code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from math import ceil
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Dict ) ->Union[str, Any]:
'''simple docstring'''
__A : Optional[Any] = list(range(0 ,snake_case_ ) )
__A : ... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
a_ = ... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConfig""",
],... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils impor... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( snake_case_ : int ) ->List[Any]:
'''simple docstring'''
__A : int = [
'... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Blip2Config""",
"""Blip2QFormerConfig""",
... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
def __lowercase ( snake_case_ : str ,snake_case_ : str ) ->Tuple:
'''simple docstring'''
assert x is not None
assert y is not None
__A : Any = len(snake_case_ )
__A : Optional[int] = len(snake_case_ )
... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : str ) ->list:
'''simple docstring'''
__A : List[Any] = [0] * len(snake_case_ )
for i in range(1 ,len(snake_case_ ) ):
# use last results for better performance - d... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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
... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : list ) ->list:
'''simple docstring'''
if len(snake_case_ ) < 2:
return collection
def circle_sort_util(snake_case_ : list ,snake_case_ : int ,snake_case_ : int ) -> bool:
... | 353 |
"""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 __snake_ca... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def __lowercase ( snake_case_ : list[list[float]] ) ->list[list[float]]:
'''simple docstring'''
__A : str = Decimal
# Check i... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __lowercase ( snake_case_ : List[Any] ) ... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArgum... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Input value must be an \'int\' type''' )
__A : str = 0
while nu... | 358 |
"""simple docstring"""
from collections import UserDict
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_availab... | 291 | 0 |
"""simple docstring"""
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __snake_case ( metaclass=SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = ["""speech"""]
def __init__( self , *__lowerCamelCase , **__lowerCamelCase ):
'''simple... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
from math import sqrt
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
__A : Any = 0
for i in range(1 ,int(sqrt(snake_case_ ) + 1 ) ):
if n % i == 0 and i != sqrt(snake_c... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 291 | 0 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerat... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowerCamelCase = """WhisperFeatureExtractor"""
_lowerCamelCase = """WhisperTokenizer"""
def __init__( self , __lowerCamelCas... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
a_ = logging.get_logger(__name__)
def __lowercase ( sna... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
a_ = 0B1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] give... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_att... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
from typing import Any
def __lowercase ( snake_case_ : list ,snake_case_ : list ,snake_case_ : dict ,snake_case_ : dict ,snake_case_ : dict ,) ->list:
'''simple docstring'''
_validation(
... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''' ,[
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.m... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""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-4... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
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 (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.s... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : str ,snake_case_ : str ) ->bool:
'''simple docstring'''
__A : Optional[int] = len(snake_case_ )
__A : List[str] = len(snake_case_ )
__A : List[An... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json"""
),
}
class... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.uti... | 353 |
"""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 __snake_ca... | 291 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ = logging.get_logger(__name__)
a_ ... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a_ = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a_ = typing.Union[np.floataa, int, float] # noqa: UP007
def __lowercase ... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
a_ = logging.get_logger(__name__)
class ... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
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_... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 358 |
"""simple docstring"""
from collections import UserDict
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_availab... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( snake_case_ : list[int] ,snake_case_ : int ) ->bool:
'''simple docstring'''
if len(snake_case_ ) == 0:
return False
__A : Union[str, Any] = len(... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import List, Optional
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self ):
'''simple docstring'''
self.test()
def UpperCamelCase__( self ):
'''si... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 291 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""si... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")
def __lowercase ( snake_case_ : Any ) ->Any:
'''simple docstring'''
... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fro... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...uti... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, c... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
import string
import numpy
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a ,snake_case_ )
... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {"""configuration_mmbt""": ["""MMBTConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:... | 350 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
#... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vi... | 351 |
"""simple docstring"""
a_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",... | 291 | 0 |
def __lowercase ( snake_case_ : Union[str, Any] ,snake_case_ : int ,snake_case_ : str=False ) ->Optional[Any]:
'''simple docstring'''
if isinstance(snake_case_ ,snake_case_ ) and isinstance(snake_case_ ,snake_case_ ):
__A : Option... | 352 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import ... | 291 | 0 |
"""simple docstring"""
class __snake_case :
"""simple docstring"""
def __init__( self , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
__A : List[str] = None
__A : str = None
__A : ... | 353 |
"""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 __snake_ca... | 291 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(... | 354 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config"""... | 355 |
"""simple docstring"""
import math
from collections.abc import Iterator
from itertools import takewhile
def __lowercase ( snake_case_ : int ) ->bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 ... | 291 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kand... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a_ = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasTo... | 291 | 0 |
import argparse
a_ = """docs/source/_static/js/custom.js"""
def __lowercase ( snake_case_ : Tuple ) ->Union[str, Any]:
'''simple docstring'''
with open(snake_case_ ,encoding='''utf-8''' ,newline='''\n''' ) as f:
__A : Any ... | 357 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_case ( SCREAMING_SNAKE_CASE__ ... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 358 |
"""simple docstring"""
from collections import UserDict
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_availab... | 291 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test... | 359 |
"""simple docstring"""
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowercase ( snake_case_ : int ) ->str:
'''simple docstring'''
if not isinstance(snake_case_ ,snake_case_ ):
raise TypeError('''Undefined for no... | 291 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaV... | 360 |
"""simple docstring"""
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 291 | 0 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import D... | 361 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
a_ = logging.get_logger(__name__)
a_ = {
"""shi-labs/dinat-mini-in1k-224""": """https:... | 291 | 0 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_se... | 362 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 291 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import... | 363 |
"""simple docstring"""
def __lowercase ( ) ->Tuple:
'''simple docstring'''
__A : str = []
__A : List[Any] = 1
while len(snake_case_ ) < 1e6:
constant.append(str(snake_case_ ) )
i += 1
__A : ... | 291 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __lowercase ( snake_case_ : str = "isbn/0140328726" ) ->dict:
'''simple docstring'''
__A : int = olid.strip().s... | 364 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ = {
"""configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
... | 291 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
a_ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang... | 365 |
"""simple docstring"""
from math import factorial
def __lowercase ( snake_case_ : int ,snake_case_ : int ) ->int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('''Please enter positive integers for n and k where n >= k''' )
re... | 291 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
i... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verb... | 367 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""PatchingSpec""",
],
... | 291 | 0 |
"""simple docstring"""
def __lowercase ( snake_case_ : list ,snake_case_ : list ,snake_case_ : int ) ->int:
'''simple docstring'''
if len(snake_case_ ) != len(snake_case_ ):
raise ValueError('''The length of profit and weight must be same.''' ... | 368 |
"""simple docstring"""
import numpy as np
import qiskit
def __lowercase ( snake_case_ : int = 8 ,snake_case_ : int | None = None ) ->str:
'''simple docstring'''
__A : str = np.random.default_rng(seed=snake_case_ )
# Roughly 25%... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowercase ( snake_case_ : list ,snake_case_ : int ) ->List[Any]:
'''simple docstring'''
if len(snake_case_ ) <= 1 or n <= 1:
return
insert_ne... | 369 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
a_ = {"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
a_ = _LazyModule(__name__, globals()["""... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
a_ = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@r... | 371 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_ = {
"""E""": 12.70,
"""T""": 9.06,
"""A""": 8.17,
"""O""": 7.51,
"""I""": 6.97,
"""N""": 6.75,
"""S""": 6.33,
"""H""": 6.09,
"""R""": 5.99,
"""D""": 4.25... | 291 | 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 (U... | 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... | 292 | 1 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
fr... | 292 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTester... | 292 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | 292 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Dict = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggi... | 292 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xop... | 292 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfi... | 292 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCA... | 292 |
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_ : Optional[Any] = {
'YituTech/conv-bert-bas... | 292 | 1 |
def snake_case (UpperCAmelCase__ = 1_0 , UpperCAmelCase__ = 1_0_0_0 , UpperCAmelCase__ = True ) -> int:
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
and isinstance(UpperCAmelCase__ ... | 292 |
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_channel_d... | 292 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : int = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}
class _lowerCAmelCase( UpperCAmelCase_ ):
"... | 292 |
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 _lowerCAmelCase( unittest.... | 292 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
A_ : List[str] = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto a... | 292 |
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
@requ... | 292 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
... | 292 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _a ( _lowerCamelCase ):
raise NotImplemen... | 292 | 1 |
A_ : List[Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A_ : Union[str, Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ) -> list[int]:
UpperCamelCase_: Optional[int] ... | 292 |
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
while b:
UpperCamelCase_ ,UpperCamelCase_: int = b, a % b
return a
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase... | 292 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def snake_case (UpperCAmelCase__ , UpperCAmelCase__=7 ) -> Optional[Any]:
UpperCamelCase_: Union[str, Any] = None
if token is not None:
UpperCamelCase... | 292 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class _lowerCAmelCase( UpperCAm... | 292 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
A_ : int = namedtuple('covid_data', 'cases deaths recovered')
def snake_case (UpperCAmelCase__ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
UpperCamelCase_: Union[str, A... | 292 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can a... | 292 | 1 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_... | 292 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertToken... | 292 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def snake_case (UpperCAmelCase__ ) -> None:
UpperCamelCase_ ,UpperCamelCase_: Dict = analyze_text(UpperCAmelCase__ )
UpperCamelCase_: List[str] = list... | 292 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def snake_case (UpperCAmelCase__ , UpperCAmelCase__=() , UpperCAmelCase__=None , UpperCAmelCase__="n... | 292 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 292 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | 292 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
A_ : int = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trap... | 292 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A_ : str = [
'word_embeddings_la... | 292 | 1 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 1_0_0 * 2**2_0, 9_0_0 * 2**2_0] )
def ... | 292 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS... | 292 | 1 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_availa... | 292 |
def snake_case (UpperCAmelCase__ ) -> int:
assert isinstance(UpperCAmelCase__ , UpperCAmelCase__ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
UpperCamelCase_: List[Any] = F'''The input value of [n={number}]... | 292 | 1 |
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 Conv... | 292 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 292 | 1 |
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 hug... | 292 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> np.array:
UpperCamelCase_: Dict = F'''{sampling_rate}'''
UpperCamelCase_: Any = '1'
UpperC... | 292 | 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 ModelTesterM... | 292 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_ : List[str] = '.'
if __name__ == "__main__":
A_ : Dict = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
... | 292 | 1 |
from __future__ import annotations
from collections.abc import Generator
def snake_case () -> Generator[int, None, None]:
UpperCamelCase_: dict[int, int] = {}
UpperCamelCase_: Union[str, Any] = 2
while True:
UpperCamelCase_: Dict = factor_map.pop(UpperCA... | 292 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...t... | 292 | 1 |
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 .... | 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... | 292 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
A_ : List[str] = logging.get_logger(__name__)
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self ... | 292 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTester... | 292 | 1 |
from functools import lru_cache
@lru_cache
def snake_case (UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError('Number should not be negative.' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod() | 292 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Tuple = logging.get_logger(__name__)
A_ : Dict = {
'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
# See all XGLM models at https://huggi... | 292 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
A_ : List[str] = logging.get_logger(__name__)
class _lowerCAmelCase( UpperCAmelCase_ ):
"""simple docstring"""
def __ini... | 292 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Tuple = {
'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfi... | 292 | 1 |
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase_: Union[str, Any] = str(bin(UpperCAmelCase__ ) )[2:] # remove the leading "0b"
UpperCamelCase_: Optional[i... | 292 |
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_ : Optional[Any] = {
'YituTech/conv-bert-bas... | 292 | 1 |
class _lowerCAmelCase:
"""simple docstring"""
def __init__( self , _lowerCamelCase ):
UpperCamelCase_: Union[str, Any] = set_counts
UpperCamelCase_: Dict = max(_lowerCamelCase )
UpperCamelCase_: L... | 292 |
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_channel_d... | 292 | 1 |
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