code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
__UpperCAmelCase = range(2, 20 + 1)
__UpperCAmelCase = [10**k for k in range(ks[-1] + 1)]
__UpperCAmelCase = {}
def __lowerCamelCase ( __magic_name__ : Optional[Any] , __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_nam... | 353 |
from math import factorial
def __lowerCamelCase ( __magic_name__ : int , __magic_name__ : int , __magic_name__ : float ):
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
... | 42 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf,... | 354 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__UpperCAmelCase = logging.get_logger(__name__)
_... | 42 | 0 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def __lowerCamelCase ( __magi... | 355 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''google... | 42 | 0 |
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()... | 356 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCAmelCase = logging.getLogger(__name... | 42 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput... | 357 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class lowerCamelCase__ ( _a ):
@require_torch
def _lowerCamelCase ( self : Union[str, Any] ):
... | 42 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
clas... | 358 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCAmelCase = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be... | 42 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_ut... | 359 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__UpperCAmelCase = 5_00_00
__UpperCAmelCase = 50_00
__UpperCAmelCase , __UpperCAmelCase = os.path.split(__file__)
__UpperCAmelCase = os.path.jo... | 42 | 0 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
... | 360 |
def __lowerCamelCase ( __magic_name__ : int ):
if not isinstance(__magic_name__ , __magic_name__ ):
a__: List[str] =F"Input value of [number={number}] must be an integer"
raise TypeError(__magic_name__ )
if number < 1:
a__: ... | 42 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
... | 361 |
from __future__ import annotations
def __lowerCamelCase ( __magic_name__ : str , __magic_name__ : Any , __magic_name__ : List[Any] , __magic_name__ : str ): # noqa: E741
while r - l > 1:
a__: Any =(l + r) // 2
if v[m] >... | 42 | 0 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCamelCase__ ( _a ):
_lowerCAmelCase = (DPMSolverSDES... | 362 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ... | 42 | 0 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...util... | 363 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_M... | 42 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=_a ):
_lowerCAmelCase = ['''flax''']
def __init__( self : Any , *_a : int , **_a : Tuple ):
requires_backends(self , ["flax"] ... | 364 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def __lowerCamelCase ( __magic_name__ : List[Any] ):
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings" , set() )
@pytest.fixture
def __lowerCamelCase ... | 42 | 0 |
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__UpperCAmelCase = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def __lowerCamelCase ( __magic_n... | 365 |
from __future__ import annotations
from math import gcd
def __lowerCamelCase ( __magic_name__ : int , __magic_name__ : int = 2 , __magic_name__ : int = 1 , __magic_name__ : int = 3 , ):
# A value less than 2 can cause an infinite loop in the algorithm.
... | 42 | 0 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class lowerCamelCase__ ( unittest.TestCase ):
def _lowerCamelCase ( self : Optional[int] ):
... | 366 |
def __lowerCamelCase ( __magic_name__ : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 | 0 |
def __lowerCamelCase ( __magic_name__ : int = 1_000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 367 |
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
__UpperCAmelCase = logging.get_logger(__name__)... | 42 | 0 |
import datasets
from .evaluate import evaluate
__UpperCAmelCase = '''\
@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},
year={2016}
}
'... | 368 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class lowerCamelCase__... | 42 | 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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.se... | 369 |
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_extraction_common import SequenceFeat... | 42 | 0 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#... | 370 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip... | 42 | 0 |
"""simple docstring"""
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampl... | 371 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class lower... | 42 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase_ ( _a , unittest.TestCase ):
"""simple docstring"""
... | 43 |
'''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-large": "https:... | 43 | 1 |
'''simple docstring'''
import datasets
__a = "\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n and Schwenk, Holger... | 43 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__a = {
"configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
"tokenization_biogpt": ["BioGptToken... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 100 ) -> int:
snake_case__ : List[str] = 0
snake_case__ : Optional[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return s... | 43 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]... | 43 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
__a = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase_ ( _a ):
"""simple d... | 43 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 | 1 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, 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_m... | 43 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from tran... | 43 | 1 |
'''simple docstring'''
import sys
import turtle
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase... | 43 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict ):
snake_case__ : List[str] = {}
def lowerCame... | 43 | 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 __snake_case( ) -> int:
snake_case__ : List[Any] = ArgumentParser(
description=(... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 1 |
'''simple docstring'''
import re
def __snake_case( _lowerCAmelCase ) -> list:
return [char.split() for char in re.split(r"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def __snake_case( _lowerCAmelCase ) -> str:
snake_case__ : Optional[Any] = split_i... | 43 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.te... | 43 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtten... | 43 |
'''simple docstring'''
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
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow... | 43 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1_000 ) -> int:
snake_case__ : Any = 1
snake_case__ : Union[str, Any] = 0
for divide_by_number in range(_lowerCAmelCase , digit + 1 ):
sn... | 43 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 1 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_... | 43 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils im... | 43 | 1 |
'''simple docstring'''
from __future__ import annotations
from math import pi
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("""One and... | 43 |
'''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.uti... | 43 | 1 |
'''simple docstring'''
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_sp... | 43 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 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 import A... | 43 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 1 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__a = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classificati... | 43 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__nam... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : List[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : Union[str... | 43 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 |
'''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-large": "https:... | 43 | 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 UpperCAmelCase_ ( enum.Enum ):
"""simple docstring"""
lowercase = "all_checks"
... | 43 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 1 |
'''simple docstring'''
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... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
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 ... | 43 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]... | 43 | 1 |
'''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.uti... | 43 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 | 1 |
'''simple docstring'''
import random
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> int:
snake_case__ : Tuple = a[left_index]
snake_case__ : Dict = left_index + 1
for j in range(left_index + 1 , ... | 43 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from tran... | 43 | 1 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict ):
snake_case__ : List[str] = {}
def lowerCame... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
__a = {
"configuration_speech_to_text": ["SPEECH_TO_TEXT_PRE... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 1 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__a = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/model... | 43 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 43 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 1 |
'''simple docstring'''
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def __snake_case( _lowerCAmelCase = True , *_lowerCAmelCase , **_lowerCAmelCase ) -> Any:
if not is_tq... | 43 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__a = 5_0000
__a = 5000
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".p... | 43 |
'''simple docstring'''
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
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective... | 43 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
... | 43 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 1 |
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
"_TestCommandArgs",
[
... | 43 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 1 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils im... | 43 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
__a = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive ... | 43 |
'''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.uti... | 43 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.js... | 43 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__a... | 43 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import re... | 43 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__nam... | 43 | 1 |
'''simple docstring'''
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __snake_case( _lowerCAmelCase ) -> Any:
snake_cas... | 43 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 1 |
'''simple docstring'''
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class Up... | 43 |
'''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-large": "https:... | 43 | 1 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__a = 3
def __snake_case( _lowerCAmelCase ) -> int:
print("""Generating primitive root of p""" )
while True:
snake_case__ : ... | 43 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedule... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSched... | 43 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
... | 43 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 | 1 |
'''simple docstring'''
__a = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : str = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
... | 43 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from tran... | 43 | 1 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __snake_cas... | 43 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict ):
snake_case__ : List[str] = {}
def lowerCame... | 43 | 1 |
'''simple docstring'''
import argparse
import json
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_see... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCH... | 43 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__a = logging.get_logger(__name__)
__a = {"vocab_file": "vocab.json", "merges_file":... | 43 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 1 |
'''simple docstring'''
from torch import nn
def __snake_case( _lowerCAmelCase ) -> List[str]:
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
... | 43 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowe... | 43 |
'''simple docstring'''
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
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 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 UpperCAmelCase_ ( _a ):
"""simple docstring"""
... | 43 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 1 |
'''simple docstring'''
__a = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def __snake_case( _lowerCAmelCase ) -> int:
... | 43 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 1 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_tru... | 43 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils im... | 43 | 1 |
'''simple docstring'''
__a = {str(digit): digit**5 for digit in range(10)}
def __snake_case( _lowerCAmelCase ) -> int:
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCAmelCase ) )
def __snake_case( ) -> int:
return sum(
number
... | 43 |
'''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.uti... | 43 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__a = TypeVar("T")
class UpperCAmelCase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self : List[Any] , snake_case_... | 43 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return x if y == 0 else greatest_common_divisor(_lowerCAmelCase , x % y )
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> int:
return (x * y) /... | 43 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_com... | 43 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> bool:
if not all(x.isalpha() for x in string ):
raise ValueError("""String must only contain alphabetic characters.""" )
snake_case__ : str = sorted(string.lower() )
return len(_lowerCAmel... | 43 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__nam... | 43 | 1 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, 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_model... | 43 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 1 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__a = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
... | 43 |
'''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-large": "https:... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__a = {
"configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"],
"tokenization_transfo_xl": ["Tran... | 43 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 1 |
'''simple docstring'''
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__a = Mapping[str, np.ndarray]
__a = Mapping[str, Any] # Is a nested dict.
__a = 0.0... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class UpperCAmelCase_ (... | 43 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> list[list[float]]:
snake_case__ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(_lowerCAmelCase ):
if len(_lowerCAmelCase ) < i + 1:
... | 43 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 | 1 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def __snake_case( ) -> Optional[int]:
snake_case__ : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=_lowerCAmelCase... | 43 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from tran... | 43 | 1 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
... | 43 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict ):
snake_case__ : List[str] = {}
def lowerCame... | 43 | 1 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def lowerCamelCase ( self : int , snake_case_ : str ):
with open(snak... | 43 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 43 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> tuple[int, int]:
try:
snake_case__ : Any = float(_lowerCAmelCase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
snake_case__ : int = deci... | 43 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> float:
_validate_point(_lowerCAmelCase )
_validate_point(_lowerCAmelCase )
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError("""Both points must be in the s... | 43 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 1 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
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... | 43 |
'''simple docstring'''
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
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__a = Lock()
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ... | 43 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
"ClapTextConfig",
... | 43 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 1 |
'''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-2.0
... | 43 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 1 |
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