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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__lowerCAmelCase : Any = {
"configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}
try:
if... | 529 |
'''simple docstring'''
import functools
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> int:
# Validation
if not isinstance(__A , __A ) or not all(isinstance(__A , __A ) for day in days ):
raise ValueError('The parameter days should b... | 495 | 0 |
def lowerCamelCase__ ( a : List[Any] ) -> int:
"""simple docstring"""
stooge(a , 0 , len(a ) - 1 )
return arr
def lowerCamelCase__ ( a : Optional[int] , a : List[str] , a : str ) -> Optional[int]:
"""simple docstring"""
if i >= h:
retur... | 373 |
import sys
from collections import defaultdict
class lowerCAmelCase_ :
def __init__( self : Optional[int] ) ->Any:
"""simple docstring"""
a__ :Optional[Any] = []
def _snake_case ( self : Optional[Any] , __A : ... | 373 | 1 |
import unittest
import numpy as np
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = None , ) -> np.ndarray:
UpperCamelCase_: str = np.shape(UpperCAmelCase__ )
UpperCamelCase_:... | 57 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
fr... | 57 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
Eul... | 704 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def ... | 178 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
snake_case__ = 42
snake_case__ = 42
class UpperCAmelCase_ :
def... | 420 |
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_to... | 17 | 0 |
import argparse
import copy
def a_ ( __magic_name__ ) -> Optional[Any]:
"""simple docstring"""
snake_case : List[str] = {}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in d... | 84 |
import string
import numpy
def a_ ( __magic_name__ , __magic_name__ ) -> int:
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , __magic_name__ )
class a_ :
A__ : List[Any] = string.asc... | 84 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _UpperCamelCase ( __UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase = No... | 42 |
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)
_snake_case : Tuple = models.Sequentia... | 441 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : List[str] = logging.get_logger(__name__)
__UpperCAmelCase : Optional[Any] = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cv... | 720 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'microsoft/wavlm-base': 'https://huggingface.co/microsoft/w... | 256 | 0 |
"""simple docstring"""
class _a :
def __init__( self : Union[str, Any] , _lowercase : int ) -> Dict:
snake_case : Optional[Any] = n
snake_case : str = [None] * self.n
snake_case : List[st... | 449 |
from math import factorial, pi
def lowerCamelCase__ ( _a , _a = 30):
if not isinstance(_a , (int, float)):
raise ValueError("maclaurin_sin() requires either an int or float for theta")
if not isinstance(_a , _a) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a... | 25 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import ... | 707 |
"""simple docstring"""
def __A ( a_ :int = 2_00) -> int:
__a : int = [1, 2, 5, 10, 20, 50, 1_00, 2_00]
__a : List[Any] = [0] * (pence + 1)
__a : Tuple = 1 # base case: 1 way to make 0 pence
for coin in coins:
... | 101 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a_ : Union[str, Any] = logging.get_logger(__name__)
a_ : List[str] ... | 73 |
_lowercase = [0, 2, 4, 6, 8]
_lowercase = [1, 3, 5, 7, 9]
def lowerCAmelCase__ ( UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : list[int] , UpperCamelCase_ : int )-> int:
if remaining_length == 0:
... | 632 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
loggin... | 300 |
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,
resize,
to_channel_d... | 300 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.im... | 458 |
"""simple docstring"""
import numpy as np
def _UpperCAmelCase ( __lowerCamelCase : np.ndarray , __lowerCamelCase : np.ndarray , __lowerCamelCase : float = 1E-1_2 , __lowerCamelCase : int = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(__lowerCamelCase ... | 224 | 0 |
from __future__ import annotations
from collections import deque
class __A :
def __init__(self , __magic_name__ ):
lowerCamelCase__ : list[dict] = []
self.adlist.append(
{"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []... | 96 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def _A (UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray , UpperCamelCase : np.ndarray , UpperCamelCase : int , UpperCamelCase : int ) ->np.n... | 96 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : float ,lowerCAmelCase_ : float ,lowerCAmelCase_ : float ) -> float:
"""simple docstring"""
if days_between_payments <= 0:
raise ValueError('days_between_p... | 220 |
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self ):
SCREAMING_SNAKE_CASE_ : Optional[int] =0
SCREAMING_SNAKE_CASE_ : str =0
SCREAMING_SNAKE_CASE_ : int ={}
def __lowerCamelCase ( se... | 220 | 1 |
'''simple docstring'''
import requests
A = 'YOUR API KEY'
def UpperCAmelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str = giphy_api_key):
lowerCamelCase : Optional[Any] = "+".join(query.split())
... | 711 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAM... | 449 | 0 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def __magic_name__ ( __a : Union[dict, list, tuple, torch.Tensor] ):
'''s... | 513 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
''' Distillation''... | 513 | 1 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
assert isi... | 246 | import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.mode... | 246 | 1 |
def __UpperCAmelCase ( __a : Dict ,__a : List[Any] ,__a : Dict ) -> Optional[Any]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__a ,n - 1 ,__a ) * a) % mod
els... | 14 |
"""simple docstring"""
from typing import Any
class a__ :
def __init__( self : List[str] , UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : str = data
__UpperCAmelCase : Optional[Any] = None
... | 77 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A__ ( ) ->Optional[Any]:
'''simple docstring'''
__A =ArgumentParser(
description=(
'''PyTorch T... | 712 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...uti... | 516 | 0 |
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
a : int = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', ... | 555 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransforme... | 555 | 1 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class _lowerCamelCase ( __lowercase ):
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ) -> List[str]:
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
def UpperCamel... | 706 | import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
... | 107 | 0 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : Dict = get_tests_dir('''fi... | 4 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( a_ ):
SCREAMING_SNAKE_CASE : Dict = (DDPMScheduler,)
def _SCREAMING_SNAKE_CASE ( self , **_SCREAMING_SNAKE_CASE ):
... | 284 | 0 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.testing_utils ... | 710 | def UpperCamelCase ( __lowercase : str ,__lowercase : int ):
'''simple docstring'''
A_ : int = word.split()
def justify(__lowercase : list ,__lowercase : int ,__lowercase : int ) -> str:
A_ : Optional[Any] = max_width - width... | 70 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
SCREAMING_SNAKE_CASE = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
... | 579 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 579 | 1 |
'''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 ..... | 617 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A: Union[str, Any] = logging.get_logger(__name__)
_A: List[str] = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/conf... | 617 | 1 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __snake_case ( _lowercase ,_lowercase ,_lowercase ):
"""simple docstring"""
UpperCamelCase = AutoConfig.from_pretrained(_l... | 34 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __UpperCAmelCase ( __magic_name__ ,__magic_name__=7 )-> Tuple:
"""simple docstring"""
snake_case_ : ... | 653 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
lowercase : int = 6378137.0
lowercase : Any = 6356752.314245
lowercase : List[str] = 637_8137
def SCREAMING_SNAKE_CASE__ ( __A , __A , ... | 703 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipeline... | 542 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.... | 93 | '''simple docstring'''
import csv
import tweepy
# Twitter API credentials
__snake_case : Union[str, Any] = ''''''
__snake_case : List[Any] = ''''''
__snake_case : List[str] = ''''''
__snake_case : Any = ''''''
def lowerCamelCase__ ( A_ ):
# authorize... | 660 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( a ):
"""simple docstring"""
UpperCamelCase__ : Tuple =["""image_processor""", """tokenizer"""]
UpperCamelCas... | 704 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
A_ :Dict = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
'... | 154 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case__ = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDepen... | 583 |
snake_case__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def lowerCamelCase_ ( ):
lowercase : Optional[Any] = input('''Enter message: ''' )
lowercase : Optional[Any] = input('''Enter key [alphanumeric]: ''' )
lowercase : Uni... | 583 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : int =logging.get_logger(__name__)
__snake_case : List[Any] ={
'vocab_file': 'vocab.json',
'merg... | 719 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to ... | 90 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_SCREAMING_SNAKE_CASE : Union[str, Any] = TypeVar("T")
_SCREAMING_SNAKE_CASE : Dict = TypeVar("U")
class _snake... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailabl... | 122 | 0 |
'''simple docstring'''
import argparse
import os
import re
__lowercase = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowercase = re.compile(R'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\... | 708 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _snake_case ( lowerCAmel... | 305 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPrio... | 640 |
'''simple docstring'''
from ....utils import logging
a : Optional[int] = logging.get_logger(__name__)
class a ( _lowerCamelCase ):
def __init__( self : int , lowercase_ : Tuple , lowercase_ : Any=None , lowercase_ ... | 640 | 1 |
'''simple docstring'''
from collections.abc import Generator
def SCREAMING_SNAKE_CASE_ ( ):
UpperCamelCase__ : Tuple = 0, 1
while True:
UpperCamelCase__ : Union[str, Any] = b, a + b
yield b
def SCREAMING_SNAKE_CASE_ ( Uppe... | 701 |
class _lowerCamelCase :
"""simple docstring"""
def __init__( self ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = ''''''
UpperCamelCase__ : int = ''''''
UpperCamelCase__ : Opt... | 462 | 0 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.t... | 85 | import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__ : Opti... | 85 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists... | 133 |
"""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.pipel... | 133 | 1 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
lowerCamelCase_ = numpy.array([0, 0])
lowerCamelCase_ = numpy.array([0.5, 0.8_660_254])
lowerCamelCase_ = ... | 498 |
"""simple docstring"""
def __lowerCamelCase ( a_ : list , a_ : int , a_ : int = 0 , a_ : int = 0 ) -> int:
__SCREAMING_SNAKE_CASE :Union[str, Any] = right or len(a_ ) - 1
if left > right:
return -1
elif list... | 498 | 1 |
import re
import string
import numpy as np
import datasets
__UpperCamelCase : List[str] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__UpperCamelCase : Optional[Any] = '\nArgs:\n predic... | 715 | import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
e... | 34 | 0 |
"""simple docstring"""
from statistics import mean, stdev
def a__ ( lowerCAmelCase , lowerCAmelCase = 3 ) -> list:
UpperCAmelCase__ : Dict = min(lowerCAmelCase )
UpperCAmelCase__ : List[str] = max(lowerCAmelCase )
# normalize data
... | 182 |
"""simple docstring"""
import math
def a__ ( lowerCAmelCase ) -> list[int]:
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Union[str, Any] = 2
UpperCAmelCase__ : List[Any] = int(math.sqrt(lowerCAmelCase ) ) # S... | 182 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCAmelCase : str = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/m... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)... | 694 | 1 |
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
lowercase_ : Union[str, Any] = logging.getLogger(__name__)
class _lowerCamelCase ( UpperCamelCase_ ):
def __init__... | 64 |
'''simple docstring'''
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbo... | 685 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowerCamelCase__ ( __snake_case ) -> Any:
"""simple docstring"""
_UpperCame... | 703 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
_a = [
# (stable-diffusion, HF Diffusers)
("""time_embed.0.weight""", "... | 78 | 0 |
"""simple docstring"""
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __lowercase ( _UpperCAmelCase):
"""simple docstring"""
_A : Union[str, Any] = (UnCLIPScheduler,)
def __UpperCam... | 480 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a_ = {
'''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 480 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class UpperCAmelCase_ ( __lower... | 578 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
a__ = '''\
@inproceedings{popovic-2015-chrf,
title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
author = "Popovi{\'c}, Maja",
booktitle = "Proceedings of the Tenth Worksho... | 578 | 1 |
from __future__ import annotations
import math
def __SCREAMING_SNAKE_CASE ( a__ : float ,a__ : int ) -> float:
__A : Dict = u
for i in range(1 ,lowercase__ ):
__A : List[Any] = temp * (u - i)
return temp
def __SCREAMING_SNAKE_CASE ( ) ... | 17 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowerCAmelCase_ :
def __snake_case ( self : Any , SCREAMING_SNAKE_CASE_ : int ):
raise NotImplementedError()
def ... | 668 | 0 |
'''simple docstring'''
lowerCAmelCase__ : List[str] = tuple[float, float, float]
lowerCAmelCase__ : Union[str, Any] = tuple[float, float, float]
def _a ( __lowerCAmelCase : Pointad , __lowerCAmelCase : Pointad ):
"""simple... | 709 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 502 | 0 |
import math
import sys
import cva
import numpy as np
def lowerCamelCase__ ( __lowerCamelCase : np.ndarray , __lowerCamelCase : float ):
# For applying gaussian function for each element in matrix.
__UpperCAmelCase : int = math.sqrt(__lo... | 63 |
'''simple docstring'''
import random
from typing import Any
def __A ( a_ : list ):
for _ in range(len(a_ ) ):
lowerCAmelCase : List[Any] = random.randint(0 ,len(a_ ) - 1 )
lowerCAmelCase : Tuple = random.randint(0 ,len... | 525 | 0 |
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
fro... | 640 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = 200 ):
"""simple docstring"""
lowercase_ : Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
lowercase_ : str = [0] * (pence + 1)
lowercase_ : Dict = 1 # base cas... | 640 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils impor... | 40 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE (A ) -> bool:
"""simple docstring"""
if not isinstance(A , A ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(A ) == 0:
raise ValueError('''Input li... | 460 | 0 |
from math import pow, sqrt
def a ( *SCREAMING_SNAKE_CASE_ : float ):
"""simple docstring"""
UpperCamelCase : Tuple = len(SCREAMING_SNAKE_CASE_ ) > 0 and all(value > 0.0 for value in values )
return result
def a ... | 643 |
import torch
from transformers import AutoModel
class UpperCAmelCase_ ( torch.nn.Module):
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
"""simple docstring"""
... | 643 | 1 |
"""simple docstring"""
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __A ( nn.Module ):
UpperCAmelCase__ = 42
Uppe... | 96 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/con... | 619 | 0 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 720 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase( SCREAMING_SNAKE_CASE ):
__A: Optional[Any] = ["""image_processor""", """tokenizer"""]
__A: List[str] = """CLIPImage... | 328 | 0 |
# 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
#
# Unless re... | 684 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvaila... | 684 | 1 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _SCREAMING_SNAKE_CASE ( datasets.BeamBasedBuilder):
def _snake_case ( self )-> ... | 75 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A : Union[str, Any] = logging.get_logger(__name__)
__A : Optional[Any] = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
class _SCRE... | 75 | 1 |
import numpy as np
def UpperCamelCase ( _a , _a , _a , _a , _a ) -> List[Any]:
'''simple docstring'''
lowercase_ :Optional[Any] = int(np.ceil((x_end - xa) / h ) )
lowercase_ :List[str] = n... | 257 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE : Union[str, Any] = re.compile(r"([A-Z]+)([A-Z][a-z])")
SCREAMING_SNAKE_CASE : Union[str, Any] = re.compile(r"([a-z\d])([A-Z])")
SCREAMING_SNAKE_CASE : Optional[int] = re.com... | 257 | 1 |
"""simple docstring"""
import math
def _lowercase ( _SCREAMING_SNAKE_CASE : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or num... | 237 | """simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase : Tuple =logging.get_logger(__name__)
lowerCamelCase : Union[str, Any] ={
... | 237 | 1 |
'''simple docstring'''
def _a (lowercase__ : str , lowercase__ : str ) -> float:
"""simple docstring"""
def get_matched_characters(lowercase__ : str , lowercase__ : str ) -> str:
__snake_case = []
__snake_case ... | 56 |
import os
import numpy
import onnx
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = a.name
SCREAMING_SNAKE_CASE_ : Dict = ... | 105 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnPr... | 603 | '''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__snake_case = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classifica... | 603 | 1 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.ser... | 88 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.ut... | 383 | 0 |
'''simple docstring'''
import string
def _SCREAMING_SNAKE_CASE( snake_case_ : str ) ->str:
'''simple docstring'''
_lowercase : str = ''''''
for i in sequence:
_lowercase : Tuple = ord(sn... | 411 |
'''simple docstring'''
from math import factorial
def _SCREAMING_SNAKE_CASE( snake_case_ : int , snake_case_ : int ) ->int:
'''simple docstring'''
# If either of the conditions are true, the function is being asked
... | 411 | 1 |
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
if index == number_of_items:
return 0
A_ = 0
A_ = 0
A_ = knapsack(__UpperCamelCase , __UpperCamelCase ... | 141 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
requ... | 141 | 1 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
... | 84 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a_ ( __magic_name__ ) -> List[Any]:
"""simple docstring"""
if "cls_token" in name:
... | 84 | 1 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureE... | 43 |
'''simple docstring'''
from __future__ import annotations
class _a :
'''simple docstring'''
def __init__( self ,__a = 0 ) -> str:
snake_case : List[Any] = key
def snake_case_... | 116 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Co... | 646 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def __UpperCamelCase ( _A : Dict ) -> int:
... | 646 | 1 |
import numpy as np
class A :
def __init__( self: Tuple ) -> Any:
'''simple docstring'''
UpperCAmelCase_ =(0, 0)
UpperCAmelCase_ =None
UpperCAmelCase_ =0
UpperCAmelCase_ =0... | 54 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 0 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
a_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or mu... | 710 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# 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
a_ =... | 92 | 0 |
'''simple docstring'''
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhoneme... | 78 |
'''simple docstring'''
import random
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: Optional[int] = num - 1
snake_case: List[str] = 0
while s % 2 == 0:
snake_case: Union[str, Any] =... | 329 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class ... | 712 |
"""simple docstring"""
from __future__ import annotations
import math
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->list:
"""simple docstring"""
if len(_lowerCamelCase ) != 2 or len(a[0] ) != 2 or len(_lowerCamelCase ) != 2 or len(b[0] ) != 2:
ra... | 281 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=_a )
class a_ (_a ):
__lowerCAmelCase : str = field(default="""audio-classi... | 384 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
UpperCamelCase_ = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
d... | 384 | 1 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> list:
if len(_UpperCAmelCase ) < 2:
return collection
def circle_sort_util(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> bool:
lowerCamelCase__ : Union[str, Any] = False
if low == hig... | 188 |
_UpperCAmelCase : str = """Tobias Carryer"""
from time import time
class lowerCAmelCase :
def __init__( self : Optional[Any] , UpperCAmelCase : int , UpperCAmelCase : Dict , UpperCAmelCase : Any , UpperCAmelCase : str=int(time()... | 188 | 1 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( _lowercase ,_lowercase = "cpu" ,_lowercase = None ):
"""simple docstring"""
UpperCamelCase = torch.load(_lowercase ,map_location=_lowercase ... | 34 |
from ... import PretrainedConfig
lowercase : Dict = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ):
"""simple docstring"""
lowercase : List[str] ... | 327 | 0 |
'''simple docstring'''
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCAmelCase ( tf.keras.layers.Layer ):
def __init__(self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1 , ... | 714 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
Aut... | 156 | 0 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
... | 274 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def snake_case__ ( SCREAMING_SNAKE_CASE_ : Tuple ):
'''simple docstring'''
if (
(cp >= 0X4_e00 and cp <= 0X9_fff)
... | 164 | 0 |
"""simple docstring"""
def snake_case ( UpperCamelCase__ : Any , UpperCamelCase__ : Any , UpperCamelCase__ : int , UpperCamelCase__ : Optional[int] ) -> List[str]:
if height >= 1:
move_tower(height - 1 , UpperCamelC... | 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'''
import random
from typing import Any
def UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
for _ in range(len(lowerCamelCase_ ) ):
lowerCAmelCase__ : Dict = random.randint(0 , len(lowerCamelCase_ ) - 1 )
lowerCAmelCase__ : Un... | 378 |
'''simple docstring'''
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_availab... | 378 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from datasets import load_dataset
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 ImageProcessingSavingTestM... | 711 | """simple docstring"""
import os
import string
import sys
SCREAMING_SNAKE_CASE__ = 1 << 8
SCREAMING_SNAKE_CASE__ = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
... | 104 | 0 |
import argparse
import os
import re
_lowercase : List[Any] ='''src/diffusers'''
# Pattern that looks at the indentation in a line.
_lowercase : Optional[Any] =re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : Optional[Any] =... | 305 | _lowercase : str ='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_lowercase : List[str] =... | 305 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
lowe... | 380 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFa... | 380 | 1 |
def snake_case_ () -> List[str]:
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
__UpperCAmelCase = generate_large_matrix()
__UpperCAmelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, ... | 651 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstri... | 4 | 0 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" ,[
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.j... | 706 |
'''simple docstring'''
def _A ( A ,A ,A ,A ,A ) -> int:
if index == number_of_items:
return 0
lowercase : Optional[int] = 0
lowercase : Union[str, Any] = 0
lowercase : Dict = knapsack(A ,A ,A ,A ,index + ... | 425 | 0 |
def _lowerCamelCase ( __lowerCamelCase ) -> list:
'''simple docstring'''
UpperCAmelCase__ : List[Any] = [0] * len(__lowerCamelCase )
for i in range(1 , len(__lowerCamelCase ) ):
# use last results for better perfo... | 79 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {
"configuration_roberta": ... | 602 | 0 |
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
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : ... | 232 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class UpperCamelCase ( _... | 232 | 1 |
'''simple docstring'''
class UpperCamelCase__ : # Public class to implement a graph
"""simple docstring"""
def __init__( self , snake_case , snake_case , snake_case ):
'''simple docstring'''
UpperCAmelCase : str = row
UpperC... | 679 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.u... | 679 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
'configuration_poolformer': [
'POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'PoolFormerConfig',
... | 719 |
lowerCAmelCase__ = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCAmelCase__ = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__ = {
"... | 1 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class UpperCamelCase__ :
"""simple docstring"""
A__ : List[str] = None
def snake_case__ ( s... | 104 |
"""simple docstring"""
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepen... | 104 | 1 |
'''simple docstring'''
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 __SCREAMING_SNAK... | 702 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow... | 665 | 0 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase : int =logging.get_logger(__name_... | 316 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
_snake_case : Any = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation ... | 22 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCAmelCase__ :
def __init__( self , UpperCamelCase__ ):
'''simple docstring'''
A__ = data
A__ = None
class lowerCAmelC... | 261 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__UpperCAme... | 261 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_AR... | 90 | import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import ... | 305 | 0 |
'''simple docstring'''
from math import ceil
def _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ ):
_lowercase = list(range(0 , UpperCamelCase__ ) )
_lowercase = [item for sublist in list(device_map.values() ) for item in sublist]
# Duplicate check
_lo... | 715 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
_lowercase = 0
_lowercase = number
while duplicate > 0:
_lowercase , _lowercase = divm... | 572 | 0 |
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,
)
lowercase : List[Any] = {'configuration_mbart': ['... | 557 |
import numpy as np
class A_ :
'''simple docstring'''
def __init__( self: Optional[int] ):
__lowerCamelCase : int = (0, 0)
__lowerCamelCase : List[str] = None
__lowerCamelCase : int = 0
__lowerCamelCa... | 669 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
Aut... | 576 | import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
Aut... | 576 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _lowerCamelCase ( snake_case = "isbn/0140328726" ):
_lowerCAmelCase = olid.strip().strip('/' ) # Remove leading/trailing whitespace & slashes
if new_olid.count('/' ) != 1:
... | 192 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase: int = logging.get_logger(__name__)
_lowercase: Union[str, Any] = {'''v... | 192 | 1 |
"""simple docstring"""
import string
def _a ( _SCREAMING_SNAKE_CASE ) -> None:
for key in range(len(string.ascii_uppercase ) ):
snake_case_ = """"""
for symbol in message:
if symbol in string.ascii_uppercase:
... | 716 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = 'Input must be a string of 8 numbers plus letter'
__SCREAMING_SNAKE_CASE : Dict = 'TRWAGMYFPDXBNJZSQVHLCKE'
def _a ( _SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCR... | 2 | 0 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppToke... | 66 |
'''simple docstring'''
from __future__ import annotations
_UpperCamelCase = 10
def a_ ( _lowerCAmelCase ) -> list[int]:
__lowerCamelCase : str = 1
__lowerCamelCase : Union[str, Any] = max(_lowerCAmelCase )
while placement <= max_di... | 459 | 0 |
"""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
if... | 721 |
"""simple docstring"""
def A_ ( __UpperCamelCase : int = 1 , __UpperCamelCase : int = 10_00 ):
lowercase = 1
lowercase = 0
for divide_by_number in range(__UpperCamelCase , digit + 1 ):
lowercase = []
lowercase ... | 396 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(__lowerCAmelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 509 |
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,
... | 509 | 1 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
"""simple docstring"""
... | 711 |
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
UpperCAmelCase_ = logging.get_logger(__name__)
def UpperCAmelCase ( A__ , A__ ... | 519 | 0 |
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