



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowerCamelCase		       =      """
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

lowerCamelCase		       =      """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


lowerCamelCase		       =      """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\"]]
    >>> wiki_split = datasets.load_metric(\"wiki_split\")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}
"""






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Union[str, Any]:

				def remove_articles(__UpperCamelCase ):
								a__	:							str      						= re.compile(R"\b(a|an|the)\b"	,				re.UNICODE )
								return re.sub(__UpperCamelCase	,				" "	,				__UpperCamelCase )

				def white_space_fix(__UpperCamelCase ):
								return " ".join(text.split() )

				def remove_punc(__UpperCamelCase ):
								a__	:							Union[str, Any]      						= set(string.punctuation )
								return "".join(ch for ch in text if ch not in exclude )

				def lower(__UpperCamelCase ):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase ) ) ) )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						Any:
				return int(normalize_answer(__UpperCamelCase ) == normalize_answer(__UpperCamelCase ) )






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase )	->						str:
				a__	:							List[Any]      						= [any(compute_exact(__UpperCamelCase	,				__UpperCamelCase ) for ref in refs ) for pred, refs in zip(__UpperCamelCase	,				__UpperCamelCase )]
				return (sum(__UpperCamelCase ) / len(__UpperCamelCase )) * 1_00






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						str:
				a__	:							Any      						= [rgram for rgrams in rgramslist for rgram in rgrams]
				a__	:							Union[str, Any]      						= Counter(__UpperCamelCase )

				a__	:							Tuple      						= Counter(__UpperCamelCase )
				a__	:							List[Any]      						= Counter()
				for sgram, scount in sgramcounter.items():
								a__	:							Dict      						= scount * numref

				a__	:							str      						= Counter(__UpperCamelCase )
				a__	:							Any      						= Counter()
				for cgram, ccount in cgramcounter.items():
								a__	:							Union[str, Any]      						= ccount * numref

				# KEEP
				a__	:							List[str]      						= sgramcounter_rep & cgramcounter_rep
				a__	:							int      						= keepgramcounter_rep & rgramcounter
				a__	:							List[Any]      						= sgramcounter_rep & rgramcounter

				a__	:							Tuple      						= 0
				a__	:							Optional[int]      						= 0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				a__	:							Union[str, Any]      						= 1
				a__	:							Tuple      						= 1
				if len(__UpperCamelCase ) > 0:
								a__	:							Any      						= keeptmpscorea / len(__UpperCamelCase )
				if len(__UpperCamelCase ) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								a__	:							str      						= keeptmpscorea / sum(keepgramcounterall_rep.values() )
				a__	:							Union[str, Any]      						= 0
				if keepscore_precision > 0 or keepscore_recall > 0:
								a__	:							Optional[Any]      						= 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				a__	:							int      						= sgramcounter_rep - cgramcounter_rep
				a__	:							List[Any]      						= delgramcounter_rep - rgramcounter
				a__	:							Tuple      						= sgramcounter_rep - rgramcounter
				a__	:							int      						= 0
				a__	:							Optional[int]      						= 0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				a__	:							Any      						= 1
				if len(__UpperCamelCase ) > 0:
								a__	:							Tuple      						= deltmpscorea / len(__UpperCamelCase )

				# ADDITION
				a__	:							int      						= set(__UpperCamelCase ) - set(__UpperCamelCase )
				a__	:							List[str]      						= set(__UpperCamelCase ) & set(__UpperCamelCase )
				a__	:							Tuple      						= set(__UpperCamelCase ) - set(__UpperCamelCase )

				a__	:							Tuple      						= 0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				a__	:							str      						= 1
				a__	:							Any      						= 1
				if len(__UpperCamelCase ) > 0:
								a__	:							Optional[int]      						= addtmpscore / len(__UpperCamelCase )
				if len(__UpperCamelCase ) > 0:
								a__	:							Any      						= addtmpscore / len(__UpperCamelCase )
				a__	:							Dict      						= 0
				if addscore_precision > 0 or addscore_recall > 0:
								a__	:							List[Any]      						= 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Optional[int]:
				a__	:							Optional[int]      						= len(__UpperCamelCase )

				a__	:							str      						= ssent.split(" " )
				a__	:							Tuple      						= csent.split(" " )
				a__	:							List[Any]      						= []
				a__	:							Dict      						= []
				a__	:							int      						= []
				a__	:							Union[str, Any]      						= []
				a__	:							str      						= []
				a__	:							Dict      						= []

				a__	:							Any      						= []
				a__	:							List[str]      						= []
				a__	:							str      						= []
				a__	:							Tuple      						= []
				for rsent in rsents:
								a__	:							List[Any]      						= rsent.split(" " )
								a__	:							str      						= []
								a__	:							Dict      						= []
								a__	:							str      						= []
								ragramslist.append(__UpperCamelCase )
								for i in range(0	,				len(__UpperCamelCase ) - 1 ):
												if i < len(__UpperCamelCase ) - 1:
																a__	:							Optional[Any]      						= ragrams[i] + " " + ragrams[i + 1]
																ragrams.append(__UpperCamelCase )
												if i < len(__UpperCamelCase ) - 2:
																a__	:							List[str]      						= ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2]
																ragrams.append(__UpperCamelCase )
												if i < len(__UpperCamelCase ) - 3:
																a__	:							Tuple      						= ragrams[i] + " " + ragrams[i + 1] + " " + ragrams[i + 2] + " " + ragrams[i + 3]
																ragrams.append(__UpperCamelCase )
								ragramslist.append(__UpperCamelCase )
								ragramslist.append(__UpperCamelCase )
								ragramslist.append(__UpperCamelCase )

				for i in range(0	,				len(__UpperCamelCase ) - 1 ):
								if i < len(__UpperCamelCase ) - 1:
												a__	:							List[Any]      						= sagrams[i] + " " + sagrams[i + 1]
												sagrams.append(__UpperCamelCase )
								if i < len(__UpperCamelCase ) - 2:
												a__	:							Union[str, Any]      						= sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2]
												sagrams.append(__UpperCamelCase )
								if i < len(__UpperCamelCase ) - 3:
												a__	:							int      						= sagrams[i] + " " + sagrams[i + 1] + " " + sagrams[i + 2] + " " + sagrams[i + 3]
												sagrams.append(__UpperCamelCase )

				for i in range(0	,				len(__UpperCamelCase ) - 1 ):
								if i < len(__UpperCamelCase ) - 1:
												a__	:							Optional[Any]      						= cagrams[i] + " " + cagrams[i + 1]
												cagrams.append(__UpperCamelCase )
								if i < len(__UpperCamelCase ) - 2:
												a__	:							Optional[Any]      						= cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2]
												cagrams.append(__UpperCamelCase )
								if i < len(__UpperCamelCase ) - 3:
												a__	:							List[Any]      						= cagrams[i] + " " + cagrams[i + 1] + " " + cagrams[i + 2] + " " + cagrams[i + 3]
												cagrams.append(__UpperCamelCase )

				((a__)						,   (a__)						,   (a__))	:							Optional[int]      						= SARIngram(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				((a__)						,   (a__)						,   (a__))	:							Optional[int]      						= SARIngram(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				((a__)						,   (a__)						,   (a__))	:							Union[str, Any]      						= SARIngram(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				((a__)						,   (a__)						,   (a__))	:							Dict      						= SARIngram(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )
				a__	:							Any      						= sum([keepascore, keepascore, keepascore, keepascore] ) / 4
				a__	:							Union[str, Any]      						= sum([delascore, delascore, delascore, delascore] ) / 4
				a__	:							Union[str, Any]      						= sum([addascore, addascore, addascore, addascore] ) / 4
				a__	:							Any      						= (avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase = True	,				__UpperCamelCase = "13a"	,				__UpperCamelCase = True )	->						Tuple:
				# Normalization is requried for the ASSET dataset (one of the primary
				# datasets in sentence simplification) to allow using space
				# to split the sentence. Even though Wiki-Auto and TURK datasets,
				# do not require normalization, we do it for consistency.
				# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
				# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

				if lowercase:
								a__	:							Union[str, Any]      						= sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__ ).major >= 2:
												a__	:							List[Any]      						= sacrebleu.metrics.bleu._get_tokenizer(__UpperCamelCase )()(__UpperCamelCase )
								else:
												a__	:							Optional[int]      						= sacrebleu.TOKENIZERS[tokenizer]()(__UpperCamelCase )
				elif tokenizer == "moses":
								a__	:							List[Any]      						= sacremoses.MosesTokenizer().tokenize(__UpperCamelCase	,				return_str=__UpperCamelCase	,				escape=__UpperCamelCase )
				elif tokenizer == "penn":
								a__	:							Any      						= sacremoses.MosesTokenizer().penn_tokenize(__UpperCamelCase	,				return_str=__UpperCamelCase )
				else:
								a__	:							str      						= sentence

				if not return_str:
								a__	:							Union[str, Any]      						= normalized_sent.split()

				return normalized_sent






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase )	->						Dict:
				if not (len(__UpperCamelCase ) == len(__UpperCamelCase ) == len(__UpperCamelCase )):
								raise ValueError("Sources length must match predictions and references lengths." )
				a__	:							Tuple      						= 0
				for src, pred, refs in zip(__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase ):
								sari_score += SARIsent(normalize(__UpperCamelCase )	,				normalize(__UpperCamelCase )	,				[normalize(__UpperCamelCase ) for sent in refs] )
				a__	:							str      						= sari_score / len(__UpperCamelCase )
				return 1_00 * sari_score






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase="exp"	,				__UpperCamelCase=None	,				__UpperCamelCase=False	,				__UpperCamelCase=False	,				__UpperCamelCase=False	,				)	->						int:
				a__	:							Union[str, Any]      						= len(references[0] )
				if any(len(__UpperCamelCase ) != references_per_prediction for refs in references ):
								raise ValueError("Sacrebleu requires the same number of references for each prediction" )
				a__	:							Union[str, Any]      						= [[refs[i] for refs in references] for i in range(__UpperCamelCase )]
				a__	:							Optional[Any]      						= sacrebleu.corpus_bleu(
				    __UpperCamelCase	,				__UpperCamelCase	,				smooth_method=__UpperCamelCase	,				smooth_value=__UpperCamelCase	,				force=__UpperCamelCase	,				lowercase=__UpperCamelCase	,				use_effective_order=__UpperCamelCase	,				)
				return output.score






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,  _KWARGS_DESCRIPTION			)
class 				_a						(       datasets.Metric			):





							'''simple docstring'''

							def 		_A	(							self				):







											"""simple docstring"""



											return datasets.MetricInfo(
											    description=_DESCRIPTION ,					citation=_CITATION ,					inputs_description=_KWARGS_DESCRIPTION ,					features=datasets.Features(
											        {
											            "predictions": datasets.Value("string" ,					id="sequence"				),
											            "references": datasets.Sequence(datasets.Value("string" ,					id="sequence"				) ,					id="references"				),
											        }				) ,					codebase_urls=[
											        "https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py",
											        "https://github.com/cocoxu/simplification/blob/master/SARI.py",
											        "https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py",
											        "https://github.com/mjpost/sacreBLEU",
											    ] ,					reference_urls=[
											        "https://www.aclweb.org/anthology/Q16-1029.pdf",
											        "https://github.com/mjpost/sacreBLEU",
											        "https://en.wikipedia.org/wiki/BLEU",
											        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
											    ] ,					)




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= {}
											result.update({"sari": compute_sari(sources=__UpperCAmelCase ,					predictions=__UpperCAmelCase ,					references=__UpperCAmelCase				)}				)
											result.update({"sacrebleu": compute_sacrebleu(predictions=__UpperCAmelCase ,					references=__UpperCAmelCase				)}				)
											result.update({"exact": compute_em(predictions=__UpperCAmelCase ,					references=__UpperCAmelCase				)}				)
											return result







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 import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
						import PIL

lowerCamelCase		       =      logging.get_logger(__name__)






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						List[List[ImageInput]]:
				if isinstance(__UpperCamelCase	,				(list, tuple) ) and isinstance(videos[0]	,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(__UpperCamelCase	,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(__UpperCamelCase ):
								return [[videos]]

				raise ValueError(F'Could not make batched video from {videos}' )






class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:List[str] =       ["pixel_values"]



							def __init__(							self ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = 1 / 255 ,					__UpperCAmelCase = True ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											super().__init__(**__UpperCAmelCase				)
											a__	:							int      						= size if size is not None else {"shortest_edge": 256}
											a__	:							str      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							Union[str, Any]      						= crop_size if crop_size is not None else {"height": 224, "width": 224}
											a__	:							int      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											a__	:							str      						= do_resize
											a__	:							Union[str, Any]      						= size
											a__	:							Any      						= do_center_crop
											a__	:							Optional[int]      						= crop_size
											a__	:							Optional[int]      						= resample
											a__	:							Optional[Any]      						= do_rescale
											a__	:							Union[str, Any]      						= rescale_factor
											a__	:							Dict      						= offset
											a__	:							List[str]      						= do_normalize
											a__	:							Dict      						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											a__	:							Tuple      						= image_std if image_std is not None else IMAGENET_STANDARD_STD



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = PILImageResampling.BILINEAR ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											if "shortest_edge" in size:
															a__	:							Optional[Any]      						= get_resize_output_image_size(__UpperCAmelCase ,					size["shortest_edge"] ,					default_to_square=__UpperCAmelCase				)
											elif "height" in size and "width" in size:
															a__	:							Union[str, Any]      						= (size["height"], size["width"])
											else:
															raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'				)
											return resize(__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= get_size_dict(__UpperCAmelCase				)
											if "height" not in size or "width" not in size:
															raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'				)
											return center_crop(__UpperCAmelCase ,					size=(size["height"], size["width"]) ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							Optional[Any]      						= image.astype(np.floataa				)
											if offset:
															a__	:							Dict      						= image - (scale / 2)
											return rescale(__UpperCAmelCase ,					scale=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											return normalize(__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					):







											"""simple docstring"""



											if do_resize and size is None or resample is None:
															raise ValueError("Size and resample must be specified if do_resize is True."				)

											if do_center_crop and crop_size is None:
															raise ValueError("Crop size must be specified if do_center_crop is True."				)

											if do_rescale and rescale_factor is None:
															raise ValueError("Rescale factor must be specified if do_rescale is True."				)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("Image mean and std must be specified if do_normalize is True."				)

											if offset and not do_rescale:
															raise ValueError("For offset, do_rescale must also be set to True."				)

											# All transformations expect numpy arrays.
											a__	:							int      						= to_numpy_array(__UpperCAmelCase				)

											if do_resize:
															a__	:							List[Any]      						= self.resize(image=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase				)

											if do_center_crop:
															a__	:							str      						= self.center_crop(__UpperCAmelCase ,					size=__UpperCAmelCase				)

											if do_rescale:
															a__	:							Optional[Any]      						= self.rescale(image=__UpperCAmelCase ,					scale=__UpperCAmelCase ,					offset=__UpperCAmelCase				)

											if do_normalize:
															a__	:							Union[str, Any]      						= self.normalize(image=__UpperCAmelCase ,					mean=__UpperCAmelCase ,					std=__UpperCAmelCase				)

											a__	:							Optional[int]      						= to_channel_dimension_format(__UpperCAmelCase ,					__UpperCAmelCase				)
											return image




							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = ChannelDimension.FIRST ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											a__	:							List[Any]      						= do_resize if do_resize is not None else self.do_resize
											a__	:							Dict      						= resample if resample is not None else self.resample
											a__	:							Tuple      						= do_center_crop if do_center_crop is not None else self.do_center_crop
											a__	:							Optional[Any]      						= do_rescale if do_rescale is not None else self.do_rescale
											a__	:							Optional[int]      						= rescale_factor if rescale_factor is not None else self.rescale_factor
											a__	:							List[Any]      						= offset if offset is not None else self.offset
											a__	:							Optional[Any]      						= do_normalize if do_normalize is not None else self.do_normalize
											a__	:							int      						= image_mean if image_mean is not None else self.image_mean
											a__	:							List[Any]      						= image_std if image_std is not None else self.image_std

											a__	:							int      						= size if size is not None else self.size
											a__	:							Tuple      						= get_size_dict(__UpperCAmelCase ,					default_to_square=__UpperCAmelCase				)
											a__	:							List[Any]      						= crop_size if crop_size is not None else self.crop_size
											a__	:							Union[str, Any]      						= get_size_dict(__UpperCAmelCase ,					param_name="crop_size"				)

											if not valid_images(__UpperCAmelCase				):
															raise ValueError(
															    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
															    "torch.Tensor, tf.Tensor or jax.ndarray."				)

											a__	:							Union[str, Any]      						= make_batched(__UpperCAmelCase				)

											a__	:							Optional[Any]      						= [
											    [
											        self._preprocess_image(
											            image=__UpperCAmelCase ,					do_resize=__UpperCAmelCase ,					size=__UpperCAmelCase ,					resample=__UpperCAmelCase ,					do_center_crop=__UpperCAmelCase ,					crop_size=__UpperCAmelCase ,					do_rescale=__UpperCAmelCase ,					rescale_factor=__UpperCAmelCase ,					offset=__UpperCAmelCase ,					do_normalize=__UpperCAmelCase ,					image_mean=__UpperCAmelCase ,					image_std=__UpperCAmelCase ,					data_format=__UpperCAmelCase ,					)
											        for img in video
											    ]
											    for video in videos
											]

											a__	:							Any      						= {"pixel_values": videos}
											return BatchFeature(data=__UpperCAmelCase ,					tensor_type=__UpperCAmelCase				)


