code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
lowerCAmelCase_ = range(2, 2_0 + 1)
lowerCAmelCase_ = [1_0**k for k in range(ks[-1] + 1)]
lowerCAmelCase_ = {}
def A_ ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Optional[int]:
_snake_case : Dict = sum(a_i[j] for j in range(__UpperCAmelCase ... | 326 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 109 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : List[Any] , UpperCamelCase : Any , UpperCamelCase : Tuple ):
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCAmelCase ... | 574 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 0 |
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError("multiplicative_persistence() only accepts integral values" )
if num < 0:
raise ValueError("multiplicative_persistence()... | 55 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a__ ( metaclass=_snake_case ):
_SCREAMING_SNAKE_CASE : Optional[Any] = ['onnx']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
"""simple docstring""... | 245 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snak... | 109 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_... | 131 |
'''simple docstring'''
from __future__ import annotations
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : list[list[int]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = TypeError(
"""Matrices must be formed ... | 109 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
a__ : List[str] ... | 368 |
'''simple docstring'''
import os
from math import logaa
def __magic_name__ ( __UpperCAmelCase = "base_exp.txt" ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.... | 109 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
SCREAMING_SNAKE_CASE__ : Union[str, Any] =list[list[float | int]]
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->Matrix:
_lowerCamelCase : Dict ... | 434 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Imag... | 109 | 0 |
def UpperCAmelCase__ ( __snake_case , __snake_case , __snake_case=False ) -> Tuple:
if isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase ):
_A = len(set_a.intersection(__UpperCAmelCase )... | 317 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 109 | 0 |
"""simple docstring"""
import numpy as np
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Any , _snake_case : int=None , _snake_case : List[str]=None , _snake_case : Tuple=None , _snake_case : Tuple=None , _snake_case : str=None ... | 232 |
'''simple docstring'''
import numpy as np
class __a :
def __init__( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCR... | 109 | 0 |
'''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 accelerat... | 143 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Image:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__UpperCAmelCase ) -> int:
... | 109 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Debe... | 348 |
'''simple docstring'''
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
a = {
"gwf-440k": ... | 109 | 0 |
def A_ ( lowercase_ , lowercase_ ) -> str:
_snake_case : str = ''''''
for word_or_phrase in separated:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise Exception('''join() accepts only strings to be joined''' )
... | 326 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ren... | 109 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale... | 574 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithP... | 109 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environme... | 55 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 109 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProc... | 245 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 109 | 0 |
'''simple docstring'''
import math
def _lowercase ( lowerCamelCase__ : Optional[Any] ):
assert isinstance(__UpperCAmelCase, __UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 131 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/re... | 109 | 0 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_Q... | 368 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 109 | 0 |
"""simple docstring"""
from math import pi
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) ->float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 434 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.se... | 109 | 0 |
def UpperCAmelCase__ ( __snake_case ) -> list[list]:
_A = current_set.copy()
for row_index, row in enumerate(__UpperCAmelCase ):
_A = row[0]
for column_index, column in enumerate(__UpperCAmelCase ):
if magnitude == 0:
_A = c... | 317 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
a = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase=None , __UpperCAmelCase=No... | 109 | 0 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 232 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
tr... | 109 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from ... | 143 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (boundary[1] - boundary[0]) / steps
__SCREAMING_SNAKE_CASE = boundary[0]
__SCREAMING_SNAKE_CASE ... | 109 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 348 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 109 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class A (unittest.TestCase ):
def __a ( self ) -> Union[str, Any]:
'''simple docstring'''
_sna... | 326 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 109 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase__ = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_ta... | 574 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 0 |
SCREAMING_SNAKE_CASE :Any = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
SCREAMING_SNAKE_CAS... | 55 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDepende... | 245 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snak... | 109 | 0 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 131 |
'''simple docstring'''
from __future__ import annotations
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : list[list[int]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = TypeError(
"""Matrices must be formed ... | 109 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Optional[Any] = logging.get_logger(__name__)
a__ : List[Any] = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''h... | 368 |
'''simple docstring'''
import os
from math import logaa
def __magic_name__ ( __UpperCAmelCase = "base_exp.txt" ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.... | 109 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
... | 434 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Imag... | 109 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__lowerCamelCase = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINE... | 317 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 109 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP... | 232 |
'''simple docstring'''
import numpy as np
class __a :
def __init__( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCR... | 109 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( ... | 143 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Image:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__UpperCAmelCase ) -> int:
... | 109 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__SCREAMING_SNAKE_CASE : Any = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggi... | 348 |
'''simple docstring'''
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
a = {
"gwf-440k": ... | 109 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class A (_snake_case ):
_SCREAMING_SNAKE_CASE = 'ctrl'
_SCR... | 326 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ren... | 109 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _SCREAMING_SNAKE_CASE ... | 574 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithP... | 109 | 0 |
from __future__ import annotations
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(__UpperCAmelCase ) ):
... | 55 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 109 | 0 |
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_snake_case = logging.getLogger(__name__)
if __name__ == "__main__":
... | 245 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 109 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fro... | 131 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/re... | 109 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __snake_case ( yaml.SafeLoader ):
def _snake_case ( self , UpperCamelCase_ ) -> List[Any]:
s... | 368 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 109 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ... | 434 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.se... | 109 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
St... | 317 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
a = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase=None , __UpperCAmelCase=No... | 109 | 0 |
"""simple docstring"""
import math
def _lowerCamelCase ( UpperCAmelCase__ ) -> bool:
'''simple docstring'''
return math.sqrt(__UpperCAmelCase ) * math.sqrt(__UpperCAmelCase ) == num
def _lowerCamelCase ( UpperCAmelCase__ ) -> bool:
'''simple docstrin... | 232 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
tr... | 109 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impo... | 143 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (boundary[1] - boundary[0]) / steps
__SCREAMING_SNAKE_CASE = boundary[0]
__SCREAMING_SNAKE_CASE ... | 109 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common i... | 348 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 109 | 0 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCAmelCase_ = 1.054571817e-34 # unit of ℏ : J * s
lowerCAmelCase_ = 3e8 # unit of c : m * s^-1
def A_ ( lowercase_ , lowercase_ ,... | 326 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 109 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import TypedDict
class _UpperCamelCase ( _snake_case):
__lowerCamelCase = 42
__lowerCamelCase = 42
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : str ):
... | 574 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 0 |
def UpperCAmelCase ( a_ , a_ , a_ ) -> float:
"""simple docstring"""
__A = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCAmelCase ( ) -> List[str]:
... | 55 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_ver... | 245 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snak... | 109 | 0 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline... | 131 |
'''simple docstring'''
from __future__ import annotations
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : list[list[int]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = TypeError(
"""Matrices must be formed ... | 109 | 0 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a__ : Any = logging.get_logger(__... | 368 |
'''simple docstring'''
import os
from math import logaa
def __magic_name__ ( __UpperCAmelCase = "base_exp.txt" ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.... | 109 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diff... | 434 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Imag... | 109 | 0 |
from torch import nn
def UpperCAmelCase__ ( __snake_case ) -> List[Any]:
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... | 317 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 109 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
__magic_name__ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _snake_case ):
"""simple docstring"""
def __init__( self : Dict , *_s... | 232 |
'''simple docstring'''
import numpy as np
class __a :
def __init__( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCR... | 109 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", ... | 143 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Image:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__UpperCAmelCase ) -> int:
... | 109 | 0 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 348 |
'''simple docstring'''
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
a = {
"gwf-440k": ... | 109 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def A_ ( lowercase_ = True , *lowercase_ , **lowercase_ ) -> Dict:
if not is_tqdm_available():
raise ImportError('''Accel... | 326 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ren... | 109 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencep... | 574 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithP... | 109 | 0 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.s... | 55 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 109 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerConfig',... | 245 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 109 | 0 |
'''simple docstring'''
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
__snake_case : Union[str, Any] = False
class A... | 131 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/re... | 109 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
a... | 368 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 109 | 0 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE__ : List[str] ={
'linear': PIL.Image.Resampling.BILINEAR,
'... | 434 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.se... | 109 | 0 |
import numpy as np
class _snake_case :
"""simple docstring"""
def __init__( self ) -> Optional[int]:
"""simple docstring"""
_A = (0, 0)
_A = None
_A = 0
_A = 0
_A = 0
def __eq__... | 317 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
a = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase=None , __UpperCAmelCase=No... | 109 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( UpperCAmelCase__ ) -> float:
'''simple docstring'''
a__ = 0.00
a__ = 0
for resistor in resistors:
if resistor <= 0:
a__ = f'''Resistor at index {index} has a negativ... | 232 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
tr... | 109 | 0 |
'''simple docstring'''
def A_ ( snake_case , snake_case ):
_enforce_args(__UpperCAmelCase , __UpperCAmelCase )
if n == 0:
return 0
SCREAMING_SNAKE_CASE:Tuple = float("-inf" )
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE:List[str] = ... | 143 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (boundary[1] - boundary[0]) / steps
__SCREAMING_SNAKE_CASE = boundary[0]
__SCREAMING_SNAKE_CASE ... | 109 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'microsoft/git-base': 'https://huggingface.co/microsof... | 348 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 109 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def A_ ( lowercase_ , lowercase_=1000 ) -> List[str]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_snake_case : Tuple = n - 1
... | 326 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 109 | 0 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTI... | 574 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 0 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCRE... | 55 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokenizer... | 245 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snak... | 109 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chan... | 131 |
'''simple docstring'''
from __future__ import annotations
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : list[list[int]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = TypeError(
"""Matrices must be formed ... | 109 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transfor... | 368 |
'''simple docstring'''
import os
from math import logaa
def __magic_name__ ( __UpperCAmelCase = "base_exp.txt" ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.... | 109 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : Any =TypeVar('T')
class _UpperCAmelCase ( Generic[T] ):
"""simple docstring"""
def __init__( self , _lowercase ) -... | 434 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Imag... | 109 | 0 |
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
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase ... | 317 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 109 | 0 |
"""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 huggingf... | 232 |
'''simple docstring'''
import numpy as np
class __a :
def __init__( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCR... | 109 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at ht... | 143 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Image:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__UpperCAmelCase ) -> int:
... | 109 | 0 |
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMi... | 348 |
'''simple docstring'''
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
a = {
"gwf-440k": ... | 109 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, 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
fr... | 326 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ren... | 109 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"... | 574 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithP... | 109 | 0 |
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE :Dict = '\\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 ... | 55 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorc... | 109 | 0 |
'''simple docstring'''
def _A ( snake_case ) -> list[int]:
if num <= 0:
raise ValueError("Input must be a positive integer" )
_lowercase : List[Any] = [True] * (num + 1)
_lowercase : Dict = 2
while p * p <= num:
if primes[p]:
for i in... | 245 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common imp... | 109 | 0 |
'''simple docstring'''
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECK... | 131 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
# TODO Update this
a = {
"facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/re... | 109 | 0 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
a__ : Union[str, Any] = log... | 368 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 109 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[int] ={
'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}
try:
if not is_to... | 434 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.se... | 109 | 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... | 317 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
a = logging.get_logger(__name__)
def __magic_name__ ( __UpperCAmelCase=None , __UpperCAmelCase=No... | 109 | 0 |
"""simple docstring"""
from collections import deque
from math import floor
from random import random
from time import time
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self : Tuple ) -> int:
'''simple docstring'''
a__ = {}
def _low... | 232 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"InformerConfig",
],
}
tr... | 109 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A_ = 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
... | 143 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (boundary[1] - boundary[0]) / steps
__SCREAMING_SNAKE_CASE = boundary[0]
__SCREAMING_SNAKE_CASE ... | 109 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __magic_name__ ( unittest.TestCas... | 348 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 109 | 0 |
import mpmath # for roots of unity
import numpy as np
class A :
def __init__( self , lowercase_=None , lowercase_=None ) -> List[Any]:
'''simple docstring'''
_snake_case : Dict = list(poly_a or [0] )[:]
_snake_case ... | 326 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modelin... | 109 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_trocr": ["TROCR_PRETRA... | 574 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 | 0 |
import cva
import numpy as np
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[int] ,A : float ,A : int ):
if k in (0.04, 0.06):
__A = k
__A = window_size
else:
raise ValueError("inval... | 55 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import ... | 109 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestM... | 245 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snak... | 109 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case : Any = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For bin... | 131 |
'''simple docstring'''
from __future__ import annotations
class __a :
def __init__( self : Optional[int] ,lowerCamelCase : list[list[int]] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = TypeError(
"""Matrices must be formed ... | 109 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
impor... | 368 |
'''simple docstring'''
import os
from math import logaa
def __magic_name__ ( __UpperCAmelCase = "base_exp.txt" ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.... | 109 | 0 |
"""simple docstring"""
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->bool:
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
_lowerCamelCase : Tuple = sorted(string.lower() )
return... | 434 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
Imag... | 109 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.model... | 317 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils... | 109 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_on... | 232 |
'''simple docstring'''
import numpy as np
class __a :
def __init__( self : Optional[int] ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (0, 0)
__SCREAMING_SNAKE_CASE = None
__SCREAMING_SNAKE_CASE = 0
__SCR... | 109 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _snake_case ( datasets.BeamBasedBuilder ):
def __... | 143 |
'''simple docstring'''
from PIL import Image
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Image:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__UpperCAmelCase ) -> int:
... | 109 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transfo... | 348 |
'''simple docstring'''
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
a = {
"gwf-440k": ... | 109 | 0 |
from __future__ import annotations
def A_ ( lowercase_ ) -> bool:
_snake_case : str = str(__UpperCAmelCase )
return n == n[::-1]
def A_ ( lowercase_ = 1000000 ) -> str:
_snake_case : Optional[int] = 0
for i in range... | 326 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import ren... | 109 | 0 |
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