code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = 2
__magic_name__ = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.append(A_ )
if n > 1:
facto... | 88 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__lowerCAmelCase : Optional[int] = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03... | 88 | 1 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _snake_case ( pl.LightningModule ):
def __init__( self , _lowerCamelCa... | 357 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _snake_case ( ... | 283 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
'RWKV/rwkv-4-169m-pile': 'https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json',
'RWKV/rwkv-4-430m-pile': '... | 290 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 268 | 0 |
"""simple docstring"""
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,
)
A_ : Union[str, Any] = {
"co... | 351 |
"""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_ : int = {
"configuration_clip": ... | 316 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : List[str] = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
... | 48 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ : Optional[Any] = {'configuration_mmbt': ['MMBTConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 48 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : List[Any] = logging.get_logger(__name__)
A__ : Dict = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/res... | 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[Any] = list[list[int]]
# assigning initial values to the grid
A__ : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
... | 0 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def __lowerCamelCase ( UpperCAmelCase_ : Optional[i... | 94 |
from __future__ import annotations
from fractions import Fraction
def __magic_name__ ( __lowerCAmelCase : int , __lowerCAmelCase : int ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def __magic_name__ ( __lowerCA... | 270 | 0 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# Checks if the entire collection has been sorted
if len(SCREAMING_SNAKE_CASE__ ) <= 1 or n <= 1:
return
insert_next(SCREAMING_SNAKE_CASE__ , n - 1 )
rec_insertion_sor... | 194 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import Onn... | 194 | 1 |
def lowerCamelCase_ ( _UpperCamelCase ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
snake_case_ : int = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
snake... | 279 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
lowerCAmelCase_ = logging.getLogger(__name__)
def lowerCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
snake_case_ : List[str] ... | 279 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 361 |
"""simple docstring"""
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
lowerCamel... | 241 | 0 |
import math
def A_ ( ) -> None:
UpperCamelCase : List[Any] = input("Enter message: " )
UpperCamelCase : Optional[Any] = int(input(F"""Enter key [2-{len(_snake_case ) - 1}]: """ ) )
UpperCamelCase : Tuple = input("Encryption/Decryption [e/d... | 52 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBe... | 315 | 0 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
_A = [
'''word_embeddings_layernorm.weight''',
... | 356 |
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,
PILImageResampling,
get_image... | 167 | 0 |
'''simple docstring'''
_lowerCamelCase : str = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def __lowerCamelCase ( A__ ) -> bytes:
"""simple docstring"""
# Make sure the supplied data is a bytes-like object
if not is... | 28 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfi... | 255 | 0 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE_ = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE_ = obj... | 362 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.test... | 193 | 0 |
"""simple docstring"""
import math
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_lowercase )
else:
if x == 0: # 0 raised to a... | 66 |
"""simple docstring"""
import random
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ : Optional[int] = [], [], []
for element in data:
if element < pivot:
less.append(A_ )
elif element > pivot:
greater.append(A_ )
else:
... | 106 | 0 |
def _UpperCAmelCase (UpperCamelCase_ : int ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _UpperCAmelCase (UpperCamelCase_ : int = 100 ):
'''simpl... | 370 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
_lowe... | 159 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : int = {
'BridgeTower/bridgetower-base': 'https://hug... | 182 | import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' , ['''default''', 0, 100 * 2**20, 900 * 2**20] )
def A ( _low... | 182 | 1 |
'''simple docstring'''
def A_( A : str):
UpperCamelCase = [int(A) for i in ip_va_address.split('.') if i.isdigit()]
return len(A) == 4 and all(0 <= int(A) <= 254 for octet in octets)
if __name__ == "__main__":
lowerCAmelCase : int = input().strip()
... | 358 |
'''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/LICENSE... | 251 | 0 |
'''simple docstring'''
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __A ( A , A ):
'''simple docstring'''
@register_to_config
def __init__(self , *,
A = 4 , A = 768 , A , ... | 211 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_mmbt": ["MMBTConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 211 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAm... | 358 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 118 | 0 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNet... | 201 |
'''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 # ... | 55 | 0 |
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 OptionalDependencyNotAvailabl... | 361 |
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
snake_case_ = False
snake_case_ = True
snake_case_ = False
if __name__ == "__main__":
snake_case_ = argparse.Argument... | 216 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
lowercase ... | 220 |
from functools import reduce
_SCREAMING_SNAKE_CASE : Any = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 314 | 0 |
"""simple docstring"""
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 To... | 367 |
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = ... | 317 | 0 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet im... | 169 |
import re
from filelock import FileLock
try:
import nltk
lowerCamelCase__ : str = True
except (ImportError, ModuleNotFoundError):
lowerCamelCase__ : Union[str, Any] = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def ... | 225 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__A : List[str] = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def __SCREAMING_S... | 370 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for test... | 57 | 0 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : float ,_lowerCamelCase : float ) -> float:
if (
not isinstance(_lowerCamelCase ,(int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError... | 44 |
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 lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> List[str]:
lowerCAmelCas... | 212 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase(__UpperCamelCase ) -> bool:
_lowerCAmelCase =str(__UpperCamelCase )
return n == n[::-1]
def _lowerCamelCase(__UpperCamelCase = 1000000 ) -> str:
_lowerCAmelCase =0
for i in range(1 , __UpperCame... | 352 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
lowerCamelCase = ['''image_processor''', '''tokenizer''']
l... | 341 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_lowerCamelCase : List[str] = logging.get_logger(... | 14 |
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 : Optional[Any] = datasets.ut... | 14 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Flax... | 218 |
import argparse
import copy
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = {}
with open(_A ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
SCREAMING_SNAKE_CASE__ = []
... | 218 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCamelCase__( )->Union[str, Any]:
A__ = 9
A__ = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2... | 193 |
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 transformers import glue_compu... | 193 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerO... | 357 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __lowerCamelCase (... | 3 | 0 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
lowercase_ = 6378137.0
lowercase_ = 6356752.314245
lowercase_ = 6_3_7_8_1_3_7
def lowercase ( lowerCAmelCase__ : float , lowerCAmelCase__ : float , lowerCAm... | 45 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, l... | 12 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase__ = {
"configuration_rembert": ["REM... | 353 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __magic_name__ (__lowercase ):
lowerCamelCase__ = ['''image_processor''', '''tokenizer''']
lowerCamelCase__ = '''ViTImageProcessor'''
lowerCamel... | 22 | 0 |
'''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = "T5Conf... | 31 |
def UpperCAmelCase__ ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# Return True if there is node that has not iterated.
lowercase :Union[str, Any] = [False] * len(lowerCamelCase )
lowercase :Union[str, Any] = []
queue.append(lowerCamelCase ... | 236 | 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_torch_a... | 347 |
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 cached_property
... | 347 | 1 |
from __future__ import annotations
__UpperCAmelCase = tuple[int, int, int]
__UpperCAmelCase = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
__UpperCAmelCase = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------... | 299 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Dict:
"""simple docstring"""
lowerCAmelCase_ : Union[str, Any] ... | 241 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.d... | 8 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__A : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( lowercase__ ):
def __init__( self :List[str] ,*_Upp... | 8 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A_ ( _lowercase, _lowercase ):
'''simple docstring'''
return math.sqrt(sum(pow(a - b, 2 ) for a, b in zip(_lowercase, _lowercase ) ) )
def A_ ( _l... | 66 |
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run t... | 66 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
A__ = ['torch']
def __init__( self : str , *_a : int , **_a : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
... | 363 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 114 | 0 |
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils imp... | 124 |
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 SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> int:
lowerCamelCase... | 50 | 0 |
"""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 : Optional[Any] = logging.get... | 150 |
"""simple docstring"""
import os
from pathlib import Path
def lowercase__() ->List[Any]:
"""simple docstring"""
from torch.utils.cpp_extension import load
lowercase__ : Any= Path(A ).resolve().parent.parent.parent / "kerne... | 150 | 1 |
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_camembert import... | 277 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
a_ :Any = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mi... | 277 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tenso... | 345 | '''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
UpperCamelCase : Optional[Any] = logging.getLogger(__name__)
class UpperCamelCase ( a_ ):
"""simple docstring"""
A : Tuple = "masked_bert"
... | 345 | 1 |
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class lowercase_ ( _UpperCAmelCase ):
'''simple docstring'''
__snake_case = CustomTokenizer
pass
| 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 306 | 0 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def ... | 259 |
"""simple docstring"""
import string
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
__lowerCAmelCase = ""
for symbol in message:
if symbol in string.ascii_uppercase:
__lowerCAmel... | 259 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common im... | 104 |
"""simple docstring"""
def lowercase ( __snake_case : int = 1_0_0 ):
lowercase_ : str = 0
lowercase_ : List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
... | 33 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a_ : str = logging.get_logger(__name__)
a_ : Optional[Any] ... | 356 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCase):
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set())
@pytest.fixture
def lowerCamelCase__ (_UpperCAmelCa... | 327 | 0 |
"""simple docstring"""
def _A (__a , __a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : list[list[str]] = [[] for _ in range(__a )]
SCREAMING_SNAKE_CASE_ : str = key - 1
if key <= 0:
raise ValueError('''... | 91 | """simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms... | 221 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = 0
lowerCAmelCase_ :int = len(lowercase__ )
for i in range(n - 1 ):
for j in range(i ... | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , *__A , ... | 1 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import... | 62 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestC... | 296 | 0 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> List[str]:
_a : Tuple = sorted(zip(lowerCAmelCase_ , ... | 107 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all file... | 107 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = ["""image_processor""", """tokenizer"""]
__lowercase = ... | 42 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowercase : List[str] = logging.get_logger("transformers.models.speecht5")
def SCREAMING_SNAKE_CASE__ ( ... | 42 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Any =logging.get_logger(__name__)
lowerCAmelCase : Any ={
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/... | 370 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class a_ ... | 147 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = ArgumentParser(
descript... | 36 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 106 | 0 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
... | 182 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.... | 182 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A : Tuple = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itert... | 118 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self, __magic_name__ ) -> Union[str, Any]:
... | 201 | 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... | 27 |
"""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 acce... | 27 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__a = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBart... | 35 |
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 | 0 |
def lowerCAmelCase__ ( _a : dict ):
snake_case_ : List[Any] = set()
# edges = list of graph's edges
snake_case_ : int = get_edges(_a )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extre... | 36 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Tuple = ['image_processor', 'tokenizer']
A : Tuple = 'AutoIm... | 36 | 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.org/licenses/LICENSE-... | 60 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
snake_cas... | 60 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ :Optional[int] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': [''... | 245 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ :List[str] = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/face... | 245 | 1 |
'''simple docstring'''
import logging
from transformers.configuration_utils import PretrainedConfig
_A : Dict = logging.getLogger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE : Any ... | 229 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_A : List[Any] = 299792458
# Symbols
_A , _A , _A , _A : Union[str, Any] = symbols('''ct x y z''')
... | 229 | 1 |
'''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... | 129 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A : Union[str, Any] =logging.get_logger(__name__)
_A : List[Any] ={
'''SenseTime/deformabl... | 129 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenizatio... | 23 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thin... | 11 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
Upper... | 368 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def U... | 164 | 0 |
import pytest
lowercase__ : Optional[int] = "__dummy_dataset1__"
lowercase__ : str = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann... | 328 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
lowercase__ : Union[str, Any] = "\\n@misc{chen2021evaluating,\n title={Eval... | 328 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
UpperCamelCase__ = '''htt... | 299 |
'''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 AutoProc... | 299 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : int ) -> list[list[int]]:
"""simple docstring"""
a_ : list[list[int]] = []
a_ : list[int] = []
a_ : Dict = 0
... | 32 | from __future__ import annotations
def UpperCamelCase__ ( A__ , A__ , A__ ) -> tuple[float, list[float]]:
snake_case__ : Optional[Any] = list(range(len(A__ ) ) )
snake_case__ : str = [v / w for v, w in zip(A__ , A__ )]
index.sor... | 143 | 0 |
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Optional[Any] = len(__lowerCamelCase )
_SCREAMING_SNAKE_CASE : List[str] = [[0] * n for i in range(__lowerCamelCase )]
for i in range(__lowerCam... | 325 |
from math import isqrt, loga
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : List[Any] = [True] * max_number
for i in range(2, isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2, __lowerCamelCase, __lo... | 325 | 1 |
from collections.abc import Callable
import numpy as np
def _SCREAMING_SNAKE_CASE ( lowercase : Callable , lowercase : float , lowercase : float , lowercase : float , lowercase : float ):
'''simple docstring'''
... | 204 |
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
IMAGENET_S... | 204 | 1 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def __a ( UpperCAmelCase ) ->bytes:
"""simple docstring"""
if len(UpperCAmelCase ) != 32:
raise ValueError("""Input must be of length 32""" )
A = B""""""
for i in [3, 2, 1, ... | 356 |
'''simple docstring'''
import os
def __a ( ) ->List[Any]:
"""simple docstring"""
A = os.path.join(os.path.dirname(UpperCAmelCase ) , """num.txt""" )
with open(UpperCAmelCase ) as file_hand:
return str(sum(int(UpperCAmelCase ) for line in file_ha... | 337 | 0 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if... | 332 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBe... | 332 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""... | 365 |
'''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_comm... | 4 | 0 |
def _lowerCamelCase( lowercase__ , lowercase__ ) -> Optional[Any]:
'''simple docstring'''
__lowercase= 0
__lowercase= len(lowercase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted... | 295 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import require_m... | 295 | 1 |
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:
from ...... | 319 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extracti... | 319 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_UpperCamelCase = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.a... | 254 |
'''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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resi... | 250 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionMode... | 104 |
'''simple docstring'''
from __future__ import annotations
def _A (lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
_a = 2
_a = []
while i * i <= n:
if n % i:
i += 1
... | 104 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
"configuration_bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig", "BloomOnnxConfig"],
}
try:
if not is_tokenizers_available()... | 299 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils im... | 299 | 1 |
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase =len(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr valu... | 356 |
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _UpperCAmelCase ( ):
print('Making key files...' )
make_key_files('rsa' , 10_24 )
print('Key files generation succes... | 117 | 0 |
def lowercase_ (A : int = 1_0_0 ):
snake_case__ : str = 0
snake_case__ : Tuple = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ == "__main... | 277 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : Tuple = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"... | 328 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 144 |
"""simple docstring"""
import argparse
import os
import re
_a = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+Order... | 144 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _lowercase ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.r... | 275 |
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_STAND... | 275 | 1 |
"""simple docstring"""
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def lowerCAmelCase__ ( _UpperCamelCase : ... | 365 | """simple docstring"""
import requests
from bsa import BeautifulSoup
def lowerCAmelCase__ ( _UpperCamelCase : str = "https://www.worldometers.info/coronavirus" ) -> dict:
"""simple docstring"""
snake_case = BeautifulSoup(requests.get(_U... | 149 | 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_dimension_format,
)
from ... | 176 | '''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 31 | 0 |
'''simple docstring'''
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__A ={
'<': operator.lt,
'<=': operator.le,
'==': operator.eq,
'!=': operator.ne,
'>=': operator.ge,
'>': operator.gt,
... | 283 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A ='\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\... | 283 | 1 |
'''simple docstring'''
def _lowerCamelCase ( lowercase : str , lowercase : Optional[Any] ) -> Optional[Any]:
return int(input_a == input_a == 0 )
def _lowerCamelCase ( ) -> Any:
print("Truth Table of NOR Gate:" )
print("| Input 1 | ... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large... | 303 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def snake_case_ ( A_ : Callable ):
'''simple docstring'''
@wraps(A_ )
def _inner_fn(*A_ : Any, **A_ : Union[str, Any] ):
warnings.warn(
... | 355 |
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : str = ... | 175 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def snake_case_ ( A_ : np.ndarray ):
'''simple docstring'''
_lowerCamelCase , _lowerCamelCase : int = np.shape(A_ )
if rows != columns:
_... | 72 |
from __future__ import annotations
def UpperCamelCase ( __magic_name__ : list[float] , __magic_name__ : list[float] ) -> float:
"""simple docstring"""
lowercase__ = sorted(numsa + numsa )
lowercase__ , lowercase__ = divmod(l... | 305 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __magic_name__ ( A... | 332 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 1 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTes... | 308 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
... | 308 | 1 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCAmelCase_ ( ):
'''simple docstring'''
A : Dict = [randint(-1000 , 1000 ) for i... | 311 |
'''simple docstring'''
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...t... | 311 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __UpperCAmelCase ( unittest.... | 42 | import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, Fla... | 348 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta imp... | 366 |
'''simple docstring'''
import torch
from torch import nn
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Any , lowerCAmelCase_ : List[str] , lowerCAmelCase_ : List[Any] , lowerCAmelCase_ : Dict , lowerCAmelCase... | 136 | 0 |
from __future__ import annotations
from collections import namedtuple
def UpperCAmelCase_ ( __snake_case , __snake_case , __snake_case ) -> tuple:
"""simple docstring"""
_lowercase =namedtuple('''result''' , '''name value''' )
if (volt... | 5 |
'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCS... | 161 | 0 |
"""simple docstring"""
_snake_case : Tuple = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_snake_case : Optional[int] = [{"type": "code", "cont... | 365 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_snake_case : List[str] = namedtuple(
"_TestCommandArgs",
[
"da... | 134 | 0 |
'''simple docstring'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_UpperCAmelCase : Any = mod... | 174 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae ... | 174 | 1 |
import functools
def __lowerCamelCase (UpperCAmelCase__ : list[int] , UpperCAmelCase__ : list[int] ):
# Validation
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for day in days ... | 365 | from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {'... | 206 | 0 |
'''simple docstring'''
import math
def a ( lowerCamelCase__ ):
'''simple docstring'''
if not isinstance(lowerCamelCase__ , lowerCamelCase__ ):
A_ : List[Any] = f'Input value of [number={number}] must be an integer'
raise TypeError(lowerCamelCase__ )
i... | 206 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
lowerCamelCase : Any = logging.get_... | 124 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def __lowercase ( _a , _a , _a , _a = 100 , ):
snake_case_ : int = x_start
snake_case_ : Union[str, Any] = fnc(_a )
snake_case_ ... | 361 |
"""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
lowercase__ : List[str] = logging... | 155 | 0 |
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