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 |
|---|---|---|---|---|
'''simple docstring'''
A = {str(digit): digit**5 for digit in range(10)}
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowerCAmelCase__))
def SCREAMING_SNAKE_CASE ... | 125 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : np.ndarray , lowerCAmelCase__ : np.ndarray) -> float:
'''simple docstring'''
return math... | 125 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_N... | 125 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Namespace) -> Optional[int]:
'''simple docstring'''
return ConvertCommand(
... | 125 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int]=7) -> Any:
'''simple docstring'''
... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information')
for cell_n in range(1 , len(grid[0])):
... | 125 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageP... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_te... | 125 |
'''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,
)
logg... | 125 | 1 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoCo... | 125 |
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmel... | 125 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ... | 125 |
'''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... | 125 | 1 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify... | 125 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __SCREAMING_SNAKE_C... | 125 | 1 |
'''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 BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.imag... | 125 |
'''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 ...... | 125 | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def SCREAMING_SNAKE_CASE ( *lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[Union[Dict, Any]] = None , lowerCAmelCase__ : List[str... | 125 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae impor... | 125 | 1 |
'''simple docstring'''
from collections import deque
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple ,UpperCamelCase : str ,UpperCamelCase : int ,UpperCamelCase : int ) -> None:
_lowercase : Dict = ... | 125 |
'''simple docstring'''
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.conver... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : List[str]) -> Dict:
'''simple docstring'''
_lowercase , _lowercase : Dict = [], []
while len(lowerCAmelCase__) > 1:
_lowercase , _lowercase : Optional[int] ... | 125 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 125 | 1 |
'''simple docstring'''
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Any , lo... | 125 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstrin... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str , lowerCAmelCase__ : int) -> list[str]:
'''simple docstring'''
return [sentence[i : i + ngram_size] for i in range(len(lowerCAmelCase__) - ngram_size + 1)]
if __name__ == "__main__"... | 125 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusi... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi, sqrt
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : float , lowerCAmelCase__ : float) -> tuple:
'''simple docstring'''
if inductance <= 0:
raise ValueError('Inductanc... | 125 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __SCREAMING_SNA... | 125 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A = {'''configuration_speech_encoder_decoder''': ['''SpeechEncoderDecoderConfig''']}
try:
if not is_torch_available():
r... | 125 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
fr... | 125 | 1 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
A = {
... | 125 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = (PNDMScheduler,)
... | 125 | 1 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageP... | 125 |
'''simple docstring'''
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : np.ndarray) -> bool:
'''simple docstring'''
return np.array_equal(lowerCAmelCase__ , matrix.conjugate().T)
def SCREAMING_SNAKE_CASE ( lowe... | 125 | 1 |
'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A = '''\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Lang... | 125 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A = logging.get_logger(__name__)
# TODO: upload to AWS
A = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/reso... | 125 | 1 |
'''simple docstring'''
import torch
from diffusers import DiffusionPipeline
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self : str ,UpperCamelCase : Optional[int] ,UpperCamelCase : Optional[int] ) -> List[... | 125 |
'''simple docstring'''
A = 9.80_665
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float = g) -> float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('I... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : int ,UpperCamelCase : int ,UpperCamelCase : int ,UpperCamelCase : float = 0 ) -> None:
... | 125 |
'''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 loggi... | 125 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def SCREAMING_SNAKE_CASE ( ... | 125 |
'''simple docstring'''
# 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-... | 125 | 1 |
'''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 SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : L... | 125 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 125 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverM... | 125 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_N... | 125 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderConfig''', ''... | 125 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int]=7) -> Any:
'''simple docstring'''
... | 125 | 1 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A = '''src/diffusers'''
# Matches is_xxx_available()
A = re.compile(R'''is\_([a-z_]*)... | 125 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageP... | 125 | 1 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from tra... | 125 |
'''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,
)
logg... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list) -> list:
'''simple docstring'''
if len(lowerCAmelCase__) <= 1:
return [tuple(lowerCAmelCase__)]
_lowercase : Union[str, Any] = []
def generate(lowerCAmelCase__... | 125 |
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmel... | 125 | 1 |
'''simple docstring'''
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, sl... | 125 |
'''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... | 125 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : List[Any] ... | 125 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __SCREAMING_SNAKE_C... | 125 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( ... | 125 |
'''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 ...... | 125 | 1 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def SCREAMING_... | 125 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae impor... | 125 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ... | 125 |
'''simple docstring'''
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.conver... | 125 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = (PNDMScheduler,)
... | 125 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 125 | 1 |
'''simple docstring'''
import collections
import os
import re
from pathlib import Path
A = '''src/transformers'''
# Matches is_xxx_available()
A = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
A = re.compile(R'''^_import_struct... | 125 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstrin... | 125 | 1 |
'''simple docstring'''
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,
... | 125 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusi... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pow, sqrt
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float) -> dict[str, float]:
'''simple docstring'''
if (re... | 125 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __SCREAMING_SNA... | 125 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/micr... | 125 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
fr... | 125 | 1 |
'''simple docstring'''
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaud... | 125 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = (PNDMScheduler,)
... | 125 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}
try:
if not is_torc... | 125 |
'''simple docstring'''
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : np.ndarray) -> bool:
'''simple docstring'''
return np.array_equal(lowerCAmelCase__ , matrix.conjugate().T)
def SCREAMING_SNAKE_CASE ( lowe... | 125 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusi... | 125 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A = logging.get_logger(__name__)
# TODO: upload to AWS
A = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/reso... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int) -> bool:
'''simple docstring'''
_lowercase : int = int(number**0.5)
return number ... | 125 |
'''simple docstring'''
A = 9.80_665
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float = g) -> float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('I... | 125 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
A = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
... | 125 |
'''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 loggi... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
A = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list[float]) -> list[float]:... | 125 |
'''simple docstring'''
# 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-... | 125 | 1 |
'''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_... | 125 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 125 | 1 |
'''simple docstring'''
import re
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str) -> bool:
'''simple docstring'''
_lowercase : Any = re.compile(
r'^(?:0|94|\+94|0{2}94)' r'7(0|1|2|4|5|6|7|8)' r'(-| |)' r'\d{7}$')
return bool(re.search(low... | 125 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_N... | 125 | 1 |
'''simple docstring'''
A = '''Alexander Joslin'''
import operator as op
from .stack import Stack
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str) -> int:
'''simple docstring'''
_lowercase : str = {'*': op.mul, '/': op.truediv, '+': op.add, ... | 125 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int]=7) -> Any:
'''simple docstring'''
... | 125 | 1 |
'''simple docstring'''
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from ... | 125 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageP... | 125 | 1 |
'''simple docstring'''
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
A = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
subsections:
- nam... | 125 |
'''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,
)
logg... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list) -> list:
'''simple docstring'''
for i in range(len(lowerCAmelCase__) - 1 , 0 , -1):
_lowercase : Any = False
for j in range(lowerCAmelCase__ ,... | 125 |
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmel... | 125 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix2s... | 125 |
'''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... | 125 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffus... | 125 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __SCREAMING_SNAKE_C... | 125 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm... | 125 |
'''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 ...... | 125 | 1 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers ... | 125 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae impor... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str) -> str:
'''simple docstring'''
_lowercase : Optional[Any] = ''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
... | 125 |
'''simple docstring'''
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.conver... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str , lowerCAmelCase__ : int) -> list:
'''simple docstring'''
_lowercase : List[str] = word.split()
def justify(lowerCAmelCase__ : list , lowerCAmelCase__ :... | 125 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 125 | 1 |
'''simple docstring'''
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Tuple ,UpperCamelCase : int ) -> Dict:
_lowercase : List[str] = n
_lowercase : List[Any] = [None] * self.n
_lowe... | 125 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstrin... | 125 | 1 |
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmel... | 125 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusi... | 125 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __SCREAMING_SNAKE_CASE ... | 125 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __SCREAMING_SNA... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list[int]) -> list[int]: # This function is recursive
'''simple docstring'''
_lowercase : List[Any] = len(lowerCAmelCase__)
# If the array contai... | 125 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
fr... | 125 | 1 |
'''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 loggi... | 125 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = (PNDMScheduler,)
... | 125 | 1 |
'''simple docstring'''
import numpy as np
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : np.array) -> np.array:
'''simple docstring'''
return 1 / (1 + np.exp(-vector))
if __name__ == "__main__":
import doctest
doctest.testmod() | 125 |
'''simple docstring'''
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : np.ndarray) -> bool:
'''simple docstring'''
return np.array_equal(lowerCAmelCase__ , matrix.conjugate().T)
def SCREAMING_SNAKE_CASE ( lowe... | 125 | 1 |
'''simple docstring'''
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf... | 125 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A = logging.get_logger(__name__)
# TODO: upload to AWS
A = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/reso... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : str) -> str:
'''simple docstring'''
_lowercase : str = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowercase : Dict = ''
_lowercase :... | 125 |
'''simple docstring'''
A = 9.80_665
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float = g) -> float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('I... | 125 | 1 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 125 |
'''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 loggi... | 125 | 1 |
'''simple docstring'''
from math import factorial
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 1_00) -> int:
'''simple docstring'''
return sum(map(lowerCAmelCase__ , str(factorial(lowerCAmelCase__))))
if __name__ == "__main__":
print(solution(int(i... | 125 |
'''simple docstring'''
# 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-... | 125 | 1 |
'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .s... | 125 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 125 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
A = logging.get_logger... | 125 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_N... | 125 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper... | 125 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int]=7) -> Any:
'''simple docstring'''
... | 125 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
... | 125 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageP... | 125 | 1 |
'''simple docstring'''
import math
from collections.abc import Iterator
from itertools import takewhile
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
eli... | 125 |
'''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,
)
logg... | 125 | 1 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
) | 125 |
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmel... | 125 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-la... | 125 |
'''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... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 1_00) -> int:
'''simple docstring'''
_lowercase : int = n * (n + 1) * (2 * n + 1) / 6
_lowercase : Dict = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum... | 125 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __SCREAMING_SNAKE_C... | 125 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os... | 125 |
'''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 ...... | 125 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Optional[Any]) -> Dict:
'''simple docstring'''
... | 125 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae impor... | 125 | 1 |
'''simple docstring'''
import warnings
warnings.warn(
'''memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '''
'''`from accelerate import find_executable_batch_size` to avoid this warning.''',
FutureWarning,
) | 125 |
'''simple docstring'''
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.conver... | 125 | 1 |
'''simple docstring'''
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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers impo... | 125 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 125 | 1 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
A = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__... | 125 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : int) -> int:
'''simple docstring'''
return int((input_a, input_a).count(0) != 0)
def SCREAMING_SNAKE_CASE ( ) -> None:
'''simple docstrin... | 125 | 1 |
'''simple docstring'''
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLM... | 125 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusi... | 125 | 1 |
'''simple docstring'''
import heapq
import sys
import numpy as np
A = tuple[int, int]
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : List[str] ) -> Optional[int]:
_lowercase : Any = []
_lowercase... | 125 |
'''simple docstring'''
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json''',
}
class __SCREAMING_SNA... | 125 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''bert-base-uncased''': '''https://hugg... | 125 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
fr... | 125 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'''configuration_xlm_roberta_xl''': [
'''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XLMRobertaXLConfig''',
''... | 125 |
'''simple docstring'''
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
'''simple docstring'''
lowerCAmelCase__ : Dict = (PNDMScheduler,)
... | 125 | 1 |
'''simple docstring'''
A = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'... | 125 |
'''simple docstring'''
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : np.ndarray) -> bool:
'''simple docstring'''
return np.array_equal(lowerCAmelCase__ , matrix.conjugate().T)
def SCREAMING_SNAKE_CASE ( lowe... | 125 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 ..... | 125 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
A = logging.get_logger(__name__)
# TODO: upload to AWS
A = {
'''yjernite/retribert-base-uncased''': (
'''https://huggingface.co/yjernite/retribert-base-uncased/reso... | 125 | 1 |
'''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 = {
'... | 125 |
'''simple docstring'''
A = 9.80_665
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAmelCase__ : float = g) -> float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError('I... | 125 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_... | 125 |
'''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 loggi... | 125 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
rais... | 125 |
'''simple docstring'''
# 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-... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
A = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : list[list[int]] , lowerCAmelCase__ : list[int] , lowerCAmelCase__ ... | 125 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
A = '''\
@article{hendrycksmath2021,
title={Measuring Mathematical Problem Solving With the MATH Dataset},
author={Dan Hendrycks
and Collin Burns
and Saurav Kadavath
... | 125 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extra... | 125 |
'''simple docstring'''
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_N... | 125 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
A = _LazyModule(__name__, gl... | 125 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int]=7) -> Any:
'''simple docstring'''
... | 125 | 1 |
'''simple docstring'''
import mpmath # for roots of unity
import numpy as np
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str ,UpperCamelCase : List[Any]=None ,UpperCamelCase : Union[str, Any]=None ) -> List[str]:
# ... | 125 |
'''simple docstring'''
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 ImageP... | 125 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
A = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_py... | 125 |
'''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,
)
logg... | 125 | 1 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
de... | 125 |
'''simple docstring'''
import math
A = 10
A = 7
A = BALLS_PER_COLOUR * NUM_COLOURS
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 20) -> str:
'''simple docstring'''
_lowercase : Union[str, Any] = math.comb(lowerCAmel... | 125 | 1 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
A = get_logger(__name__)
class __SCREAMING_SNAKE_CASE ( enum.Enum ):
'''simple docstring'''
lowerCAmelCa... | 125 |
'''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... | 125 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedCon... | 125 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A = logging.get_logger(__name__) # pylint: disable=invalid-name
class __SCREAMING_SNAKE_C... | 125 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int) -> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__):
raise TypeError('only integers accepted as input')
else:
_lowercase : i... | 125 |
'''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 ...... | 125 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'''vocab_file''': '''vocab.json''',
''... | 125 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae impor... | 125 | 1 |
'''simple docstring'''
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_... | 125 |
'''simple docstring'''
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.conver... | 125 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
'''simple docstring'''
_lowercase : Union... | 125 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate impo... | 125 | 1 |
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