code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def lowerCamelCase_ (... | 506 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Union[str, Any] = logging.get_logger(__name__)
a__ : Optional[int] = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
... | 51 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase ( __SCREAMING_SNAKE_CASE , unittest.TestCase ):
a : ... | 721 |
'''simple docstring'''
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCAmelCase :
def __init__( self , UpperCamelCase ):
if isinstance(UpperCamelCase... | 493 | 0 |
'''simple docstring'''
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_log... | 418 |
'''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def SCREAMING_SNAKE_CASE_ ( __A : float ) -> float:
if num <= 0:
raise ValueError("math domain error" )
return quad(__A , 0 , __A , args=(__A) )[0]
def SCREAM... | 418 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils impor... | 87 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_a : Optional[int] = {
"""configuration_pix2struct""": [
"""PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Pix2Struc... | 87 | 1 |
"""simple docstring"""
from itertools import permutations
def __snake_case ( __A ) -> Optional[Any]:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
return False
lowercase :... | 607 | def A__ ( snake_case_ : int ):
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
SCREAMING_SNAKE_CASE__: List[Any]= [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
SCREAMING_SNAKE_CASE__: List[str]= 1
if upper_limit > 0:
SCREAMING_SNAKE_CASE_... | 64 | 0 |
"""simple docstring"""
def snake_case (A_ :int ):
'''simple docstring'''
if not isinstance(A_ , A_ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept negative va... | 118 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.roc_bert.tokenization_roc_bert import (
VOCAB_FILES_NAMES,
RoCBertBasicTokenizer,
RoCBertTokenizer,
RoCBertWordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.tes... | 118 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowercase__ :
"""simple docstring"""
__UpperCamelCase ... | 648 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict = logging.get_logger(__name__)
_A : Union[str, Any] = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke... | 315 | 0 |
'''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... | 703 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from t... | 399 | 0 |
'''simple docstring'''
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
... | 131 |
'''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... | 131 | 1 |
def _a ( ):
'''simple docstring'''
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
SCREAMING_SNAKE_CASE__ : Tuple = generate_large_matrix()
SCREAMING_SNAKE_CASE__ : List[Any] = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -... | 636 | import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['i... | 636 | 1 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils... | 57 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _UpperCAmelCase ( _lowerCamelCase : int ) -> int:
_lowerCAmelCase : Any = prime_factors(_lowerCamelCase )
if is_square_free(_lowerCamelCase ):
... | 384 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def snake_case__ ( __lowerCamelCase : Union[str, Any] ):
"""simple docstring"""
lowerCamelCase__ : Dict ={}
lowerCamelCase__ : Optional[int] =job["... | 720 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : str ):
"""simple docstring"""
return " ".join(
''''''.join(word[::-1] ) if len(__lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 625 | 0 |
from ..utils import DummyObject, requires_backends
class __a( metaclass=_a ):
"""simple docstring"""
lowerCAmelCase = ['''torch''', '''torchsde''']
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
requires_bac... | 30 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
cl... | 232 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
SCREAMING_SNAKE_CASE_: int =""""""
for word_or_phrase in separated:
if not isinstance(lowercase , lowercase ):
raise Exception("""join() accepts only strings to be joined""" )
... | 708 |
"""simple docstring"""
def __magic_name__ ( lowercase = 200_0000 ):
SCREAMING_SNAKE_CASE_: List[Any] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE_: Union[str, Any] =1
SCREAMING_SNAKE_CASE_: Optional[Any] =1
for i in range(2 , int(n**0.5 ) + 1 ):
... | 36 | 0 |
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 A__ ( UpperCAmelCase_ ):
UpperCAmelCase = [... | 691 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lice... | 601 | 0 |
"""simple docstring"""
from math import factorial, pi
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ = 30 ):
if not isinstance(UpperCamelCase_ , (int, float) ):
raise ValueError("""maclaurin_sin() requires either an int or float for theta""" ... | 248 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# ... | 248 | 1 |
'''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
lowerCAmelCase_ : Dict = datasets.utils.logging.get_logger(__name__)... | 489 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor... | 489 | 1 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=_lowerCamelCase ):
lowerCamelCase_ =['transformers', 'torch', 'note_seq']
def __init__( self : Any , *__lowerCAmelCase : Any , **__lowerCAmelCase : List[str]) -> Union[str, Any]... | 700 | '''simple docstring'''
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __a ( __lowerCamelCase : List[Any] ) -> Tuple:
'''simple docstring'''
... | 461 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class lowercase_ ( _... | 7 |
"""simple docstring"""
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 7 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVec... | 702 | '''simple docstring'''
from collections.abc import Iterable
from typing import Any
class __UpperCAmelCase :
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE = None ) -> List[str]:
A_ = value
A_ = None #... | 174 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ : str = {
"google/pix2struct-textcaps-b... | 85 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A_ ( SCREAMING_SNAKE_CASE_ ) ->List[Any]:
lowercase_ = [
"""encoder.version""",
"""decoder.version""",
"""mo... | 451 | 0 |
import os
__snake_case :Tuple = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def __snake_case ( _UpperCAmelCase ) -> Union[str, Any]:
__a = 0
__a = 0
while index < len(_UpperCAmelCase ) - 1:
__a... | 702 |
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
__snake_case :Optional[int] = collections.namedtuple('''_Datasets''', ['''trai... | 60 | 0 |
from __future__ import annotations
def lowercase__ ( A_: list[int] , A_: list[int] , A_: list[int] , A_: list[list[str]] , A_: int , ) -> None:
"""simple docstring"""
__UpperCAmelCase =len(A_ )
... | 68 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
snake_case : List[Any] = {"""vocab_file""": """vocab.txt""", "... | 545 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
... | 709 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMSchedule... | 372 | 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, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kand... | 129 |
"""simple docstring"""
def _A ( __lowercase = 10 , __lowercase = 22 ):
"""simple docstring"""
lowerCamelCase__ = range(1 , __lowercase )
lowerCamelCase__ = range(1 , __lowercase )
return sum(
... | 129 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _UpperCamelCase ( UpperCamelCase_ : bool = True , *UpperCamelCase_ : Optional[Any] , **UpperCamelCase_ : int... | 365 |
from typing import Dict, Optional
import numpy as np
import datasets
__snake_case : str = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or ... | 365 | 1 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
UpperCAmelCase_ : str = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
t... | 24 |
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self , _a , _a , _a ) -> List[str]:
_a : List[Any] = name
_a : List[str] = value
_a : List[str... | 14 | 0 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
SCREAMING_SNAKE_CASE : Union[str, Any] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calc... | 709 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( ... | 229 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_co... | 58 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE ={
"configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
"feature_extraction_mctct": ["MCTCTFeatureE... | 425 | 0 |
from collections.abc import Callable
import numpy as np
def _A ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
a__ : Any = int(np.ceil((x_end - xa) / step_size ) )
a__ : Any = np.zeros((n + 1,)... | 706 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE__ : List[str] = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_torch_av... | 629 | 0 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_snake_case = 2_9979_2458
# Symbols
_snake_case , _snake_case , _snake_case , _snake_case = symbols("ct x y z")
def s... | 510 |
"""simple docstring"""
import os
import sys
_snake_case = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
Auto... | 510 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={"vocab_file": "vocab.json",... | 241 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase__ ( __UpperCamelCase ):
... | 241 | 1 |
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
a__ = {
'''n_samples''': 64,
'''horizon''': 32,
'''num_inference_steps''': 20,
'''n_guide_steps''': 2, # can set to 0 for faster sampling, does not use value network
... | 14 |
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,
)
from transformers.testing_utils import DUM... | 14 | 1 |
def UpperCAmelCase_ ( UpperCAmelCase__ = 1_0_0_0 ):
lowercase_ = 3
lowercase_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 1_5 == 0:
result -= a
a += 1
return result
if __name__ == ... | 650 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 650 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> int:
# encoder.embeddings are double... | 33 |
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(... | 234 | 0 |
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowerCamelCase : Any = ... | 713 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKIN... | 649 | 0 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _lowerCAmelCase ( __magic_name__ : Tuple , __magic_name__ : str , __magic_name__ : Union[str, Any] ) -> List[Any]:
lowercase : Union[str, ... | 92 |
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 UpperCAmelCase__ ( self : Any ):
_UpperCAmelCase ... | 684 | 0 |
'''simple docstring'''
class a__ :
def __init__( self ) -> str:
__A = 0
__A = 0
__A = {}
def _lowerCamelCase ( self , lowercase__ ) -> List[Any]:
if vert... | 717 |
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_availabl... | 205 | 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/... | 22 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARC... | 373 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__lowercase = """\
@misc{chen2021eval... | 135 |
"""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
fro... | 135 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : int = logging.get_logger(__name__)
__magic_name__ : List[str] = {
... | 280 |
from manim import *
class A__ ( __snake_case ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = Rectangle(height=0.5 ... | 280 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BridgeT... | 296 | """simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__lowercase = 299_792_458
# Symbols
__lowercase , __lowercase , __lowercase , __lowercase = symbols('''ct x y z''')
def low... | 296 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class _lowerCamelCase ( yaml.SafeLoader ):
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( self , __SCREAMING_SNAKE_CASE ) -> str:
"""simple do... | 285 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_model... | 285 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( SCREAMING_SNAKE_CASE__: str ) -> list:
"""simple docstring"""
if n_term == "":
return []
__a = []
for temp in range(int(SCREAMING_SNAKE_CASE__ ) ):
series.append(f"""1/{temp + 1}""" if series ... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Tuple = {
"""configuration_blip_2""": [
"""BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 270 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowercase__ = parse(importlib.metadata.version("torch"))
def __magic_name__ ( _lowerCamelCase : Union[s... | 581 |
"""simple docstring"""
from __future__ import annotations
import math
def __magic_name__ ( _lowerCamelCase : float , _lowerCamelCase : int ):
__a : Tuple = u
for i in range(1 , _lowerCamelCase ):
__a : List... | 581 | 1 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_m... | 713 |
'''simple docstring'''
from collections.abc import Callable
def UpperCamelCase ( a , a , a ) -> float:
'''simple docstring'''
__magic_name__ = a
__magic_name__ = b
if function(a ) == 0: # one of the a or b is a root for the function
... | 245 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
f... | 81 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetP... | 134 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCamelCase ) )
def lowerCAmelCase_ ( __lowerCamelCase... | 203 |
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import torch.nn as nn
fro... | 203 | 1 |
'''simple docstring'''
import requests
__snake_case = '''YOUR API KEY'''
def a ( __a , __a = giphy_api_key ) -> list:
'''simple docstring'''
UpperCamelCase__ :int = '''+'''.join(query.split() )
UpperCamelCase__ :Tuple = f'''https://api.gip... | 189 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 189 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execu... | 624 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, rando... | 624 | 1 |
'''simple docstring'''
def _A ( ):
"""simple docstring"""
__lowercase = 0
for i in range(1 , 1001 ):
total += i**i
return str(A__ )[-10:]
if __name__ == "__main__":
print(solution())
| 41 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqCo... | 41 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_fo... | 713 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""... | 36 | 0 |
from string import ascii_uppercase
_lowerCAmelCase = {str(ord(c) - 55): c for c in ascii_uppercase}
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
raise TypeError("""int() can't convert non-string... | 569 |
import os
import numpy
import onnx
def _lowerCAmelCase ( _lowerCAmelCase ,_lowerCAmelCase ):
'''simple docstring'''
A_ : List[str] = a.name
A_ : int = b.name
A_ : int = """"""
A_ : Union[str, Any] = """"""
A_ ... | 569 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a= logging.get_logger(__name__)
a= {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class __lowercase ( _lowerCamelCase ):
... | 721 | '''simple docstring'''
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... | 287 | 0 |
'''simple docstring'''
import copy
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 ..auto import CONFIG_MAPPING
UpperCAmelCase_... | 539 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( a_ : str = "" ):
__a = url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
__a = Beautiful... | 539 | 1 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's ... | 721 |
from __future__ import annotations
from collections.abc import MutableSequence
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) != degree + 1:
raise V... | 412 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : Any = logging.get_logger(__name__)
a_ : int = {
'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/config.json',
'google/fnet-large': 'https://huggingface.co/go... | 623 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def __lowercase( UpperCAmelCase__ = "laptop" ):
"""simple docstring"""
lowerCamelCase = F"""https://www.amazon.in/laptop/s?k={product}"""
l... | 623 | 1 |
from __future__ import annotations
def __snake_case ( _UpperCAmelCase ):
"""simple docstring"""
lowercase = len(SCREAMING_SNAKE_CASE_ )
# We need to create solution object to save path.
lowercase = [[0 for _ in range(SCREAM... | 700 |
from __future__ import annotations
import os
from collections.abc import Mapping
__magic_name__ = tuple[int, int]
class a__ :
"""simple docstring"""
def __init__( self :List[Any] , lowercase__ :set[int] , lowercase__ :Mapping[EdgeT, int] ... | 314 | 0 |
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_case__ , snake_case__ ) -> float:
__UpperCAmelCase =(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def SCREAMING_SNAKE_CASE ( ) -> str:
pri... | 132 |
from importlib import import_module
from .logging import get_logger
SCREAMING_SNAKE_CASE__ : Union[str, Any] = get_logger(__name__)
class UpperCAmelCase_ :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=None ):
UpperC... | 79 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__lowerCamelCase = logging.get_logger(__name__)
# TODO: upload to AWS
__lowerCamelCase = {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-... | 190 |
"""simple docstring"""
from pathlib import Path
import fire
def lowercase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
__magic_name__ = Path(__UpperCamelCase )
__magic_name__ = Path(__UpperCamelCase )
dest_dir.m... | 190 | 1 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCamelCase : Tuple = get_logger(__name__)
lowerCamelCase : List[str] = r'''
Args:
input_ids (`jnp.ndarray` of s... | 149 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
... | 149 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impo... | 89 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __magic_name__ ( unittest.TestCase):
... | 89 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
fro... | 569 |
class _UpperCAmelCase :
def __init__( self , a__ , a__ ):
A_ : str = name
A_ : int = val
def __str__( self ):
return F"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self ... | 569 | 1 |
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 as compute... | 714 |
def a__ ( snake_case , snake_case ):
"""simple docstring"""
while a != 0:
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Any = b % a, a
return b
def a__ ( snake_case , snake_case ):
"""simple docstring"""
if gcd(snake_case , sn... | 131 | 0 |
'''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
lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
lowerC... | 444 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list ):
if len(_lowerCamelCase ) <= 1:
return [tuple(_lowerCamelCase )]
__SCREAMING_SNAKE_CASE : Optional[int] = []
def generate(_lowerCamelCase: int , _lowerCamelCase: list ):
__SCREAMI... | 578 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ : List[Any] =logging.get_logger(__name__)
A_ : Any ={
'''camembert-... | 606 | '''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def snake_case_ ... | 606 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity... | 39 |
import collections
import importlib.util
import os
import re
from pathlib import Path
snake_case__ : Optional[int] = '''src/transformers'''
# Matches is_xxx_available()
snake_case__ : Union[str, Any] = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a o... | 392 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils impor... | 714 |
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
if index == r:
for j in range(_lowerCAmelCase ):
print(data[j] , end=" " )
print(" " )
... | 405 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _a ( UpperCamelCase__ ):
def __init__( self: int , U... | 43 |
class _a :
def __init__( self: Tuple , UpperCamelCase_: Dict ) -> List[str]:
"""simple docstring"""
lowercase__ = val
lowercase__ = None
lowercase__ = None
def lowerCamelCase_ ( ... | 43 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCAmelCase: Optio... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available()... | 195 | 0 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import ... | 447 |
'''simple docstring'''
def lowerCamelCase__ ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
UpperCAmelCase = (boundary[1] - boundary[0]) / steps
UpperCAmelCase = ... | 447 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
A : Tuple = 50000
A : Optional[int] = 5000
A , A : Dict = os.path.split(__file__)
A : Tuple ... | 473 |
import os
from datetime import datetime as dt
from github import Github
A : Union[str, Any] = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _lowe... | 473 | 1 |
import pytest
_lowerCamelCase : Dict = '''__dummy_dataset1__'''
_lowerCamelCase : Optional[int] = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-tr... | 686 |
import random
def a_ ( __lowercase : str , __lowercase : Any , __lowercase : Any ) -> Optional[Any]:
_snake_case = a[left_index]
_snake_case = left_index + 1
for j in range(left_index + 1 , __lowercase ):
if a[j] ... | 686 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 324 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, s... | 324 | 1 |
from __future__ import annotations
from random import choice
def _A ( lowerCamelCase ):
return choice(lowerCamelCase )
def _A ( lowerCamelCase , lowerCamelCase ):
a__ : List[str] = random_pivot(lowerCamelCase )
# partition based on pivot
# linear time
a__ ... | 112 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case , snake_case=0.2 , snake_case=0.2 ... | 112 | 1 |
'''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,... | 702 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class __snake_case( _lowerCAmelCase ):
'''simple docstring'''
def __init__( ... | 344 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusio... | 67 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - usefu... | 290 | 0 |
'''simple docstring'''
def a ( __a = 1000 ) -> int:
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution()) | 280 |
'''simple docstring'''
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def a ( __a = "" ) -> dict[str, float]:
'''simple docstring'''
UpperCamelCase__ :Optional[Any] = url or '''https://www.imdb.com/chart/top/?r... | 280 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowercase ( snake_case__ ):
@staticmethod
@abstractmethod
def _lowercase ( __lowerCamelCase : ArgumentParser ) -> Union[str, Any]:
... | 377 |
from manim import *
class lowercase ( snake_case__):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Union[str, Any]:
UpperCAmelCase_= Rectangle(height=0.5 , width=0.5 )
UpperCAmelCase_= Rectangle(height=0.25 , w... | 593 | 0 |
def A__ ( _a : str ):
'''simple docstring'''
snake_case__ : Union[str, Any] =""""""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A__ ( _a : str ):
'''simple ... | 720 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__lowerCamelCase : str = models.Sequential()
... | 448 | 0 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
def __lowerCAme... | 367 |
from __future__ import annotations
def __lowerCAmelCase ( __snake_case ):
__lowerCAmelCase = len(__snake_case )
# We need to create solution object to save path.
__lowerCAmelCase = [[0 for _ in range(__snake_case )] for _ in range(... | 367 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common i... | 1 |
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
from accelerate import Acceler... | 1 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 610 |
"""simple docstring"""
def __lowerCamelCase ( __UpperCamelCase ) -> tuple[int, int]:
"""simple docstring"""
try:
lowerCAmelCase_ : Tuple = float(__UpperCamelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
lowerCAmelCa... | 610 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, Tok... | 582 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.u... | 582 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _A ( A ) -> int:
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod... | 372 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase : Dict = {"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}
try:
if not is_to... | 372 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 91 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( UpperCAmelCase_ ):
lowerCAmelCase = """ClapFeatureExtractor"""
lowerCAmelCase = ("""RobertaTokenizer""", """RobertaToken... | 91 | 1 |
"""simple docstring"""
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self , A , A , A = True... | 506 |
"""simple docstring"""
from math import factorial
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int = 2_0 ):
"""simple docstring"""
snake_case_ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...... | 480 | 0 |
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 import stable_softmax
if is_to... | 706 | lowercase_ = {str(digit): digit**5 for digit in range(10)}
def __lowerCAmelCase ( __SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(__SCREAMING_SNAKE_CASE ) )
def __lowerCAmelCase ... | 390 | 0 |
'''simple docstring'''
import itertools
import math
def _snake_case ( A ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even n... | 90 |
"""simple docstring"""
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... | 553 | 0 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 143 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class __lowercase ( enum.Enum ... | 143 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 395 |
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,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaToke... | 395 | 1 |
'''simple docstring'''
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokeniza... | 187 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgum... | 187 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQua... | 613 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devic... | 504 | 0 |
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = test_file.split(os.path.sep )
if c... | 720 |
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 = {
"facebook/xmod-base": "https://huggingface.co/faceb... | 297 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __a ( __magic_name__ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE_ ( snake_case ):
"""simple docstrin... | 453 |
"""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,
)
if is_sentencepiece_available():
from ..ta.toke... | 453 | 1 |
def _lowerCAmelCase ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = 0
for i in range(1 , 1001 ):
total += i**i
return str(__lowerCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 447 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class _SCREAMING_SNAKE_CASE (UpperCamelCase ):
def __init__( sel... | 447 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
A__ : str = '''bert-generation'''
def __init__( self : Tuple , __lowerCamelCase : Optional[int]=5_0_3_5_8 ... | 103 |
import math
from collections.abc import Iterator
from itertools import takewhile
def UpperCamelCase__ ( _A: int ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 479 | 0 |
"""simple docstring"""
def snake_case ( UpperCamelCase__ : int ) -> Dict:
lowerCamelCase : int = generate_pascal_triangle(UpperCamelCase__ )
for row_idx in range(UpperCamelCase__ ):
# Print left spaces
for _ in range(num_rows - r... | 705 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_atten... | 42 | 0 |
'''simple docstring'''
from collections.abc import Sequence
from queue import Queue
class a_ :
def __init__( self : Union[str, Any] , __lowerCAmelCase : str , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : List[str] , __lowerCAmelCase ... | 356 |
'''simple docstring'''
from collections import defaultdict
def lowerCamelCase__ ( a , a ):
__snake_case = first_str.lower().strip()
__snake_case = second_str.lower().strip()
# Remove whitespace
__snake_case = first_str.replace(' ' , '' ... | 356 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.js... | 48 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Thread... | 48 | 1 |
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