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"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCamelCase__ ( lowercase__ : int ):
return getitem, k
def UpperCamelCase__ ( lowercase__ : Union[str, Any] , lowercase__ : ... | 134 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/... | 134 | 1 |
'''simple docstring'''
import operator as op
__A = '''scaler.pt'''
__A = '''pytorch_model'''
__A = '''random_states'''
__A = '''optimizer'''
__A = '''scheduler'''
__A = '''pytorch_model.bin'''
__A = '''pytorch_model.bin.index.json'''
__A ... | 61 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'... | 61 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
'''xlnet-large-cased''': '... | 14 |
'''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 Backbo... | 284 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a : int = logging.get_logger(__name__)
__a : Tuple = {
'''vocab_file''': '''vocab.json'''... | 298 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import... | 298 | 1 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
UpperCamelCase__ : Any = 1_00
UpperCamelCase__ : List[str] = set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCamelCase__ : int
for prime in range(3, ceil... | 578 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _UpperCamel... | 578 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cach... | 265 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowerCamelCas... | 265 | 1 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 39 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_snake_case : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--dump_path", defau... | 713 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case : Optional[int] = ... | 203 | 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 required by appli... | 647 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case : int ={
'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}
try:
if not is_torch_available... | 647 | 1 |
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 transformers.utils imp... | 716 |
'''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
a__ = logging.get_logger(__name__)
a__ = {'''vocab_file''': '... | 566 | 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
from... | 110 |
"""simple docstring"""
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCamelCase__ = logging.getLogger(__name__)
class a :
def __init__( self ):
... | 110 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
... | 558 | """simple docstring"""
SCREAMING_SNAKE_CASE__ : Any =9.8_0665
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = g ) ->float:
if fluid_density <= 0:
raise ValueError('''Impossible fluid density''' )
if volume < 0:
... | 558 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case = {
"configuration_vision_encoder_decoder": ["VisionEncoderDecoderConfig", "VisionEncoderDecoderOnnxConfig"]
}
try... | 424 | import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __A ( snake_case__ ,unittest.TestCase ):
'''simple ... | 424 | 1 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ : Tuple = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 711 |
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
snake_case__ : Tuple = logging.get_log... | 171 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a = {
"""configuration_longformer""": [
"""LONGFORMER_PRET... | 19 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowercase : Optional[int] ={
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
i... | 136 | 0 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__lowerCAmelCase : Any =logging.getLogger()
@unittest.skip('Temporarily disable t... | 716 | """simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( lowerCAmelCase__ :list[float] , lowerCAmelCase__ :list[float] ) -> float:
'''simple docstring'''
lowercase = sorted(numsa + numsa )
lowercase , l... | 197 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProce... | 111 |
'''simple docstring'''
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import To... | 111 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
... | 702 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 0 |
'''simple docstring'''
def UpperCAmelCase_ ( lowerCAmelCase_ ):
"""simple docstring"""
lowercase = len(lowerCAmelCase_ )
for i in range(1 , lowerCAmelCase_ ):
lowercase = collection[i]
lowercase = 0
lowercase = i - 1
... | 310 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
... | 310 | 1 |
'''simple docstring'''
SCREAMING_SNAKE_CASE : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE : List[str] = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
SCREAMING_SNAKE_CASE : Any = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wedn... | 238 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : str = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if... | 238 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowerCAmelCase__ ( __magic_name__ , __magic_name__ ) ->int:
if len(__magic_name__ ) < k or k < 0:
raise ValueError("Invalid Input" )
__lowercase = __lowercase = sum(... | 118 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTConfig
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_configurati... | 118 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : Any = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerConfig",
],
}
try:
... | 453 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase = False ):
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 > 3317044064679887385961981 and not allow_probable:
raise Val... | 453 | 1 |
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
... | 285 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase =logging.get_logger(__name__)
lowerCamelCase ={
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2... | 285 | 1 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
UpperCamelCase__ = logging.g... | 702 |
def UpperCAmelCase__ ( lowercase__ , lowercase__ ) -> bool:
__lowercase = len(lowercase__ )
__lowercase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
... | 634 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google... | 90 |
"""simple docstring"""
from __future__ import annotations
def _A ( __lowercase ):
"""simple docstring"""
if len(__lowercase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
... | 129 | 0 |
import math
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> list[int]:
snake_case__ = []
snake_case__ = 2
snake_case__ = int(math.sqrt(__lowerCAmelCase ) ) # Size of every segment
snake_case__ = [True] * (end + 1)
... | 208 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCamelCase__ : Any = logging.get_logger(__name__)
lowerCamelCase__ : Union[str, Any] = {
"""CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
"""https... | 208 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase__ : int = {
'''configuration_mobilevit''': ['''MOBILEV... | 238 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 594 | 0 |
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from ... | 714 |
from typing import Dict, Optional
import numpy as np
import datasets
lowerCAmelCase = """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class seg... | 675 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def _A ( SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float , SC... | 563 |
import math
from numpy import inf
from scipy.integrate import quad
def _A ( SCREAMING_SNAKE_CASE : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(SCREAMING_SNAKE_CASE , 0 , SCREAMING_SNAKE_CASE , arg... | 563 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_tens... | 721 |
"""simple docstring"""
class _lowercase :
def __init__( self , UpperCamelCase_ ):
__magic_name__ = size
__magic_name__ = [0] * size
__magic_name__ = [0] * size
@staticmethod
def lowerCAmelCase__ ( Upper... | 190 | 0 |
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disa... | 679 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class UpperCamelCase__ :
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int
SCREAMING_SNAKE_CASE__ : TreeNode | None = None
SCREA... | 679 | 1 |
def __lowerCAmelCase ( lowercase : str ) -> int:
"""simple docstring"""
assert column_title.isupper()
snake_case : Dict = 0
snake_case : Tuple = len(lowercase ) - 1
snake_case : Optional[Any] = 0
while in... | 711 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""google/bigbird-rober... | 117 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import... | 541 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase__ : List[Any] = pytest.mark.integration
@pytest.mark.parametrize('path' , ... | 105 | 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,
)
lowerCAmelCase__ = {
'''configuration_blenderbot''': [
... | 544 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
UpperCamelCase = t... | 544 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
while a != 0:
__lowercase ,__lowercase : Tuple = b % a, a
return b
def __UpperCAmelCase ( __UpperCamelCase , __UpperCamelCase ):
if gcd(__Up... | 76 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : str = logging.get_logger(__name__)
class a_ ( a ):
def __init__( self : List[str] , *UpperCAmelCase__ : Optional[int] , **UpperCAmelC... | 598 | 0 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A__ ( lowercase_ ):
lowercase =... | 719 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase__ = {
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""ChineseCLIPConfig""",
... | 69 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase : str = {
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ChineseCLIPConfig",
"Chinese... | 323 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __ve... | 323 | 1 |
'''simple docstring'''
# Algorithm for the pigeonhole sorting
def lowerCamelCase__ ( a ):
__snake_case = min(a ) # min() finds the minimum value
__snake_case = max(a ) # max() finds the maximum value
__snake_case = max_val - min_val + 1... | 427 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_lowercase = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_lowercase = """
Args:
predictions: List ... | 427 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase ( ) ->Generator[int, None, None]:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = 2
while True:
_SCREAMING_SNAKE_CASE = factor_m... | 314 |
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 (
AutoTokeni... | 632 | 0 |
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
UpperCAmelCase_ = False
UpperCAmelCase_ = True
UpperCAmelCase_ = False
if __name__ == "__main__":
UpperCAmelCase_ =... | 2 |
import collections
import os
import re
from pathlib import Path
UpperCAmelCase_ = """src/transformers"""
# Matches is_xxx_available()
UpperCAmelCase_ = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
UpperCAmelCase_ = re.compile(r"""^_im... | 2 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase ( SCREAMING_SNAKE_CASE ):
@staticmethod
@abstractmethod
def UpperCamelCase ( snake_case__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError()
... | 673 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
a_ : List[str] = version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from nltk import word_tokenize
a_ : Di... | 673 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__a :Tuple = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderO... | 86 |
from __future__ import annotations
def lowerCAmelCase_ (lowercase__ : list[int] , lowercase__ : list[int] , lowercase__ : int ) -> tuple[float, list[float]]:
'''simple docstring'''
lowerCAmelCase__ = list(range(len(lowercase... | 668 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE = logging.get_logg... | 8 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def lowercase_ ( __A : str = "mumbai" ) -> ... | 8 | 1 |
'''simple docstring'''
from math import pi
def lowerCamelCase ( lowerCamelCase : int , lowerCamelCase : int):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 665 |
'''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,
)
UpperCAmelCase = {
'''configuration_owlvit''': [
... | 119 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def a_ ( __snake_case , __snake_case ) -> Tuple:
UpperCamelCase_ = args.log_... | 703 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__a : Optional[Any] = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_A... | 559 | 0 |
def _lowercase ( ):
"""simple docstring"""
return 1
def _lowercase ( SCREAMING_SNAKE_CASE_ : int ):
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def _lowercase ( SCR... | 386 |
def _lowercase ( SCREAMING_SNAKE_CASE_ : int = 10 , SCREAMING_SNAKE_CASE_ : int = 22 ):
"""simple docstring"""
UpperCamelCase = range(1 , SCREAMING_SNAKE_CASE_ )
UpperCamelCase = range(1 , SCREAMING_SNAKE_CASE_ )
... | 386 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCamelCase : Dict = logging.get_logger(__name__)
# TODO: upload to AWS
_UpperCamelCase : Tuple = {
"yjernite/retribert-base-uncased": (
"https://hu... | 713 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_UpperCamelCase : List[Any] = "\\n\n"
_UpperCamelCase : List[Any] = "\... | 514 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_im... | 57 |
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 OptionalDependencyNotAvailable:
... | 551 | 0 |
from collections.abc import Generator
def __magic_name__ ( ) -> Generator[int, None, None]:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = 0, 1
while True:
UpperCamelCase , UpperCamelCase = b, a + b
... | 414 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a : Union[str, Any] = logging.get_logger(__name__)
def __magic_name__ ( lowercase_ ) -> ... | 414 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( __snake_case : Tuple , __snake_case : ... | 88 |
"""simple docstring"""
from math import isqrt, loga
def _snake_case ( __snake_case : int ):
"""simple docstring"""
_lowerCamelCase : List[str] = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:... | 88 | 1 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import ... | 713 |
# using dfs for finding eulerian path traversal
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: Any , __lowerCamelCase: Tuple , __lowerCamelCase: List[Any] , __lowerCamelCase: Union[str, Any]=None ):
'''simple docstring'''
lowercase_ = (path or []) + [u]
for v in gra... | 601 | 0 |
"""simple docstring"""
a = 256
# Modulus to hash a string
a = 1_000_003
def _snake_case ( _snake_case : str , _snake_case : str ) -> bool:
'''simple docstring'''
_A = len(_snake_case )
_A = len(_snake_ca... | 7 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''image_processor''', '''tokenizer''']
UpperCAmel... | 7 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase : Any = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConf... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__UpperCAmelCase = {
'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}
try:
if not is_torch_availab... | 503 | 0 |
'''simple docstring'''
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
a = "▁"
a... | 109 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __a ( _snak... | 109 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def snake_case (A_ :Optional[int] , A_ :int , A_ :Dict , A_ :Any , ):
'''simple docstring'''
a : List[Any] = ... | 719 |
"""simple docstring"""
_UpperCamelCase : Tuple = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transform... | 118 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __UpperCamelCase : int ):
'''simple docstring'''
if n == 1 or not isinstance(__UpperCamelCase , __UpperCamelCase ):
return 0
elif n == 2:
return 1
else:... | 58 |
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import... | 70 | 0 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class lowerCAmelCase_ :
de... | 416 |
from typing import List
from .keymap import KEYMAP, get_character
def _A ( _UpperCamelCase ):
def decorator(_UpperCamelCase ):
_UpperCAmelCase : Optional[int] = getattr(_UpperCamelCase , '''handle_key''' , [] )
handle += [key]
setattr(_UpperCamelCase ,... | 416 | 1 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class snake_case_ ... | 375 |
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case ) -> int:
# Return True if there is node that has not iterated.
__lowercase = [False] * len(snake_case )
__lowercase = []
queue.append(snake_case )
... | 375 | 1 |
class _a( __A ):
pass
class _a( __A ):
pass
class _a:
def __init__( self ) -> Tuple:
'''simple docstring'''
_snake_case : Tuple = [
[],
[],
... | 707 |
import re
def A ( UpperCAmelCase ):
_snake_case : Any = re.compile(R"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(UpperCAmelCase , UpperCAmelCase ):
return match.string == phone
return False
if __na... | 278 | 0 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case( lowerCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase : Dict = (DDPMScheduler,)
def __snake_case... | 433 |
'''simple docstring'''
import requests
_SCREAMING_SNAKE_CASE = '''YOUR API KEY'''
def _lowerCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : str = giphy_api_key ):
__lowercase = '''+'''.join(query.split() )
__lowercase ... | 502 | 0 |
import importlib
import inspect
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__SCREAMING_SNAKE_CASE : List[Any] ='src/transformers'
# This is to make sure the transformers... | 717 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCamelCase__ ( lowerCAmelCase__ ):
lowercase = args.pruning_method
lowercase = args.threshold
lowercase = args.model_na... | 72 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def lowerCamelCase ( ) ->List[str]:
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = 2
while True:
_SCREAMING_SNAKE_CASE = factor_map.pop(_snake_cas... | 314 |
"""simple docstring"""
def lowerCamelCase ( _snake_case ,_snake_case ):
return int((input_a, input_a).count(0 ) == 0 )
def lowerCamelCase ( ):
assert and_gate(0 ,0 ) == 0
assert and_gate(0 ,1 ) == 0
assert and_gate(1 ,0 ) == 0
assert and_gate(1 ,1 ) == 1
if... | 110 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...... | 363 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils impo... | 363 | 1 |
'''simple docstring'''
__lowercase : Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def lowerCamelCase (_SCREAMING_SNAKE_CASE : bytes ):
# Make sure the supplied data is a bytes-like object
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE... | 476 |
'''simple docstring'''
import argparse
import gc
import json
import os
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 a... | 476 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor... | 709 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
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 TokenizerTesterM... | 535 | 0 |
"""simple docstring"""
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteSche... | 426 |
"""simple docstring"""
import math
def A__ ( A__ , A__ ) -> float:
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("The value of intensity cannot be negative" )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
... | 426 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__A : List[Any] = (3, 9, -11, 0, 7, 5, 1, -1)
__A : List[str] = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class _U... | 141 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForOb... | 141 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : list[int] , snake_case__ : list[list[str]] , snake_case__ : int , ):
snake_case__ : A... | 261 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase ) -> int:
"""simple docstring"""
snake_case__ : Any ... | 261 | 1 |
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def UpperCamelCase_ ( lowerCAmelCase__ = "isbn/0140328726" ):
"""simple docstring"""
_lowerCAmelCase : List[Any] = olid.strip().strip("/" ) # Remove leading/trailing... | 587 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDCondi... | 587 | 1 |
SCREAMING_SNAKE_CASE : Dict = "Input must be a string of 8 numbers plus letter"
SCREAMING_SNAKE_CASE : List[str] = "TRWAGMYFPDXBNJZSQVHLCKE"
def UpperCamelCase_( lowerCamelCase_ ) -> bool:
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ):
_low... | 89 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase ... | 84 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https://huggingface.... | 71 | def _snake_case ( __snake_case , __snake_case , __snake_case ):
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(__snake_case , n - 1 , __snake_case ) * a) % mod
else:
_UpperCamelCase = binary_exponentiation(__s... | 71 | 1 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,... | 30 | """simple docstring"""
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TY... | 359 | 0 |
import heapq
def snake_case ( snake_case__ :dict) -> set[int]:
_A = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min... | 701 | # Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 83 | 0 |
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, floats_tensor
if i... | 101 |
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"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
SCREAM... | 183 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _UpperCAmelCase ( SCREAMING_SNAKE_CASE_ ):
@staticmethod
@abstractmethod
def a_ ( lowercase_ ) -> Optional[Any]:
raise NotIm... | 183 | 1 |
"""simple docstring"""
from __future__ import annotations
def _a ( _snake_case ):
"""simple docstring"""
if not nums:
raise ValueError("""List is empty""" )
return sum(_snake_case ) / len(_snake_case )
if __name__ == "__main__":
import d... | 341 |
"""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 Config... | 341 | 1 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def UpperCamelCase__ ... | 307 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar('T')
__lowerCamelCase = TypeVar('U')
class UpperCamelCase_ ( Generic[T, U] ):
def __init__( self , lowercase , lowercase ) -> An... | 307 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCAmelCase : Dict = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pa... | 671 |
lowerCAmelCase : List[str] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def A_ ( _Upp... | 671 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : int ):
"""simple docstring"""
snake_case_ : Tuple = int(SCREAMING_SNAKE_CASE__ )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE__ )
... | 48 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list ):
"""simple docstring"""
snake_case_ : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case_ : Tuple ... | 48 | 1 |
'''simple docstring'''
def A ( UpperCamelCase_ : list ) -> list:
'''simple docstring'''
lowerCAmelCase__ = len(UpperCamelCase_ )
for i in range(1 , UpperCamelCase_ ):
lowerCAmelCase__ = collection[i]
lowerCAmelCase__ ... | 48 |
"""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_... | 180 | 0 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _snake_case ( SCREAMING_SNAKE_CASE ) -> Dict[str, torch.Tensor]:
"""simple docstring"""
_lowerCA... | 714 |
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, r... | 503 | 0 |
'''simple docstring'''
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
fro... | 13 | import torch
def lowerCAmelCase_ ( ) -> int:
'''simple docstring'''
if torch.cuda.is_available():
_UpperCamelCase: Any = torch.cuda.device_count()
else:
_UpperCamelCase: Union[str, Any] = 0
print(F"""Successfully ran on {num_gpus} GPUs""" )
if __name__ == "__ma... | 271 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 538 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_A = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
_A ... | 538 | 1 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
UpperCamelCase : List[str] = argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument(""... | 37 |
'''simple docstring'''
from __future__ import annotations
import os
from collections.abc import Mapping
_UpperCamelCase : Any = tuple[int, int]
class snake_case__ :
def __init__( self : List[str] , _A : set[int] , _A : Mapping[EdgeT, int] ) -> Non... | 541 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> Any:
_UpperCAmelCase = a... | 402 |
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> float:
return 1_0 - x * x
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(__snake_case ) * equ... | 402 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase_ = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Traj... | 28 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase : Union[str, Any] = logging.get_logger(__name__)
_lowercase : Li... | 49 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ : Union[str, Any] ={"""configuration_fnet""": ["""FNET_PRETRAINED_... | 720 | '''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 ... | 606 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
lowerCAmelCase_ = ['''small''', '''medium''', '''large''']
lowerCAmelCase_ = '''lm_head.decoder.weight'''
lowerCAmelCase_ = '''lm_head.weight'''
def lowerCamelCase... | 338 | """simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
lowerCAmelCase_ = '''path-to-your-trained-model'''
lowerCAmelCase_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
lowerCAmelCase_ = '''A photo of ... | 338 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__UpperCAmelCase = ... | 220 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def SCREAMING_SNAKE_CASE_ ( snake_case_ : np.ndarray ) -> np.ndarray:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] ... | 220 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase = {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConf... | 84 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _A ( __lowercase ):
lowercase__: Any = ['''image_processor''', '''tokenizer''']
lowercase__: Any = ''... | 26 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __UpperCAmelCase ( __A ):
"""... | 209 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOut... | 209 | 1 |
import argparse
from argparse import Namespace
import torch
from torch import nn
from transformers import XGLMConfig, XGLMForCausalLM
def _A ( __magic_name__ ):
lowercase__ = [
"decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"decoder.emb... | 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case ... | 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Optional[int] = {
"""configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 704 | import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCamelCase_ : Any = datasets.utils.loggi... | 246 | 0 |
__A = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
__A = [{"type": "code", "content": INSTALL_C... | 68 |
def _lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE ) < 2:
return collection
def circle_sort_util(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
A_ = Fal... | 203 | 0 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"snap-research/efficientformer-l1-300": (
"https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"
),
}
clas... | 707 |
def lowerCamelCase__ ( __lowerCAmelCase : list[list[int]] , __lowerCAmelCase : int , __lowerCAmelCase : int , __lowerCAmelCase : set ):
"""simple docstring"""
lowerCAmelCase_ , lowerCAmelCase_ = le... | 279 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTraine... | 526 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
... | 481 | 0 |
import fire
from utils import calculate_rouge, save_json
def UpperCamelCase (lowercase_: List[Any] , lowercase_: List[str] , lowercase_: Union[str, Any]=None , **lowercase_: List[Any] ) -> List[Any]:
A__ : List[Any] = [x.strip() for x in open(lowercase_ ... | 717 |
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_async, require_cuda
from ... | 64 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slo... | 167 | from __future__ import annotations
import numpy as np
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase :Optional[Any] = np.shape(SCREAMING_SNAKE_CASE )
if rows != columns:
__UpperCamelCase :Dict ... | 167 | 1 |
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def A_ ( __a : str = "laptop" ):
"""simple docstring"""
a__ = F'''https://www.amazon.in/laptop/s?k={product}'''
a__ = {
"""User-Agent... | 351 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transfo... | 351 | 1 |
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