code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'BAAI/AltCLIP': 'https://huggingface.co/BAAI/AltCLIP/resolve/mai... | 76 |
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 UpperCamelCase_( ) -> Any:
_lowercase... | 89 | 0 |
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@datacl... | 311 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowerCAmelCase__: Dict = {
"configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}
try:
if no... | 311 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
UpperCAmelCase =[8, 5, 9, 7]
UpperCAmelCase =[
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
UpperCAmelCase =[
[3, 2, ... | 617 |
"""simple docstring"""
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_... | 617 | 1 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class UpperCAmelCase__ :
def __init__( self ,A__ ,A__ ,A__ ):
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
_A : Uni... | 718 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase : Any ={'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', '... | 332 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is... | 610 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 610 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 106 |
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer... | 106 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils i... | 379 |
import requests
from bsa import BeautifulSoup
def _lowerCamelCase ( SCREAMING_SNAKE_CASE_ : str = "AAPL" ):
"""simple docstring"""
a_ : Optional[Any] = F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
a_ : Optional[int] = ... | 419 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthC... | 712 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {'''processing_layoutxlm''': ['''LayoutXLMProcessor'... | 73 | 0 |
def __lowerCamelCase (UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any] ):
SCREAMING_SNAKE_CASE = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
SCREAMING_SNAKE_CASE = n - k
# Calculate C(n,k)
... | 403 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __A :
UpperCamelCase = 42
UpperCamelCase ... | 21 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimensi... | 718 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import loggin... | 198 | 0 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallba... | 9 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the re... | 291 | 0 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
SCREAMING_SNAKE_CASE_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5,... | 714 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 579 | 0 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ , unittest.TestCase ):
__lowerCamelCase : Tuple ... | 225 |
import random
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
__a = num - 1
__a = 0
while s % 2 == 0:
__a = s // 2
t += 1
for _ in range(5 ):
__a = random.randrange(2 , num - 1 )
... | 225 | 1 |
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 lowerCamelCase__ ( __lowercase , __lowercase):
'''simple docstring'''
@re... | 94 |
import re
import string
import numpy as np
import datasets
lowercase : List[str] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
lowercase : List[str] = '\... | 94 | 1 |
def a ( A__ : Optional[int] ) -> List[str]:
"""simple docstring"""
if isinstance(_A , _A ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(_A , _A ):
raise TypeError('\'str\' object cannot be in... | 291 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __UpperCamelCase ( ):
lowerCAmelCase_ = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ... | 431 | 0 |
'''simple docstring'''
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __snake_case ( SCREAMING_SNAKE_CASE_ : Optional[int] , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple = "x" , SCREAMING_SNAKE_CASE_ : Optional[Any] = 10**-10 , SCREAMIN... | 719 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
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_backbone_common import Backbone... | 570 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {'vocab_file': 'spi... | 147 |
import sys
import turtle
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def a ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , ):
... | 183 | 0 |
"""simple docstring"""
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.versio... | 718 | """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_comm... | 93 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 133 |
'''simple docstring'''
def a_ ( lowerCamelCase : float , lowerCamelCase : float ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_... | 133 | 1 |
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def UpperCamelCase__ ( __magic_... | 716 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transforme... | 419 | 0 |
def lowerCamelCase__ ( lowercase = 50 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - ... | 62 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_... | 62 | 1 |
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available... | 180 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDepend... | 180 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_... | 673 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'''facebook/wav2vec2-base-960h''': '''https:... | 673 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__a = 5_0_0_0_0
__a = 5_0_0_0
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", ""... | 719 |
from __future__ import annotations
import math
def UpperCamelCase_ ( a_ , a_ ) ->float:
A =u
for i in range(1 , a_ ):
A =temp * (u - i)
return temp
def UpperCamelCase_ ( ) ->None:
A =int(input("enter the numbers of values: " ) )
A =[]
for _ in ... | 689 | 0 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
... | 29 |
"""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
from t... | 29 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
UpperCAmelCase = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP}... | 706 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
Upp... | 565 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr... | 21 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since t... | 562 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 537 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCamelCase( UpperCamelCase__ : int ) -> list[int]:
if num <= 0:
A : str = F'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(UpperCamelCase__ ... | 537 | 1 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floa... | 270 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 270 | 1 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Any:
"""simple docstring"""
lowerCAmelCase__ ... | 711 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class _lowerCAmelCase ( a ):
"""simple docstring"""
__magic_name__ :List[str] = """SpeechT5FeatureExtractor"""
__magic_name__ :List[Any] = """SpeechT5Tokenizer"""
... | 560 | 0 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_lowercase : Tuple ={"""UserAgent""": UserAgent().random}
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ):
lowerCamelCase_ : Union[str... | 364 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from... | 610 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def UpperCamelCase ( ):
'''simple docstring'''
__snake_case :List[str] = A... | 717 |
from __future__ import annotations
import time
import numpy as np
lowerCamelCase__ = [8, 5, 9, 7]
lowerCamelCase__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase__ = [
[3, 2, 1, 4],
[0,... | 291 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, t... | 118 |
"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
_lowercase = get_logger(__name__)
class __a ( enum.Enum ):
'''simple docstring'''
... | 118 | 1 |
# 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_... | 336 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord impor... | 336 | 1 |
'''simple docstring'''
import collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutpu... | 350 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from... | 350 | 1 |
"""simple docstring"""
def lowercase ( __UpperCamelCase ) -> bool:
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise ValueError('''Input series is not valid, valid series - [2, 4, 6]''' )
if len(__UpperCamelCase ) == 0:
raise ValueError('''Input list mu... | 190 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def lowercase ( __UpperCamelCase ) -> Any:
return choice(__UpperCamelCase )
def lowercase ( __UpperCamelCase , __UpperCamelCase ) -> int:
__magic_name__ = ran... | 190 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import Back... | 42 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {'vocab_file': 'sentencepi... | 61 | 0 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprec... | 547 |
from __future__ import annotations
lowerCamelCase__ = """Muhammad Umer Farooq"""
lowerCamelCase__ = """MIT"""
lowerCamelCase__ = """1.0.0"""
lowerCamelCase__ = """Muhammad Umer Farooq"""
lowerCamelCase__ = """contact@muhammadumerfarooq.me"""
lowerCamelCase__ = """Alpha"""
import re
from html.parser im... | 547 | 1 |
from __future__ import annotations
_lowerCAmelCase = """#"""
class _UpperCAmelCase :
def __init__( self ):
A_ : dict = {}
def _lowerCamelCase ( self , a__ ):
A_ : List[Any] = self._trie
for char in t... | 569 |
from ...configuration_utils import PretrainedConfig
class _UpperCAmelCase ( _lowerCamelCase ):
a = '''bert-generation'''
def __init__( self , a__=50358 , a__=1024 , a__=24 , a__=16 , a__=4096 , a__="gelu" , a__=0.1 , ... | 569 | 1 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCamelCase = TypeVar('''T''')
__lowerCamelCase = Union[List[T], Tuple[T, ...]]
__lowerCamelCase = Union[T, List[T], Dict[str, T]]
__lowerCamelCase = Union[str, bytes, os.PathLike]
| 708 |
import operator
def _snake_case ( __snake_case , __snake_case = False , __snake_case = None ) -> list:
'''simple docstring'''
UpperCAmelCase_ : Optional[int] = operator.lt if reverse else operator.gt
UpperCAmelCase_ : int = so... | 455 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
__lowercase : List[str] = True
except (ImportError, ModuleNotFoundError):
__lowercase : Dict = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt"""... | 142 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokeni... | 142 | 1 |
'''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 TFModelTester... | 644 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LIC... | 644 | 1 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _lowerCAmelCase ( __magic_name__ : str = "https://www.worldometers.info/coronavirus" ) -> dict:
lowercase : List[Any] =BeautifulSoup(requests.get(__magic_name__ ).text , '''html.parser... | 92 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
# TODO Update this
UpperCamelCase_ = {
"""facebook/esm... | 92 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig... | 215 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowerCAmelCase ( __UpperCamelCase ):
""... | 215 | 1 |
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 transformers.testing_utils import... | 74 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=_snake_case ):
UpperCAmelCase = ["speech"]
def __init__( self : List[Any] , *__lowerCamelCase : List[Any] , **__lowerCamelCase : List[str]... | 467 | 0 |
from __future__ import annotations
def UpperCamelCase__ ( A__ ) -> List[Any]:
snake_case__ : List[Any] = 0.0_0
snake_case__ : str = 0
for resistor in resistors:
if resistor <= 0:
snake_case__ : Union[str, Any] ... | 720 | 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,
)
lowerCAmelCase__ : Any = {'''configuration_xglm''': [''... | 699 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
... | 38 | def lowerCAmelCase__ ( a__ , a__ , a__ , a__ , a__ ) ->int:
'''simple docstring'''
if index == number_of_items:
return 0
_UpperCamelCase = 0
_UpperCamelCase = 0
_UpperCamelCase = knapsack(a__ , a__ , a__... | 547 | 0 |
'''simple docstring'''
from __future__ import annotations
def __a ( lowerCAmelCase__ : list[int | str] ):
create_state_space_tree(lowerCAmelCase__ , [] , 0 , [0 for i in range(len(lowerCAmelCase__ ) )] )
def __a ( lowerCAmelCase__ : list[int | st... | 340 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImag... | 340 | 1 |
"""simple docstring"""
import argparse
import copy
def _snake_case ( __snake_case : Tuple ):
"""simple docstring"""
_lowerCamelCase : str = {}
with open(__snake_case ) as f:
for line in f:
if line.split()[0] not in dict_of_... | 88 |
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> int:
return x if y == 0 else greatest_common_divisor(_UpperCamelCase , x % y )
def __snake_case ( _UpperCamelCase , _UpperCamelCase ) -> int:
return (x * y) // greatest_common_divisor(_UpperCamelCase , _U... | 487 | 0 |
'''simple docstring'''
import requests
_a : Optional[Any] = "YOUR API KEY"
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ = giphy_api_key ) -> list:
"""simple docstring"""
__UpperCAmelCase : Tuple = "+".join(query.... | 10 | '''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : ... | 10 | 1 |
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --host <host> --key_path <... | 618 |
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaTokenizerFast,
XLMRober... | 618 | 1 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.... | 383 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase = logging.getLogger()
def A ( lowercase__ : ... | 383 | 1 |
def lowerCAmelCase_ ( lowerCamelCase = 1000 ):
__magic_name__ : Optional[int] =2**power
__magic_name__ : int =0
while n:
__magic_name__ , __magic_name__ : List[str] =r + n % 10, n // 10
return r
if __name__... | 21 |
UpperCAmelCase_ : int = range(2, 20 + 1)
UpperCAmelCase_ : Tuple = [10**k for k in range(ks[-1] + 1)]
UpperCAmelCase_ : dict[int, dict[int, list[list[int]]]] = {}
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ... | 21 | 1 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECK... | 715 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class a__ ( A__ ):
def __init__( self :List[str] , *_lowerCamelCase :int ... | 395 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_commo... | 144 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_git': ['GitProcessor'],
}
try:
if ... | 144 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( __lowerCAmelCase : int , __lowerCAmelCase : bool = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n... | 239 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"... | 239 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a : ... | 63 |
'''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__ : int = logging.get_logger(__name__)
lowercase__ : Any = ... | 98 | 0 |
import torch
from diffusers import StableDiffusionPipeline
lowercase_ = 'path-to-your-trained-model'
lowercase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
lowercase_ = 'A photo of sks dog in a bucket'
lowerca... | 380 |
from math import pow, sqrt
def a ( *A__ : float ) -> bool:
"""simple docstring"""
_lowercase =len(A__ ) > 0 and all(value > 0.0 for value in values )
return result
def a ( A__ : float , A__ : float ) -> float | ValueErro... | 380 | 1 |
from math import factorial
def a__ ( _UpperCamelCase : int = 1_00 ):
return sum(int(snake_case__ ) for x in str(factorial(snake_case__ ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 175 | from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require... | 312 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 716 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ... | 57 | 0 |
'''simple docstring'''
from __future__ import annotations
class a_ :
def __init__( self : Optional[Any] , lowercase : str , lowercase : str ):
"""simple docstring"""
lowercase_ , lowercase_ :int ... | 172 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int ):
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
lowercase_ :Optional[int] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
l... | 172 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_SCREAMING_SNAKE_CASE )
class __A ( _SCREAMING_SNAKE_CASE ):
lowerCamelCase... | 704 |
'''simple docstring'''
def A_ ( __SCREAMING_SNAKE_CASE : int ) -> None:
"""simple docstring"""
__A : Tuple = generate_pascal_triangle(__SCREAMING_SNAKE_CASE )
for row_idx in range(__SCREAMING_SNAKE_CASE ):
# Print left spaces
for _ in ran... | 499 | 0 |
"""simple docstring"""
_snake_case = {str(digit): digit**5 for digit in range(1_0)}
def __snake_case ( SCREAMING_SNAKE_CASE: int ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(SCREAMING_SNAKE_CASE ) ... | 580 |
"""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, rand... | 580 | 1 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODE... | 709 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowercase : Dict = {
"configuration_vision_text_dual_encoder": ["Visi... | 93 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def a__ ( lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
... | 54 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import... | 141 | 0 |
"""simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_g... | 2 |
"""simple docstring"""
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 2 | 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__)
UpperCAmelCase_... | 616 |
"""simple docstring"""
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCAmelCase_ ( ):
"""simple docstring"""
wit... | 616 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case ( metaclass=_UpperCAmelCase ):
_A : Tuple = ['''flax''', '''transformers''']
def __init__( self : Optional[Any] ,*SCREAMING_SNAKE_CASE__ : int ... | 708 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def A_ ( snake_case ):
@wraps(snake_case )
def _inner_fn(*snake_case , **snake_case ):
warnings.warn(
(F'''\'{fn.__name__}\' is experimental and might be subject to bre... | 465 | 0 |
'''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 __magic_name__ ( tf.keras.layers.Layer ):
d... | 358 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_d... | 358 | 1 |
"""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 _UpperCAmelCas... | 711 |
"""simple docstring"""
import json
import sys
def lowerCamelCase__ ( __snake_case, __snake_case ) -> Union[str, Any]:
"""simple docstring"""
with open(__snake_case, encoding='''utf-8''' ) as f:
_UpperCamelCase = json.load(__snake... | 78 | 0 |
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
Upper... | 32 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeat... | 171 | 0 |
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
lowerCAmelCase_ = 'facebook/wmt19-en-de'
lowerCAmelCase_ = FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCAmelCase_ = FSMTConfig.from_pretrained(... | 720 |
from functools import reduce
lowerCAmelCase_ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""66... | 669 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
def __UpperCAmelCase ( self , _lowerCAmelCase ):
raise NotImplementedError()
... | 79 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 652 | 0 |
import math
def UpperCamelCase ( snake_case__):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# ... | 683 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def UpperCamelCase ( ):
lowerCAmelCase_ : List[str] = HfArgumentParser(snake_case__)
lowerCAmelCase_ : List[Any] = parser.parse_args_into_dataclasses()[0]
lowerCAmelCase_... | 683 | 1 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: ... | 399 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
a__ : str =argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=N... | 399 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a_ = {
"""configuration_efficientformer""": [
"""EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Effi... | 622 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clipseg""": [
"""CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPSegConfig""",
"""CLIPSegTextConfig""",
"""CLIPSegVisionConfig""... | 622 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class a__ ( a_ ):
__l... | 361 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
... | 361 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : int = 1000000 ) -> int:
lowercase : Dict =set(range(3 , __magic_name__ , 2 ) )
primes.add(2 )
for p in range(3 , __magic_name__ , 2 ):
if p not in primes... | 88 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = ... | 88 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_... | 407 |
"""simple docstring"""
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
stooge(_UpperCamelCase , 0 , len(_UpperCamelCase ) - 1 )
return arr
def _lowerCAmelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
"""simple ... | 353 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird import B... | 703 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A_ : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str ... | 64 | 0 |
import requests
from bsa import BeautifulSoup
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: dict ) -> str:
_UpperCAmelCase : List[str] = BeautifulSoup(requests.get(__lowerCamelCase , params=__lowerCamelCase ).content , "html.parser" )
_UpperCAm... | 300 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, ge... | 344 | 0 |
'''simple docstring'''
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
# TODO Update this
__lowerCamelCase = {
'''facebook/esm-1b'... | 711 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_UpperCamelCase = ... | 459 |
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io ... | 459 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.hu... | 329 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
lowerCAmelCase__ : List[str] = TypeVar("KEY")
lowerCAmelCase__ : str = TypeVar("VAL")
@dataclass(frozen=snake_case__ ,slots=snake_case__... | 329 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.or... | 103 |
"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logg... | 103 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ... | 308 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : List[str] = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json"... | 308 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import Att... | 102 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def SCREAMING_SNAKE_CASE ( snake_case__ , snake_c... | 132 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] = logging.get_logger(__name__)
a__ : Dict = {
"Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/co... | 642 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a__ : int = logging.get_logger(__name__)
a__ : Tuple = {"vocab_file": "vocab.txt"}
a__ : int ... | 642 | 1 |
'''simple docstring'''
from __future__ import annotations
a : Optional[Any] = '''Muhammad Umer Farooq'''
a : int = '''MIT'''
a : Dict = '''1.0.0'''
a : Optional[int] = '''Muhammad Umer Farooq'''
a : Optional[Any] ... | 69 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCAmelCase_ = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not ... | 539 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a = {
'''configuration_owlvit''': [
... | 715 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from tran... | 505 | 0 |
'''simple docstring'''
def __UpperCamelCase ( a : list , a : int = 0 ) ->Optional[Any]:
snake_case = length or len(lowerCAmelCase_ )
snake_case = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
snake_case ... | 342 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
... | 53 | 0 |
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Union[str, Any], snake_case :Optional[int]):
"""simple docstring"""
_lowercase =arr.split(',')
def UpperCamelCase__ ( self :List[Any]):
... | 713 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_ ( _a ):
"""simple docstring"""
def __init__( self :str, *snake_c... | 557 | 0 |
'''simple docstring'''
import logging
from transformers import PretrainedConfig
SCREAMING_SNAKE_CASE_: Any =logging.getLogger(__name__)
SCREAMING_SNAKE_CASE_: Any ={
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/conf... | 78 |
"""simple docstring"""
def _snake_case ( lowercase__ = 1 , lowercase__ = 1000 ):
_lowerCamelCase : Optional[int] = 1
_lowerCamelCase : List[Any] = 0
for divide_by_number in range(lowercase__ , digit + 1 ):
... | 630 | 0 |
'''simple docstring'''
def a_ ( __UpperCAmelCase ) -> Dict:
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 347 |
'''simple docstring'''
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transfor... | 347 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
f... | 99 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
p... | 99 | 1 |
'''simple docstring'''
import math
def lowerCAmelCase ( snake_case__ : int )-> str:
A_ = 0
A_ = 0
while num > 0:
A_ = num % 8
A_ = octal + (remainder * math.floor(math.pow(10 , _lowercas... | 709 |
from __future__ import annotations
__magic_name__ : List[Any] = 8.9_8_8e9 # units = N * m^s * C^-2
def lowerCAmelCase ( snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float )-> ... | 608 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 19 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'''The conver... | 279 | 0 |
class __magic_name__ :
def __init__( self : Optional[Any] ,__SCREAMING_SNAKE_CASE : List[str] ):
UpperCAmelCase = n
UpperCAmelCase = [None] * self.n
UpperCAmelCase = 0 # index of the first element
UpperCAmelCas... | 707 |
def __UpperCamelCase ( _lowerCAmelCase = 10 ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or n < 0:
raise ValueError("Invalid input" )
UpperCAmelCase = 10**n
UpperCAmelCase = 2_84_33 * (pow(2 , 7_83_... | 405 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
_a : Optional[Any] = logging.get_logger(__name__)
class _lowercase ( __lowercase ):
def __init__( self : Any , *SCREAMING_SNAKE_CAS... | 56 |
def __snake_case ( _lowerCAmelCase : list , _lowerCAmelCase : list , _lowerCAmelCase : int ) -> int:
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:... | 454 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase : Dict = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 709 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : list[str] | None = None ):
__lowercase : Tuple = word_bank or []
# create a table
__lowercase : int = len(lower... | 649 | 0 |
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