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 warnings
from ..trainer import Trainer
from ..utils import logging
_UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
class a ( a_ ):
def __init__( self , _lowerCamelCase=None , **_lowerCamelCase ):
war... | 220 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=__snake_case , defaul... | 220 | 1 |
"""simple docstring"""
def a__ ( snake_case__ ) -> List[Any]:
lowerCamelCase , lowerCamelCase = [], []
while len(snake_case__ ) > 1:
lowerCamelCase , lowerCamelCase = min(snake_case__ ), max(snake_case__ )
start.append(snake_case__ ... | 168 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 168 | 1 |
from collections.abc import Sequence
def __snake_case ( _lowerCAmelCase : Sequence[float] , _lowerCAmelCase : float ) -> float:
return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase ) )
def __snake_case ( _lowerCAmelCase : Sequence[float] , _l... | 300 |
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
class __magic_name__ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self :Unio... | 300 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_avail... | 325 |
from __future__ import annotations
import typing
from collections import Counter
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : typing.Counter[int] = Counter()
for base in range(1, max_perimeter + 1 ):
for perpendicular in range(__lower... | 325 | 1 |
"""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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from ... | 69 | """simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = '''T5Config'''
class UpperCamelCase ... | 69 | 1 |
import warnings
warnings.warn(
'''memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '''
'''`from accelerate import find_executable_batch_size` to avoid this warning.''',
FutureWarning,
)
| 173 |
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 __SCREAMING_SNAKE_CASE ( tf.keras.optimizers.schedules.LearningRateSchedule )... | 173 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Sessi... | 160 |
"""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, prepare_ima... | 160 | 1 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext... | 100 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 100 | 1 |
from __future__ import annotations
def UpperCamelCase ( __lowerCamelCase : list[int] , __lowerCamelCase : int ):
snake_case : list[list[int]] = []
snake_case : list[int] = []
snake_case : Optional[Any] = ... | 59 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta... | 59 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __lowerCAmelCase ( lowercase : list ) -> int:
"""simple docstring"""
if not postfix_notation:
return 0
snake_case : Union[str, Any] = {"+", "-", "*", "/"}
... | 112 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
fro... | 112 | 1 |
def _A ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return credit_card_number.startswith(("34", "35", "37", "4", "5", "6") )
def _A ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
a__ : Tuple... | 95 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mo... | 295 | 0 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__UpperCAmelCase : int = logging.get_logger(__name__)
class ... | 315 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 315 | 1 |
"""simple docstring"""
def _A ( lowercase = 1_00 ):
"""simple docstring"""
a =(n * (n + 1) // 2) ** 2
a =n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'{solution() = }') | 81 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPText... | 208 | 0 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRCo... | 28 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger('transformers.models.speecht5')
def __UpperCamelCase ( lowercase__ : Optional[Any] , lowe... | 28 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase : str = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""facebook/c... | 52 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def A_ ( ) -> List[Any]:
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.rais... | 52 | 1 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
A_ : Optional[int] = mo... | 368 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...... | 316 | 0 |
"""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 , lo... | 150 | """simple docstring"""
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 Padd... | 150 | 1 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB 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... | 132 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme impor... | 132 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = '''timm_backbone''... | 323 |
'''simple docstring'''
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 ):
"... | 323 | 1 |
"""simple docstring"""
def a_ ( _lowercase ):
_UpperCamelCase : Any = len(_lowercase )
_UpperCamelCase : Tuple = len(matrix[0] )
_UpperCamelCase : str = min(_lowercase , _lowercase )
fo... | 128 |
"""simple docstring"""
from __future__ import annotations
import math
def a_ ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ):
if depth < 0:
raise ValueError('''Depth cannot be less than 0''' )
... | 128 | 1 |
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 lowerCAmelCase ( ... | 50 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test... | 167 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _a ( _lowerCAmelCase ):
UpperCamelCase = ['''image_processor''', '''feature_extractor''']
UpperCamelCase = '''TvltImageProcessor'''
UpperCamelCase = '''TvltFe... | 128 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dens... | 128 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ : Union[str, Any] = {
'configuration_convbert': ['CONVBERT_PRETRAINED_... | 324 |
'''simple docstring'''
import math
def a__ ( lowercase : list, lowercase : int = 0, lowercase : int = 0 ) -> list:
"""simple docstring"""
_UpperCamelCase = end or len(lowercase )
for i in range(lowercase, lowercas... | 324 | 1 |
'''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
UpperCamelCase = logging.get_logger(__name__)
UpperC... | 364 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
... | 334 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""microsoft/swinv2-tiny-patch4-window8-256""": (
"""https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-... | 173 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
# See all V... | 173 | 1 |
'''simple docstring'''
import argparse
import os
from accelerate.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_... | 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 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
A__ = datasets.logging.get_logger(__name__)
A__ = '''\
@InProceedings{moosavi2019minimum,
author = { Naf... | 230 |
A__ = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__ = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A__ = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
6: '''Saturday''',
}
def _... | 230 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase : List[str] = logging.get_logger(__name__)
UpperCAmelCase : List[Any] = {
"BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/conf... | 358 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPanoramaPipeline,
UN... | 66 | 0 |
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.... | 112 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: list[int] , _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : str = int(_lowerCamelCase )
# Initialize Result
__SCREAMING_SNAKE_CASE : Tuple = []
# Traverse through all denomin... | 112 | 1 |
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__)
class _a ( A_ , A_ ):
_lowercase ... | 368 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers... | 93 | 0 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
__a :Tuple = False
class _a ( unittest.TestCase ):
... | 312 |
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
fro... | 312 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def __A ( a_ :int) -> list[int]:
if num <= 0:
__a : Dict = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(a_)
__a : Unio... | 355 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
... | 188 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def __lowerCamelCase ( A__ ) -> bool:
"""simple docstring"""
UpperCamelCase = int(number**0.5 )
return number == sq * sq
... | 28 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 28 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : list[list[int]] , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : set ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = le... | 364 | '''simple docstring'''
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datas... | 274 | 0 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepa... | 196 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main... | 196 | 1 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a_ = logging.... | 362 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingfac... | 222 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( ) -> int:
return 1
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def lowerCamelCase_ ( SCREAMING_SNAKE_CAS... | 125 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_a... | 125 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNA... | 319 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
snake_case ... | 319 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a :List[str] = logging.get_logger(__name__)
__a :int = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all GLPN models at http... | 312 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _a ( snake_case_ , snake_case_ ):
"""simple docstring"""
@register_t... | 312 | 1 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
... | 59 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.t... | 59 | 1 |
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 36 |
"""simple docstring"""
def lowercase (snake_case__ : list[int] , snake_case__ : list[int] ) -> tuple[float, float]:
'''simple docstring'''
if not len(snake_case__ ) == len(snake_case__ ) == 3:
raise ValueError("""Please e... | 155 | 0 |
def __snake_case ( __UpperCamelCase : int ,__UpperCamelCase : Any ):
"""simple docstring"""
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(__lowerCAmelCase ):
for j in range(__lowerCAmelCase ):
... | 353 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engi... | 329 | 0 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE = 4_000_000 ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase , UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_SCREAMING_SNAKE_CASE )
UpperCamelCase , Upper... | 153 |
"""simple docstring"""
class _lowerCamelCase :
def __init__(self , __a ) -> None:
UpperCamelCase = len(__a )
UpperCamelCase = [0] * len_array
if len_array > 0:
UpperCamelCase = array[0]
for i in rang... | 153 | 1 |
'''simple docstring'''
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def UpperCAmelCase_ ( ) -> Optional[Any]:
'''simple docstring'''
raise RuntimeErro... | 156 |
'''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_bert import BertTokenizer
__SCREAMING_SNAKE_CASE :List[str] = logging.g... | 156 | 1 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class lowerCamelCase_ :
... | 181 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ = {
'''configuration_re... | 181 | 1 |
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] =[4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowerCAmelCase : Union[str, Any] =[3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
__lowerCAmelCase : List[Any] ={
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wedn... | 123 |
'''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
fr... | 123 | 1 |
def snake_case_ ( snake_case = 1_00 ) -> Tuple:
lowercase__: int = set()
lowercase__: Tuple = 0
lowercase__: str = n + 1 # maximum limit
for a in range(2 , A__ ):
for b in range(2 , A__ ):
... | 196 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __snake_ca... | 120 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before token... | 363 |
from math import ceil, sqrt
def __lowercase ( __lowerCAmelCase : int = 1_0_0_0_0_0_0 ):
a__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a__ = max(ceil(sqrt(outer_width**2 ... | 109 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ : Union[s... | 70 |
'''simple docstring'''
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__lowercase : Dict = logging.get_logger(__name__)
__lowercase : str = ... | 318 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_mod... | 352 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import l... | 175 | 0 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_common import... | 106 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __lowercase ( ) ->int:
"""simple docstring"""
lowercase : Tuple = HfArgumentParser(_UpperCamelCase )
lowercase : List[str] = parser.parse_args_in... | 337 | 0 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _SCREAMING_SNAKE_CASE :
def __init__( self : Optional[Any] , __lowerCamelCase : List[A... | 351 |
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCAmelCase_ : Optional[Any] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 62 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a ( datasets.BeamBasedBuilder):
def UpperCAmelCase__( self ... | 131 |
def lowerCamelCase_ ( _a ):
"""simple docstring"""
lowerCAmelCase__ : Optional[Any] = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowerCamelC... | 131 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.... | 60 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniz... | 60 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase ( __A ):
'''simple docstr... | 76 |
'''simple docstring'''
from functools import lru_cache
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = 2
UpperCAmelCase : str = set()
while i * i <= n:
if n % i:
... | 311 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_nump... | 36 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAme... | 36 | 1 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e ... | 254 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : int = 50 ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_l... | 254 | 1 |
"""simple docstring"""
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_extractio... | 363 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def _snake_case ( _snake_case : Dict ):
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org... | 314 | 0 |
class UpperCAmelCase :
def __init__(self : Optional[int] , snake_case__ : list[int] ) -> None:
'''simple docstring'''
snake_case : str = len(snake_case__ )
snake_case : int = [0] * len_array
if ... | 59 |
from __future__ import annotations
__lowerCamelCase = list[list[int]]
# assigning initial values to the grid
__lowerCamelCase = [
[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, 0, 8, 0],
[9, 0, 0, 8, 6, ... | 59 | 1 |
"""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 import require_vision
from tr... | 166 |
"""simple docstring"""
def a__ ( lowerCAmelCase ) -> bool:
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : Tuple = F"""Input value of [number={number}] must be an integer"""
raise TypeError(lowerCAmelCase )
if number < 0:
... | 166 | 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/licenses/LICENSE-2.0
... | 75 |
'''simple docstring'''
__lowerCAmelCase = [
(1_000, 'M'),
(900, 'CM'),
(500, 'D'),
(400, 'CD'),
(100, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def __SCREAMING_SNAKE_CASE ( _SCREA... | 341 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optio... | 351 |
def __lowercase ( _UpperCamelCase, _UpperCamelCase ) ->list[int]:
"""simple docstring"""
lowercase : Dict = int(_UpperCamelCase )
# Initialize Result
lowercase : Union[str, Any] = []
# Traverse through all denomi... | 173 | 0 |
import string
def __magic_name__ ( A : str ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
a = ""
for symbol in message:
if symbol in string.ascii_uppercase:
a = string.ascii_uppercase.find(A )
... | 107 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : str = {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}
class snake_case__ (_Up... | 107 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
fr... | 123 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : int ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
A__ = F"Input value of [number={number}] must be an integer"
raise TypeError(_lowerCamelCase )
if number < 1:
A__ = F"I... | 123 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
... | 67 | '''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase =log... | 67 | 1 |
from typing import Any
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ ):
A__ = data
A__ = None
def __repr__( self ):
return F"""Node({self.data})"""
class UpperCamelCase :
def __init__( self ):
... | 356 |
from __future__ import annotations
from random import random
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ = None ):
A__ = value
A__ = random()
A__ = None
A__ = None
def __repr__( self )... | 198 | 0 |
"""simple docstring"""
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def A ( snake_case :List[Any] ) -> Tuple:
__UpperCamelCase = [
'decoder.version',
'decoder.output_project... | 316 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
UpperCamelCase : List[str] = TypeVar("KEY")
UpperCamelCase : List[str] = TypeVar("VAL")
@dataclass(frozen=__SCREAMING_SNAKE_CASE , slots=__SCREAMING_SNAKE_CA... | 316 | 1 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configurat... | 37 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def __UpperCamelCase ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = grid.shape
__UpperCAmelCase : List[... | 37 | 1 |
'''simple docstring'''
def snake_case_ (_a : Tuple ):
UpperCAmelCase = [0] * len(_a )
UpperCAmelCase = []
UpperCAmelCase = [1] * len(_a )
for values in graph.values():
for i in values:
indegree[i] += 1
for ... | 34 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _a , _a):
# Load checkpoint
SCREAMING_SNAKE_CA... | 76 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_to... | 82 |
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
f... | 82 | 1 |
"""simple docstring"""
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__":
A: Optional[Any] = pd.read_csv("sample_data.csv", header=None)
... | 109 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfi... | 62 | 0 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
f... | 354 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def UpperCamelCase ( _A, _A ):
"""simple docstring"""
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip(_A, _A ) ) )
def UpperCamelCase ( _A, _A ):
... | 138 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : list[int] ):
if not nums: # Makes sure that the list is not empty
raise ValueError('''List is empty''' )
lowerCAmelCase : Tuple = sum(_snake_case ) / len(_snake_case ) # Calculate the average
return sum(abs(... | 60 |
"""simple docstring"""
import numpy as np
def _snake_case ( _snake_case : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 60 | 1 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
a_ = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (t... | 163 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ = "."
# Intern... | 163 | 1 |
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 36 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec... | 36 | 1 |
def A (__A : str , __A : str ) -> Optional[Any]:
"""simple docstring"""
assert x is not None
assert y is not None
UpperCAmelCase_ = len(__A )
UpperCAmelCase_ = len(__A )
# decl... | 7 |
from maths.prime_factors import prime_factors
def A (__A : int ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
UpperCAmelCase_ = F"""Input value of [number={number}] must be an integer"""
... | 7 | 1 |
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Tuple = len(__lowerCAmelCase)
print("The following activities are selected:")
# The first activity is always selected
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
print(__lowerCAmelCase , end=",")
#... | 76 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class lowerCAmelCase__ :
def __init__( self : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
... | 234 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _a ( lowerCamelCase_ ):
SCREAMING_SNAKE_CASE_ : List[Any] = ['''image_processor''', '''feature_extractor''']
SCREAMING_SNAKE_CASE_ : Any = '''TvltImageProcessor'''
... | 371 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : str = logging.get_logger(__name__)
UpperCamelCase_ : Optional[Any] = {
'''asapp/sew-tiny-100k''': '''http... | 142 | 0 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def a__ ( snake_case__ ) -> Optional[Any]:
lowerCamelCase = [
"""encoder.version""",
"""decoder.version""",
... | 291 |
"""simple docstring"""
from math import ceil
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = list(range(0 , snake_case__ ) )
lowerCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]... | 291 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from... | 231 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : Any = {
"""configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}
try:
if not is_torch_avai... | 231 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_im... | 185 |
'''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_retribert import RetriBertTokenizer
a_ : str = ... | 168 | 0 |
"""simple docstring"""
import re
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = s... | 357 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
A : Tuple = logging.get_logger(__name__)
A : ... | 259 | 0 |
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
UpperCamelCase__ = logging.getLogger(__name__)
@dataclass
class a_... | 92 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : List[str] ):
__lowerCAmelCase = 0
if start < end:... | 92 | 1 |
def lowerCAmelCase_ ( _lowercase : int , _lowercase : int) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 266 |
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
f... | 266 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__lowerCAmelCase : Optional[int] = 4
__lowerCAmelCase : Optional[Any] = ... | 88 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[int] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : Optional[Any] = 0
while number > 0:
lowercase_... | 198 | 0 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *A , **A ) -> List[str]:
... | 176 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import ... | 176 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gp... | 37 |
'''simple docstring'''
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_ident... | 37 | 1 |
def a__ ( UpperCAmelCase : str ) -> List[str]:
stooge(UpperCAmelCase , 0 , len(UpperCAmelCase ) - 1 )
return arr
def a__ ( UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : Unio... | 368 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 99 | 0 |
import unittest
from transformers import DonutProcessor
__UpperCAmelCase : Union[str, Any] = "naver-clova-ix/donut-base"
class __snake_case ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase__ ( self : List[str]... | 111 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 111 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _UpperCamelCase ( snake_case__ = "laptop" ) -> DataFrame:
__UpperCAmelCase : Tuple = f'''https://www.amazon.in/laptop/s?k={product}'''
... | 342 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class _snake_case ... | 342 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_g... | 72 |
"""simple docstring"""
def snake_case_ ( A_ : list[int], A_ : str ):
'''simple docstring'''
_lowerCamelCase : Tuple = int(A_ )
# Initialize Result
_lowerCamelCase : Dict = []
# Traverse through all deno... | 72 | 1 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( A__ ,A__ = None ):
UpperCAmelCase_ : Optional[int] = word_bank or []
# create a table
UpperCAmelCase_ : int = len(A__ ) + 1
UpperCAmelCase_ : list[list[list[st... | 253 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 253 | 1 |
'''simple docstring'''
__A ='0.21.0'
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip... | 163 |
'''simple docstring'''
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
clas... | 163 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase ( _UpperCAmelCase ):
@staticmethod
@abstractmethod
def __A ( UpperCAmelCase__ ):
raise NotImplementedError()
@abstractmethod
def __A ( self ):
... | 198 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import loggin... | 198 | 1 |
def _snake_case( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str ) -> int:
'''simple docstring'''
assert x is not None
assert y is not None
A__ = len(SCREAMING_SNAKE_CASE__ )
A__ = len(SCREAMING... | 7 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def _snake_case( SCREAMING_SNAKE_CASE__ : Any ) -> ... | 7 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
... | 359 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_ava... | 322 | 0 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __lowerCAmelCase ( UpperCamelCase__):
@require_torch
def _lowercase (... | 95 |
def _A ( SCREAMING_SNAKE_CASE : int = 50 ):
"""simple docstring"""
a__ : Any =[1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_st... | 95 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE__ = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE__ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
... | 359 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def lowercase__ ( __UpperCamelCase )-> str:
return "".join(sorted(__UpperCamelCase ) )
def lowercase__ ( __UpperCamelCase... | 183 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
... | 231 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _lowerCAmelCase ( __a , unittest.TestCase ):
_lo... | 231 | 1 |
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test... | 191 |
import qiskit
def _a ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> qiskit.result.counts.Counts:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = qiskit.Aer.get_backend("aer_simulator" ... | 191 | 1 |
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