code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
def lowercase__( __SCREAMING_SNAKE_CASE : list[int] , __SCREAMING_SNAKE_CASE : int ):
if len(__SCREAMING_SNAKE_CASE ) == 0:
return False
lowercase_ : Any = len(__SCREAMING_SNAKE_CASE ) // 2
... | 425 | """simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
clas... | 425 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtract... | 717 |
"""simple docstring"""
def A__ ( UpperCamelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = int(UpperCamelCase__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(UpperCamelCase__ )
_SCREAMING_SNAKE_CASE , ... | 168 | 0 |
'''simple docstring'''
import unittest
import numpy as np
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ):
'''simple docstring'''
A : Tuple = np.shape(snake_case... | 634 |
'''simple docstring'''
from random import randint, random
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ = False , snake_case__ = False , snake_case__ = 5 , ):
'''simple... | 634 | 1 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCamelCase( _A : bytes , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = F'''{sampling_rate}'''
Up... | 718 | '''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
... | 496 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : List[str] = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfi... | 210 |
'''simple docstring'''
from __future__ import annotations
import math
_lowercase : Dict = """2020.9.26"""
_lowercase : Any = """xcodz-dot, cclaus, dhruvmanila"""
def lowerCamelCase__ ( A : float , A : float , A : float , A : float , A : float ):
... | 210 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaV... | 245 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils ... | 245 | 1 |
def _lowerCAmelCase ( UpperCamelCase__: int = 50 ) -> int:
"""simple docstring"""
A = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ... | 641 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( __snake_case ):
"""simple docstring"""
lowerCAmelCase = (IPNDMScheduler,)
lowerCAmelCase = (('num_inference_steps', 5_0)... | 641 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...t... | 710 | import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_lowerCAmelCase = logging.get_logger(__name__)
de... | 236 | 0 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_singl... | 199 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """D... | 199 | 1 |
"""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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .... | 87 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils impor... | 87 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_config... | 334 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__A : int = logging.get_logger(__name__)
__A : List[Any] = {
'post_extract_proj':... | 334 | 1 |
'''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class snake_case__ :
def __init__( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
__snake_case : str = psutil.Process()
__snake_case :... | 718 |
'''simple docstring'''
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_... | 124 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
"""microsoft/unispeech-sat-base-100h-libri-ft""": (
"""https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/... | 221 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
a = False
class SCREAMING_SNAKE_CASE__ ( unittest.Tes... | 169 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__ = logging.get_logger(__name__)
A__ = {
'''sa... | 219 |
import unittest
from transformers import DonutProcessor
A__ = '''naver-clova-ix/donut-base'''
class a ( unittest.TestCase ):
def __lowerCamelCase ( self :Optional[int] ):
snake_case__ : str = DonutProcessor.from_pretrained(__lowercase )... | 219 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCAmelCase = ["""image_processor""", """tokenizer""... | 101 | import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def UpperCAmelCase_... | 423 | 0 |
from math import sqrt
def A ( _UpperCAmelCase : Optional[int] ) -> bool:
'''simple docstring'''
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
_UpperCAmelCase = True
... | 712 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet impo... | 639 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impo... | 143 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE = (boundary[1] - boundary[0]) / steps
__SCREAMING_SNAKE_CASE = boundary[0]
__SCREAMING_SNAKE_CASE ... | 109 | 0 |
import operator
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase = False , UpperCAmelCase = None ):
lowercase__ : Optional[int] = operator.lt if reverse else operator.gt
lowercase__ : Optional[int] = solution or []
if not arr:
return solution
lowercas... | 716 | '''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compu... | 428 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_SCREAMING_SNAKE_CASE = "\\n\n"
_SCREAMING_SNAKE_CASE = "\nPerplexity (PPL) is one of the most common metrics for evaluating la... | 181 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE_ (... | 181 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelF... | 41 |
'''simple docstring'''
import torch
from diffusers import StableDiffusionPipeline
lowercase = '''path-to-your-trained-model'''
lowercase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
lowercase = '''A ph... | 41 | 1 |
def _UpperCamelCase ( snake_case__, snake_case__, snake_case__, snake_case__ ) -> int:
__UpperCAmelCase , __UpperCAmelCase : Dict = len(snake_case__ ), len(grid[0] )
if (
min(snake_case__, snake_case__ ) < 0
or row == ro... | 382 | import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
_snake_case = '''src/transformers'''
_snake_cas... | 382 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def UpperCAmelCase ( lowercase , lowercase , lowercase = "x" , lowercase = 10**-10 , lowercase = 1 , ):
"""simple docstring"""
__lowercase = ... | 717 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 522 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
UpperCAmelCase__ = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapane... | 186 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_ten... | 186 | 1 |
"""simple docstring"""
import heapq as hq
import math
from collections.abc import Iterator
class A_:
"""simple docstring"""
def __init__( self , A ):
_lowerCamelCase : Optional[int] = str(id_ )
_lowerCamelCase : Tuple ... | 703 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase_ ( __a : np.array ):
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 349 | 0 |
def a_ ( lowerCAmelCase_ : str ):
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def a_ ( lowerCAmelCase_ : str ):
__lowerCAmelCase = credit_card_number
__lowerCAmelCase = 0
__lowerCAmelCase ... | 53 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_... | 215 | 0 |
from __future__ import annotations
class A_ :
def __init__( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : int ):
__a = order
# a_{0} ... a_{k}
__a = [1.0] + [0.0] * order
# b_{0} ... b_{k}
__a = [1.0] + [0.0] * order
# x[n-1] ... x... | 525 | import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class A_ ( a_ , a_ , ... | 525 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.con... | 82 |
def _A ( __snake_case :int = 400_0000 ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__snake_case )
__SCRE... | 693 | 0 |
'''simple docstring'''
import sys
import turtle
def _lowerCAmelCase ( __snake_case : tuple[float, float] , __snake_case : tuple[float, float] ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def _lower... | 338 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxMod... | 338 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
UpperCAmelCase_ : Optional[int] = False
class SCREAMING_SNAKE_C... | 570 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAut... | 570 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__ : str = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t... | 715 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ) -> List[Any]:
"""simple docstring"""
with offline(Offli... | 0 | 0 |
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 DPRContextEncoderTokenize... | 12 | import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def A__ ( ):
SCREAMING_SNAKE_CASE__: U... | 64 | 0 |
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_available, logging
from .ben... | 711 |
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> list[int]:
_UpperCAmelCase = [0 for i in range(len(__snake_case ) )]
# initialize interval's left pointer and right pointer
_UpperCAmelCase , _UpperCAmelCase = 0, 0
for i in range(1 , len(__sn... | 402 | 0 |
"""simple docstring"""
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torc... | 337 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'encoder-decoder'
SCREAMING... | 242 | 0 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class UpperCAmelCase__ ( datasets.BeamBasedBuilder ):
"""simple docstring"""
... | 709 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCAmelCase_ (__a : str = "isbn/0140328726" ):
"""simple docstring"""
_a : Dict = olid.strip().strip('/' ) # Remove leading/tr... | 319 | 0 |
"""simple docstring"""
import cmath
import math
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> complex:
UpperCAmelCase__ : str = math.radians(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = math.radians(... | 182 |
"""simple docstring"""
import requests
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> None:
UpperCAmelCase__ : List[str] = {"""Content-Type""": """application/json"""}
UpperCAmelCase__ : List[str] = requests.post(lowerCAmelCase , json={"""text"""... | 182 | 1 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule,... | 704 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTok... | 369 | 0 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import T... | 20 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark i... | 264 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_rembert impor... | 701 |
from PIL import Image
def _a ( lowerCamelCase__ , lowerCamelCase__ ) -> Image:
def brightness(lowerCamelCase__ ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -255.0 (black) and 255.0 (white)' ... | 144 | 0 |
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = 0 , UpperCamelCase__ = 0 ) -> int:
'''simple docstring'''
UpperCAmelCase = right or len(lowerCAmelCase_ ) - 1
if left > right:
return -1
... | 130 |
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def __a ( lowerCAmelCase_ : Namespace ) -> Optional[int]:
'''simple docstring'''
return ConvertCommand(
args.model_type ,args.tf_checkpoint... | 593 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase: int =logging.get_logger(__name__)
_UpperCamelCase: str ={'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class __lowercase( SCREAMING_SNAKE_CASE ):
"""simp... | 585 |
from __future__ import annotations
import math
class __lowercase:
"""simple docstring"""
def __init__( self : str , _lowerCAmelCase : int ) -> None:
_lowerCAmelCase = size
# approximate the overall size of segment tree with given value
... | 585 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_availabl... | 523 | '''simple docstring'''
import numpy as np
def UpperCamelCase__ ( _lowercase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 523 | 1 |
import torch
def _lowerCAmelCase ( ):
if torch.cuda.is_available():
lowercase__ = torch.cuda.device_count()
else:
lowercase__ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main()
| 700 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {"configuration_reformer": ["REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "ReformerConfig"]}
tr... | 642 | 0 |
"""simple docstring"""
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_A : Tuple = (DDPMScheduler,)
def lowerCamelCase(self , **lowerC... | 180 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cache... | 180 | 1 |
'''simple docstring'''
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... | 347 |
'''simple docstring'''
import sys
from collections import defaultdict
class a_ :
def __init__( self : Union[str, Any] ) -> Optional[int]:
snake_case: Any =[]
def UpperCamelCase ( self : List[str] , a_ : ... | 347 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils impo... | 552 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_... | 552 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common imp... | 352 |
'''simple docstring'''
import numpy as np
import qiskit
def lowerCAmelCase (__A = 8 , __A = None):
"""simple docstring"""
_a = np.random.default_rng(seed=__A)
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
_... | 352 | 1 |
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __magic_name__ ( lowercase_ ):
U... | 628 |
'''simple docstring'''
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class... | 379 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
a_ : List[str] = logging.get_logger(__name__)
a_ : ... | 710 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
f... | 678 | 0 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
SCREAMING_SNAKE_CASE_ = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase ( ... | 597 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
return abs(SCREAMING_SNAKE_CASE__ ) if a == 0 else greatest_common_divisor(b % a , SCREAMING_SNAKE_CASE__ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_C... | 597 | 1 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowercase ( ):
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import di... | 704 |
"""simple docstring"""
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
sl... | 141 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase ):
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] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase_ : ... | 718 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 418 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int ) -> int:
'''simple docstring'''
UpperCAmelCase_ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase_ ( snake_c... | 78 | '''simple docstring'''
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table imp... | 78 | 1 |
import numpy as np
from PIL import Image
def A_ ( lowercase_ , lowercase_ , lowercase_ ) ->np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE = np.array(lowercase_ )
if arr.shape[0] != arr.shape[1]:
raise ValueError('The input array is not a square matrix' ... | 259 |
import argparse
from collections import defaultdict
import yaml
__UpperCAmelCase = "docs/source/en/_toctree.yml"
def A_ ( lowercase_ ) ->Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE = defaultdict(lowercase_ )
for doc in model_doc:
counts[doc["... | 259 | 1 |
from bisect import bisect
from itertools import accumulate
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
_snake_case : Any = sorted(zip(__lowerCAmelCase , __lowerCAmelCase ) , key=lambda __l... | 304 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowercase ( a_ ):
"""simple ... | 304 | 1 |
def A_( A , A ):
_validate_point(A )
_validate_point(A )
if len(A ) != len(A ):
raise ValueError("""Both points must be in the same n-dimensional space""" )
return float(sum(abs(a - b ) for a, b in zip(A , A ) ) )
def A_( A ):
if point:... | 486 |
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,... | 486 | 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 a__ :
def __init__( self , _a , ... | 361 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://... | 654 | 0 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_a... | 702 |
def __lowercase ( UpperCAmelCase__ = 10 , UpperCAmelCase__ = 1_000 , UpperCAmelCase__ = True ):
"""simple docstring"""
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
... | 102 | 0 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Model... | 610 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSche... | 426 | 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,
Wa... | 537 |
'''simple docstring'''
# Usage:
# ./gen-card-facebook-wmt19.py
import os
from pathlib import Path
def _lowerCamelCase( UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] ) -> List[Any]:
A : Any = {
... | 537 | 1 |
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 ...test_tokenization_... | 6 |
'''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 | 0 |
lowerCAmelCase__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowerCamelCase_ ( UpperCAmelCase_ : float , UpperCAmelCase_ : float , UpperCAmelCase_ : float ) -> float:
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume <... | 702 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
lowerCAmelCase__ = False
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def... | 648 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pr... | 698 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 698 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfi... | 98 |
'''simple docstring'''
def _snake_case ( A ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _snake_case ( A ) -> bool:
lowerCAmelCase__ = 0
lowerCAmelCase__ = number
while duplicate > 0:
... | 98 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : List[Any] = {
'''kssteven/ibert-roberta-base''':... | 613 | '''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
__snake_case : int = logging.get_logger(__name__... | 660 | 0 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 711 | """simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
fr... | 296 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[int] = {
... | 527 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase ( A : list[int] , A : int ):
if len(A ) < k or k < 0:
raise ValueError('''Invalid Input''' )
SCREAMING_SNAKE_CASE : Dict = sum(array[:k] ... | 527 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE = {
"""configuration_roc_bert""": ["""ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoCBertConfig"""],
"""tokenization_... | 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""I... | 17 | 0 |
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transfo... | 97 | """simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
UpperCAme... | 420 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCAmelCase = {
"configuration_perceiver": ["PERCEIVER_PRETRAINED_CONFIG_A... | 709 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAtt... | 692 | 0 |
from math import ceil, sqrt
def lowerCamelCase__ ( _a = 1000000):
SCREAMING_SNAKE_CASE : List[Any] = 0
for outer_width in range(3 , (limit // 4) + 2):
if outer_width**2 > limit:
SCREAMING_SNAKE_CASE : str = max(ceil(sqrt(outer_width**2 - limit)) , 1)
e... | 25 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import ... | 527 | 0 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...... | 662 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from... | 662 | 1 |
from __future__ import annotations
from collections.abc import Iterator
class _a :
def __init__( self: Union[str, Any] , UpperCamelCase_: int ) -> None:
"""simple docstring"""
lowercase__ = value
lowercase... | 43 |
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 _A ( _lowercase , unittest.TestCase ):
... | 402 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 604 |
import heapq
import sys
import numpy as np
_lowerCAmelCase : str = tuple[int, int]
class __snake_case :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ = set()
def SCREAMING_SNAKE_CASE_ ( ... | 604 | 1 |
UpperCamelCase__ : Optional[Any] = 0 # The first color of the flag.
UpperCamelCase__ : Dict = 1 # The second color of the flag.
UpperCamelCase__ : int = 2 # The third color of the flag.
UpperCamelCase__ : Tuple = (red, white, blue)
... | 387 |
import os
import pytest
from attr import dataclass
_a : int = 'us-east-1' # defaults region
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
A = 42
A = '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
A = {
'... | 479 | 0 |
'''simple docstring'''
from __future__ import annotations
def A__ ( A : str , A : str):
'''simple docstring'''
UpperCamelCase : List[Any] = get_failure_array(A)
# 2) Step through text searching for pattern
UpperCamelCase : Any = 0... | 720 |
'''simple docstring'''
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state ... | 435 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_UpperCAmelCase = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""),
("""k... | 558 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 0 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def __UpperCAmelCase ( _UpperCAmelCase : Union[str, Any] ) -> List[str]:
__snake_case = FileLock(str(tmpdir / "foo.lock" ) )
__snake_case = FileLock(s... | 680 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
... | 680 | 1 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAM... | 519 |
def _UpperCAmelCase ( UpperCAmelCase : int = 600_851_475_143 ):
"""simple docstring"""
try:
__lowerCamelCase : Any = int(UpperCAmelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable... | 519 | 1 |
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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMas... | 704 |
from __future__ import annotations
import numpy as np
def lowercase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
lowerCamelCase , lowerCamelCase : Dict = np.shape(SCREAMING_SNAKE_CASE_ )
if rows != columns:
lowerCamelCase : int ... | 231 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
a_ :Tuple = logging.get_logger(__name__)
a_ :int = {'vo... | 35 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_sha... | 230 | 0 |
from __future__ import annotations
def __lowerCAmelCase ( __magic_name__ , __magic_name__ , __magic_name__ ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
i... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See ... | 206 | 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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltFo... | 639 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _lowerCAmelCase ( __magic_name__ : List[str]... | 92 | 0 |
from collections.abc import Sequence
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__ ) )
def __lowerCamelCase ( UpperCamelCase__ , Uppe... | 108 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_UpperCAmelCase : Dict = logging.get_logger(__name__)
class lowercase ( lowercase_ ):
__SCREAMING_SNAKE_CASE : Any = '... | 108 | 1 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
class _lowerCamelCase ( UpperCamelCase_ ):
def __init__... | 64 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowercase ( __snake_case ):
_A = (DEISMultistepSc... | 461 | 0 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class lowerCamelCase_ ( unittest.TestCase ):
def __magic_name__ ( self ):
a_ = Vector([1, 2, 3] )
self.ass... | 703 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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_... | 403 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( snake... | 341 |
"""simple docstring"""
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = len(_snake_case )
for i in range(_snake_case ):
for j in range(i + 1 , _snake_case ):
if numbers[j] < numbers[i]:
... | 341 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 30 | '''simple docstring'''
_lowercase : int = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase ( ) -> None:
lowercase_ : List[Any] = input("""Enter message: """ )
lowercase_ : str = input("""Enter key [alphanumeric]: """ )
lowerca... | 30 | 1 |
"""simple docstring"""
class lowercase__ :
'''simple docstring'''
def __init__( self , snake_case = "" , snake_case = False ) -> None:
# Mapping from the first character of the prefix of the node
_UpperCAmel... | 573 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
'''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config... | 573 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
__snake_case: Any = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHead... | 460 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A_ : int ):
"""simple docstring"""
a_ : Optional[Any] = 2
a_ : int = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 460 | 1 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditi... | 532 | '''simple docstring'''
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputF... | 435 | 0 |
# 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 req... | 711 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedde... | 89 | 0 |
"""simple docstring"""
from string import ascii_uppercase
UpperCAmelCase : Optional[Any] = {str(ord(c) - 55): c for c in ascii_uppercase}
def _SCREAMING_SNAKE_CASE (__lowerCAmelCase , __lowerCAmelCase ) -> str:
'''simple docstring'''
if isinstance(_... | 567 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _SCREAMING_SNAKE_CASE () -> Generator[int, None, None]:
'''simple docstring'''
lowercase_ = {}
lowercase_ = 2
while True:
lowe... | 567 | 1 |
"""simple docstring"""
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _con... | 720 |
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeli... | 628 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.trai... | 486 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
lowerCAmelCase :Dict = datasets.utils.logging.get_logger(__name__)
class _lowerCamelCase ( folder_based_builder.FolderBasedBuil... | 561 | 0 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class ... | 716 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCAmelCase_ : List[str] = {
'''configuration_layoutlmv2''': ['''L... | 156 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
a = '''\
@misc{chen2021evaluating,
title={Evaluating L... | 7 | import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from... | 140 | 0 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_A = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def lowercase_ ( ) -> List[Any]:
lowerCAmelCase__ : int = os.path.dirname(os.path.realpath(__UpperCAmelCase ) )
... | 507 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
_A = TypeVar("""_T""")
class _lowerCamelCase ( Generic[_T] ):
def __init__( self : Optional[Any] , UpperCamelCase : Iterable[_T] | None = None ) -> None:
... | 507 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : Optional[int] = {
... | 368 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( UpperCAmelCase_ ) ->int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError('Input value ... | 368 | 1 |
def _A( UpperCamelCase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
stooge(UpperCamelCase__ , 0 , len(UpperCamelCase__ ) - 1 )
return arr
def _A( UpperCamelCase__ : Tuple , UpperCamelCase__ : ... | 362 |
import argparse
import copy
def _A( UpperCamelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
__lowercase = {}
with open(UpperCamelCase__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbour... | 362 | 1 |
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