code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 281 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common impor... | 281 | 1 |
"""simple docstring"""
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 transform... | 719 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'''shi-labs/n... | 401 | 0 |
from __future__ import annotations
from typing import Any
class lowerCamelCase_ ( _lowercase ):
pass
class lowerCamelCase_ :
def __init__( self : Optional[int] , __A : Any ):
__A : Any = data
__A : Node | None = None
... | 17 |
"""simple docstring"""
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 i... | 573 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, Random... | 703 |
"""simple docstring"""
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"""):
A__ : str= {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""... | 20 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ = {
'''configuration_perceiver''': ['''PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Per... | 122 |
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
lowerCamelCase__ = logging.getLogger()
@unittest.skip('''Temporarily disable the doc tests.''' )... | 122 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.def... | 708 |
# 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: F401
deprecate(
... | 108 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ : Tuple = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Opti... | 98 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 15 | 0 |
'''simple docstring'''
def A_( A : int = 10):
if not isinstance(A , A) or n < 0:
raise ValueError('Invalid input')
UpperCamelCase = 10**n
UpperCamelCase = 2_8433 * (pow(2 , 783_0457 , A)) + 1
return str(number % modulus)
if __nam... | 432 |
'''simple docstring'''
import argparse
import copy
def A_( A : Optional[int]):
UpperCamelCase = {}
with open(A) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
UpperCamelCase = ... | 432 | 1 |
"""simple docstring"""
class lowerCAmelCase :
'''simple docstring'''
def __init__( self :str ) -> Optional[int]:
"""simple docstring"""
UpperCamelCase__ = {}
def lowerCamelCase__ ( sel... | 516 | """simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
A : int = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHead... | 516 | 1 |
'''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> list[int]:
'''simple docstring'''
_UpperCamelCase : str = int(UpperCAmelCase )
# Initialize Result
_UpperCamelCase : Optional[int] = []
... | 204 | '''simple docstring'''
import requests
from bsa import BeautifulSoup
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> str:
'''simple docstring'''
_UpperCamelCase : Dict = BeautifulSoup(requests.get(UpperCAmelCase ,params=UpperCAmelCase ... | 204 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __a ( _... | 554 |
"""simple docstring"""
from dataclasses import dataclass
from typing import 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 .modeling_utils import ModelMixin
from .vae im... | 554 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
fro... | 458 |
def _UpperCAmelCase ( UpperCAmelCase : str ):
"""simple docstring"""
__lowerCamelCase : List[Any] = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
__lowerCamelCas... | 458 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
_lowerCamelCase = get_tests_... | 114 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''xlm-mlm-en-2048''': '''https://huggingfa... | 154 | 0 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A : Optional[Any] = logging.getLogger(__name__)
if __name__ == "__main__":
__A : int... | 334 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__A : str = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if not is_torch_available():
rais... | 334 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import... | 93 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_lowerCAmelCase: int = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path... | 20 | 0 |
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_... | 701 |
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 OptionalDependencyNotAvailable()
exce... | 678 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 0 ) -> N... | 104 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase_, UpperCAmelCase_ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise Value... | 104 | 1 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class UpperCAmelCase__ ( tf.keras.optimizers.schedules.LearningRateSchedule... | 704 | import inspect
import unittest
class UpperCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self: Union[str, Any] ) -> Dict:
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
... | 286 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( __a , unitte... | 619 | '''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def A_ ( SCREAMING... | 451 | 0 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class UpperCAmelCase ( tf.keras.layers.Layer ):
d... | 346 |
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.import_uti... | 346 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 439 |
import re
from filelock import FileLock
try:
import nltk
a_ : Optional[Any] = True
except (ImportError, ModuleNotFoundError):
a_ : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def ... | 439 | 1 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
__lowerCAmelCase : int = pd.read_csv('sample_data.csv', header=None)
__lowerCAme... | 705 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_for... | 76 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class lowercase_ :
"""simple docstring"""
def __init__( self : Tuple, UpperCamelCase__ : list[tuple[float, float]] ) -> Any:
_A = list_of_poi... | 107 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : Tuple = logging.get_logger(__name__)
__magic_name__ : Optional[Any] = ... | 497 | 0 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeMod... | 713 |
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 SCREAMING_SNAKE_CASE ( snake_case_ : dict )... | 25 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCAmelCase ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : ... | 202 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : List[str] = {
"""configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}
try:
i... | 202 | 1 |
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verb... | 701 | import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowerCAmelCase__ = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowerCAmelCase__ = requests.get(url, header... | 594 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import... | 396 | '''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.mo... | 396 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedC... | 707 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __magic_name__ ( _lowerCamelCase : int , _lowerCamelCase : Optional[Any]=False ):
__a : Dict = Omeg... | 63 | 0 |
import torch
from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool
if is_datasets_available():
from datasets import load_dataset
class A_ ( lowerCAmelCase__ ):
'''simp... | 303 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : str = logging.get_logger(__name__)
lowercase__ : List[Any] = {
'''bigcode/gpt_bigcode-santacoder''': '''https://huggingface.co/bigcode/gpt_big... | 123 | 0 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help='''Pa... | 231 |
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 import AutoProcessor
from transformers... | 231 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 | 1 |
"""simple docstring"""
import cmath
import math
def lowercase_ ( _lowercase : float , _lowercase : float , _lowercase : float , _lowercase : float ):
'''simple docstring'''
UpperCAmelCase : Opt... | 719 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case__ ( lowerCAmelCase_ ):
... | 292 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_logger(__n... | 470 |
"""simple docstring"""
def A_ ( lowercase ) -> None:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = generate_pascal_triangle(lowercase )
for row_idx in range(lowercase ):
# Print left spaces
for _ in range(num_r... | 470 | 1 |
'''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/license... | 710 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(SCREAMING_SNAKE_CASE_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").t... | 181 | 0 |
import heapq
def lowerCAmelCase_ (lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_: str = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be fill... | 556 |
# 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
#
# Unles... | 518 | 0 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( _UpperCAmelCase : Optional[Any] ,_UpperCAmelCase : Tuple ,_UpperCAmelCase : List[str] ) -> Union[str... | 718 |
'''simple docstring'''
def A ( _UpperCAmelCase : int = 1_0 ,_UpperCAmelCase : int = 1_0_0_0 ,_UpperCAmelCase : bool = True ) -> int:
'''simple docstring'''
assert (
isinstance(_UpperCAmelCase ,_UpperCAmelCase )
and isinstance(_Upp... | 123 | 0 |
# Algorithm for the pigeonhole sorting
def UpperCamelCase_( _A :List[Any] )-> int:
UpperCamelCase__ = min(_A ) # min() finds the minimum value
UpperCamelCase__ = max(_A ) # max() finds the maximum value
UpperCamelCase__ = max_val - min_val + 1 # size is ... | 551 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
loggin... | 551 | 1 |
"""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 : O... | 595 | """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, torch_device
from t... | 595 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
"""facebook/data2vec-text... | 5 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
re... | 595 | 0 |
'''simple docstring'''
import logging
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,
... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
import requests
def a ( snake_case__: str , snake_case__: str ):
'''simple docstring'''
lowercase_ = {'''Content-Type''': '''application/json'''}
lowercase_ = requests.post(snake_case__ , json={'''text''': message_body} , headers=snake_case__ )
if ... | 97 |
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_bar... | 97 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
_lowerCamelCase : Any = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/s... | 157 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase (__lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase_ = ["image_processor", "tokenizer"]
UpperCA... | 157 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationSt... | 93 |
def UpperCamelCase ( snake_case__ : float ,snake_case__ : int ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(snake_case__ ) ,snake_case__ )
return number - int(snake_case__ )
if __name__ == "__main__":
... | 455 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase :int = logging.get_logger(__name__)
__lowerCamelCase ... | 42 |
"""simple docstring"""
import os
def snake_case ( ) -> Optional[Any]:
with open(os.path.dirname(UpperCamelCase__ ) + """/grid.txt""" ) as f:
lowerCamelCase : int = [] # noqa: E741
for _ in range(20 ):
l.append([int(UpperCame... | 42 | 1 |
"""simple docstring"""
def lowerCAmelCase_( ) -> list[list[int]]:
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
__SCREAMING_SNAKE_CASE : Union[str, Any] = generate_large_matrix()
__SCREAMING_SNAKE_CASE : Union[str, Any] = ... | 661 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastap... | 661 | 1 |
"""simple docstring"""
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.mo... | 442 |
"""simple docstring"""
import os
import numpy
import onnx
def lowerCAmelCase_ ( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[str] ):
"""simple docstring"""
__lowercase = a.name
__lowercase = b.name
__lowercase = """... | 442 | 1 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_lowercase = False
class lowerCamelCase__ ( unittest.TestCase ):
def ... | 306 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.uti... | 306 | 1 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_ = Lock()
def lowercase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ... | 713 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class _UpperCAmelCase ( SCREAMING_SNAKE_CASE_ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] ... | 183 | 0 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class _a ( _lowercase ):
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Dict ):
super().__ini... | 510 |
import math
class lowerCamelCase_ :
def __init__( self : Union[str, Any] , __A : List[str]=0 ): # a graph with Node 0,1,...,N-1
__A : List[str] = n
__A : List[str] = [
[math.inf for j in range(0 , __A )] for i in ran... | 17 | 0 |
__UpperCamelCase : Dict = [0, 2, 4, 6, 8]
__UpperCamelCase : Optional[Any] = [1, 3, 5, 7, 9]
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : list[int] , UpperCAmelCase : int ... | 705 |
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCamelCase ( A ):
'''simple docstring'''
a_ : List[Any] = ["image_processor", "tokenizer"]
a_ : Tuple = "AutoIm... | 458 | 0 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
assert (
isinstance(__A , __A ) and number_of_steps > 0
), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
if number_of_steps == 1:
r... | 94 |
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
from huggingfac... | 20 | 0 |
def _lowercase ( lowercase__ , lowercase__ ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowerCAmelCase : Tuple = str(bin(lowercase__ ) )[2:] # remove the leading "0b"
__lowerCAmelCase : List[Any] = ... | 583 |
from __future__ import annotations
def _lowercase ( lowercase__ ):
if len(lowercase__ ) == 0:
return array
__lowerCAmelCase, __lowerCAmelCase : List[str] = min(lowercase__ ), max(lowercase__ )
# Compute the variables
__lowerCAmelCase : ... | 583 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
UpperCamelCase__ = {
'gwf-440k': {
... | 322 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from data... | 322 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ =logging.get_logger(__name__)
lowerCAmelCase__ ={
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-em... | 720 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def _a ( UpperCAmelCase__ = "isbn/0140328726" ) -> dict:
__SCREAMING_SNAKE_CASE = olid.strip().strip('''/''' ) # Remove leading/trailing w... | 690 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
... | 248 | import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : List[Any] = OmegaConf.load(_lowercase )
SCREAMING_SNAKE_... | 248 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def A_ ( _lowerCAmelCase : int ):
"""si... | 721 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_t... | 11 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_devi... | 539 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
_lowercase = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def _snake_case ( ):
A = os.path.dirname(os.path.realpath(snake_case__ ) )
A = os.path.join(snake_case__ , 'words.txt' )
... | 91 | 0 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : Optional[int] = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCo... | 705 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING... | 155 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __A ... | 21 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCAmelCase_ : Optional[int] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
... | 692 | 0 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
... | 714 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase__ : int = {
"google/pix2struct-textcaps-base": (
... | 329 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#... | 7 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( snake_case_ :list , snake_case_ :int ):
# Checks if the entire collection has been sorted
if len(snake_case_ ) <= 1 or n <= 1:
return
insert_next(snake_case_ , n - 1 )
rec... | 49 | 0 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGEN... | 413 |
def lowerCamelCase_ ( A : int = 1_00 ):
"""simple docstring"""
lowerCAmelCase_ = (n * (n + 1) // 2) ** 2
lowerCAmelCase_ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 413 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConf... | 484 |
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
# TODO Update this
__A = {
'facebook/esm-1b': 'https://huggingface.co/facebook/esm-1b/resolve/main/conf... | 484 | 1 |
"""simple docstring"""
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def __magic_name__ ( *__snake_case : int ) -> int:
with open(__snake_case , "r" ) as fh:
fcntl.flock(__snake_case , fcntl.LOCK... | 708 |
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,... | 518 | 0 |
'''simple docstring'''
from PIL import Image
def __lowerCamelCase ( A__ , A__ ) -> int:
"""simple docstring"""
UpperCamelCase = (259 * (level + 255)) / (255 * (259 - level))
def contrast(A__ ) -> int:
ret... | 430 |
def _lowercase( __a : list[int] ):
a__ =len(__a )
for i in range(__a ):
for j in range(i + 1 , __a ):
if numbers[j] < numbers[i]:
a__ , a__ =numbers[j], numbers[i]
return numbers
... | 20 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCamelCase ( __A : Optional[Any] ... | 704 |
def _lowerCamelCase ( __A : int ) -> bool:
if not isinstance(__A , __A ):
_UpperCAmelCase : Tuple = f'''Input value of [number={number}] must be an integer'''
raise TypeError(__A )
if number < 0:
return False
_UpperCAme... | 186 | 0 |
def __lowerCAmelCase ( UpperCAmelCase__ : Any ) -> Union[str, Any]:
lowerCamelCase_ = 1
lowerCamelCase_ = 2
while i * i <= n:
lowerCamelCase_ = 0
while n % i == 0:
n //= i
... | 272 |
import numpy as np
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(snake_case__ )[0] == np.shape(snake_case__ )[1]
# Ensure proper... | 193 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class lowercase_ :
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] ... | 706 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _A ( A__ ):
"""simple docstring"""
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class lowercase_ (lowerCa... | 624 | 0 |
"""simple docstring"""
def _snake_case ( ):
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def _snake_case ( snake_case__ : List[Any] ):
A = 1
A = 2
while i * i <= n:
A = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity ... | 91 |
def UpperCamelCase__ ( lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ ,lowerCAmelCase__ ):
lowercase = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowerCAmelCase__ )
if number < 1:
lowercase = f"""Input va... | 428 | 0 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
A = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year... | 109 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTest... | 109 | 1 |
'''simple docstring'''
def a__ ( UpperCamelCase_ : int ):
assert column_title.isupper()
UpperCAmelCase__ :Union[str, Any] = 0
UpperCAmelCase__ :Optional[Any] = len(__UpperCamelCase ) - 1
UpperCAmelCase__ :Any = 0
while index >= 0:
... | 467 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {"vocab_file": "vocab.json", "merges... | 42 | 0 |
# Function to print upper half of diamond (pyramid)
def _UpperCAmelCase ( UpperCAmelCase : Optional[int] ):
"""simple docstring"""
for i in range(0 , UpperCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
... | 458 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Any = {
'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}
try:
if not is_torch_available... | 458 | 1 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = 50 ):
"""simple docstring"""
lowercase_ : int = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_... | 620 |
'''simple docstring'''
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 620 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 Ima... | 443 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def SCREAMING_SNAKE_CASE_ ( ) -> str:
"""simple docstring"""
a_ : str = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
a_ ... | 443 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedul... | 537 | import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_SCREAMING_SNAKE_CASE = """\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
title = {BLEU: a Method for Automatic... | 537 | 1 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def UpperCAmelCase_ ( *_A , _A = None , _A=True , _A=2 ):
'''simple docstring'''
from .. import __version__
SCREAMING_SNAKE_CASE__ ... | 707 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
... | 472 | 0 |
"""simple docstring"""
import qiskit
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase ) -> Tuple:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit ... | 77 |
'''simple docstring'''
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
... | 578 | 0 |
"""simple docstring"""
def _lowerCamelCase ( __a ):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''' )
for cell_n in range(1, len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
SCREAMING_SNAKE_CASE_ = gri... | 716 |
"""simple docstring"""
from __future__ import annotations
import typing
from collections import Counter
def _lowerCamelCase ( __a ):
SCREAMING_SNAKE_CASE_ = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__a, max_perimeter + 1 ):
S... | 628 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class SCREAMING_SNAKE_CASE__ :
def __init__( self , A_ )-> List[str]:
'''simple docstring'''
UpperCamelCase = []
... | 3 |
'''simple docstring'''
from __future__ import annotations
def A_ ( __SCREAMING_SNAKE_CASE : list , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ) -> list:
__SCREAMING_SNAKE_CASE : Optio... | 158 | 0 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__lowerCAmelCase : str = "naver-clova-ix/donut-base"
class A ( unittest.TestCase ):
def snake_case__ ( self : Tuple ) -> List[Any]:
... | 654 | '''simple docstring'''
import requests
from bsa import BeautifulSoup
def lowerCAmelCase ( UpperCamelCase__ : str = "AAPL" ):
"""simple docstring"""
__UpperCAmelCase = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
__UpperCAmelCase = Beautif... | 654 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/reso... | 67 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not ... | 67 | 1 |
"""simple docstring"""
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torc... | 538 | """simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_A = {'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}
try:
if not is_torch_available():
... | 538 | 1 |
from math import factorial
def _UpperCAmelCase ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : float ):
"""simple docstring"""
if successes > trials:
raise ValueError("""successes must be lower or equal t... | 519 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCamelCase : Any = {
"""repo_name""": ["""test_rep... | 519 | 1 |
from math import isqrt
def snake_case_ ( __lowercase ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(_lowerCamelCase ) + 1 ) )
def snake_case_ ( __lowercase = 1_0**6 ):
UpperCAmelCase_ : Dict = 0
UpperCAme... | 716 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCAmelCase__:
'''simple docstring'''
def __init__( self : List[str] , __snake_case : Union[str, Any] ):
'''simple docstring'''
... | 641 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowercase = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
author={Wang, Alex and Pru... | 203 |
import random
class _lowercase :
@staticmethod
def UpperCamelCase ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
"""simple docstring"""
A_ = [ord(lowerCamelCase__ ) for i in text]
A_ = []
A_ ... | 203 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a : Optional[int] = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
... | 522 | import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__lowercase = [
'''encoder.version''',
'''decoder.version''',
'... | 522 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__magic_name__ : Optional[Any] = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TableTransfo... | 672 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 205 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class SCREAMING_SNAKE_CASE ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase = 42
__UpperCamelCase = 42
def lowerCAmelCase_ ( __... | 703 |
'''simple docstring'''
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
... | 692 | 0 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowercase_ : Optional[Any] = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowercase_ : Optional[Any]... | 572 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, ... | 590 | 0 |
import copy
import random
from transformers import CLIPTokenizer
class lowerCAmelCase_ ( _UpperCAmelCase ):
def __init__( self ,*snake_case__ ,**snake_case__ ):
super().__init__(*A_ ,**A_ )
SCREAMING_SNAKE_CASE_ : Dict = {}
def s... | 705 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 1_00 * 2**20, 9_00 * 2**20] )
def __... | 685 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"configuration_autoformer": [
"AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Autoforme... | 517 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
SCREAMING_SNAKE_CASE_ = version.parse(ver... | 517 | 1 |
import re
def __UpperCAmelCase ( __A ) -> List[Any]:
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def __UpperCAmelCase ( __A ) -> List[Any]:
'''simple ... | 705 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A = logging.getLogger(__name__)
@dataclass
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
A__= field(
... | 277 | 0 |
"""simple docstring"""
from pathlib import Path
import fire
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->List[Any]:
"""simple docstring"""
lowerCAmelCase__ :Any = Path(_SCREAMING_SNAKE_CASE )
lowerCAmelCase__ :List[str... | 93 |
__lowerCamelCase : Optional[Any] = {
"""A""": """.-""", """B""": """-...""", """C""": """-.-.""", """D""": """-..""", """E""": """.""", """F""": """..-.""", """G""": """--.""",
"""H""": """....""", """I""": """..""", """J""": """.---""", """K""": """-.-""", """L""": """.-..""", """M""": ""... | 385 | 0 |
"""simple docstring"""
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_uti... | 374 |
"""simple docstring"""
def lowerCamelCase_ ( ) ->str:
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def lowerCamelCase_ ( UpperCAmelCase_ ) ->Union[str, Any]:
"""simple docstring... | 374 | 1 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNet... | 393 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.... | 393 | 1 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
UpperCA... | 367 |
from math import pow, sqrt
def lowerCAmelCase_ ( *lowerCamelCase ):
__magic_name__ : Tuple =len(lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return (
ro... | 367 | 1 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCamelCase__ = TypeVar('KEY')
UpperCamelCase__ = TypeVar('VAL')
@dataclass(frozen=_UpperCAmelCase , slots=_UpperCAmelCase )
class ... | 110 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at https://huggingface.co/... | 586 | 0 |
A_ : int = tuple[float, float, float]
A_ : Optional[int] = tuple[float, float, float]
def UpperCamelCase (lowercase_: Dict , lowercase_: Optional[int] ) -> Vectorad:
A__ : List[Any] = end_pointa[0] - end_pointa[0]
A__ : Optional[int] ... | 719 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 64 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _snake_case (__lowercase , __lowercase , __lowercase):
# Initialise PyTorch model
Upp... | 23 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = data
UpperCAmelCase__ : List[str] = None
def __repr__( self ):
retur... | 110 | 0 |
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.mod... | 706 | import re
def a_ (_lowerCAmelCase : str )-> list:
return [char.split() for char in re.split(R"""[^ a-z A-Z 0-9 \s]""" , str_ )]
def a_ (_lowerCAmelCase : str )-> str:
snake_case: Tuple = split_input(str_ )
return "".join(
... | 164 | 0 |
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