code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMT... | 113 | """simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def a_ ( _lowerCAmelCase : jnp.ndarray , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1.0E4 , _lowerCAmelCase : bool = False , _lo... | 77 | 0 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
... | 269 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_we... | 269 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = ... | 147 |
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 : List[str] ... | 147 | 1 |
"""simple docstring"""
lowerCAmelCase_ : dict[str, float] = {
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609344,
"knot": 1.852,
}
lowerCAmelCase_ : dict[str, float] = {
"km/h": 1.0,
"m/s": 0.277777778,
"mph": 0.621371192,
"knot": 0.539956803,
}
... | 248 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
lowerCAmelCase_ : Any = {
'''google/tapas-base-finetuned-sqa''': (
'''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
),
'''google/tapas-base-finetuned-wtq'''... | 248 | 1 |
from collections.abc import Callable
def lowerCAmelCase_ (lowerCAmelCase__: Callable[[float], float] , lowerCAmelCase__: float , lowerCAmelCase__: float ):
"""simple docstring"""
UpperCAmelCase_: str = a
UpperCAmelCase_: ... | 147 |
import os
def UpperCAmelCase__ ( _A : Any ):
'''simple docstring'''
a__ =len(grid[0] )
a__ =len(_A )
a__ =0
a__ =0
a__ =0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(_A ):
for... | 188 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE_: str =get... | 352 | '''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 106 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'andreasmadsen/efficient_mlm_m0.40': (
'https://huggingface.co... | 235 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase_ : Optional[Any] = {
'huggingface/informe... | 286 | 0 |
'''simple docstring'''
import qiskit
def UpperCAmelCase_ (__a : int , __a : int ):
"""simple docstring"""
_a : Any = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_a : List[Any] = qi... | 371 |
'''simple docstring'''
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
fr... | 5 | 0 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,) ... | 269 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__snake_case : List[Any] = logging.get_logger(__name__)
__snake_case : Any = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-c... | 269 | 1 |
from __future__ import annotations
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : int ) -> list[str]:
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of... | 357 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __snake_case ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] ) -> Dict:
A_ : Optional[Any] = 0
if start < end:
A_ : Tuple = ... | 70 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A__(a_ ):
"""simple docstring"""
def UpperCamelCase__ ( self ) -> str:
return [
{"col_1": 3, "col_2": "a"},
{"col_1": 2, "... | 248 |
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 ):
"""simple docstring"""
_A : int
_A : int
_A : float =... | 248 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils i... | 353 |
"""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.util... | 79 | 0 |
"""simple docstring"""
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_modelin... | 106 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def __SCREAMING_SNAKE_CASE ( A_ = 1_00_00_00 , A_ = 10 ):
lowerCAmelCase__ : defaultdict = defaultdict(A_ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_wi... | 106 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)... | 368 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_blenderbot": [
... | 347 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase : Optional[int] = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
UpperCAmelCase : Any = ... | 95 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class lowe... | 5 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaF... | 89 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optiona... | 89 | 1 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torchaudio
@re... | 333 |
'''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, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
A__ : Any =logging.g... | 70 | 0 |
import os
import sys
import unittest
_A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping... | 366 |
class lowerCamelCase :
def __init__(self : List[Any] , _A : str ) -> Any:
# we need a list not a string, so do something to change the type
snake_case = arr.split("," )
def UpperCAmelCase(self : str ) ... | 137 | 0 |
import os
import unicodedata
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 = logging.get_logger(__name__)
__A = {... | 90 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, requir... | 79 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers... | 351 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : int = logging.get_logger(__name__)
lowercase : int = {
'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/... | 311 | 0 |
"""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.or... | 40 |
"""simple docstring"""
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 ... | 347 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor... | 96 |
'''simple docstring'''
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start... | 96 | 1 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils im... | 89 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> np.array:
_a : Optional[int] = f"""{sampling_rate}"""
_a ... | 89 | 1 |
def snake_case_ ( lowerCAmelCase_ : int ):
if number > 0:
raise ValueError("""input must be a negative integer""" )
__lowercase : Optional[Any] = len(bin(lowerCAmelCase_ )[3:] )
__lowercase : int = bin(abs(lowerCAmelCase_ ) - ... | 306 |
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... | 306 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowerCamelCase_ = logging.get_logg... | 79 |
import os
import unicodedata
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 SPIECE_UNDERLINE, logging
a_ : Optional[Any] = logging.get_logger(__n... | 137 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...uti... | 150 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a : Optional[Any] = [
"""Prosecutor: \"No videos were used in the crash investigat... | 150 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a ( ) -> Union[str, Any]:
"""simple docstring"""
_lowercase =[randint(-1000 , 1000 ) for i in range(10 )]
... | 205 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
... | 311 | 0 |
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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
Vi... | 125 |
def __lowerCamelCase ( snake_case__ ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = len(snake_case__ )
for i in range(length - 1 ):
_SCREAMING_SNAKE_CASE = i
for k in range(i + 1 ,snake_c... | 125 | 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_form... | 96 |
"""simple docstring"""
lowercase__ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": ""... | 96 | 1 |
import numpy as np
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_, snake_case_ = 1e-12, snake_case_ = 1_0_0, ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(snake_case_ )[0] == np.shape(snake_case_ )[1]
# Ensur... | 330 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ : str = logging.get_logger(__name__)
UpperCamelCase__ : Optional[int] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.j... | 330 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
... | 306 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
i... | 306 | 1 |
def lowerCamelCase_ ( UpperCamelCase__ : int ):
'''simple docstring'''
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCamelCase__ = gray_code_sequence_string(UpperCam... | 35 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase = {
"""configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
"""tokenization_xlm""": ["""XLMT... | 35 | 1 |
"""simple docstring"""
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE__ = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
SCREAMING_SNAKE_CASE__ = ... | 150 | """simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab_file": "vocab.json"}
SCREAMING_S... | 150 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_util... | 370 | """simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa:... | 54 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
snake_case_ : List[str] = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_C... | 125 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class __a (lowerCamelCase ):
__a : Dict = "SpeechT5FeatureExtractor"
__a : Any = "SpeechT5Tokenizer"
def __init__( self : Any , __magic_name__ : Union... | 125 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class lowerCamelCase (__lowerCamelCase ):
"""simple... | 357 |
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> str:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(S... | 191 | 0 |
import numpy as np
def a__ ( _UpperCamelCase : np.ndarray ,_UpperCamelCase : np.ndarray ,_UpperCamelCase : float = 1e-12 ,_UpperCamelCase : int = 1_00 ,):
assert np.shape(_UpperCamelCase )[0] == np.shape(_UpperCamelCase )[1]
# Ensure ... | 330 |
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_tensor, random_attention_mask
if is_torch... | 330 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
snake_case_ = BeautifulSoup(requests.get(lowercase_ ).text , """html.parser""" )
snake_case_ ... | 34 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 1 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Optional[Any] = np.max(_lowerCAmelCase , axis=-1 , keepdims=_lowerCAmelCase )
snake_case__ : List[str]... | 35 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
# See all GLPN models at https://hugg... | 35 | 1 |
from typing import List
import numpy as np
def _A ( __magic_name__ ):
lowercase__ = {key: len(UpperCamelCase__ ) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__ , UpperCamelCase__ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
... | 352 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCam... | 201 | 0 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
f... | 283 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
@staticmethod
@abstractmethod
def UpperCAmelCase_ ( UpperCAmelCase__ : ArgumentParser ) -> int:
raise NotImple... | 54 | 0 |
"""simple docstring"""
_UpperCamelCase: Optional[int] = {
'A': '.-', 'B': '-...', 'C': '-.-.', 'D': '-..', 'E': '.', 'F': '..-.', 'G': '--.',
'H': '....', 'I': '..', 'J': '.---', 'K': '-.-', 'L': '.-..', 'M': '--', 'N': '-.',
'O': '---', 'P': '.--.', 'Q': '--.-', 'R': '... | 53 |
"""simple docstring"""
_UpperCamelCase: Dict = 2_5_6
# Modulus to hash a string
_UpperCamelCase: Union[str, Any] = 1_0_0_0_0_0_3
def lowercase__ ( _UpperCAmelCase , _UpperCAmelCase ) -> bool:
'''simple docstring''... | 53 | 1 |
'''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... | 41 |
"""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/LI... | 191 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class __SCREAMING_SNAKE_CASE :
"""simple docstring"""
pass
| 346 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ : Optional[int] = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ : Tuple = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
... | 346 | 1 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
A =logging.getLogger(__name__)
A ={
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class _a ( __a ):
... | 34 |
'''simple docstring'''
import os
def snake_case_ ():
UpperCAmelCase = os.path.join(os.path.dirname(_a ) , '''num.txt''' )
with open(_a ) as file_hand:
return str(sum(int(_a ) for line in file_hand ) )[:1_0]
if __name__ == "__main__":
... | 34 | 1 |
"""simple docstring"""
_UpperCamelCase : str = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase : List[Any] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_UpperCamelCase : Tuple = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursda... | 186 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCamelCase : Optional[int] = {
'configuration_rag': ['RagConfig'],
'retrieval_rag': ['RagRetriever'],
'tokenization_ra... | 186 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
... | 64 |
UpperCAmelCase_ = 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_ = 'TRWAGMYFPDXBNJZSQVHLCKE'
def lowerCAmelCase_ ( __UpperCAmelCase: str ) -> bool:
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
UpperCamel... | 201 | 0 |
"""simple docstring"""
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__magic_name__ = {
... | 255 |
"""simple docstring"""
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=1024 ):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE... | 255 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from... | 53 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTok... | 53 | 1 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineO... | 367 | import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
lowercase = logging.get_logger(__name__)
class __lowercase ( A ):
'''simple docstring'''
def __init__( self : Any , *_a : Optional[A... | 35 | 0 |
'''simple docstring'''
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class lowerCAmelCase_ :
'''simple docstring'''
pass
| 346 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
UpperCAmelCase_ = logging.getLogger()
@unittest.skip("""Temporarily disab... | 346 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:int = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLB... | 115 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
... | 115 | 1 |
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 = logging.get_logger(__name__)
UpperCamelCase = {
"""nvidi... | 186 |
from typing import Dict
from .base import GenericTensor, Pipeline
class _lowerCamelCase ( UpperCamelCase ):
"""simple docstring"""
def _snake_case ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=... | 186 | 1 |
'''simple docstring'''
def _UpperCamelCase ( UpperCamelCase__ ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
UpperCAmelCase__ : str ... | 283 |
'''simple docstring'''
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 _snake_case ( ... | 283 | 1 |
from __future__ import annotations
from collections import deque
class _SCREAMING_SNAKE_CASE :
def __init__( self , _SCREAMING_SNAKE_CASE )-> List[Any]:
lowerCamelCase_ =[]
self.adlist.append(
{"""value""": """""", """next_states... | 154 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...te... | 254 | 0 |
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=__lowercase):
_SCREAMING_SNAKE_CASE : Optional[int] = ['''speech''']
def __init__( self , *_UpperCamelCase , **_UpperCamelCase ):
"""simple doc... | 122 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
__s... | 122 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__lowerCAmelCase : List[str] = 'src/transformers'
# T... | 88 |
'''simple docstring'''
from PIL import Image
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Image:
def brightness(_lowerCAmelCase ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be ... | 35 | 0 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __lowerCamelCase ( snake_case__ ,snake_case__ ,snake_case__ ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CAS... | 353 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_... | 125 | 0 |
"""simple docstring"""
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_ava... | 115 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Any = {
'configuration_blenderbot': [
... | 115 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _lowercase ( __snake_case = True ,*__snake_case ,**__snake_case ) -> str:
if not is_tqdm_availab... | 58 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowercase ( __snake_case ,__snake_case = 2 ,__snake_case = 1 ,__snake_case = 3 ,) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
... | 58 | 1 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
__A : List[Any] = JukeboxTokenizer
__A : Optional[int] = {
"arti... | 283 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SqueezeBertConfig''',... | 283 | 1 |
"""simple docstring"""
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase_ ():
_UpperCAmelCase : int = HfArgumentParser(UpperCamelCase__ )
_UpperCAmelCase : Optional[Any] = parser.parse_args... | 68 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCAmelCase ( a ,unittest.TestCase ):
'''simple docstring'''... | 68 | 1 |
def lowerCamelCase__ ( a__ : Tuple , a__ : Tuple , a__ : Optional[Any] , a__ : Tuple , a__ : Any , a__ : Tuple ) -> List[str]:
if index == r:
for j in range(a__ ):
print(data[j] , end="""... | 122 |
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shape... | 122 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : int ):
lowerCAmelCase = len(_UpperCAmelCase )
lowerCAmelCase = [[0] * n for i in range(_UpperCAmelCase )]
for i in range(_UpperCAmelCa... | 309 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 | 1 |
"""simple docstring"""
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
"""simple docstring"""
return 1 if input_a == input_a else 0
def snake_case__ ( ):
"""simple docstring"""
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0... | 238 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class __a :
def __init__( self : Union[str, Any] , __magic_name__ : Dict=2 , __magic_name__ : Di... | 125 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCAmelCase ( UpperCAmelCase ) -> List[Any]:
snake_case_ = [
'encoder.version',
'decoder.version',
'model.encoder.ve... | 312 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except O... | 312 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase_ = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase_ = {
"""yjernite/retribert-base-uncased""": (
"""https://huggingface.co/yjernite/retribert-base-uncased/resolv... | 58 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-bas... | 58 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct... | 352 |
lowerCAmelCase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squa... | 93 | 0 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
... | 75 | '''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
else:
... | 67 | 0 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules i... | 18 | '''simple docstring'''
def _UpperCAmelCase ( _UpperCamelCase : float, _UpperCamelCase : list[float] ) -> float:
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows ... | 18 | 1 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : list , _lowerCamelCase : list , _lowerCamelCase : int ) -> list:
_lowerCAmelCase : int = len(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = [[0] * n for i in range(_... | 309 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _UpperCAmelCase ( _lowerCamelCase : Callable , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> np.ndarray:
... | 309 | 1 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fro... | 366 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->Optional[Any]:
'''simple docstring'''
stooge(snake_case_ ,0 ,len(snake_case_ ) - 1 )
return arr
def __lowercase ( snake_case_ : Optional[Any] ,snake_case_ : Un... | 291 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __snake_case ( __UpperCamelCase : Any ):
"""simple docstring"""
A_ = [
"encoder.version",
"decoder.version",
... | 312 |
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 _a ( snake_case_ ):
"""simple doc... | 312 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase : Tuple = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MA... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__a = {
"configuration_mobilebert": [
"MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 66 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowercase : int = logging.get_logger(__name__)
_lowercase : List[Any] ... | 93 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : str = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microso... | 351 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 249 | 0 |
from collections.abc import Sequence
from queue import Queue
class a__ :
def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ):
"""simple docstring"""
... | 18 | from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_ba... | 18 | 1 |
'''simple docstring'''
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
lowerCAmelCase : Dict =500_000
lowerCAmelCase , lowerCAmelCase : Tuple =os.path.split(__file__)
lowerC... | 147 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Tuple =logging.get_logger(__name__)
lowerCAmelCase : str ={
'''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config... | 147 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowercase_ = TypeVar("T")
lowercase_ = TypeVar("U")
class A ( Generic[T, U] ):
"""simple docstring"""
def __init__( self : ... | 7 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
fro... | 291 | 0 |
"""simple docstring"""
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->int:
"""simple docstring"""
while second != 0:
lowerCAmelCase__ :List[Any] = first & second
first ^= second
lowerCAmelCase__ :Optional[Any] = ... | 358 |
"""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/LICEN... | 254 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ) -> str:
__a = ''''''
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise Exception('''join(... | 45 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .... | 204 | from __future__ import annotations
__lowerCamelCase : Tuple = list[list[int]]
# assigning initial values to the grid
__lowerCamelCase : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0... | 204 | 1 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _lowerCAmelCase ( pl.LightningModule ):
"""simple docstring"""
def __init__( self : Option... | 17 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_ac... | 249 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase ( metaclass=A__ ):
UpperCAmelCase : Any = ["""flax"""]
def __init__(self : int , *_A : Optional[Any] , **_A : Optional[Any]) -> Option... | 353 | """simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def __UpperCAmelCase ( UpperCAmelCase_ : Iterable[str] , UpperCAmelCase_ : int ) -> Generator[tuple[str, ...], None, None]:
'''simple docstring'''
... | 95 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase_ (l... | 147 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderb... | 147 | 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.activations import gelu_new, gelu_python, get_activation
@require_torch
class lowerCamel... | 299 |
'''simple docstring'''
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
B... | 299 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbo... | 68 |
'''simple docstring'''
from collections.abc import Sequence
def lowercase_ ( lowerCAmelCase__ : Sequence[float] , lowerCAmelCase__ : float ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase__ ) )
def ... | 254 | 0 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCamelCase :Any = ['''note_seq''']
def __init__( self , *lowerCAmelCase_ , **lowerCAmelCase_ ) -> List[str]:
... | 364 | from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routing impor... | 81 | 0 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _SCREAMING_SNAKE_CASE ( lowercase : str , lowercase : str , lowercase : str , lowercase : Path... | 204 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_... | 204 | 1 |
"""simple docstring"""
class __UpperCamelCase ( _A ):
pass
class __UpperCamelCase ( _A ):
pass
class __UpperCamelCase :
def __init__(self : Tuple):
A = [
[],
[],
[],
]
def SCREAMING_SNAKE_CA... | 57 |
"""simple docstring"""
import numpy as np
import datasets
__A : Optional[int] = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\... | 57 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import requir... | 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 tra... | 95 | 0 |
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
if len(lowerCamelCase_ ) != len(lowerCamelCase_ ):
raise ValueError('String lengths must match!' )
_lowercase : Optional[Any] = 0
for chara, chara in zip(lowerCamelCase_ , lowerCam... | 365 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _lowerCamelCase( unittest.TestCase ):
def UpperCamelCase ( self) -> Tuple:
... | 84 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available... | 299 |
import math
import random
def A__ ( __lowerCamelCase, __lowerCamelCase = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
__UpperCAmelCase = 0.02
def A__ ( __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNA... | 299 | 1 |
'''simple docstring'''
import functools
from typing import Any
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ ) -> bool:
# Validation
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or len(__lowerCAmelCase ) == 0:
raise ValueError('''the string shou... | 353 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> Optional[Any]:
UpperCAmelCase__ : Optional[Any] = len(lowerCAmelCase__ )
for i in range(length - 1 ):
UpperCAmelCase__ : Optional[Any] = i
for k in range(i + 1 , low... | 299 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_ax... | 56 |
"""simple docstring"""
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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def _A ( ... | 81 | 0 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowercase = (
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
"""JH 8S TH ... | 369 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
log... | 226 | 0 |
"""simple docstring"""
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 OptionalDependencyNotAva... | 57 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 | 1 |
'''simple docstring'''
import socket
def UpperCAmelCase__ ( ) -> List[str]:
A_ = socket.socket(socket.AF_INET, socket.SOCK_STREAM )
A_ = socket.gethostname()
A_ = 1_23_12
sock.connect((host, port) )
sock.send(b"""Hello server!""" )
... | 101 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> 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__ == "__m... | 101 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-... | 199 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
d... | 84 | 0 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__A = logging.getLogger(__name__)
class snake_case ( a_ ):
def __init__( ... | 364 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available()... | 108 | 0 |
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