code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : Union[str, Any] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if n... | 20 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__UpperCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .s... | 29 | 0 |
from __future__ import annotations
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance < 0:
rais... | 21 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 0 |
'''simple docstring'''
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_inf... | 22 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 29 | 0 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuratio... | 23 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 29 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)... | 24 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 | 0 |
"""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
UpperCAmelCase__ : List[str... | 25 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self ... | 29 | 0 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : int = len(snake_case_ )
_A : int = len(snake_case_ )
_A : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
_A... | 26 |
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:... | 29 | 0 |
'''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
__lowercase : Any =... | 27 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDepen... | 29 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
_lowerCamelCase : Tuple = {
"configuration_trocr": ["TROCR_PR... | 28 |
__UpperCAmelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'd... | 29 | 0 |
def a ( snake_case__: int ):
'''simple docstring'''
lowercase_ = [0] * len(snake_case__ )
lowercase_ = []
lowercase_ = [1] * len(snake_case__ )
for values in graph.values():
for i in values:
indegree[i] +... | 30 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 29 | 0 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = """Alexander Joslin"""
import operator as op
from .stack import Stack
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> int:
"""simple docstring"""
_UpperCAmelCase : Tuple = {"... | 31 |
def lowercase__ ( __snake_case : int , __snake_case : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase_ : Tuple = str(bin... | 29 | 0 |
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 ...test_configur... | 32 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)... | 29 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
'''configuration_lxmert''': ['''LXMERT_PRETR... | 33 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/ef... | 29 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 34 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDis... | 29 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"bigcode/gpt_bigcode-santacoder": "https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json",
}
class Up... | 35 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params im... | 29 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from... | 36 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerCo... | 29 | 0 |
'''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 Session, TrainingJobAnalytics
from... | 37 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as o... | 29 | 0 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
UpperCAmelCase_ : Tuple = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(... | 38 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ... | 29 | 0 |
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_attention_mask
... | 39 |
from __future__ import annotations
def lowercase__ ( __snake_case : tuple[int, int] , __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = position
UpperCAmelCas... | 29 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transforme... | 40 |
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : list[list[int]] = [[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase_ : Optional[A... | 29 | 0 |
'''simple docstring'''
_A : List[str] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def SCREAMING_SNAKE_CASE_ (UpperCamelCase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinsta... | 41 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..model... | 29 | 0 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
lowercase : List[str] = [
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wip",
]
def ... | 42 |
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = os.path.dirname(os.pa... | 29 | 0 |
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.test_... | 43 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__UpperCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .s... | 29 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def... | 44 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 0 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : int ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 45 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 29 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import c... | 46 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 29 | 0 |
'''simple docstring'''
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class A__ ( A__ ):
def __init__( self : List[str] , _a : Optional[Any]="" , _a : Any="train" ) -> int:
'''simple docstring... | 47 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Tuple = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
rai... | 48 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self ... | 29 | 0 |
from __future__ import annotations
from collections import Counter
from random import random
class _A :
def __init__( self : Optional[Any]):
'''simple docstring'''
__a = {}
def _lowerCamelCase ( self : List[str] , ... | 49 |
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:... | 29 | 0 |
# 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 app... | 50 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDepen... | 29 | 0 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
snake_case_ : Optional[int] = datasets.logging.get_logger(__name__)
snake_case_ : Tuple = "\\n@inproceedings{bleurt,\n title={BLEURT: Learning Robust Metrics for... | 51 |
__UpperCAmelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'd... | 29 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( __snake_case ):
_UpperCAmelCase :List[Any] = (DDIMParallelScheduler,)
_UpperCAmelCase :Any = (('eta', 0.0), ('num_inference_steps', 5_0))
... | 52 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 29 | 0 |
'''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_identifi... | 53 |
def lowercase__ ( __snake_case : int , __snake_case : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase_ : Tuple = str(bin... | 29 | 0 |
"""simple docstring"""
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__S... | 54 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)... | 29 | 0 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
# TODO Update this
a_ : List[str] = {
"""facebook/esm-1b""":... | 55 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/ef... | 29 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[Any] = logging.get_logger(__name__)
a : str = {
'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/config.json',
'funnel-... | 56 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDis... | 29 | 0 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from .... | 57 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params im... | 29 | 0 |
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
... | 58 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerCo... | 29 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""xlm-roberta-base""": """http... | 59 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as o... | 29 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _snake_case ( _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snak... | 60 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ... | 29 | 0 |
"""simple docstring"""
import os
_a = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def __a ( __lowerCamelCase ):
UpperCAmelCase_ : Union[str, Any] = 0
UpperCAmelCase_ : List[str] = 0
while index < len(__lowerCamelCase... | 61 |
from __future__ import annotations
def lowercase__ ( __snake_case : tuple[int, int] , __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = position
UpperCAmelCas... | 29 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'sail/poolformer_s12': 'https://hugg... | 62 |
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : list[list[int]] = [[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase_ : Optional[A... | 29 | 0 |
'''simple docstring'''
import math
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = sum(i * i for i in range(1 , n + 1 ) )
_a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of... | 63 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..model... | 29 | 0 |
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational impo... | 64 |
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = os.path.dirname(os.pa... | 29 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax impor... | 65 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__UpperCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .s... | 29 | 0 |
"""simple docstring"""
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :str... | 66 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> list:
__lowerCamelCase = len(UpperCamelCase__ )
__lowerCamelCase = []
for i in range(len(UpperCamelCase__ ) - pat_len + 1 ):
__lowerCamelCase = True
for j in range(UpperC... | 67 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 29 | 0 |
lowerCAmelCase__ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_: List[Any] , SCREAMING_SNAKE_CASE_: Union[str, Any]... | 68 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 29 | 0 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
A... | 69 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 | 0 |
'''simple docstring'''
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
A__ : Optional[int] =[
os.path.join(os.path.dirname(__file__), dirname)
... | 70 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self ... | 29 | 0 |
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 (
... | 71 |
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:... | 29 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def snake_case_ ( A_ : float, A_ : int ):
'''simple docstring'''
_lowerCamelCase : Tuple = u
for i in range(1, A_ ):
_lowerCamelCase ... | 72 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDepen... | 29 | 0 |
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
a =logging.get_logger(__name__)
a ={"""vocab_file""": """vocab.txt""", "... | 73 |
__UpperCAmelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'd... | 29 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_t... | 74 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 29 | 0 |
'''simple docstring'''
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig... | 75 |
def lowercase__ ( __snake_case : int , __snake_case : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase_ : Tuple = str(bin... | 29 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
a_ = datasets.utils.logging.get_logger(__name__)
class _UpperCamelCase ( folder_based_builder.FolderBasedBuilderConfig ):
'''simple docstri... | 76 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)... | 29 | 0 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequence... | 77 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/ef... | 29 | 0 |
"""simple docstring"""
snake_case_ = {
0: """0""",
1: """1""",
2: """2""",
3: """3""",
4: """4""",
5: """5""",
6: """6""",
7: """7""",
8: """8""",
9: """9""",
10: """a""",
11: """b""",
12: """c""",
13: """d""",
14: """... | 78 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDis... | 29 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
... | 79 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params im... | 29 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import... | 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerCo... | 29 | 0 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCa... | 81 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as o... | 29 | 0 |
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
_lowerCAmelCase = """"""
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 _UpperCAmelCase ( snake_case ):
... | 82 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ... | 29 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import Counter
from random import random
class lowercase__ :
def __init__( self : Optional[Any] ):
'''simple docstring'''
_UpperCamelCase : str = {}
def UpperCamelCase_ ( sel... | 83 |
from __future__ import annotations
def lowercase__ ( __snake_case : tuple[int, int] , __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = position
UpperCAmelCas... | 29 | 0 |
"""simple docstring"""
__UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _snake_case ( lowercase__ : dict[int, list[int]] , lowercase__ : int , lowerca... | 84 |
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : list[list[int]] = [[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase_ : Optional[A... | 29 | 0 |
'''simple docstring'''
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
snake_case_ = len(snake_case )
while cur > 1:
# Find the maximum number in arr
snake_case_ = arr.index(max(arr[0:cur] ... | 85 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..model... | 29 | 0 |
"""simple docstring"""
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
lowerCamelCase__ = {
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """a... | 86 |
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = os.path.dirname(os.pa... | 29 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowercase_ ( _lowerCamelCase : List[str]):
if not is_accelerate_available():
return method
lowercase__ : Optional[int] = ... | 87 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__UpperCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .s... | 29 | 0 |
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
__lowerCAmelCase : str = 637_8137.0
__lowerCAmelCase : Optional[Any] = 635_6752.31_4245
__lowerCAmelCase : List[str] = 6378137
def a__ ( A_, A_... | 88 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 0 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
fro... | 89 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}
try:... | 90 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 29 | 0 |
"""simple docstring"""
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
UpperCAmelCase_ : Optional[Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase_ : List[str] ... | 91 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class a__ :
def __init__( self , _A ):
"""simple docstring"""
__lowerCAmelCase = list_of_points
# Degree determines the flexibility of the curve.
... | 92 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self ... | 29 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowercase : str = logging.get_logger(__name__)
_lowercase : str = {
... | 93 |
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:... | 29 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
snake_case : Optional[Any] = {
'''sample_size''': 32,
'''in_channels''': 3,
'''out_channels''': 3,
'''layers_per_block''': 2,
... | 94 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDepen... | 29 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int = 1_000_000 ):
"""simple docstring"""
a__ : Optional[int] =[i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , ... | 95 |
__UpperCAmelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'd... | 29 | 0 |
"""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
lowercase__ =... | 96 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 29 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
__snake_case = '''\
'''
__snake_case = '''
Perplexity (PPL) is one of the most co... | 97 |
def lowercase__ ( __snake_case : int , __snake_case : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase_ : Tuple = str(bin... | 29 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_... | 98 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)... | 29 | 0 |
import logging
import os
from .state import PartialState
class A__ ( logging.LoggerAdapter ):
"""simple docstring"""
@staticmethod
def __lowercase ( lowercase) -> Any:
'''simple docstring'''
a__ : int = PartialState()
... | 99 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'snap-research/efficientformer-l1-300': (
'https://huggingface.co/snap-research/ef... | 29 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import ... | 100 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDis... | 29 | 0 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQu... | 101 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params im... | 29 | 0 |
"""simple docstring"""
def lowercase ( _snake_case : list[list[int]] , _snake_case : int , _snake_case : int , _snake_case : set ) ->int:
"""simple docstring"""
__snake_case , __snake_case : Optional[Any] = len(_s... | 102 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'configuration_time_series_transformer': [
'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TimeSeriesTransformerCo... | 29 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils i... | 103 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as o... | 29 | 0 |
'''simple docstring'''
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def _A ( *A__ , A__ = None , A__=True , A__=2 ):
"""simple docstring"""
from .. import __version__
__lowercase = take_from
... | 104 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def lowercase__ ( ... | 29 | 0 |
"""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` in... | 105 |
from __future__ import annotations
def lowercase__ ( __snake_case : tuple[int, int] , __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = position
UpperCAmelCas... | 29 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from tran... | 106 |
def lowercase__ ( __snake_case : int ):
'''simple docstring'''
UpperCAmelCase_ : list[list[int]] = [[0 for _ in range(__snake_case )] for _ in range(m + 1 )]
for i in range(m + 1 ):
UpperCAmelCase_ : Optional[A... | 29 | 0 |
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def __magic_name__ ( A : NDArray[floataa], A : NDArray[floataa], A : list[int], A : int, ):
'''simple docstring'''
a , a = coefficient_matr... | 107 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..model... | 29 | 0 |
"""simple docstring"""
lowerCAmelCase__ = range(2, 20 + 1)
lowerCAmelCase__ = [10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__ = {}
def a__ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : ... | 108 |
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCAmelCase = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def lowercase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = os.path.dirname(os.pa... | 29 | 0 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
UpperCAmelCase : Any = [True] * (num + 1)
UpperCAmelCase : Any = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , ... | 109 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__UpperCAmelCase = importlib.util.find_spec('s3fs') is not None
if _has_safs:
from .s... | 29 | 0 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
lowercase__ = _modexpt(SCREAMING_SNAKE_CASE , exponent // 2 , SCREAMI... | 110 |
def lowercase__ ( __snake_case : list ):
'''simple docstring'''
for i in range(len(__snake_case ) - 1 , 0 , -1 ):
UpperCAmelCase_ : Dict = False
for j in range(__snake_case , 0 ,... | 29 | 0 |
'''simple docstring'''
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_SCREAMI... | 85 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 29 | 0 |
'''simple docstring'''
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class A__ ( _snake_case ):
"""simple docstring"""
def _lowerCAmelCase ( self : Optional[Any] , low... | 145 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 29 | 0 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__s... | 203 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess... | 29 | 0 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | 90 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class lowerCamelCase (_snake_case ):
'''simple docstring'''
def __init__( self ... | 29 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : Dict ) -> Tuple:
'''simple docstring'''
if not head:
return True
# split the list to two parts
lowerCAmelCase = head.next, head
while fast and fast.next:
lowerCAmelC... | 155 |
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
if not head:
return True
# split the list to two parts
UpperCAmelCase_ , UpperCAmelCase_ : Any = head.next, head
while fast and fast.next:... | 29 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingfa... | 74 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDepen... | 29 | 0 |
# Lint as: python3
import itertools
import os
import re
_A : Optional[Any] = re.compile(r'([A-Z]+)([A-Z][a-z])')
_A : str = re.compile(r'([a-z\d])([A-Z])')
_A : Optional[Any] = re.compile(r'(?<!_)_(?!_)')
_A : str = re.compile(r'(_{2,})')
_A : str = r'^\w+(\.\... | 142 |
__UpperCAmelCase = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'd... | 29 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
UpperCAmelCase__ = {
'''configuration_speecht5''': [
'''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SPEECHT5_PRETRAI... | 5 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProce... | 29 | 0 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = 9
lowerCamelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, ... | 252 |
def lowercase__ ( __snake_case : int , __snake_case : int ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCAmelCase_ : Tuple = str(bin... | 29 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : Namespace ) -> List[str]:
return ConvertCommand(
args.model_type ,args.t... | 276 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCAmelCase = logging.get_logger(__name__)... | 29 | 0 |
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