerCamelCase : Dict = set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
__lowerCamelCase : Dict = s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
__lowerCamelCase : Tuple = len(UpperCAmelCase ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
__lowerCamelCase : Optional[int] = node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
__lowerCamelCase : int = True
if len(UpperCAmelCase ) != 0:
__lowerCamelCase : Any = stack[len(UpperCAmelCase ) - 1]
else:
__lowerCamelCase : Tuple = False
indirect_parents.append(UpperCAmelCase )
__lowerCamelCase : Optional[Any] = s
__lowerCamelCase : Union[str, Any] = ss
# check if se have reached the starting point
if len(UpperCAmelCase ) == 0:
return False
def lowerCamelCase__ ( self : Any ):
return list(self.graph )
def lowerCamelCase__ ( self : Optional[int] , UpperCAmelCase : Union[str, Any]=-2 , UpperCAmelCase : Optional[Any]=-1 ):
__lowerCamelCase : int = time()
self.dfs(UpperCAmelCase , UpperCAmelCase )
__lowerCamelCase : Any = time()
return end - begin
def lowerCamelCase__ ( self : int , UpperCAmelCase : List[Any]=-2 ):
__lowerCamelCase : int = time()
self.bfs(UpperCAmelCase )
__lowerCamelCase : Tuple = time()
return end - begin
| 135
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 135
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case :List[str] = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''TimeSeriesTransformerConfig''',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__snake_case :Union[str, Any] = [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimeSeriesTransformerForPrediction''',
'''TimeSeriesTransformerModel''',
'''TimeSeriesTransformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__snake_case :int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 131
|
def __snake_case ( _UpperCAmelCase = 1000000 ):
__a = limit + 1
__a = [0] * limit
for first_term in range(1 , _UpperCAmelCase ):
for n in range(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
__a = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
__a = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(f'{solution() = }')
| 131
| 1
|
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case :int = logging.get_logger(__name__)
__snake_case :Union[str, Any] = '''▁'''
__snake_case :List[str] = {'''vocab_file''': '''prophetnet.tokenizer'''}
__snake_case :List[str] = {
'''vocab_file''': {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
),
}
}
__snake_case :Dict = {
'''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}
__snake_case :str = {
'''microsoft/xprophetnet-large-wiki100-cased''': 512,
}
def __snake_case ( _UpperCAmelCase ):
__a = collections.OrderedDict()
with open(_UpperCAmelCase , '''r''' , encoding='''utf-8''' ) as reader:
__a = reader.readlines()
for index, token in enumerate(_UpperCAmelCase ):
__a = token.rstrip('''\n''' )
__a = index
return vocab
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[Any] = VOCAB_FILES_NAMES
UpperCamelCase__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase__ : Dict = ['''input_ids''', '''attention_mask''']
def __init__( self : str , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Optional[Any]="[SEP]" , __SCREAMING_SNAKE_CASE : List[str]="[SEP]" , __SCREAMING_SNAKE_CASE : str="[SEP]" , __SCREAMING_SNAKE_CASE : Optional[int]="[UNK]" , __SCREAMING_SNAKE_CASE : str="[PAD]" , __SCREAMING_SNAKE_CASE : Union[str, Any]="[CLS]" , __SCREAMING_SNAKE_CASE : Tuple="[MASK]" , __SCREAMING_SNAKE_CASE : Optional[Dict[str, Any]] = None , **__SCREAMING_SNAKE_CASE : Union[str, Any] , ):
'''simple docstring'''
__a = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__SCREAMING_SNAKE_CASE , eos_token=__SCREAMING_SNAKE_CASE , sep_token=__SCREAMING_SNAKE_CASE , unk_token=__SCREAMING_SNAKE_CASE , pad_token=__SCREAMING_SNAKE_CASE , cls_token=__SCREAMING_SNAKE_CASE , mask_token=__SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **__SCREAMING_SNAKE_CASE , )
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
''' pip install sentencepiece''')
raise
__a = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(__SCREAMING_SNAKE_CASE))
__a = 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'
# put special tokens and [unused] tokens into the vocab
__a = {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}
for i in range(10):
__a = F'[unused{i}]'
__a = 5 + i
# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
__a = 12
__a = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
for k in self.fairseq_tokens_to_ids.keys():
self.unique_no_split_tokens.append(__SCREAMING_SNAKE_CASE)
def __getstate__( self : Dict):
'''simple docstring'''
__a = self.__dict__.copy()
__a = None
return state
def __setstate__( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int]):
'''simple docstring'''
__a = d
try:
import sentencepiece as spm
except ImportError:
logger.warning(
'''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
''' pip install sentencepiece''')
raise
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
__a = {}
__a = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _lowerCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None , __SCREAMING_SNAKE_CASE : bool = False):
'''simple docstring'''
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 ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]
return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1]
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None):
'''simple docstring'''
__a = [self.sep_token_id]
if token_ids_a is None:
return len(token_ids_a + sep) * [0]
return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]
@property
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
return len(self.sp_model) + self.fairseq_offset
def _lowerCamelCase ( self : int):
'''simple docstring'''
__a = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _lowerCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : str):
'''simple docstring'''
return self.sp_model.encode(__SCREAMING_SNAKE_CASE , out_type=__SCREAMING_SNAKE_CASE)
def _lowerCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : Dict):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__a = 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 _lowerCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]):
'''simple docstring'''
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 _lowerCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple):
'''simple docstring'''
__a = ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE , ''' ''').strip()
return out_string
def _lowerCamelCase ( self : List[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(__SCREAMING_SNAKE_CASE):
logger.error(F'Vocabulary path ({save_directory}) should be a directory')
return
__a = 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:
__a = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE)
return (out_vocab_file,)
def _lowerCamelCase ( self : int , __SCREAMING_SNAKE_CASE : List[int] , __SCREAMING_SNAKE_CASE : Optional[List[int]] = None):
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.sep_token_id]
__a = [self.sep_token_id]
return token_ids_a + sep + token_ids_a + sep
| 49
|
import math
class _snake_case :
def __init__( self , _lowerCamelCase=0 ): # a graph with Node 0,1,...,N-1
a :Optional[int] = n
a :Union[str, Any] = [
[math.inf for j in range(0 , _lowerCamelCase )] for i in range(0 , _lowerCamelCase )
] # adjacency matrix for weight
a :List[Any] = [
[math.inf for j in range(0 , _lowerCamelCase )] for i in range(0 , _lowerCamelCase )
] # dp[i][j] stores minimum distance from i to j
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
a :Tuple = w
def SCREAMING_SNAKE_CASE__ ( self ):
for k in range(0 , self.n ):
for i in range(0 , self.n ):
for j in range(0 , self.n ):
a :Union[str, Any] = min(self.dp[i][j] , self.dp[i][k] + self.dp[k][j] )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase ):
return self.dp[u][v]
if __name__ == "__main__":
snake_case : str = Graph(5)
graph.add_edge(0, 2, 9)
graph.add_edge(0, 4, 10)
graph.add_edge(1, 3, 5)
graph.add_edge(2, 3, 7)
graph.add_edge(3, 0, 10)
graph.add_edge(3, 1, 2)
graph.add_edge(3, 2, 1)
graph.add_edge(3, 4, 6)
graph.add_edge(4, 1, 3)
graph.add_edge(4, 2, 4)
graph.add_edge(4, 3, 9)
graph.floyd_warshall()
graph.show_min(1, 4)
graph.show_min(0, 3)
| 94
| 0
|
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> bool:
snake_case__ : List[str] = len(_lowerCAmelCase )
snake_case__ : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
snake_case__ : str = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
snake_case__ : Tuple = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
snake_case__ : Optional[int] = subset[i - 1][j]
if arr[i - 1] <= j:
snake_case__ : int = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43
|
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : int ):
snake_case__ : List[str] = """hf-internal-testing/tiny-random-t5"""
snake_case__ : Any = AutoTokenizer.from_pretrained(snake_case_ )
snake_case__ : Optional[Any] = AutoModelForSeqaSeqLM.from_pretrained(snake_case_ )
snake_case__ : Union[str, Any] = tokenizer("""This is me""" , return_tensors="""pt""" )
snake_case__ : str = model.to_bettertransformer()
self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
snake_case__ : Optional[int] = model.generate(**snake_case_ )
snake_case__ : Any = model.reverse_bettertransformer()
self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(snake_case_ )
snake_case__ : int = AutoModelForSeqaSeqLM.from_pretrained(snake_case_ )
self.assertFalse(
any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
snake_case__ : Optional[Any] = model_reloaded.generate(**snake_case_ )
self.assertTrue(torch.allclose(snake_case_ , snake_case_ ) )
def lowerCamelCase ( self : List[Any] ):
snake_case__ : Optional[Any] = """hf-internal-testing/tiny-random-t5"""
snake_case__ : List[str] = AutoModelForSeqaSeqLM.from_pretrained(snake_case_ )
snake_case__ : int = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(snake_case_ ):
model.save_pretrained(snake_case_ )
snake_case__ : int = model.reverse_bettertransformer()
model.save_pretrained(snake_case_ )
| 43
| 1
|
'''simple docstring'''
from jiwer import compute_measures
import datasets
A__ : int ='''\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'''
A__ : str ='''\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'''
A__ : Dict ='''\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n >>> references = [\"this is the reference\", \"there is another one\"]\n >>> wer = datasets.load_metric(\"wer\")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
def lowercase__ ( self : Dict ) -> List[str]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/jitsi/jiwer/"""] , reference_urls=[
"""https://en.wikipedia.org/wiki/Word_error_rate""",
] , )
def lowercase__ ( self : Union[str, Any] , __snake_case : Optional[int]=None , __snake_case : Optional[Any]=None , __snake_case : int=False ) -> str:
if concatenate_texts:
return compute_measures(__snake_case , __snake_case )["wer"]
else:
_lowerCAmelCase = 0
_lowerCAmelCase = 0
for prediction, reference in zip(__snake_case , __snake_case ):
_lowerCAmelCase = compute_measures(__snake_case , __snake_case )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 70
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base/resolve/main/config.json",
}
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Any = '''git_vision_model'''
def __init__( self, A=768, A=3_072, A=12, A=12, A=3, A=224, A=16, A="quick_gelu", A=1E-5, A=0.0, A=0.02, **A, ):
'''simple docstring'''
super().__init__(**A )
SCREAMING_SNAKE_CASE : Any = hidden_size
SCREAMING_SNAKE_CASE : Any = intermediate_size
SCREAMING_SNAKE_CASE : Union[str, Any] = num_hidden_layers
SCREAMING_SNAKE_CASE : str = num_attention_heads
SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE : List[str] = patch_size
SCREAMING_SNAKE_CASE : Optional[int] = image_size
SCREAMING_SNAKE_CASE : Dict = initializer_range
SCREAMING_SNAKE_CASE : List[Any] = attention_dropout
SCREAMING_SNAKE_CASE : List[str] = layer_norm_eps
SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_act
@classmethod
def UpperCamelCase_ ( cls, A, **A ):
'''simple docstring'''
cls._set_token_in_kwargs(A )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = cls.get_config_dict(A, **A )
# get the vision config dict if we are loading from GITConfig
if config_dict.get('model_type' ) == "git":
SCREAMING_SNAKE_CASE : Optional[Any] = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls, 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(A, **A )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A : Optional[Any] = '''git'''
def __init__( self, A=None, A=30_522, A=768, A=6, A=12, A=3_072, A="gelu", A=0.1, A=0.1, A=1_024, A=0.02, A=1E-12, A=0, A="absolute", A=True, A=False, A=101, A=102, A=None, **A, ):
'''simple docstring'''
super().__init__(bos_token_id=A, eos_token_id=A, pad_token_id=A, **A )
if vision_config is None:
SCREAMING_SNAKE_CASE : List[str] = {}
logger.info('vision_config is None. initializing the GitVisionConfig with default values.' )
SCREAMING_SNAKE_CASE : List[str] = GitVisionConfig(**A )
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = hidden_size
SCREAMING_SNAKE_CASE : Any = num_hidden_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = num_attention_heads
SCREAMING_SNAKE_CASE : Dict = hidden_act
SCREAMING_SNAKE_CASE : List[Any] = intermediate_size
SCREAMING_SNAKE_CASE : str = hidden_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : int = max_position_embeddings
SCREAMING_SNAKE_CASE : List[str] = initializer_range
SCREAMING_SNAKE_CASE : Union[str, Any] = layer_norm_eps
SCREAMING_SNAKE_CASE : int = position_embedding_type
SCREAMING_SNAKE_CASE : str = use_cache
SCREAMING_SNAKE_CASE : int = tie_word_embeddings
SCREAMING_SNAKE_CASE : Optional[int] = num_image_with_embedding
SCREAMING_SNAKE_CASE : List[str] = bos_token_id
SCREAMING_SNAKE_CASE : int = eos_token_id
def UpperCamelCase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE : int = self.__class__.model_type
return output
| 251
| 0
|
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__lowerCAmelCase = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__lowerCAmelCase = [file for file in filepaths if file != file.lower()]
if upper_files:
print(f"""{len(upper_files)} files contain uppercase characters:""")
print('''\n'''.join(upper_files) + '''\n''')
__lowerCAmelCase = [file for file in filepaths if ''' ''' in file]
if space_files:
print(f"""{len(space_files)} files contain space characters:""")
print('''\n'''.join(space_files) + '''\n''')
__lowerCAmelCase = [file for file in filepaths if '''-''' in file]
if hyphen_files:
print(f"""{len(hyphen_files)} files contain hyphen characters:""")
print('''\n'''.join(hyphen_files) + '''\n''')
__lowerCAmelCase = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(f"""{len(nodir_files)} files are not in a directory:""")
print('''\n'''.join(nodir_files) + '''\n''')
__lowerCAmelCase = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 367
|
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def __lowerCamelCase ( lowerCAmelCase_ ) -> int:
for i in range(0 , lowerCAmelCase_ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 ): # printing stars
print('* ' , end='' )
print()
def __lowerCamelCase ( lowerCAmelCase_ ) -> Union[str, Any]:
for i in range(lowerCAmelCase_ , 0 , -1 ):
for _ in range(lowerCAmelCase_ , 0 , -1 ): # printing stars
print('* ' , end='' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(' ' , end='' )
def __lowerCamelCase ( lowerCAmelCase_ ) -> Tuple:
if n <= 0:
print(' ... .... nothing printing :(' )
return
floyd(lowerCAmelCase_ ) # upper half
reverse_floyd(lowerCAmelCase_ ) # lower half
if __name__ == "__main__":
print(r'''| /\ | |- | |- |--| |\ /| |-''')
print(r'''|/ \| |- |_ |_ |__| | \/ | |_''')
__lowerCAmelCase = 1
while K:
__lowerCAmelCase = int(input('''enter the number and , and see the magic : '''))
print()
pretty_print(user_number)
__lowerCAmelCase = int(input('''press 0 to exit... and 1 to continue...'''))
print('''Good Bye...''')
| 107
| 0
|
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matrix.shape
a , a = constant_matrix.shape
if rowsa != colsa:
a = F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
raise ValueError(A )
if colsa != 1:
a = F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
raise ValueError(A )
if rowsa != rowsa:
a = (
"Coefficient and constant matrices dimensions must be nxn and nx1 but "
F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
)
raise ValueError(A )
if len(A ) != rowsa:
a = (
"Number of initial values must be equal to number of rows in coefficient "
F"""matrix but received {len(A )} and {rowsa}"""
)
raise ValueError(A )
if iterations <= 0:
raise ValueError("Iterations must be at least 1" )
a = np.concatenate(
(coefficient_matrix, constant_matrix), axis=1 )
a , a = table.shape
strictly_diagonally_dominant(A )
# Iterates the whole matrix for given number of times
for _ in range(A ):
a = []
for row in range(A ):
a = 0
for col in range(A ):
if col == row:
a = table[row][col]
elif col == cols - 1:
a = table[row][col]
else:
temp += (-1) * table[row][col] * init_val[col]
a = (temp + val) / denom
new_val.append(A )
a = new_val
return [float(A ) for i in new_val]
def __magic_name__ ( A : NDArray[floataa] ):
'''simple docstring'''
a , a = table.shape
a = True
for i in range(0, A ):
a = 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()
| 107
|
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if board[row][i] == 1:
return False
for i in range(len(__A ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__A , -1 , -1 ) , range(__A , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__A , -1 , -1 ) , range(__A , len(__A ) ) ):
if board[i][j] == 1:
return False
return True
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int ) -> bool:
"""simple docstring"""
if row >= len(__A ):
solution.append(__A )
printboard(__A )
print()
return True
for i in range(len(__A ) ):
if is_safe(__A , __A , __A ):
a_ : Any = 1
solve(__A , row + 1 )
a_ : Tuple = 0
return False
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] ) -> None:
"""simple docstring"""
for i in range(len(__A ) ):
for j in range(len(__A ) ):
if board[i][j] == 1:
print('Q' , end=' ' )
else:
print('.' , end=' ' )
print()
# n=int(input("The no. of queens"))
UpperCAmelCase_ : List[str] = 8
UpperCAmelCase_ : str = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print('The total no. of solutions are :', len(solution))
| 32
| 0
|
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
raise Exception('''requires fairseq < v2''')
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = '''Hello, World!'''
__snake_case = '''en_XX'''
def lowerCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] =Path('''data_bin''' )
UpperCAmelCase : Optional[int] =FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(__SCREAMING_SNAKE_CASE ).parent ) , checkpoint_file=Path(__SCREAMING_SNAKE_CASE ).name , _name='''xmod_base''' , arch='''xmod_base''' , task='''multilingual_masked_lm''' , data_name_or_path=str(__SCREAMING_SNAKE_CASE ) , bpe='''sentencepiece''' , sentencepiece_model=str(Path(__SCREAMING_SNAKE_CASE ).parent / '''sentencepiece.bpe.model''' ) , src_dict=str(data_dir / '''dict.txt''' ) , )
xmod.eval() # disable dropout
print(__SCREAMING_SNAKE_CASE )
UpperCAmelCase : Tuple =xmod.model.encoder.sentence_encoder
UpperCAmelCase : Dict =XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings , hidden_size=xmod.cfg.model.encoder_embed_dim , num_hidden_layers=xmod.cfg.model.encoder_layers , num_attention_heads=xmod.cfg.model.encoder_attention_heads , intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1e-5 , pre_norm=xmod.cfg.model.encoder_normalize_before , adapter_reduction_factor=getattr(xmod.cfg.model , '''bottleneck''' , 2 ) , adapter_layer_norm=xmod.cfg.model.adapter_layer_norm , adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm , ln_before_adapter=xmod.cfg.model.ln_before_adapter , languages=xmod.cfg.model.languages , )
if classification_head:
UpperCAmelCase : str =xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print('''Our X-MOD config:''' , __SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[Any] =XmodForSequenceClassification(__SCREAMING_SNAKE_CASE ) if classification_head else XmodForMaskedLM(__SCREAMING_SNAKE_CASE )
model.eval()
# Now let's copy all the weights.
# Embeddings
UpperCAmelCase : str =xmod_sent_encoder.embed_tokens.weight
UpperCAmelCase : Optional[Any] =xmod_sent_encoder.embed_positions.weight
UpperCAmelCase : Tuple =torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
UpperCAmelCase : List[str] =xmod_sent_encoder.layernorm_embedding.weight
UpperCAmelCase : int =xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
UpperCAmelCase : int =model.roberta.encoder.layer[i]
UpperCAmelCase : Dict =xmod_sent_encoder.layers[i]
# self attention
UpperCAmelCase : int =layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError('''Dimensions of self-attention weights do not match.''' )
UpperCAmelCase : int =xmod_layer.self_attn.q_proj.weight
UpperCAmelCase : Tuple =xmod_layer.self_attn.q_proj.bias
UpperCAmelCase : Dict =xmod_layer.self_attn.k_proj.weight
UpperCAmelCase : Any =xmod_layer.self_attn.k_proj.bias
UpperCAmelCase : Tuple =xmod_layer.self_attn.v_proj.weight
UpperCAmelCase : str =xmod_layer.self_attn.v_proj.bias
# self-attention output
UpperCAmelCase : List[str] =layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError('''Dimensions of self-attention output weights do not match.''' )
UpperCAmelCase : List[Any] =xmod_layer.self_attn.out_proj.weight
UpperCAmelCase : List[Any] =xmod_layer.self_attn.out_proj.bias
UpperCAmelCase : Optional[int] =xmod_layer.self_attn_layer_norm.weight
UpperCAmelCase : List[Any] =xmod_layer.self_attn_layer_norm.bias
# intermediate
UpperCAmelCase : str =layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of intermediate weights do not match.''' )
UpperCAmelCase : Any =xmod_layer.fca.weight
UpperCAmelCase : Tuple =xmod_layer.fca.bias
# output
UpperCAmelCase : Optional[Any] =layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError('''Dimensions of feed-forward weights do not match.''' )
UpperCAmelCase : Optional[Any] =xmod_layer.fca.weight
UpperCAmelCase : Tuple =xmod_layer.fca.bias
UpperCAmelCase : List[str] =xmod_layer.final_layer_norm.weight
UpperCAmelCase : Optional[int] =xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
UpperCAmelCase : Optional[Any] =xmod_layer.adapter_layer_norm.weight
UpperCAmelCase : str =xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError('''Lists of language adapters do not match.''' )
for lang_code, adapter in xmod_layer.adapter_modules.items():
UpperCAmelCase : Dict =bert_output.adapter_modules[lang_code]
UpperCAmelCase : List[str] =xmod_layer.adapter_modules[lang_code]
UpperCAmelCase : Any =from_adapter.fca.weight
UpperCAmelCase : Any =from_adapter.fca.bias
UpperCAmelCase : Tuple =from_adapter.fca.weight
UpperCAmelCase : Optional[int] =from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
UpperCAmelCase : Dict =xmod_sent_encoder.layer_norm.weight
UpperCAmelCase : Any =xmod_sent_encoder.layer_norm.bias
if classification_head:
UpperCAmelCase : int =xmod.model.classification_heads["mnli"].dense.weight
UpperCAmelCase : int =xmod.model.classification_heads["mnli"].dense.bias
UpperCAmelCase : Optional[Any] =xmod.model.classification_heads["mnli"].out_proj.weight
UpperCAmelCase : Union[str, Any] =xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
UpperCAmelCase : Optional[int] =xmod.model.encoder.lm_head.dense.weight
UpperCAmelCase : Optional[Any] =xmod.model.encoder.lm_head.dense.bias
UpperCAmelCase : Any =xmod.model.encoder.lm_head.layer_norm.weight
UpperCAmelCase : Tuple =xmod.model.encoder.lm_head.layer_norm.bias
UpperCAmelCase : Any =xmod.model.encoder.lm_head.weight
UpperCAmelCase : Tuple =xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
UpperCAmelCase : str =xmod.encode(__SCREAMING_SNAKE_CASE ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(__SCREAMING_SNAKE_CASE )
UpperCAmelCase : Union[str, Any] =model(__SCREAMING_SNAKE_CASE )[0]
if classification_head:
UpperCAmelCase : Optional[int] =xmod.model.classification_heads["mnli"](xmod.extract_features(__SCREAMING_SNAKE_CASE ) )
else:
UpperCAmelCase : int =xmod.model(__SCREAMING_SNAKE_CASE , lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape , their_output.shape )
UpperCAmelCase : Optional[int] =torch.max(torch.abs(our_output - their_output ) ).item()
print(f'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
UpperCAmelCase : Tuple =torch.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , atol=1e-3 )
print('''Do both models output the same tensors?''' , '''🔥''' if success else '''💩''' )
if not success:
raise Exception('''Something went wRoNg''' )
Path(__SCREAMING_SNAKE_CASE ).mkdir(parents=__SCREAMING_SNAKE_CASE , exist_ok=__SCREAMING_SNAKE_CASE )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
__snake_case = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 369
|
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__lowerCamelCase : Union[str, Any] = """CLIPImageProcessor"""
__lowerCamelCase : Any = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case__=None , snake_case__=None , **snake_case__ ) -> List[str]:
'''simple docstring'''
UpperCAmelCase : Optional[int] =None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , snake_case__ , )
UpperCAmelCase : int =kwargs.pop('''feature_extractor''' )
UpperCAmelCase : Tuple =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`.''' )
super().__init__(snake_case__ , snake_case__ )
def __call__( self , snake_case__=None , snake_case__=None , snake_case__=None , **snake_case__ ) -> Optional[Any]:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
UpperCAmelCase : List[Any] =self.tokenizer(snake_case__ , return_tensors=snake_case__ , **snake_case__ )
if images is not None:
UpperCAmelCase : Tuple =self.image_processor(snake_case__ , return_tensors=snake_case__ , **snake_case__ )
if text is not None and images is not None:
UpperCAmelCase : List[Any] =image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**snake_case__ ) , tensor_type=snake_case__ )
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> Optional[int]:
'''simple docstring'''
return self.tokenizer.batch_decode(*snake_case__ , **snake_case__ )
def UpperCAmelCase__ ( self , *snake_case__ , **snake_case__ ) -> List[Any]:
'''simple docstring'''
return self.tokenizer.decode(*snake_case__ , **snake_case__ )
@property
def UpperCAmelCase__ ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[str] =self.tokenizer.model_input_names
UpperCAmelCase : Union[str, Any] =self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def UpperCAmelCase__ ( self ) -> str:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , snake_case__ , )
return self.image_processor_class
@property
def UpperCAmelCase__ ( self ) -> Tuple:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , snake_case__ , )
return self.image_processor
| 78
| 0
|
from math import isclose, sqrt
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = point_y / 4 / point_x
lowerCAmelCase__ : int = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
lowerCAmelCase__ : Tuple = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
lowerCAmelCase__ : Dict = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
lowerCAmelCase__ : int = outgoing_gradient**2 + 4
lowerCAmelCase__ : Optional[int] = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
lowerCAmelCase__ : Tuple = (point_y - outgoing_gradient * point_x) ** 2 - 100
lowerCAmelCase__ : Union[str, Any] = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
lowerCAmelCase__ : Dict = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
lowerCAmelCase__ : Optional[int] = x_minus if isclose(_a , _a ) else x_plus
lowerCAmelCase__ : Optional[Any] = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def lowerCamelCase_ ( _a = 1.4 , _a = -9.6 ):
"""simple docstring"""
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : float = first_x_coord
lowerCAmelCase__ : float = first_y_coord
lowerCAmelCase__ : float = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Dict = next_point(_a , _a , _a )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'''{solution() = }''')
| 131
|
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
lowerCamelCase = collections.namedtuple('''_Datasets''', ['''train''', '''validation''', '''test'''])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
lowerCamelCase = '''https://storage.googleapis.com/cvdf-datasets/mnist/'''
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Dict = numpy.dtype(numpy.uintaa ).newbyteorder('''>''' )
return numpy.frombuffer(bytestream.read(4 ) , dtype=_a )[0]
@deprecated(_a , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _a ):
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_a ) as bytestream:
lowerCAmelCase__ : Any = _readaa(_a )
if magic != 2_051:
raise ValueError(
'''Invalid magic number %d in MNIST image file: %s''' % (magic, f.name) )
lowerCAmelCase__ : Any = _readaa(_a )
lowerCAmelCase__ : Tuple = _readaa(_a )
lowerCAmelCase__ : List[Any] = _readaa(_a )
lowerCAmelCase__ : Union[str, Any] = bytestream.read(rows * cols * num_images )
lowerCAmelCase__ : List[Any] = numpy.frombuffer(_a , dtype=numpy.uinta )
lowerCAmelCase__ : int = data.reshape(_a , _a , _a , 1 )
return data
@deprecated(_a , '''Please use tf.one_hot on tensors.''' )
def lowerCamelCase_ ( _a , _a ):
"""simple docstring"""
lowerCAmelCase__ : List[Any] = labels_dense.shape[0]
lowerCAmelCase__ : Optional[Any] = numpy.arange(_a ) * num_classes
lowerCAmelCase__ : str = numpy.zeros((num_labels, num_classes) )
lowerCAmelCase__ : Optional[Any] = 1
return labels_one_hot
@deprecated(_a , '''Please use tf.data to implement this functionality.''' )
def lowerCamelCase_ ( _a , _a=False , _a=10 ):
"""simple docstring"""
print('''Extracting''' , f.name )
with gzip.GzipFile(fileobj=_a ) as bytestream:
lowerCAmelCase__ : Optional[int] = _readaa(_a )
if magic != 2_049:
raise ValueError(
'''Invalid magic number %d in MNIST label file: %s''' % (magic, f.name) )
lowerCAmelCase__ : Union[str, Any] = _readaa(_a )
lowerCAmelCase__ : Tuple = bytestream.read(_a )
lowerCAmelCase__ : Dict = numpy.frombuffer(_a , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(_a , _a )
return labels
class _a :
@deprecated(
_SCREAMING_SNAKE_CASE , '''Please use alternatives such as official/mnist/_DataSet.py'''
''' from tensorflow/models.''' , )
def __init__( self : Dict , _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : Tuple=False , _SCREAMING_SNAKE_CASE : Any=False , _SCREAMING_SNAKE_CASE : Optional[Any]=dtypes.floataa , _SCREAMING_SNAKE_CASE : List[str]=True , _SCREAMING_SNAKE_CASE : List[str]=None , )-> List[Any]:
lowerCAmelCase__ , lowerCAmelCase__ : List[Any] = random_seed.get_seed(_SCREAMING_SNAKE_CASE )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowerCAmelCase__ : Optional[int] = dtypes.as_dtype(_SCREAMING_SNAKE_CASE ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError('''Invalid image dtype %r, expected uint8 or float32''' % dtype )
if fake_data:
lowerCAmelCase__ : int = 1_0000
lowerCAmelCase__ : List[Any] = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), F'images.shape: {images.shape} labels.shape: {labels.shape}'
lowerCAmelCase__ : List[Any] = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowerCAmelCase__ : Tuple = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowerCAmelCase__ : Any = images.astype(numpy.floataa )
lowerCAmelCase__ : Any = numpy.multiply(_SCREAMING_SNAKE_CASE , 1.0 / 255.0 )
lowerCAmelCase__ : Tuple = images
lowerCAmelCase__ : Tuple = labels
lowerCAmelCase__ : List[Any] = 0
lowerCAmelCase__ : Tuple = 0
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self._images
@property
def UpperCAmelCase__( self : Tuple )-> Optional[int]:
return self._labels
@property
def UpperCAmelCase__( self : Tuple )-> Dict:
return self._num_examples
@property
def UpperCAmelCase__( self : Tuple )-> Any:
return self._epochs_completed
def UpperCAmelCase__( self : List[Any] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Dict=False , _SCREAMING_SNAKE_CASE : Optional[int]=True )-> List[str]:
if fake_data:
lowerCAmelCase__ : Dict = [1] * 784
lowerCAmelCase__ : Union[str, Any] = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_SCREAMING_SNAKE_CASE )],
[fake_label for _ in range(_SCREAMING_SNAKE_CASE )],
)
lowerCAmelCase__ : str = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowerCAmelCase__ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : List[Any] = self.images[perma]
lowerCAmelCase__ : Tuple = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowerCAmelCase__ : Any = self._num_examples - start
lowerCAmelCase__ : List[str] = self._images[start : self._num_examples]
lowerCAmelCase__ : Tuple = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowerCAmelCase__ : Union[str, Any] = numpy.arange(self._num_examples )
numpy.random.shuffle(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ : str = self.images[perm]
lowerCAmelCase__ : List[Any] = self.labels[perm]
# Start next epoch
lowerCAmelCase__ : Dict = 0
lowerCAmelCase__ : Union[str, Any] = batch_size - rest_num_examples
lowerCAmelCase__ : Any = self._index_in_epoch
lowerCAmelCase__ : Optional[Any] = self._images[start:end]
lowerCAmelCase__ : Optional[Any] = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowerCAmelCase__ : Dict = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(_a , '''Please write your own downloading logic.''' )
def lowerCamelCase_ ( _a , _a , _a ):
"""simple docstring"""
if not gfile.Exists(_a ):
gfile.MakeDirs(_a )
lowerCAmelCase__ : str = os.path.join(_a , _a )
if not gfile.Exists(_a ):
urllib.request.urlretrieve(_a , _a ) # noqa: S310
with gfile.GFile(_a ) as f:
lowerCAmelCase__ : Optional[Any] = f.size()
print('''Successfully downloaded''' , _a , _a , '''bytes.''' )
return filepath
@deprecated(
_a , '''Please use alternatives such as:''' ''' tensorflow_datasets.load(\'mnist\')''' )
def lowerCamelCase_ ( _a , _a=False , _a=False , _a=dtypes.floataa , _a=True , _a=5_000 , _a=None , _a=DEFAULT_SOURCE_URL , ):
"""simple docstring"""
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=_a , one_hot=_a , dtype=_a , seed=_a )
lowerCAmelCase__ : Tuple = fake()
lowerCAmelCase__ : Union[str, Any] = fake()
lowerCAmelCase__ : Tuple = fake()
return _Datasets(train=_a , validation=_a , test=_a )
if not source_url: # empty string check
lowerCAmelCase__ : Optional[Any] = DEFAULT_SOURCE_URL
lowerCAmelCase__ : Tuple = '''train-images-idx3-ubyte.gz'''
lowerCAmelCase__ : Dict = '''train-labels-idx1-ubyte.gz'''
lowerCAmelCase__ : List[str] = '''t10k-images-idx3-ubyte.gz'''
lowerCAmelCase__ : Optional[int] = '''t10k-labels-idx1-ubyte.gz'''
lowerCAmelCase__ : Optional[Any] = _maybe_download(
_a , _a , source_url + train_images_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : Optional[Any] = _extract_images(_a )
lowerCAmelCase__ : Any = _maybe_download(
_a , _a , source_url + train_labels_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : Any = _extract_labels(_a , one_hot=_a )
lowerCAmelCase__ : Any = _maybe_download(
_a , _a , source_url + test_images_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : str = _extract_images(_a )
lowerCAmelCase__ : Dict = _maybe_download(
_a , _a , source_url + test_labels_file )
with gfile.Open(_a , '''rb''' ) as f:
lowerCAmelCase__ : int = _extract_labels(_a , one_hot=_a )
if not 0 <= validation_size <= len(_a ):
lowerCAmelCase__ : Dict = (
'''Validation size should be between 0 and '''
f'{len(_a )}. Received: {validation_size}.'
)
raise ValueError(_a )
lowerCAmelCase__ : List[str] = train_images[:validation_size]
lowerCAmelCase__ : Any = train_labels[:validation_size]
lowerCAmelCase__ : Optional[Any] = train_images[validation_size:]
lowerCAmelCase__ : Optional[int] = train_labels[validation_size:]
lowerCAmelCase__ : Optional[Any] = {'''dtype''': dtype, '''reshape''': reshape, '''seed''': seed}
lowerCAmelCase__ : List[str] = _DataSet(_a , _a , **_a )
lowerCAmelCase__ : Dict = _DataSet(_a , _a , **_a )
lowerCAmelCase__ : Dict = _DataSet(_a , _a , **_a )
return _Datasets(train=_a , validation=_a , test=_a )
| 131
| 1
|
"""simple docstring"""
UpperCamelCase_ = '0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 362
|
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class snake_case ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , __UpperCAmelCase = "▁" , __UpperCAmelCase = True , __UpperCAmelCase = "<unk>" , __UpperCAmelCase = "</s>" , __UpperCAmelCase = "<pad>" , ) ->str:
a_ = {
"pad": {"id": 0, "token": pad_token},
"eos": {"id": 1, "token": eos_token},
"unk": {"id": 2, "token": unk_token},
}
a_ = [None] * len(self.special_tokens)
for token_dict in self.special_tokens.values():
a_ = token_dict["token"]
a_ = Tokenizer(Unigram())
a_ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(" {2,}") , " "),
normalizers.Lowercase(),
])
a_ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase),
pre_tokenizers.Digits(individual_digits=__UpperCAmelCase),
pre_tokenizers.Punctuation(),
])
a_ = decoders.Metaspace(replacement=__UpperCAmelCase , add_prefix_space=__UpperCAmelCase)
a_ = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' , special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])] , )
a_ = {
"model": "SentencePieceUnigram",
"replacement": replacement,
"add_prefix_space": add_prefix_space,
}
super().__init__(__UpperCAmelCase , __UpperCAmelCase)
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = 80_00 , __UpperCAmelCase = True , ) ->Optional[Any]:
a_ = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
if isinstance(__UpperCAmelCase , __UpperCAmelCase):
a_ = [files]
self._tokenizer.train(__UpperCAmelCase , trainer=__UpperCAmelCase)
self.add_unk_id()
def UpperCAmelCase__ ( self , __UpperCAmelCase , __UpperCAmelCase = 80_00 , __UpperCAmelCase = True , ) ->int:
a_ = trainers.UnigramTrainer(
vocab_size=__UpperCAmelCase , special_tokens=self.special_tokens_list , show_progress=__UpperCAmelCase , )
self._tokenizer.train_from_iterator(__UpperCAmelCase , trainer=__UpperCAmelCase)
self.add_unk_id()
def UpperCAmelCase__ ( self) ->Union[str, Any]:
a_ = json.loads(self._tokenizer.to_str())
a_ = self.special_tokens["unk"]["id"]
a_ = Tokenizer.from_str(json.dumps(__UpperCAmelCase))
| 303
| 0
|
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCamelCase__ ( self) -> List[str]:
__UpperCamelCase :Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=__lowercase).to(__lowercase)
__UpperCamelCase :List[Any] = AutoTokenizer.from_pretrained('''google/mt5-small''')
__UpperCamelCase :int = tokenizer('''Hello there''' , return_tensors='''pt''').input_ids
__UpperCamelCase :Tuple = tokenizer('''Hi I am''' , return_tensors='''pt''').input_ids
__UpperCamelCase :int = model(input_ids.to(__lowercase) , labels=labels.to(__lowercase)).loss
__UpperCamelCase :Optional[Any] = -(labels.shape[-1] * loss.item())
__UpperCamelCase :List[Any] = -84.91_27
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1E-4)
| 43
|
# Copyright 2023 The HuggingFace Inc. 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.
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : Optional[Any] = """facebook/bart-large-mnli"""
a__ : int = (
"""This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which """
"""should be the text to classify, and `labels`, which should be the list of labels to use for classification. """
"""It returns the most likely label in the list of provided `labels` for the input text."""
)
a__ : Optional[Any] = """text_classifier"""
a__ : Any = AutoTokenizer
a__ : str = AutoModelForSequenceClassification
a__ : str = ["""text""", ["""text"""]]
a__ : Optional[int] = ["""text"""]
def UpperCamelCase__ ( self) -> Union[str, Any]:
super().setup()
__UpperCamelCase :int = self.model.config
__UpperCamelCase :Optional[Any] = -1
for idx, label in config.idalabel.items():
if label.lower().startswith('''entail'''):
__UpperCamelCase :List[Any] = int(__lowercase)
if self.entailment_id == -1:
raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')
def UpperCamelCase__ ( self , __lowercase , __lowercase) -> Union[str, Any]:
__UpperCamelCase :Any = labels
return self.pre_processor(
[text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )
def UpperCamelCase__ ( self , __lowercase) -> Optional[Any]:
__UpperCamelCase :List[Any] = outputs.logits
__UpperCamelCase :Any = torch.argmax(logits[:, 2]).item()
return self._labels[label_id]
| 43
| 1
|
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging
lowerCAmelCase : List[Any] =logging.get_logger(__name__) # pylint: disable=invalid-name
class a_ ( _lowerCAmelCase ):
def __init__( self : Optional[Any] , lowercase : AutoencoderKL , lowercase : CLIPTextModel , lowercase : CLIPTokenizer , lowercase : UNetaDConditionModel , lowercase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , lowercase : StableDiffusionSafetyChecker , lowercase : CLIPImageProcessor , ):
"""simple docstring"""
super().__init__()
self.register_modules(
vae=lowercase , text_encoder=lowercase , tokenizer=lowercase , unet=lowercase , scheduler=lowercase , safety_checker=lowercase , feature_extractor=lowercase , )
def lowercase__ ( self : Tuple , lowercase : Optional[Union[str, int]] = "auto" ):
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowercase_ :Optional[int] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowercase )
def lowercase__ ( self : int ):
"""simple docstring"""
self.enable_attention_slicing(lowercase )
@torch.no_grad()
def __call__( self : Union[str, Any] , lowercase : Union[str, List[str]] , lowercase : int = 512 , lowercase : int = 512 , lowercase : int = 50 , lowercase : float = 7.5 , lowercase : Optional[Union[str, List[str]]] = None , lowercase : Optional[int] = 1 , lowercase : float = 0.0 , lowercase : Optional[torch.Generator] = None , lowercase : Optional[torch.FloatTensor] = None , lowercase : Optional[str] = "pil" , lowercase : bool = True , lowercase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , lowercase : int = 1 , lowercase : Optional[torch.FloatTensor] = None , **lowercase : int , ):
"""simple docstring"""
if isinstance(lowercase , lowercase ):
lowercase_ :Tuple = 1
elif isinstance(lowercase , lowercase ):
lowercase_ :str = len(lowercase )
else:
raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(lowercase )}' )
if height % 8 != 0 or width % 8 != 0:
raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.' )
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(lowercase , lowercase ) or callback_steps <= 0)
):
raise ValueError(
F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
F' {type(lowercase )}.' )
# get prompt text embeddings
lowercase_ :int = self.tokenizer(
lowercase , padding="max_length" , max_length=self.tokenizer.model_max_length , return_tensors="pt" , )
lowercase_ :Optional[int] = text_inputs.input_ids
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
lowercase_ :Dict = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
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}' )
lowercase_ :List[Any] = text_input_ids[:, : self.tokenizer.model_max_length]
if text_embeddings is None:
lowercase_ :Optional[int] = self.text_encoder(text_input_ids.to(self.device ) )[0]
# duplicate text embeddings for each generation per prompt, using mps friendly method
lowercase_ , lowercase_ , lowercase_ :Optional[int] = text_embeddings.shape
lowercase_ :str = text_embeddings.repeat(1 , lowercase , 1 )
lowercase_ :Union[str, Any] = text_embeddings.view(bs_embed * num_images_per_prompt , lowercase , -1 )
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
lowercase_ :int = guidance_scale > 1.0
# get unconditional embeddings for classifier free guidance
if do_classifier_free_guidance:
lowercase_ :List[str]
if negative_prompt is None:
lowercase_ :Tuple = [""]
elif type(lowercase ) is not type(lowercase ):
raise TypeError(
F'`negative_prompt` should be the same type to `prompt`, but got {type(lowercase )} !='
F' {type(lowercase )}.' )
elif isinstance(lowercase , lowercase ):
lowercase_ :Optional[Any] = [negative_prompt]
elif batch_size != len(lowercase ):
raise ValueError(
F'`negative_prompt`: {negative_prompt} has batch size {len(lowercase )}, but `prompt`:'
F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
" the batch size of `prompt`." )
else:
lowercase_ :Union[str, Any] = negative_prompt
lowercase_ :Optional[int] = text_input_ids.shape[-1]
lowercase_ :Any = self.tokenizer(
lowercase , padding="max_length" , max_length=lowercase , truncation=lowercase , return_tensors="pt" , )
lowercase_ :Tuple = self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
lowercase_ :List[Any] = uncond_embeddings.shape[1]
lowercase_ :str = uncond_embeddings.repeat(lowercase , lowercase , 1 )
lowercase_ :Optional[Any] = uncond_embeddings.view(batch_size * num_images_per_prompt , lowercase , -1 )
# 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
lowercase_ :Dict = torch.cat([uncond_embeddings, text_embeddings] )
# get the initial random noise unless the user supplied it
# Unlike in other pipelines, latents need to be generated in the target device
# for 1-to-1 results reproducibility with the CompVis implementation.
# However this currently doesn't work in `mps`.
lowercase_ :Union[str, Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
lowercase_ :Optional[Any] = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
lowercase_ :int = text_embeddings.dtype
if latents is None:
if self.device.type == "mps":
# randn does not exist on mps
lowercase_ :Optional[Any] = torch.randn(
lowercase , generator=lowercase , device="cpu" , dtype=lowercase ).to(self.device )
lowercase_ :Union[str, Any] = torch.randn(lowercase , generator=lowercase , device="cpu" , dtype=lowercase ).to(
self.device )
else:
lowercase_ :Any = torch.randn(
lowercase , generator=lowercase , device=self.device , dtype=lowercase )
lowercase_ :int = torch.randn(lowercase , generator=lowercase , device=self.device , dtype=lowercase )
else:
if latents_reference.shape != latents_shape:
raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}' )
lowercase_ :Optional[int] = latents_reference.to(self.device )
lowercase_ :int = latents.to(self.device )
# This is the key part of the pipeline where we
# try to ensure that the generated images w/ the same seed
# but different sizes actually result in similar images
lowercase_ :List[str] = (latents_shape[3] - latents_shape_reference[3]) // 2
lowercase_ :List[Any] = (latents_shape[2] - latents_shape_reference[2]) // 2
lowercase_ :str = latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
lowercase_ :str = latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
lowercase_ :Dict = 0 if dx < 0 else dx
lowercase_ :str = 0 if dy < 0 else dy
lowercase_ :Optional[Any] = max(-dx , 0 )
lowercase_ :Tuple = max(-dy , 0 )
# import pdb
# pdb.set_trace()
lowercase_ :List[str] = latents_reference[:, :, dy : dy + h, dx : dx + w]
# set timesteps
self.scheduler.set_timesteps(lowercase )
# Some schedulers like PNDM have timesteps as arrays
# It's more optimized to move all timesteps to correct device beforehand
lowercase_ :List[Any] = self.scheduler.timesteps.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
lowercase_ :Dict = 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]
lowercase_ :Tuple = "eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
lowercase_ :Optional[int] = {}
if accepts_eta:
lowercase_ :Tuple = eta
for i, t in enumerate(self.progress_bar(lowercase ) ):
# expand the latents if we are doing classifier free guidance
lowercase_ :int = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase_ :Any = self.scheduler.scale_model_input(lowercase , lowercase )
# predict the noise residual
lowercase_ :Tuple = self.unet(lowercase , lowercase , encoder_hidden_states=lowercase ).sample
# perform guidance
if do_classifier_free_guidance:
lowercase_ , lowercase_ :Union[str, Any] = noise_pred.chunk(2 )
lowercase_ :List[str] = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
lowercase_ :Tuple = self.scheduler.step(lowercase , lowercase , lowercase , **lowercase ).prev_sample
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(lowercase , lowercase , lowercase )
lowercase_ :str = 1 / 0.1_82_15 * latents
lowercase_ :Dict = self.vae.decode(lowercase ).sample
lowercase_ :Tuple = (image / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase_ :Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if self.safety_checker is not None:
lowercase_ :int = self.feature_extractor(self.numpy_to_pil(lowercase ) , return_tensors="pt" ).to(
self.device )
lowercase_ , lowercase_ :Union[str, Any] = self.safety_checker(
images=lowercase , clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype ) )
else:
lowercase_ :Tuple = None
if output_type == "pil":
lowercase_ :List[Any] = self.numpy_to_pil(lowercase )
if not return_dict:
return (image, has_nsfw_concept)
return StableDiffusionPipelineOutput(images=lowercase , nsfw_content_detected=lowercase )
| 147
|
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a_ ( _lowerCAmelCase ):
__A = ["image_processor", "tokenizer"]
__A = "LayoutLMv3ImageProcessor"
__A = ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")
def __init__( self : int , lowercase : Optional[Any]=None , lowercase : List[str]=None , **lowercase : Optional[int] ):
"""simple docstring"""
lowercase_ :int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , lowercase , )
lowercase_ :Optional[int] = kwargs.pop("feature_extractor" )
lowercase_ :Union[str, Any] = 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`." )
super().__init__(lowercase , lowercase )
def __call__( self : Optional[Any] , lowercase : List[str] , lowercase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowercase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowercase : Union[List[List[int]], List[List[List[int]]]] = None , lowercase : Optional[Union[List[int], List[List[int]]]] = None , lowercase : bool = True , lowercase : Union[bool, str, PaddingStrategy] = False , lowercase : Union[bool, str, TruncationStrategy] = None , lowercase : Optional[int] = None , lowercase : int = 0 , lowercase : Optional[int] = None , lowercase : Optional[bool] = None , lowercase : Optional[bool] = None , lowercase : bool = False , lowercase : bool = False , lowercase : bool = False , lowercase : bool = False , lowercase : bool = True , lowercase : Optional[Union[str, TensorType]] = None , **lowercase : List[Any] , ):
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
# first, apply the image processor
lowercase_ :Dict = self.image_processor(images=lowercase , return_tensors=lowercase )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowercase , lowercase ):
lowercase_ :str = [text] # add batch dimension (as the image processor always adds a batch dimension)
lowercase_ :Union[str, Any] = features["words"]
lowercase_ :Optional[Any] = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_token_type_ids=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
# add pixel values
lowercase_ :Any = features.pop("pixel_values" )
if return_overflowing_tokens is True:
lowercase_ :Any = self.get_overflowing_images(lowercase , encoded_inputs["overflow_to_sample_mapping"] )
lowercase_ :Any = images
return encoded_inputs
def lowercase__ ( self : List[Any] , lowercase : Any , lowercase : Optional[Any] ):
"""simple docstring"""
lowercase_ :Union[str, Any] = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(lowercase ) != len(lowercase ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
F' {len(lowercase )} and {len(lowercase )}' )
return images_with_overflow
def lowercase__ ( self : Union[str, Any] , *lowercase : List[Any] , **lowercase : Optional[Any] ):
"""simple docstring"""
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def lowercase__ ( self : List[Any] , *lowercase : Any , **lowercase : str ):
"""simple docstring"""
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def lowercase__ ( self : Optional[Any] ):
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def lowercase__ ( self : str ):
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , lowercase , )
return self.image_processor_class
@property
def lowercase__ ( self : Tuple ):
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , lowercase , )
return self.image_processor
| 147
| 1
|
"""simple docstring"""
from math import log
from scipy.constants import Boltzmann, physical_constants
SCREAMING_SNAKE_CASE : Union[str, Any] = 300 # TEMPERATURE (unit = K)
def lowercase ( _snake_case : float , _snake_case : float , _snake_case : float , ) ->float:
"""simple docstring"""
if donor_conc <= 0:
raise ValueError('''Donor concentration should be positive''' )
elif acceptor_conc <= 0:
raise ValueError('''Acceptor concentration should be positive''' )
elif intrinsic_conc <= 0:
raise ValueError('''Intrinsic concentration should be positive''' )
elif donor_conc <= intrinsic_conc:
raise ValueError(
'''Donor concentration should be greater than intrinsic concentration''' )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
'''Acceptor concentration should be greater than intrinsic concentration''' )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 102
|
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase : List[Any] = get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')
@require_sentencepiece
@require_tokenizers
class snake_case__ (_UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = GPTSwaTokenizer
SCREAMING_SNAKE_CASE_ : int = False
SCREAMING_SNAKE_CASE_ : Union[str, Any] = True
SCREAMING_SNAKE_CASE_ : Any = False
def __UpperCAmelCase ( self : Tuple ) -> Any:
super().setUp()
# We have a SentencePiece fixture for testing
a = GPTSwaTokenizer(__lowerCamelCase , eos_token="<unk>" , bos_token="<unk>" , pad_token="<unk>" )
tokenizer.save_pretrained(self.tmpdirname )
def __UpperCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Optional[Any] ) -> Any:
a = "This is a test"
a = "This is a test"
return input_text, output_text
def __UpperCAmelCase ( self : List[Any] ) -> List[str]:
a = "<s>"
a = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase ) , __lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase ) , __lowerCamelCase )
def __UpperCAmelCase ( self : List[Any] ) -> int:
a = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<unk>" )
self.assertEqual(vocab_keys[1] , "<s>" )
self.assertEqual(vocab_keys[-1] , "j" )
self.assertEqual(len(__lowerCamelCase ) , 20_00 )
def __UpperCAmelCase ( self : Optional[int] ) -> Dict:
self.assertEqual(self.get_tokenizer().vocab_size , 20_00 )
def __UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
a = GPTSwaTokenizer(__lowerCamelCase )
a = tokenizer.tokenize("This is a test" )
self.assertListEqual(__lowerCamelCase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase ) , [4_65, 2_87, 2_65, 6_31, 8_42] )
a = tokenizer.tokenize("I was born in 92000, and this is falsé." )
# fmt: off
self.assertListEqual(
__lowerCamelCase , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] , )
# fmt: on
a = tokenizer.convert_tokens_to_ids(__lowerCamelCase )
self.assertListEqual(
__lowerCamelCase , [2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60] , )
a = tokenizer.convert_ids_to_tokens(__lowerCamelCase )
# fmt: off
self.assertListEqual(
__lowerCamelCase , ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."] )
# fmt: on
def __UpperCAmelCase ( self : List[Any] ) -> str:
a = GPTSwaTokenizer(__lowerCamelCase )
a = ["This is a test", "I was born in 92000, and this is falsé."]
a = [
[4_65, 2_87, 2_65, 6_31, 8_42],
[2_62, 2_72, 15_25, 2_86, 2_71, 2_68, 60, 9_16, 6_33, 6_33, 6_33, 2_59, 2_66, 3_01, 2_87, 3_84, 3_67, 2_63, 1_98, 1_72, 2_60],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertListEqual(tokenizer.encode_fast(__lowerCamelCase ) , __lowerCamelCase )
# Test that decode_fast returns the input text
for text, token_ids in zip(__lowerCamelCase , __lowerCamelCase ):
self.assertEqual(tokenizer.decode_fast(__lowerCamelCase ) , __lowerCamelCase )
@slow
def __UpperCAmelCase ( self : Optional[int] ) -> List[Any]:
a = [
"<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')",
"Hey there, how are you doing this fine day?",
"This is a text with a trailing spaces followed by a dot .",
"Häj sväjs lillebrör! =)",
"Det är inget fel på Mr. Cool",
]
# fmt: off
a = {"input_ids": [[6_34_23, 5, 68_11, 1_49_54, 2_82, 8_16, 38_21, 6_34_66, 6_34_25, 6_34_62, 18, 6_39_78, 6_78, 3_01, 13_20, 6_34_23, 6_34_55, 6_34_58, 18, 6_39_82, 42_46, 39_40, 19_01, 4_77_89, 55_47, 1_89_94], [1_96_30, 11_00, 6_34_46, 13_42, 6_33, 5_44, 44_88, 5_93, 51_02, 24_16, 6_34_95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [16_52, 4_28, 2_68, 19_36, 5_15, 2_68, 5_85_93, 2_24_13, 91_06, 5_46, 2_68, 3_32_13, 6_39_79, 6_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_51_30, 6_34_50, 9_24, 6_34_49, 22_49, 40_62, 15_58, 3_18, 6_35_04, 2_14_98, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_09, 3_77, 28_27, 25_59, 3_32, 65_75, 6_34_43, 2_68_01, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__lowerCamelCase , model_name="AI-Sweden/gpt-sw3-126m" , sequences=__lowerCamelCase , )
| 107
| 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_ ) -> Tuple:
_a : Tuple = []
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_ , lowerCAmelCase_ ) -> int:
_a : int = []
for d in reversed(lowerCAmelCase_ ):
idx.append(flat_idx % d )
_a : Dict = flat_idx // d
return tuple(reversed(lowerCAmelCase_ ) )
@torch.jit.ignore
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = None , lowerCAmelCase_ = None , ) -> List[str]:
# 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_ ) -> None:
_a : Union[str, Any] = True
for i in range(len(lowerCAmelCase_ ) ):
_a : int = -1 * (i + 1)
l[reversed_idx] &= tally
_a : Tuple = l[reversed_idx]
if start_edges is None:
_a : Any = [s == 0 for s in start]
reduce_edge_list(lowerCAmelCase_ )
if end_edges is None:
_a : List[str] = [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 ),)]
_a : List[Tuple[slice, ...]] = []
_a : List[slice] = []
# 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
_a : Tuple[slice, ...] = tuple(lowerCAmelCase_ )
_a : Union[str, Any] = 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
_a : int = 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
_a : Optional[Any] = 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() )
_a : Optional[Any] = 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_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> Dict:
_a : List[Any] = t.shape[:no_batch_dims]
_a : Optional[int] = list(_flat_idx_to_idx(lowerCAmelCase_ , lowerCAmelCase_ ) )
# _get_minimal_slice_set is inclusive
_a : Optional[Any] = list(_flat_idx_to_idx(flat_end - 1 , lowerCAmelCase_ ) )
# Get an ordered list of slices to perform
_a : Tuple = _get_minimal_slice_set(
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , )
_a : int = [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_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = False , lowerCAmelCase_ = None , lowerCAmelCase_ = False , ) -> Dict:
if not (len(lowerCAmelCase_ ) > 0):
raise ValueError('Must provide at least one input' )
_a : List[str] = [shape[:no_batch_dims] for shape in _fetch_dims(lowerCAmelCase_ )]
_a : List[Any] = tuple([max(lowerCAmelCase_ ) for s in zip(*lowerCAmelCase_ )] )
def _prep_inputs(lowerCAmelCase_ ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
_a : Optional[Any] = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
_a : List[str] = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
_a : int = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
_a : Dict[str, Any] = tensor_tree_map(_prep_inputs , lowerCAmelCase_ )
_a : str = None
if _out is not None:
_a : Dict = tensor_tree_map(lambda lowerCAmelCase_ : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
_a : List[Any] = 1
for d in orig_batch_dims:
flat_batch_dim *= d
_a : Any = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(lowerCAmelCase_ ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
_a : Tuple = 0
_a : List[Any] = prepped_outputs
for _ in range(lowerCAmelCase_ ):
# Chunk the input
if not low_mem:
_a : int = _select_chunk
else:
_a : List[str] = partial(
_chunk_slice , flat_start=lowerCAmelCase_ , flat_end=min(lowerCAmelCase_ , i + chunk_size ) , no_batch_dims=len(lowerCAmelCase_ ) , )
_a : Dict[str, Any] = tensor_tree_map(lowerCAmelCase_ , lowerCAmelCase_ )
# Run the layer on the chunk
_a : Any = layer(**lowerCAmelCase_ )
# Allocate space for the output
if out is None:
_a : List[Any] = 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_ , lowerCAmelCase_ ) -> 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:
_a : List[Any] = 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:
_a : Union[str, Any] = xa
elif isinstance(lowerCAmelCase_ , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
_a : List[Any] = output_chunk
else:
raise ValueError('Not supported' )
i += chunk_size
_a : Optional[int] = tensor_tree_map(lambda lowerCAmelCase_ : t.view(orig_batch_dims + t.shape[1:] ) , lowerCAmelCase_ )
return out
class __magic_name__ :
def __init__( self : Optional[int] ,_UpperCAmelCase : int = 512 ,):
_a : Tuple = max_chunk_size
_a : Optional[int] = None
_a : Optional[tuple] = None
def __lowercase ( self : Union[str, Any] ,_UpperCAmelCase : Callable ,_UpperCAmelCase : tuple ,_UpperCAmelCase : int ):
logging.info('Tuning chunk size...' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
_a : List[int] = [2**l for l in range(int(math.log(self.max_chunk_size ,2 ) ) + 1 )]
_a : str = [c for c in candidates if c > min_chunk_size]
_a : Dict = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(_UpperCAmelCase : int ) -> bool:
try:
with torch.no_grad():
fn(*_a ,chunk_size=_a )
return True
except RuntimeError:
return False
_a : Optional[Any] = 0
_a : Any = len(_a ) - 1
while i > min_viable_chunk_size_index:
_a : int = test_chunk_size(candidates[i] )
if not viable:
_a : Optional[int] = (min_viable_chunk_size_index + i) // 2
else:
_a : List[str] = i
_a : Union[str, Any] = (i + len(_a ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def __lowercase ( self : str ,_UpperCAmelCase : Iterable ,_UpperCAmelCase : Iterable ):
_a : Optional[Any] = True
for aa, aa in zip(_a ,_a ):
assert type(_a ) == type(_a )
if isinstance(_a ,(list, tuple) ):
consistent &= self._compare_arg_caches(_a ,_a )
elif isinstance(_a ,_a ):
_a : List[str] = [v for _, v in sorted(aa.items() ,key=lambda _UpperCAmelCase : x[0] )]
_a : int = [v for _, v in sorted(aa.items() ,key=lambda _UpperCAmelCase : x[0] )]
consistent &= self._compare_arg_caches(_a ,_a )
else:
consistent &= aa == aa
return consistent
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : Callable ,_UpperCAmelCase : tuple ,_UpperCAmelCase : int ,):
_a : Tuple = True
_a : tuple = tree_map(lambda _UpperCAmelCase : a.shape if isinstance(_a ,torch.Tensor ) else a ,_a ,_a )
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(_a )
_a : Union[str, Any] = self._compare_arg_caches(self.cached_arg_data ,_a )
else:
# Otherwise, we can reuse the precomputed value
_a : Any = False
if not consistent:
_a : Dict = self._determine_favorable_chunk_size(
_a ,_a ,_a ,)
_a : Tuple = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 370
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class __magic_name__ :
def __init__( self : Dict ,_UpperCAmelCase : Any ):
_a : Any = data
_a : Node | None = None
class __magic_name__ :
def __init__( self : Any ):
_a : int = None
_a : Optional[int] = None
def __iter__( self : Optional[int] ):
_a : List[Any] = self.head
while self.head:
yield node.data
_a : str = node.next
if node == self.head:
break
def __len__( self : Any ):
return sum(1 for _ in self )
def __repr__( self : int ):
return "->".join(str(_UpperCAmelCase ) for item in iter(self ) )
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : Any ):
self.insert_nth(len(self ) ,_UpperCAmelCase )
def __lowercase ( self : str ,_UpperCAmelCase : Any ):
self.insert_nth(0 ,_UpperCAmelCase )
def __lowercase ( self : List[str] ,_UpperCAmelCase : int ,_UpperCAmelCase : Any ):
if index < 0 or index > len(self ):
raise IndexError('list index out of range.' )
_a : List[str] = Node(_UpperCAmelCase )
if self.head is None:
_a : Tuple = new_node # first node points itself
_a : int = new_node
elif index == 0: # insert at head
_a : Any = self.head
_a : Tuple = new_node
else:
_a : Any = self.head
for _ in range(index - 1 ):
_a : int = temp.next
_a : Optional[int] = temp.next
_a : int = new_node
if index == len(self ) - 1: # insert at tail
_a : Optional[int] = new_node
def __lowercase ( self : List[Any] ):
return self.delete_nth(0 )
def __lowercase ( self : Dict ):
return self.delete_nth(len(self ) - 1 )
def __lowercase ( self : Union[str, Any] ,_UpperCAmelCase : int = 0 ):
if not 0 <= index < len(self ):
raise IndexError('list index out of range.' )
_a : Optional[int] = self.head
if self.head == self.tail: # just one node
_a : Optional[int] = None
elif index == 0: # delete head node
_a : Dict = self.tail.next.next
_a : Dict = self.head.next
else:
_a : List[Any] = self.head
for _ in range(index - 1 ):
_a : Union[str, Any] = temp.next
_a : Optional[int] = temp.next
_a : List[str] = temp.next.next
if index == len(self ) - 1: # delete at tail
_a : int = temp
return delete_node.data
def __lowercase ( self : int ):
return len(self ) == 0
def __lowerCamelCase ( ) -> None:
_a : int = CircularLinkedList()
assert len(lowerCAmelCase_ ) == 0
assert circular_linked_list.is_empty() is True
assert str(lowerCAmelCase_ ) == ""
try:
circular_linked_list.delete_front()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_tail()
raise AssertionError # This should not happen
except IndexError:
assert True # This should happen
try:
circular_linked_list.delete_nth(-1 )
raise AssertionError
except IndexError:
assert True
try:
circular_linked_list.delete_nth(0 )
raise AssertionError
except IndexError:
assert True
assert circular_linked_list.is_empty() is True
for i in range(5 ):
assert len(lowerCAmelCase_ ) == i
circular_linked_list.insert_nth(lowerCAmelCase_ , i + 1 )
assert str(lowerCAmelCase_ ) == "->".join(str(lowerCAmelCase_ ) for i in range(1 , 6 ) )
circular_linked_list.insert_tail(6 )
assert str(lowerCAmelCase_ ) == "->".join(str(lowerCAmelCase_ ) for i in range(1 , 7 ) )
circular_linked_list.insert_head(0 )
assert str(lowerCAmelCase_ ) == "->".join(str(lowerCAmelCase_ ) for i in range(0 , 7 ) )
assert circular_linked_list.delete_front() == 0
assert circular_linked_list.delete_tail() == 6
assert str(lowerCAmelCase_ ) == "->".join(str(lowerCAmelCase_ ) for i in range(1 , 6 ) )
assert circular_linked_list.delete_nth(2 ) == 3
circular_linked_list.insert_nth(2 , 3 )
assert str(lowerCAmelCase_ ) == "->".join(str(lowerCAmelCase_ ) for i in range(1 , 6 ) )
assert circular_linked_list.is_empty() is False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 107
| 0
|
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