code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def lowerCamelCase__ ( snake_case_ : List[Any] ) -> Optional[Any]:
__snake_case = 1
__snake_case = 2
while i * i <= n:
__snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divi... | 24 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
snake_case_ = logging.get_logger(__name__)
snake_... | 24 |
import socket
def lowerCamelCase__ ( ) -> Any:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) ... | 24 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json'
... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] ) -> list[int]: # This function is recursive
__snake_case = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition o... | 24 | 1 |
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... | 24 |
import unittest
from transformers import LiltConfig, 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... | 24 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__(self : Any , *a__ : Tupl... | 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipelin... | 24 | 1 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]:
__snake_case = []
__snake_case = []
__snake_case = 0
__snake_case ... | 24 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://hug... | 24 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterM... | 24 | 1 |
import datasets
snake_case_ = '\\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\n ... | 24 |
def lowerCamelCase__ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(snake_case_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'... | 24 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 24 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir('fixtures/test_sentencepi... | 24 | 1 |
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
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'microsoft/b... | 24 |
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if not isinstance(snake_case_ , snake_case_ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__snake_case = 0
while number:
# This way we... | 24 | 1 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerat... | 24 |
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : list ) -> list:
if len(snake_case_ ) <= 1:
return lst
__snake_case = 1
while i < len(snake_case_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__snake_case , __sn... | 24 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 24 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
snake_case_ = 0
snake_case_ = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0... | 24 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> Any:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 24 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase__ ( ) -> tuple[list[int], int]:
__snake_case = [randint(-1000 , 1000 ) for i in range(10 ... | 24 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 24 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
snake_case_ = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trap... | 24 |
from math import pi
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : int ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 24 | 1 |
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... | 24 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://hug... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : int = 6008_5147_5143 ) -> int:
try:
__snake_case = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError('''Parameter n must be int or castable to int.''' )
if n <= 0:
raise... | 24 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 24 | 1 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( snake_case_ : str = "" ) -> dict[str, float]:
__snake_case = url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
__sna... | 24 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaToke... | 24 | 1 |
import math
def lowerCamelCase__ ( snake_case_ : float , snake_case_ : float ) -> float:
return math.pow(snake_case_ , 2 ) - a
def lowerCamelCase__ ( snake_case_ : float ) -> float:
return 2 * x
... | 24 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : List[str] = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'CLI... | 24 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 24 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_pr... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : Optional[Any] ) -> Dict:
__snake_case = len(snake_case_ )
__snake_case = sum(snake_case_ )
__snake_case = [[False for x in range(s + 1 )] for y in range(n + 1 )]
... | 24 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class SCREAMING_SNAKE_CASE__ ( ... | 24 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common... | 24 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...te... | 24 | 1 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
snake_case_ = logging.get_logger(__name__)
... | 24 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 | 1 |
import os
from collections.abc import Iterator
def lowerCamelCase__ ( snake_case_ : str = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(snake_case_ ):
__snake_case = [d for d in dir_names if d != '''scripts''' and ... | 24 |
import socket
def lowerCamelCase__ ( ) -> Any:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) ... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : float , snake_case_ : float , snake_case_ : float , snake_case_ : float , snake_case_ : float , ) -> float:
__snake_case = [redshift, radiation_density, matter_density, dark_e... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] ) -> list[int]: # This function is recursive
__snake_case = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition o... | 24 | 1 |
from string import ascii_uppercase
snake_case_ = {char: i for i, char in enumerate(ascii_uppercase)}
snake_case_ = dict(enumerate(ascii_uppercase))
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : str ) -> str:
__snake_case ... | 24 |
import unittest
from transformers import LiltConfig, 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... | 24 | 1 |
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging
... | 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipelin... | 24 | 1 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=1337 , ... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]:
__snake_case = []
__snake_case = []
__snake_case = 0
__snake_case ... | 24 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] ) -> list[int]: # This function is recursive
__snake_case = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition o... | 24 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterM... | 24 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | 24 |
def lowerCamelCase__ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(snake_case_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'... | 24 | 1 |
from collections.abc import Callable
def lowerCamelCase__ ( snake_case_ : Callable[[float], float] , snake_case_ : float , snake_case_ : float ) -> float:
__snake_case = a
__snake_case = b
if function(snak... | 24 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir('fixtures/test_sentencepi... | 24 | 1 |
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
snake_case_ = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models... | 24 |
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if not isinstance(snake_case_ , snake_case_ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__snake_case = 0
while number:
# This way we... | 24 | 1 |
from __future__ import annotations
snake_case_ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
snake_case_ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def lowerCamelCase__ ( snake_case_ : list[float] ) -> list[float]:
__sn... | 24 |
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
snake_case_ = datasets.load_iris()
snake_case_ = np.array(data['data'])
snake_case_ = np.array(data['target'])
snake_case_ = data['target_name... | 24 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 24 | 1 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> Any:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 24 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> Any:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : int = 400_0000 ) -> int:
__snake_case = [0, 1]
__snake_case = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
__snake_case ... | 24 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 24 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow a... | 24 |
from math import pi
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : int ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 24 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstri... | 24 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://hug... | 24 | 1 |
from __future__ import annotations
class SCREAMING_SNAKE_CASE__ :
def __init__(self : str , a__ : int = 0 ):
"""simple docstring"""
__snake_case = key
def a (self : str , a__ ... | 24 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 24 | 1 |
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__(self : Optional[int] , a__ : Any ):
"""simple docstring"""
__snake_case = data
__snake_case = None
cl... | 24 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaToke... | 24 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_al... | 24 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : List[str] = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'CLI... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : int ) -> bool:
if not isinstance(snake_case_ , snake_case_ ):
__snake_case = f"""Input value of [number={number}] must be an integer"""
raise TypeError(snake_case_ )
if number < 0:
... | 24 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_pr... | 24 | 1 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase__ ( ) -> Optional[int]:
__snake_case = ArgumentParser(
description=(
... | 24 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class SCREAMING_SNAKE_CASE__ ( ... | 24 | 1 |
from pathlib import Path
import numpy as np
from PIL import Image
def lowerCamelCase__ ( snake_case_ : np.ndarray ) -> np.ndarray:
__snake_case , __snake_case , __snake_case = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_989 ... | 24 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...te... | 24 | 1 |
# Lint as: python3
import itertools
import os
import re
snake_case_ = re.compile(R'([A-Z]+)([A-Z][a-z])')
snake_case_ = re.compile(R'([a-z\d])([A-Z])')
snake_case_ = re.compile(R'(?<!_)_(?!_)')
snake_case_ = re.compile(R'(_{2,})')
snake_case_ = R'^\w+(\.\w+)*$'
sn... | 24 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
snake_case_ = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingfa... | 24 |
import socket
def lowerCamelCase__ ( ) -> Any:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) ... | 24 | 1 |
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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffuse... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] ) -> list[int]: # This function is recursive
__snake_case = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition o... | 24 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__(self : int , a__ : int , a__ : int , a__ : float = 0 ):
"""simple docstring"""
__sna... | 24 |
import unittest
from transformers import LiltConfig, 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... | 24 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils im... | 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipelin... | 24 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> bool:
if len(snake_case_ ) == 0:
return False
__snake_case = len(snake_case_ ) // 2
if a_list[midpoint] ==... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]:
__snake_case = []
__snake_case = []
__snake_case = 0
__snake_case ... | 24 | 1 |
# 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
#
# Unl... | 24 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterM... | 24 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'facebook/vit-mae-base': 'https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json',
# See all ViT MAE models at https://huggi... | 24 |
def lowerCamelCase__ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(snake_case_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'... | 24 | 1 |
import argparse
import os
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_task_guides.py
snake_case_ = 'src/transformers'
snake_case_ = 'docs/source/... | 24 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir('fixtures/test_sentencepi... | 24 | 1 |
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,
... | 24 |
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if not isinstance(snake_case_ , snake_case_ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__snake_case = 0
while number:
# This way we... | 24 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]:
__snake_case = []
__snake_case = []
__snake_case = 0
__snake_case ... | 24 |
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__snake_case = set()
# Replace all the whitespace in our sentence
__snake_case = input_str.replace(''' ''' , '''''' ... | 24 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 24 | 1 |
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
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sail/poolfo... | 24 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> Any:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : str ) -> list:
__snake_case = len(snake_case_ )
__snake_case = []
for i in range(len(snake_case_ ) - pat_len + 1 ):
__snake_case = Tr... | 24 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 24 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=_UpperCAmelCase ):
A_ : List[Any] = ['transformers', 'torch', 'note_seq']
def __init__(self : List[str] , *a__ : Any , **a__ : ... | 24 |
from math import pi
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : int ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 24 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
snake_case_ = loggi... | 24 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://hug... | 24 | 1 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 24 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)... | 24 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaToke... | 24 | 1 |
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_proces... | 24 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : List[str] = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'CLI... | 24 | 1 |
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 24 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_pr... | 24 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 24 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class SCREAMING_SNAKE_CASE__ ( ... | 24 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig',... | 24 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...te... | 24 | 1 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,... | 24 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case_ = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig... | 24 |
import socket
def lowerCamelCase__ ( ) -> Any:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) ... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : str ) -> Tuple:
__snake_case = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def lowerCamelCase__ ( snake_case_ : Li... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] ) -> list[int]: # This function is recursive
__snake_case = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition o... | 24 | 1 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def a (self : Union[str, Any] ):
"""simple docstring"""
return ... | 24 |
import unittest
from transformers import LiltConfig, 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... | 24 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
A_ : int
A_ : TreeNode | None = None
A_ : TreeNode | None = None
snake_ca... | 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipelin... | 24 | 1 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
snake_case_ = logging.get_logg... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]:
__snake_case = []
__snake_case = []
__snake_case = 0
__snake_case ... | 24 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( snake_case_ : List[str] ,... | 24 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterM... | 24 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax imp... | 24 |
def lowerCamelCase__ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(snake_case_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'... | 24 | 1 |
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 (
TFBaseModelOutputWithNoAttention,
TF... | 24 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir('fixtures/test_sentencepi... | 24 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
snake_case_ = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'ou... | 24 |
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if not isinstance(snake_case_ , snake_case_ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__snake_case = 0
while number:
# This way we... | 24 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
snake_case_ = 'http://www.mocksite.com/file1... | 24 |
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at https://hugging... | 24 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 24 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determi... | 24 |
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def lowerCamelCase__ ( ) -> Any:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dir... | 24 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE__ ( unittest.Tes... | 24 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqL... | 24 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'post_extract_proj': 'feature_projection.pro... | 24 |
from math import pi
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : int ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 24 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : Optional[Any] = 'ClapFeatureExtractor'
A_ : Optional[int] = ('RobertaTokenizer', 'Roberta... | 24 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://hug... | 24 | 1 |
from collections import deque
class SCREAMING_SNAKE_CASE__ :
def __init__(self : Any , a__ : str , a__ : int , a__ : int ):
"""simple docstring"""
__snake_case = process_name... | 24 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_... | 24 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
'XCLIPVisionConfig',
... | 24 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaToke... | 24 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)... | 24 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
A_ : List[str] = ['image_processor', 'tokenizer']
A_ : Optional[Any] = 'CLI... | 24 | 1 |
from itertools import permutations
def lowerCamelCase__ ( snake_case_ : tuple ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
__snake_case ... | 24 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_pr... | 24 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
),
}
... | 24 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class SCREAMING_SNAKE_CASE__ ( ... | 24 | 1 |
import argparse
import json
from tqdm import tqdm
def lowerCamelCase__ ( ) -> Tuple:
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--src_path''' , type=snake_case_ , default=... | 24 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...te... | 24 | 1 |
from __future__ import annotations
import os
from typing import Any
import requests
snake_case_ = 'https://api.github.com'
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
snake_case_ = BASE_URL + '/user'
# https://github.com/settin... | 24 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
snake_case_ = '\nimport os\n'
snake_case_ = '\ndef foo():\n import os\n return False\n'
snake_case_ = '\ndef foo():\n def bar():\n if True:\n import os\n return... | 24 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'google/canine-s': 'https://huggingface.co/google/canine-s/resolve/main/config.json',
# See all CANINE models at https://huggingface.co/mod... | 24 |
import socket
def lowerCamelCase__ ( ) -> Any:
__snake_case = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__snake_case = socket.gethostname()
__snake_case = 1_2312
sock.connect((host, port) ... | 24 | 1 |
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if not isinstance(snake_case_ , snake_case_ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__snake_case = 0
while number:
# This way we... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] ) -> list[int]: # This function is recursive
__snake_case = len(snake_case_ )
# If the array contains only one element, we return it (it's the stop condition o... | 24 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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/LICEN... | 24 |
import unittest
from transformers import LiltConfig, 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... | 24 | 1 |
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_avai... | 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipelin... | 24 | 1 |
from collections import defaultdict
from math import gcd
def lowerCamelCase__ ( snake_case_ : int = 150_0000 ) -> int:
__snake_case = defaultdict(snake_case_ )
__snake_case = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:... | 24 |
from __future__ import annotations
def lowerCamelCase__ ( snake_case_ : list[int] , snake_case_ : int ) -> list[list[int]]:
__snake_case = []
__snake_case = []
__snake_case = 0
__snake_case ... | 24 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class SCR... | 24 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterM... | 24 | 1 |
import argparse
from collections import defaultdict
def lowerCamelCase__ ( snake_case_ : Optional[Any] , snake_case_ : Any , snake_case_ : Optional[Any] , snake_case_ : List[Any] , snake_case_ : str ) -> List[str]:
__s... | 24 |
def lowerCamelCase__ ( ) -> int:
return [
a * b * (1000 - a - b)
for a in range(1 , 999 )
for b in range(snake_case_ , 999 )
if (a * a + b * b == (1000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'... | 24 | 1 |
from math import pi
def lowerCamelCase__ ( snake_case_ : int , snake_case_ : int ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 24 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
snake_case_ = get_tests_dir('fixtures/test_sentencepi... | 24 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_c... | 24 |
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if not isinstance(snake_case_ , snake_case_ ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
__snake_case = 0
while number:
# This way we... | 24 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterM... | 24 |
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def lowerCamelCase__ ( snake_case_ : str , snake_case_ : complex , snake_case_ : str = "x" , snake_case_ : float = 10**-10 , snake_case_ : int = 1 ... | 24 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_... | 24 | 1 |
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