code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def a_ ( _A ) -> list[int]:
"""simple docstring"""
return [ord(_A ) - 96 for elem in plain]
def a_ ( _A ) -> str:
"""simple docstring"""
return "".join(chr(elem + 96 ) for elem in encoded )... | 328 |
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_im... | 328 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_lowercase = {
... | 706 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : str ) -> int:
def wrapper(*UpperCAmelCase_ : str , **UpperCAmelCase_ : str ... | 431 | 0 |
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 ... | 519 |
def _UpperCAmelCase ( UpperCAmelCase : int ):
"""simple docstring"""
if n == 1 or not isinstance(UpperCAmelCase , UpperCAmelCase ):
return 0
elif n == 2:
return 1
else:
__lowerCamelCase : Union[str, Any] ... | 519 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from .... | 708 | """simple docstring"""
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_mod... | 668 | 0 |
'''simple docstring'''
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_t... | 150 |
'''simple docstring'''
from torch import nn
def __lowercase (_lowercase ) -> Union[str, Any]:
"""simple docstring"""
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
el... | 150 | 1 |
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str , __UpperCamelCase : str ) -> Any:
"""simple docstring"""
assert x is not None
assert y is not None
A__ : int = len(__UpperCamelCase )
A__ : Union[str, Any] = ... | 55 |
def SCREAMING_SNAKE_CASE ( ) -> Optional[int]:
"""simple docstring"""
A__ : Optional[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(__UpperCamelCase )[-10:]
if __name__ == "__main__":
print(solution()) | 55 | 1 |
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_en... | 648 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCL... | 597 | 0 |
def __A ( _A = 10**12 ):
"""simple docstring"""
__a = 1
__a = 0
__a = 1
__a = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_d... | 710 | from __future__ import annotations
def __A ( _A ):
"""simple docstring"""
__a = [True] * limit
__a = False
__a = False
__a = True
for i in range(3 , int(limit**0.5 + 1 ) , 2 ):
__a = i * 2
while index < limit:
... | 525 | 0 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order... | 52 | import os
def A__ ( lowercase: str = "input.txt" ) -> int:
with open(os.path.join(os.path.dirname(lowercase ), lowercase ) ) as input_file:
A : Dict =[
[int(lowercase ) for element in line.split(',' )]
... | 305 | 0 |
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInp... | 330 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : List[str] = logging.get_logger(__name__)
_A : Any = {
'''facebook/nllb-moe-54B''': '''https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json'... | 330 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A_ = {
"debug": logging.DEBUG,
... | 393 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_plan... | 393 | 1 |
"""simple docstring"""
import operator as op
__A : Optional[Any] = 'scaler.pt'
__A : Dict = 'pytorch_model'
__A : Optional[Any] = 'random_states'
__A : List[Any] = 'optimizer'
__A : Optional[int] = 'scheduler'
__A : Union[str, Any] = ... | 281 |
"""simple docstring"""
from collections import deque
def snake_case__ ( _lowerCamelCase ) ->Dict:
"""simple docstring"""
__lowercase : Any = len(_lowerCamelCase )
__lowercase : str = deque()
__lowercase : Tuple = ... | 281 | 1 |
"""simple docstring"""
from copy import deepcopy
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ,A_ : list[int] | None = None ,A_ : int | None = None ) -> None:
if arr is None and size is not None:
A = size
... | 91 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
snake_case : List[str] = [8, 5, 9, 7]
snake_case : int = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
snake_case : Optional[Any] = [
[3, 2, 1, 4... | 566 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( snake_case__ ):
"""simple docstring"""
_snake_case : Optional[int] = (UnCLIPScheduler,)
def A ( self ... | 228 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
fro... | 228 | 1 |
'''simple docstring'''
def _a ( _lowerCamelCase ) -> Tuple:
"""simple docstring"""
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
__snake_case : List[Any] = F'''Input value of [number={number}] must be an integer'''... | 26 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
... | 379 | 0 |
from math import factorial
def UpperCamelCase (lowercase_: int = 100 ) -> int:
return sum(map(lowercase_ , str(factorial(lowercase_ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 716 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase : Dict = {
'configuration_mobilebert': [
'MOBILEBERT_PR... | 50 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
r... | 533 | 0 |
'''simple docstring'''
from pathlib import Path
import fire
from tqdm import tqdm
def _a ( __lowerCAmelCase : str="ro" , __lowerCAmelCase : Tuple="en" , __lowerCAmelCase : Tuple="wmt16" , __lowerCAmelCase : Any=None ):
"""simple docstring"""
... | 716 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
lowerCAmelCase__ : Dict = argparse.ArgumentParser()
parser.add_argum... | 502 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] =logging.get_logger(__name__)
__SCREAMING_SNAKE... | 135 | '''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ImageProcessingSavingTes... | 309 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def __lowerCAmelCase ( UpperCamelCase ) -> Dict:
# getting number of pixels in the image
lowerCAmelCase__ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(UpperCamelCase... | 707 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase ( _lowercase ):
@staticmethod
@abstractmethod
def __magic_name__( __UpperCAmelCase ):
raise NotImplementedError()
@abstractmethod
def __magic_name__( self ... | 470 | 0 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__a = logging.get_logger(__name__)
class __a( _a ):
"""simple docstring"""
lowerCAmelCase ... | 30 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__a = logging.get... | 30 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list[list] ):
__SCREAMING_SNAKE_CASE : Optional[Any] = current_set.copy()
for row_index, row in enumerate(_lowerCamelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = row[0]
for column_index, column in ... | 178 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def lowerCAmelCase_ ( _lowerCamelCase: Dict , _lowerCamelCase: List[Any] ):
# ===== initialization =====... | 178 | 1 |
'''simple docstring'''
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 tensorfl... | 350 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
a = '.'
if __name__ == "__main__":
a = os.path.join(REPO_PATH, 'utils/documentation_tests.txt')... | 350 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def _A ( ):
a__ : Optional[Any] = 9
a__ : Dict = [
[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... | 708 |
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
SCREAMING_SNAKE_CASE__ : List[str] = {
"""tiny.en""": """https://openaipublic.azureedg... | 629 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.u... | 3 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _UpperCAmelCase ( a__):
'''simple docstring'''
a_ : Optional[Any] = [
"""encoder.version""",
"""decoder.version""",
"""model.enco... | 540 | 0 |
"""simple docstring"""
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
f... | 404 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 404 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase__ = (3, 9, -1_1, 0, 7, 5, 1, -1)
lowercase__ = (4, 6, 2, 0, 8, 1_0, 3, -2)
@dataclass
class snake_case__ ... | 638 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class _UpperCAmelCase ( lowercase ):
def __init__( s... | 631 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (
'''https://huggingface.co/microsoft/unispeech-sat-... | 704 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("""3.8"""):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
__snake... | 117 | 0 |
def lowercase__ ( A_: int , A_: Tuple ) -> Union[str, Any]:
"""simple docstring"""
__UpperCAmelCase =[1]
for i in range(2 , A_ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out ... | 68 |
from typing import List
from .keymap import KEYMAP, get_character
def lowercase__ ( A_: str ) -> str:
"""simple docstring"""
def decorator(A_: int ):
__UpperCAmelCase =getattr(A_ , """handle_key""" , [] )
... | 68 | 1 |
'''simple docstring'''
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, prep... | 245 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase ( a ) -> bool:
'''simple docstring'''
return len(set(a ) ) == len(a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 245 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _lowerCamelCase ( lowerCamelCase_: Union[str, Any] , lowerCamelCase_: Any , lowerCamelCase_: Dict , lowerCamelCase_: Tuple ):
'''simple docstring'''
A ... | 256 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase : List[str] = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARC... | 164 | def a_ (_lowerCAmelCase : int = 100 )-> int:
snake_case: int = n * (n + 1) * (2 * n + 1) / 6
snake_case: Optional[int] = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F"""{solution() = }"""... | 164 | 1 |
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 transfo... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
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__ : List[Any] = collections.namedtuple("""_Datasets""", ["""train""... | 703 |
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
return str(UpperCamelCase ) == str(UpperCamelCase )[::-1]
def snake_case (UpperCamelCase : int ):
'''simple docstring'''
return int(UpperCamelCase ) + int(str(Upper... | 235 | 0 |
'''simple docstring'''
def lowerCAmelCase (__A = 1 , __A = 1_000):
"""simple docstring"""
_a = 1
_a = 0
for divide_by_number in range(__A , digit + 1):
_a = []
_a = numerator
for _ in... | 11 |
'''simple docstring'''
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_... | 11 | 1 |
"""simple docstring"""
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transform... | 401 |
"""simple docstring"""
_lowerCamelCase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.co... | 401 | 1 |
"""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.pipelin... | 34 |
from math import isclose, sqrt
def _a ( __UpperCamelCase : float ,__UpperCamelCase : float ,__UpperCamelCase : float ):
lowerCAmelCase__ : Union[str, Any] = point_y / 4 / point_x
lowerCAmelCase__ : str = 2 * normal_gradient / (1 + normal_g... | 233 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__snake_case )
class A__ ( __snake_case ):
'''simple docstring'''
... | 410 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ : str = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'... | 410 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from ... | 34 |
"""simple docstring"""
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/c... | 169 | 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 require... | 73 |
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 import MAX_SHARD_... | 73 | 1 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _lowerCAmelCase ( __magic_name__ : Any , __magic_name__ : Tuple=7 ) -> List[Any]:
lowercase : str =None
... | 92 | def _lowerCamelCase ( a_ : int , a_ : list[int] , a_ : int):
def count_of_possible_combinations(a_ : int) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item) for ... | 166 | 0 |
'''simple docstring'''
from collections import defaultdict
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Optional[int] = first_str.lower().strip()
UpperCAmelCase__ : List[str] = second_str.lower().stri... | 714 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_util... | 113 | 0 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModel... | 562 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf... | 331 | 0 |
"""simple docstring"""
def _lowerCAmelCase(a : int ) -> list:
_SCREAMING_SNAKE_CASE =int(a )
if n_element < 1:
_SCREAMING_SNAKE_CASE =ValueError('''a should be a positive number''' )
raise my_error
_SCREAMING_SNAKE_CASE =[1]
_SCREAMING_SNAKE_CA... | 700 |
"""simple docstring"""
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
UpperCAmelCase_ : Tuple = coll... | 165 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE = ['small', 'medium', 'large']
SCREAMING_SNAKE_CASE = 'lm_head.decoder.weight'
SCREAMING_SNAKE_CASE = 'lm_head.weight'
def lowercase_ (... | 94 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird i... | 619 | 0 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__lowerCamelCase : Dict = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTM... | 713 |
'''simple docstring'''
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__lowerCamelCase : ... | 656 | 0 |
from __future__ import annotations
def __a ( A__ : str ):
return [ord(A__ ) - 96 for elem in plain]
def __a ( A__ : list[int] ):
return "".join(chr(elem + 96 ) for elem in encoded )
def __a ( ):
SCREAMI... | 16 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 16 | 1 |
"""simple docstring"""
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available(... | 560 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__A = logging.get_logger(__name__)
def __A (_SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=... | 560 | 1 |
from __future__ import annotations
from collections.abc import Callable
A_ : Any = list[list[float | int]]
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Matrix:
'''simple docstring'''
__UpperCAmelCase = len(SCREAMING_SNA... | 303 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import... | 303 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 707 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase__ : Any = logging.get_logger(__name__)
UpperCamelCase__ : int = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https://huggingface.co/CarlCochet/tra... | 486 | 0 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedul... | 641 | '''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
if number < 0:
raise ValueError("number must not be negative" )
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
do... | 78 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProc... | 702 | """simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def UpperCamelCase ( _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ... | 173 | 0 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowerCAmelCase = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def lowerCAmelCase_ ( ) ->Dict:
lowerCamelCase__ : List[str] =Github(o... | 174 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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-... | 174 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ : List[str] = TypeVar("""T""")
class __lowerCAmelCase ( Gen... | 629 |
from PIL import Image
def _A ( lowerCamelCase , lowerCamelCase ):
def brightness(lowerCamelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("level must be between -255.0 (black) and 255.0 (white)" )
return img.po... | 629 | 1 |
"""simple docstring"""
from random import randint, random
def lowerCamelCase__ ( _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : int , _lowerCamelCase : bool = False , _lowerCamelCase ... | 549 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class a ( unittest.TestCa... | 549 | 1 |
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from u... | 712 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : list , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
__lowerCamelCase : Optional[An... | 363 | 0 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__UpperCAmelCase = '\\n\n'
__UpperCAmelCase = '\nPerplexity (PPL) is one of the m... | 65 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableData... | 28 | 0 |
'''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 ...test_tokenizat... | 717 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_avai... | 419 | 0 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel
f... | 51 |
'''simple docstring'''
import json
import sys
def _lowerCAmelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
with open(_UpperCamelCase , encoding='utf-8' ) as f:
_SCREAMING_SNAKE_CASE ... | 405 | 0 |
def __lowerCamelCase ( __lowerCAmelCase : int , __lowerCAmelCase : float , __lowerCAmelCase : float ) -> float:
return round(float(moles / volume ) * nfactor )
def __lowerCamelCase ( __lowerCAmelCase ... | 515 |
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`')
| 515 | 1 |
from __future__ import annotations
def lowerCAmelCase__( lowercase : list[int] , lowercase : int ) -> list[int]:
__snake_case : Optional[int] = 0
__snake_case : Union[str, Any] = len(lowercase ) - 1
while i < j:
if ... | 243 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 243 | 1 |
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : list ):
'''simple docstring'''
if len(__SCREAMING_SNAKE_CASE ) <= 1:
return lst
__snake_case : Tuple = 1
while i < len(__SCREAMING_SNAKE_CASE ):
if lst[i - 1] <= lst[i]:
... | 716 | from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
... | 390 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Optional[Any] ... | 400 |
"""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__ ):
"""simple docstring"""
_snake_case :... | 505 | 0 |
"""simple docstring"""
def A__ ( _UpperCAmelCase : float , _UpperCAmelCase : list[float] ) -> float:
'''simple docstring'''
if discount_rate < 0:
raise ValueError("Discount rate cannot be negative" )
if not cash_flows:
raise ValueError("Cash flows list cannot be... | 150 |
"""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 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSe... | 400 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
_SCREAMING_SNAKE_CASE : Optional[Any] = datasets.utils.logging.get_logger(__name... | 400 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
lowercase__ =[
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
' final s... | 326 |
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
if is_tor... | 326 | 1 |
'''simple docstring'''
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> bool:
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("""Program to check whether a number is... | 71 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {
"configuration_bigbird_pegasus": [
"BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
"BigBirdPegasusConfig",
"BigBirdPegasu... | 374 | 0 |
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transformers import glue_compute_metrics... | 706 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 328 | 0 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
__snake_case : List[str] =logging.get_logger(__name__)
class lowerCamelCase__ ( a_):
'''simple docstring'''
def __init__(self ,*__lowerCamelCase ,**__lowerCamelCa... | 647 |
"""simple docstring"""
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = ... | 237 | 0 |
"""simple docstring"""
from collections import deque
from .hash_table import HashTable
class UpperCamelCase_ (__A ):
def __init__( self : Dict , *lowerCAmelCase_ : Optional[int] , **lowerCAmelCase_ : Union[str, Any] ) -> Any:
... | 712 |
"""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_ = logging.get_logger(__name__)
lowerCamelCase_ =... | 463 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_: Optional[int] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: List[str] ={
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-... | 78 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, 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 ModelTesterMixin, ids_tensor... | 6 | 0 |
class _snake_case :
def __init__( self : Union[str, Any] ):
lowercase__ = ""
lowercase__ = ""
lowercase__ = []
def A__ ( self : Optional[Any], __lowercase : int, __lowercase : ... | 37 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
cl... | 37 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class lowerCamelCase :
def __init__( self):
__UpperCAmelCase : Dict = {}
def A( self , lowercase__):
__UpperCAmelCase : Optional[int] = {}
def A( ... | 462 |
'''simple docstring'''
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
__a : Tuple = len(SCREAMING_SNAKE_CASE__ )
__a : int = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr va... | 597 | 0 |
import string
import numpy
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> Tuple:
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , _lowerCamelCase )
class __l... | 700 |
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> int:
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ), F"""The input value of [n={number}] is not an integer"""
if number == 1:
return 2
elif number < 1:
_... | 167 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
A__ : List[Any] = logging.get... | 13 |
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 = collections.namedtuple('''_Datasets''', ['''train''', '''validation'... | 431 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
lowerCAmelCase_ : Optional[int] = logging.getLogger(__name__)
class ... | 716 | '''simple docstring'''
import requests
from bsa import BeautifulSoup
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> str:
'''simple docstring'''
_UpperCamelCase : Dict = BeautifulSoup(requests.get(UpperCAmelCase ,params=UpperCAmelCase ... | 204 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.jso... | 352 |
from __future__ import annotations
def __a ( __lowerCAmelCase , __lowerCAmelCase = None ) -> list[list[str]]:
SCREAMING_SNAKE_CASE : Dict = word_bank or []
# create a table
SCREAMING_SNAKE_CASE : int = len(__lowerCAmelCase ) + ... | 352 | 1 |
def UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print("Program to check whether a number is a Perfect number or not...")
lowercase__ :int = ... | 633 |
import os
def UpperCamelCase ( lowerCAmelCase__ = "input.txt" ):
'''simple docstring'''
with open(os.path.join(os.path.dirname(lowerCAmelCase__ ) , lowerCAmelCase__ ) ) as input_file:
lowercase = [
[int(lowerCAmelCase__ ) for element in line.split(''',''' )]
... | 633 | 1 |
"""simple docstring"""
def lowercase__ ( snake_case_ :float ):
return 10 - x * x
def lowercase__ ( snake_case_ :float , snake_case_ :float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(snake_case_ ) *... | 49 |
"""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, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
f... | 695 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResN... | 596 |
import os
lowerCAmelCase_ = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 1_00, 'D': 5_00, 'M': 10_00}
def snake_case( __magic_name__ ) -> int:
'''simple docstring'''
lowercase : Any = 0
lowercase : Any ... | 596 | 1 |
from __future__ import annotations
import typing
from collections import Counter
def _SCREAMING_SNAKE_CASE ( snake_case ) -> typing.Counter[int]:
_UpperCAmelCase = Counter()
for base in range(1 , max_perimeter + 1 ):
for perp... | 518 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ : Optional[Any] = {
'''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 17 | 0 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
A__ = logging.getLogger(__name__)
class _lowerCAmelCase :
def __init__( self : int ):
... | 49 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( A_ : int , A_ : Tuple , A_ : List[str] , A_ : str ): # noqa: E741
"""simple docstring"""
while r - l > 1:
a_ : Any = (l ... | 460 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 460 | 1 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ : Dict = logging.get_logger(__name__)
# TODO Update this
__magic_name__ : Optional[int] = {
... | 497 |
'''simple docstring'''
import math
def A__ ( A_ , A_ ) -> int:
_lowercase = len(A_ )
_lowercase = int(math.floor(math.sqrt(A_ ) ) )
_lowercase = 0
while arr[min(A_ , A_ ) - 1] < x:
_lowercase = step
step += int(math.floor(ma... | 497 | 1 |
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_tokenization_common import Tokenize... | 713 |
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 129 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested... | 92 |
"""simple docstring"""
from math import ceil
def __magic_name__ ( UpperCamelCase : int = 1001 ) -> int:
a__ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
a__ = 2 * i + 1
a__ = 2 * i
a__ = total + 4 * odd**2 - 6 * even
... | 273 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : List[Any] = {
'configuration_informer': [
'INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 350 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
UpperCamelCase = ArgumentParser(
description=(
'''PyTorch TP... | 350 | 1 |
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.
_lowercase : List[Any] =1_0
def A__ ( lowercase: int, lowercase: int, lowercase: list[int], lowercase: int ... | 305 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_... | 184 | 0 |
from math import factorial
_UpperCamelCase : List[str] ={str(digit): factorial(digit) for digit in range(10)}
def lowerCamelCase_ ( A_ ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError('''Parameter number must be int''' )
if numbe... | 708 |
'''simple docstring'''
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_UpperCamelCase : Optional[int] =version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from n... | 575 | 0 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A = logging.getLogger(__name__)
class lowerCamelCase :
'''simple docstring'''
def __init__(self ... | 182 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils impo... | 182 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ : List[Any] = logging.get_logger(__name__)
snake_case_ : List[Any] = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-1... | 710 |
"""simple docstring"""
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_configura... | 292 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SC... | 466 |
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output... | 318 | 0 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
lowerCamelCase__ : Any = logging.get_logger(__name__)
def __A ( a_ : str , ... | 704 |
"""simple docstring"""
def __A ( a_ : list , a_ : int , a_ : int = 0 , a_ : int = 0 )-> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = right or len(a_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
... | 18 | 0 |
import functools
from typing import Any
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
# Validation
if not isinstance(SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ) or len(SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("the string should be not empty string" )
if not isinsta... | 113 |
_lowerCAmelCase : int ="""
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.com/huggingface/transformers.g... | 113 | 1 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMix... | 13 |
'''simple docstring'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be check... | 13 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .test_pipelin... | 165 |
'''simple docstring'''
import argparse
import os
import re
lowercase_ : Optional[Any] = '''src/transformers'''
# Pattern that looks at the indentation in a line.
lowercase_ : int = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowercase... | 588 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
SCREAMING_SNAKE_CASE_ : Dict = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'... | 719 |
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForCon... | 500 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : Any = 'bert-generation'
def __init__(self , A=50_358 , A=1_024 , A=24 , A=16 , A=4_096 , A="gelu" , A... | 11 |
'''simple docstring'''
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A , __A):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accep... | 11 | 1 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
def a (lowerCAmelCase__ , lowerC... | 709 |
from __future__ import annotations
SCREAMING_SNAKE_CASE = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class __UpperCAmelCase :
"""simple docstring... | 209 | 0 |
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