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 |
|---|---|---|---|---|
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_... | 88 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class lowerCAmelCase__ :
def __init__( self : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> Dict:
... | 270 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCamelCase: Dict = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_av... | 53 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _A ( _a ):
"""simple docstring"""
UpperCAmelCase : List[str] = ["""image_processor... | 40 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowercase ( A_ )-> List[Any]:
'''simple docstring'''
monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_w... | 40 | 1 |
import math
class _a :
"""simple docstring"""
def __init__( self : List[str] , UpperCAmelCase : Union[str, Any]=0 ): # a graph with Node 0,1,...,N-1
A_ = n
A_ = [
[math.inf for j in ran... | 350 |
from maths.prime_factors import prime_factors
def __snake_case ( __UpperCamelCase : int ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Input value of [number={number}] must be an integer'''
... | 329 | 0 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # He... | 317 |
"""simple docstring"""
from __future__ import annotations
lowerCamelCase__ = list[tuple[int, int]]
lowerCamelCase__ = [
[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, 0],
[1, 0, 1, 0, 0, 0... | 86 | 0 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
... | 365 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_seri... | 93 | 0 |
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
lowerCamelCase_ = logging.getLogger(__name__)
if is_torch_tpu_available(check_device=False):... | 244 |
import torch
from diffusers import StableDiffusionPipeline
lowerCamelCase_ = '''path-to-your-trained-model'''
lowerCamelCase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
lowerCamelCase_ = '''A photo of sks dog in a bucket'''
lowerCamel... | 244 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, ... | 363 |
'''simple docstring'''
__UpperCamelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _a ( ) -> None:
"""simple docstring"""
__snake_case : Dict = input("""Enter message: """ )
__snake_case : Optional[int] = ... | 13 | 0 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDi... | 67 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class a__:
def __init__( self : Tuple ):
a : List[Any] = {}
def lowercase_ ( self : str , __snake_case ... | 96 |
'''simple docstring'''
lowerCAmelCase: Union[str, Any] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
low... | 96 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowercase = logging.get_logger(__name__)
class UpperCamelCase_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , *a , **a ) ... | 178 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowercase = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ ( snake_case_ ):
'''simple docstr... | 178 | 1 |
def a_ ( __lowercase : List[Any] ) -> Tuple:
_snake_case = len(__lowercase )
for i in range(length - 1 ):
_snake_case = i
for k in range(i + 1 , __lowercase ):
if collection[k] < collection[least]:
_snake_case = k
... | 130 |
def a_ ( __lowercase : list[int] , __lowercase : list[int] ) -> tuple[float, float]:
# Check if the input is valid
if not len(__lowercase ) == len(__lowercase ) == 3:
raise ValueError('Please enter a valid equation.' )
if equationa[0] == equationa[1] == equationa[0] == ... | 130 | 1 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_snake_case = logging.get_logger('transformers.models.speecht5')
def lowerCAmelCase__ ( ... | 294 |
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(A_ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(A_ ) ==... | 88 | 0 |
from collections.abc import Callable
import numpy as np
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Callable , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float , __UpperCamelCase : float ) -> np.array:
"""simp... | 204 | from math import isqrt
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> list[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 204 | 1 |
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 lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''... | 43 |
import doctest
from collections import deque
import numpy as np
class snake_case__ :
def __init__( self ) -> None:
__magic_name__ : Any = [2, 1, 2, -1]
__magic_name__ : Tuple = [1, 2, 3, 4]
def __magic_name__ ( self ) ... | 342 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : Union[str, Any] = logging.get_logger(__name__)
__A : int = {
... | 369 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_util... | 27 | 0 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [or... | 102 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentPa... | 347 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class _A ( _a ):
"""simple docstring"""
def __snake_case ( self : ... | 226 |
"""simple docstring"""
import datasets
__lowercase = """\
@InProceedings{conneau2018xnli,
author = \"Conneau, Alexis
and Rinott, Ruty
and Lample, Guillaume
and Williams, Adina
and Bowman, Samuel R.
and... | 226 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ..... | 23 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('''Undefined for non-integers''' )
elif pre... | 8 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def lowercase( UpperCamelCase_ ) -> int:
'''simple docstring'''
return 1 / (1 + np.exp(-z ))
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> Dict:
'''simple docstr... | 356 | import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 165 | 0 |
'''simple docstring'''
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines... | 85 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config i... | 85 | 1 |
'''simple docstring'''
def __lowerCamelCase ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : str ) -> bool:
snake_case = len(__lowerCAmelCase )
snake_case = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
... | 371 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
fr... | 3 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.ut... | 30 |
"""simple docstring"""
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transfor... | 260 | 0 |
'''simple docstring'''
from __future__ import annotations
def __UpperCamelCase ( UpperCAmelCase , UpperCAmelCase ):
lowercase__ , lowercase__ : int = set(UpperCAmelCase ), [start]
while stack:
lowercase__ : Optional[int] = stack.pop()
explored.add(UpperCAmelC... | 214 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
lowercase__ : Tuple = sorted(string.lower() )
return len(UpperCAmelCase ) == len(set(UpperCA... | 214 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def __UpperCamelCase ( *_A ):
if not isinstance(_A , _A ):
lowerCAmelCase_ = list(_A )
for i in range(len(_A ) ):
lowerCAmelCase_ ... | 278 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def __UpperCamelCase ( _A... | 278 | 1 |
"""simple docstring"""
from sklearn.metrics import mean_squared_error
import datasets
lowercase : str = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grise... | 364 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
lowercase : Dict = (720, 1280) # Height, Width
lowercase : Any = (0.4, 0.6) # if height or width lower than this scale, drop it.
lowercase : Tuple ... | 171 | 0 |
"""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
#... | 150 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_zsta... | 281 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_d... | 304 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
lowerCAmelCase = '''<<<<<<< This should probably be modified because it mentions: '''
lowerCAmelCase = '''=======
>>>>>>>
'''... | 304 | 1 |
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
if num < 0:
return False
lowerCamelCase__ = num
lowerCamelCase__ = 0
while num > 0:
lowerCamelCase__ = rev_num * 10 + (num % 10)
num //= 10
return num_copy == rev_num
if __name__... | 209 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import tor... | 209 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class UpperCAmelCase ( snake_case_ ):
_lowercase:... | 371 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
... | 220 | 0 |
"""simple docstring"""
import enum
import shutil
import sys
_a , _a : int = shutil.get_terminal_size()
_a : Optional[Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class __A ( enum.Enum ):
_UpperCamelCase : int = 0
_UpperC... | 44 | """simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertE... | 44 | 1 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def lowerCamelCase__ ( A__ : Optional[int] ):
'''simple docstring'''
__lowerCa... | 363 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tor... | 29 | 0 |
'''simple docstring'''
from math import factorial
def UpperCamelCase_( snake_case : int = 1_0_0 ):
'''simple docstring'''
return sum(map(snake_case , str(factorial(snake_case ) ) ) )
if __name__ == "__main__":
print(solution(int(input("E... | 85 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 85 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : str = logging.get_logger(__name__)
lowerCamelCase_ : List[Any] = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config... | 370 |
"""simple docstring"""
import os
import sys
import unittest
lowerCamelCase_ : Tuple = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from ge... | 215 | 0 |
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixi... | 185 |
'''simple docstring'''
A__ : Any = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCAmelCase__ ( UpperCAmelCase_ : float , UpperCAmelCase_ : float , UpperCAmelCase_ : float ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
rai... | 185 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Optional[int] = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggin... | 351 | '''simple docstring'''
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_fla... | 18 | 0 |
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import Tokenizer... | 338 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at ... | 211 | 0 |
from math import sqrt
def UpperCamelCase_( _snake_case : int ):
"""simple docstring"""
__a =0
for i in range(1 , int(sqrt(_snake_case ) + 1 ) ):
if n % i == 0 and i != sqrt(_snake_case ):
total += i + n // i
... | 351 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
r... | 308 | 0 |
from bisect import bisect
from itertools import accumulate
def lowercase ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Any ) -> Dict:
_snake_case : List[str] = sorted(zip... | 317 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 317 | 1 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', da... | 188 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apach... | 188 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ) -> int:
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ValueError("""Pleas... | 124 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCamelCase : List[Any] = 1_0
def SCREAMING_SNAKE_CASE__ ( lowercase ,lowercase ,low... | 124 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
from math import ceil
def __snake_case ( __UpperCamelCase : Dict ,__UpperCamelCase : int ):
"""simple docstring"""
A_ = list(range(0 ,__UpperCamelCase ) )
A_ = [item for sublist in list(device_map.values() ) for item in s... | 312 |
import functools
from typing import Any
def __snake_case ( __UpperCamelCase : str ,__UpperCamelCase : list[str] ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ) or len(__UpperCamelCase ) == 0:
raise ValueE... | 312 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Tuple = list(__snake_case )
... | 356 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class A ( __snake_case ):
def __init__( self , SCREAMING_SNAKE_CAS... | 311 | 0 |
'''simple docstring'''
# Lint as: python3
import itertools
import os
import re
lowerCAmelCase__ = re.compile(R'''([A-Z]+)([A-Z][a-z])''')
lowerCAmelCase__ = re.compile(R'''([a-z\d])([A-Z])''')
lowerCAmelCase__ = re.compile(R'''(?<!_)_(?!_)''')
lowerCAmelCase__ = re.com... | 104 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', def... | 104 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : List[A... | 351 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
'vocab_... | 337 | 0 |
import math
import unittest
def __magic_name__ ( A : int ):
'''simple docstring'''
assert isinstance(A, A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number... | 107 | '''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, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from d... | 1 | 0 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ (_UpperCAmelCase ,... | 327 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 327 | 1 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : int = 50 ):
'''simple docstring'''
_lowerCAmelCase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_l... | 158 |
'''simple docstring'''
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
_SCREAMING_SNAKE_CASE = datasets.utils.logging.get_logger(__name__)
class lowerCAmelCase_ ( folder_based_builde... | 158 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 279 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transf... | 279 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase__ ( __lowercase , unittes... | 232 |
def lowerCAmelCase__(__snake_case ) -> str:
'''simple docstring'''
return "".join(chr(ord(__snake_case ) - 32 ) if '''a''' <= char <= '''z''' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 209 | 0 |
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json... | 224 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __lowerCAmelCase ( ):
lowercase__ = 9
lowercase__ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
... | 224 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_p... | 66 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenizer... | 248 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'micro... | 368 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'vocab_file': 'vocab.json',
'merg... | 222 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 166 |
'''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
#
... | 166 | 1 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''vocab_file''': '''vocab.txt''',
... | 356 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_... | 125 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__UpperCamelCase : Optional[Any] ... | 106 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar('''T''')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
lowerc... | 106 | 1 |
A_ :Any = '''0.18.2'''
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa... | 361 |
import math
import tensorflow as tf
from packaging import version
def A ( a_ ) -> Optional[Any]:
__UpperCamelCase : Dict =tf.convert_to_tensor(a_ )
__UpperCamelCase : str =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) ,x.dtype ) ))
... | 245 | 0 |
lowerCAmelCase__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def __lowerCamelCase ( lowerCamel... | 130 |
"""simple docstring"""
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _snake_case ( _snake_case : str ) -> str:
'''simple docstring'''
return "".join(sorted(_snake_case ) )
def ... | 315 | 0 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : Dict = 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 smaller t... | 49 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__A : List[str] = logging.getLogger(__name__)
def __UpperCamelCase ( ) ->int:
"""simple docstring"""
lowerCamelCase_ =argparse.ArgumentPa... | 49 | 1 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filen... | 181 |
"""simple docstring"""
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
... | 217 | 0 |
"""simple docstring"""
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils i... | 350 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''tokenizer''']
lowercase__ = '''ViTImageProcessor'''
... | 230 | 0 |
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_pipeline_test,
ne... | 130 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class snake_case__(_UpperCamelCase ):
"""simple docstring"""
def __init__( self : Tuple , *SCREAMING_SNAKE_C... | 130 | 1 |
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,
RobertaTokenizerFast,
XLMR... | 319 |
def lowerCamelCase__ ( lowercase , lowercase = 0 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = length or len(lowercase )
SCREAMING_SNAKE_CASE : Optional[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 319 | 1 |
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_trust_remote_code
from .... | 212 |
class A__ :
def __init__( self : Optional[Any] , a : list ):
'''simple docstring'''
lowerCAmelCase__ : Dict = set_counts
lowerCAmelCase__ : str = max(a )
lowerC... | 212 | 1 |
'''simple docstring'''
import numpy
# List of input, output pairs
snake_case_ : Tuple = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
snake_case_ : Optional[Any] = (((515, 22, 13), 555), ((61, 35, 49), 150))
snake_case_ : ... | 351 |
'''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
@require_optimu... | 236 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor,... | 46 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = field(def... | 46 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow... | 354 | 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 ...test_backbone_... | 258 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.p... | 221 | """simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCamelCase = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, M... | 221 | 1 |
import json
import sys
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
with open(_SCREAMING_SNAKE_CASE , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE = json.lo... | 193 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_robert... | 193 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def lowerCamelCase ( __lowerCamelCase : Sequence[float] , __lowerCamelCase : bool = False ) ->float:
if not arr:
return 0
_SCREAMING_SNAKE_CASE = 0 if allow_empty_subarrays else float("""-... | 58 |
'''simple docstring'''
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
fr... | 58 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Tuple =logging.get_logger(__name__)
lowerCamelCase : Tuple ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config... | 354 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
assert isinstance(__lowerCAmelCase , __lowerCAmelCase ), f'The input value of [n={number}] is not an integer'
if number == 1:
return 2
elif number < 1:
UpperCamelCase__ : Li... | 196 | 0 |
"""simple docstring"""
import warnings
from ..trainer import Trainer
from ..utils import logging
A_ = logging.get_logger(__name__)
class lowercase( __a ):
'''simple docstring'''
def __init__( self: Optional[int], a_: int=None, ... | 64 |
"""simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tok... | 64 | 1 |
from math import loga
def lowerCAmelCase__ ( a__: int ) -> int:
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(a__ , a__ ):
raise TypeError('Input value must be a \'int\' type' )
... | 367 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __a ( UpperCAmelCase ):
_a : Optional[int] = 'MCTCTFeatureExtractor'
_a : int = 'AutoTokenizer'
def __init__( self , _SCREAMING_SNAKE_CASE... | 185 | 0 |
def _a ( SCREAMING_SNAKE_CASE : int | float | str ):
"""simple docstring"""
try:
UpperCamelCase__ : List[Any] = float(SCREAMING_SNAKE_CASE )
except ValueError:
raise ValueError('''Please enter a valid number''' )
UpperCamelCase__ : Optional[int] = d... | 146 |
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_available():
from... | 146 | 1 |
import inspect
import unittest
from transformers import BitConfig
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 BackboneTesterMixi... | 145 |
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = [1]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ : Tuple = 0, 0, 0
UpperCAmelCase_ : Union[str, Any] = ugly_... | 145 | 1 |
import requests
from bsa import BeautifulSoup
def snake_case( __magic_name__ , __magic_name__ ) -> str:
'''simple docstring'''
lowercase : int = BeautifulSoup(requests.get(__magic_name__ , params=__magic_name__ ).content , '''html.par... | 308 |
from heapq import heappop, heappush
import numpy as np
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , ) -> tuple[float | int, list[tuple[int, int]]]:
'''simple docstring'''
lowercase , lowercase : Op... | 308 | 1 |
def _A ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
a__ : Union[str, Any] =word.split()
def justify(SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : ... | 148 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase__)
class __lowerCAmelCase ( UpperCamelCase__):
_lowercase : str ... | 148 | 1 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
__a = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def A_ ( ):
'''simple docstring'''
snake_case_ :str = os.path.dirname(os.path.realpath(snake_case__ ) )
snake_case_ :Tuple... | 66 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.nu... | 306 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs... | 335 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 335 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
lowercase : int = logging.get_logger(__name__)
def A_ ( A__ ) -> Union[str, Any]:
a__ : Any = R'\w+[.]\d+'
... | 99 |
from collections.abc import Iterable
from typing import Generic, TypeVar
lowercase : Any = TypeVar("""_T""")
class A__ ( Generic[_T] ):
"""simple docstring"""
def __init__( self , lowercase = None) -> None:
'''simple docstring'''
... | 99 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_v... | 240 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class a ( _a ):
"""simple docstring"""
def __init__( self : Optional[Any] ) -> int:
# test for the above condition
self.tes... | 240 | 1 |
"""simple docstring"""
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import r... | 289 | """simple docstring"""
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 289 | 1 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCAmelCase__ :Optional[int] = logging.get_l... | 359 |
from importlib import import_module
from .logging import get_logger
lowerCAmelCase__ :Optional[Any] = get_logger(__name__)
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> List[Any]:
"""simple docstring"""
... | 185 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class snake_case__:
'''simple docstring'''
def __init__( self ) -> Any:
lowerCAmelCase_ : List[str] = ''''''
lowerCAmelCase_ : Any = ''''''... | 262 |
def lowerCAmelCase ( lowerCAmelCase_ )-> set:
lowerCAmelCase_ : Optional[int] = set()
# edges = list of graph's edges
lowerCAmelCase_ : List[str] = get_edges(lowerCAmelCase_ )
# While there are still elements in edges list, take an arbitrary ed... | 262 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : Any = logging.get_logger(__name__)
lowerCAmelCase__ ... | 369 |
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available... | 37 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''google/pix2struct-textcaps-base''': (
'''https://... | 108 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-... | 108 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'xlm-... | 1 |
"""simple docstring"""
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.ber... | 1 | 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 : str = logging.get_logger(__name__)
snake_case : Optional[int] ... | 94 |
from math import pi, sqrt
def __A ( __lowerCamelCase ) -> float:
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in (0, 0.5):
raise NotImplementedError... | 228 | 0 |
'''simple docstring'''
import unittest
from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import Con... | 61 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase_ = logging.get_logger(__name__)
... | 61 | 1 |
from __future__ import annotations
def A_ ( _lowerCAmelCase ) -> list[int]: # This function is recursive
UpperCamelCase : Union[str, Any] = len(_lowerCAmelCase )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_leng... | 52 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
__A : Tuple = collections.namedtuple('''_Datasets''', ['''train''', '... | 138 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase = 1000 ) -> Dict:
'''simple docstring'''
snake_case_ = 2**power
snake_case_ = str(_A )
snake_case_ = list(_A )
snake_case_ = 0
for i in list_num:
... | 353 |
'''simple docstring'''
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : Union[str, Any] = 'src/transformers'
# This is to make sure the transf... | 72 | 0 |
# 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
#
# Unless req... | 90 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logg... | 90 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cach... | 369 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase ) -> Optional[Any]:
A_ : Any = data
... | 164 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-... | 282 |
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 a_ ( __lowercase : Dict , __lowercase : int , __low... | 282 | 1 |
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 __lowerCamelCase ( lowerCamelCase__ ... | 368 |
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Union[str, Any] = []
lowercase__ : Tuple = []
lowercase__ : Any = {
"^": 3,
"*": 2,
"/": 2,
"%": 2,
... | 121 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError("""Undefined for non... | 88 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..... | 260 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( lowerCamelCase : str ):
_A : List[str] = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_A : Any = ''
_A : Optional[int] = ''
# append each character ... | 371 |
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : list[list[int]]):
_A : Dict = TypeError(
'Matrices must be fo... | 227 | 0 |
"""simple docstring"""
import torch
from transformers import AutoModel
class SCREAMING_SNAKE_CASE__ ( torch.nn.Module ):
def __init__( self : Optional[int] , lowerCAmelCase : Dict="sayef/fsner-bert-base-uncased" ):
super(lowerCAmelCase , self ).__init__... | 155 |
"""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_T... | 155 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNot... | 361 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
UpperCamelCase = len(A__ ) // 2
# choose the middle 3 elements
UpperCamelCase = lst[m - 1 : m + 2]
... | 249 | 0 |
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