



import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


SCREAMING_SNAKE_CASE__   			=   '''
@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",
}
'''

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


SCREAMING_SNAKE_CASE__   			=   '''
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 A    (							__UpperCamelCase       )    ->   str:

			def remove_articles(__UpperCamelCase       ):
						A__	  =     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__	  =     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 A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   List[Any]:
			return int(normalize_answer(__UpperCamelCase       ) == normalize_answer(__UpperCamelCase       )       )


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     [any(compute_exact(__UpperCamelCase			,						__UpperCamelCase       ) for ref in refs       ) for pred, refs in zip(__UpperCamelCase			,						__UpperCamelCase       )]
			return (sum(__UpperCamelCase       ) / len(__UpperCamelCase       )) * 100


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     [rgram for rgrams in rgramslist for rgram in rgrams]
			A__	  =     Counter(__UpperCamelCase       )

			A__	  =     Counter(__UpperCamelCase       )
			A__	  =     Counter()
			for sgram, scount in sgramcounter.items():
						A__	  =     scount * numref

			A__	  =     Counter(__UpperCamelCase       )
			A__	  =     Counter()
			for cgram, ccount in cgramcounter.items():
						A__	  =     ccount * numref

			# KEEP
			A__	  =     sgramcounter_rep & cgramcounter_rep
			A__	  =     keepgramcounter_rep & rgramcounter
			A__	  =     sgramcounter_rep & rgramcounter

			A__	  =     0
			A__	  =     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__	  =     1
			A__	  =     1
			if len(__UpperCamelCase       ) > 0:
						A__	  =     keeptmpscorea / len(__UpperCamelCase       )
			if len(__UpperCamelCase       ) > 0:
						# Fix an alleged bug [2] in the keep score computation.
						# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
						A__	  =     keeptmpscorea / sum(keepgramcounterall_rep.values()       )
			A__	  =     0
			if keepscore_precision > 0 or keepscore_recall > 0:
						A__	  =     2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

			# DELETION
			A__	  =     sgramcounter_rep - cgramcounter_rep
			A__	  =     delgramcounter_rep - rgramcounter
			A__	  =     sgramcounter_rep - rgramcounter
			A__	  =     0
			A__	  =     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__	  =     1
			if len(__UpperCamelCase       ) > 0:
						A__	  =     deltmpscorea / len(__UpperCamelCase       )

			# ADDITION
			A__	  =     set(__UpperCamelCase       ) - set(__UpperCamelCase       )
			A__	  =     set(__UpperCamelCase       ) & set(__UpperCamelCase       )
			A__	  =     set(__UpperCamelCase       ) - set(__UpperCamelCase       )

			A__	  =     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__	  =     1
			A__	  =     1
			if len(__UpperCamelCase       ) > 0:
						A__	  =     addtmpscore / len(__UpperCamelCase       )
			if len(__UpperCamelCase       ) > 0:
						A__	  =     addtmpscore / len(__UpperCamelCase       )
			A__	  =     0
			if addscore_precision > 0 or addscore_recall > 0:
						A__	  =     2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Tuple:
			A__	  =     len(__UpperCamelCase       )

			A__	  =     ssent.split(' '       )
			A__	  =     csent.split(' '       )
			A__	  =     []
			A__	  =     []
			A__	  =     []
			A__	  =     []
			A__	  =     []
			A__	  =     []

			A__	  =     []
			A__	  =     []
			A__	  =     []
			A__	  =     []
			for rsent in rsents:
						A__	  =     rsent.split(' '       )
						A__	  =     []
						A__	  =     []
						A__	  =     []
						ragramslist.append(__UpperCamelCase       )
						for i in range(0			,						len(__UpperCamelCase       ) - 1       ):
									if i < len(__UpperCamelCase       ) - 1:
												A__	  =     ragrams[i] + ' ' + ragrams[i + 1]
												ragrams.append(__UpperCamelCase       )
									if i < len(__UpperCamelCase       ) - 2:
												A__	  =     ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
												ragrams.append(__UpperCamelCase       )
									if i < len(__UpperCamelCase       ) - 3:
												A__	  =     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__	  =     sagrams[i] + ' ' + sagrams[i + 1]
									sagrams.append(__UpperCamelCase       )
						if i < len(__UpperCamelCase       ) - 2:
									A__	  =     sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
									sagrams.append(__UpperCamelCase       )
						if i < len(__UpperCamelCase       ) - 3:
									A__	  =     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__	  =     cagrams[i] + ' ' + cagrams[i + 1]
									cagrams.append(__UpperCamelCase       )
						if i < len(__UpperCamelCase       ) - 2:
									A__	  =     cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
									cagrams.append(__UpperCamelCase       )
						if i < len(__UpperCamelCase       ) - 3:
									A__	  =     cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
									cagrams.append(__UpperCamelCase       )

			((A__)  ,			(A__)  ,			(A__))	  =     SARIngram(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			((A__)  ,			(A__)  ,			(A__))	  =     SARIngram(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			((A__)  ,			(A__)  ,			(A__))	  =     SARIngram(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			((A__)  ,			(A__)  ,			(A__))	  =     SARIngram(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			A__	  =     sum([keepascore, keepascore, keepascore, keepascore]       ) / 4
			A__	  =     sum([delascore, delascore, delascore, delascore]       ) / 4
			A__	  =     sum([addascore, addascore, addascore, addascore]       ) / 4
			A__	  =     (avgkeepscore + avgdelscore + avgaddscore) / 3
			return finalscore


def A    (							__UpperCamelCase			,						__UpperCamelCase = True			,						__UpperCamelCase = "13a"			,						__UpperCamelCase = True       )    ->   Dict:
			# 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__	  =     sentence.lower()

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

			if not return_str:
						A__	  =     normalized_sent.split()

			return normalized_sent


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			if not (len(__UpperCamelCase       ) == len(__UpperCamelCase       ) == len(__UpperCamelCase       )):
						raise ValueError('Sources length must match predictions and references lengths.'       )
			A__	  =     0
			for src, pred, refs in zip(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       ):
						sari_score += SARIsent(normalize(__UpperCamelCase       )			,						normalize(__UpperCamelCase       )			,						[normalize(__UpperCamelCase       ) for sent in refs]       )
			A__	  =     sari_score / len(__UpperCamelCase       )
			return 100 * sari_score


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase="exp"			,						__UpperCamelCase=None			,						__UpperCamelCase=False			,						__UpperCamelCase=False			,						__UpperCamelCase=False			,						)    ->   Union[str, Any]:
			A__	  =     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__	  =     [[refs[i] for refs in references] for i in range(__UpperCamelCase       )]
			A__	  =     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   __lowerCAmelCase  (		datasets.Metric   ):
					"""simple docstring"""






					def      _a				(  self       :							List[Any]       ):






								"""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       :							Any					,       _snake_case       :							Optional[int]					,       _snake_case       :							List[Any]					,       _snake_case       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     {}
								result.update({'sari': compute_sari(sources=_snake_case					,       predictions=_snake_case					,       references=_snake_case       )}       )
								result.update({'sacrebleu': compute_sacrebleu(predictions=_snake_case					,       references=_snake_case       )}       )
								result.update({'exact': compute_em(predictions=_snake_case					,       references=_snake_case       )}       )
								return result









import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					A__					:		Optional[Any]      							=     ["input_values", "attention_mask"]



					def __init__(  self       :							str					,       _snake_case       :							int = 1					,       _snake_case       :							int = 1_60_00					,       _snake_case       :							float = 0.0					,       _snake_case       :							bool = False					,       _snake_case       :							int = 80					,       _snake_case       :							int = 16					,       _snake_case       :							int = 64					,       _snake_case       :							str = "hann_window"					,       _snake_case       :							float = 1.0					,       _snake_case       :							float = 80					,       _snake_case       :							float = 76_00					,       _snake_case       :							float = 1E-10					,       _snake_case       :							int = 2					,       _snake_case       :							bool = True					,       **_snake_case       :							Union[str, Any]					,       ):






								"""simple docstring"""


								super().__init__(feature_size=_snake_case					,       sampling_rate=_snake_case					,       padding_value=_snake_case					,       **_snake_case       )
								A__	  =     do_normalize
								A__	  =     return_attention_mask

								A__	  =     num_mel_bins
								A__	  =     hop_length
								A__	  =     win_length
								A__	  =     win_function
								A__	  =     frame_signal_scale
								A__	  =     fmin
								A__	  =     fmax
								A__	  =     mel_floor
								A__	  =     reduction_factor

								A__	  =     win_length * sampling_rate // 10_00
								A__	  =     hop_length * sampling_rate // 10_00
								A__	  =     optimal_fft_length(self.sample_size       )
								A__	  =     (self.n_fft // 2) + 1

								A__	  =     window_function(window_length=self.sample_size					,       name=self.win_function					,       periodic=_snake_case       )

								A__	  =     mel_filter_bank(
								    num_frequency_bins=self.n_freqs					,       num_mel_filters=self.num_mel_bins					,       min_frequency=self.fmin					,       max_frequency=self.fmax					,       sampling_rate=self.sampling_rate					,       norm='slaney'					,       mel_scale='slaney'					,       )

								if frame_signal_scale != 1.0:
											warnings.warn(
											    'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'					,       _snake_case					,       )
								if reduction_factor != 2.0:
											warnings.warn(
											    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'					,       _snake_case					,       )



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def      _a				(  _snake_case       :							List[np.ndarray]					,       _snake_case       :							List[np.ndarray]					,       _snake_case       :							float = 0.0       ):






								"""simple docstring"""


								if attention_mask is not None:
											A__	  =     np.array(_snake_case					,       np.intaa       )
											A__	  =     []

											for vector, length in zip(_snake_case					,       attention_mask.sum(-1       )       ):
														A__	  =     (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
														if length < normed_slice.shape[0]:
																	A__	  =     padding_value

														normed_input_values.append(_snake_case       )
								else:
											A__	  =     [(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

								return normed_input_values



					def      _a				(  self       :							Tuple					,       _snake_case       :							np.ndarray					,       ):






								"""simple docstring"""


								A__	  =     spectrogram(
								    _snake_case					,       window=self.window					,       frame_length=self.sample_size					,       hop_length=self.sample_stride					,       fft_length=self.n_fft					,       mel_filters=self.mel_filters					,       mel_floor=self.mel_floor					,       log_mel='log10'					,       )
								return log_mel_spec.T



					def __call__(  self       :							List[str]					,       _snake_case       :							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					,       _snake_case       :							Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       _snake_case       :							Optional[int] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								if audio is None and audio_target is None:
											raise ValueError('You must provide either `audio` or `audio_target` values.'       )

								if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
														raise ValueError(
														    F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
														    F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
														    F''' {self.sampling_rate} and not {sampling_rate}.'''       )
								else:
											logger.warning(
											    'It is strongly recommended to pass the ``sampling_rate`` argument to this function. '
											    'Failing to do so can result in silent errors that might be hard to debug.'       )

								if audio is not None:
											A__	  =     self._process_audio(
											    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       **_snake_case					,       )
								else:
											A__	  =     None

								if audio_target is not None:
											A__	  =     self._process_audio(
											    _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       _snake_case					,       **_snake_case					,       )

											if inputs is None:
														return inputs_target
											else:
														A__	  =     inputs_target['input_values']
														A__	  =     inputs_target.get('attention_mask'       )
														if decoder_attention_mask is not None:
																	A__	  =     decoder_attention_mask

								return inputs



					def      _a				(  self       :							Tuple					,       _snake_case       :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,       _snake_case       :							bool = False					,       _snake_case       :							Union[bool, str, PaddingStrategy] = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							bool = False					,       _snake_case       :							Optional[int] = None					,       _snake_case       :							Optional[bool] = None					,       _snake_case       :							Optional[Union[str, TensorType]] = None					,       **_snake_case       :							Tuple					,       ):






								"""simple docstring"""


								A__	  =     isinstance(_snake_case					,       np.ndarray       ) and len(speech.shape       ) > 1
								if is_batched_numpy and len(speech.shape       ) > 2:
											raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''       )
								A__	  =     is_batched_numpy or (
								    isinstance(_snake_case					,       (list, tuple)       ) and (isinstance(speech[0]					,       (np.ndarray, tuple, list)       ))
								)

								if is_batched:
											A__	  =     [np.asarray(_snake_case					,       dtype=np.floataa       ) for speech in speech]
								elif not is_batched and not isinstance(_snake_case					,       np.ndarray       ):
											A__	  =     np.asarray(_snake_case					,       dtype=np.floataa       )
								elif isinstance(_snake_case					,       np.ndarray       ) and speech.dtype is np.dtype(np.floataa       ):
											A__	  =     speech.astype(np.floataa       )

								# always return batch
								if not is_batched:
											A__	  =     [speech]

								# needed to make pad() work on spectrogram inputs
								A__	  =     self.feature_size

								# convert into correct format for padding
								if is_target:
											A__	  =     [self._extract_mel_features(_snake_case       ) for waveform in speech]
											A__	  =     BatchFeature({'input_values': features}       )
											A__	  =     self.num_mel_bins
								else:
											A__	  =     BatchFeature({'input_values': speech}       )

								A__	  =     self.pad(
								    _snake_case					,       padding=_snake_case					,       max_length=_snake_case					,       truncation=_snake_case					,       pad_to_multiple_of=_snake_case					,       return_attention_mask=_snake_case					,       **_snake_case					,       )

								A__	  =     feature_size_hack

								# convert input values to correct format
								A__	  =     padded_inputs['input_values']
								if not isinstance(input_values[0]					,       np.ndarray       ):
											A__	  =     [np.asarray(_snake_case					,       dtype=np.floataa       ) for array in input_values]
								elif (
								    not isinstance(_snake_case					,       np.ndarray       )
								    and isinstance(input_values[0]					,       np.ndarray       )
								    and input_values[0].dtype is np.dtype(np.floataa       )
								):
											A__	  =     [array.astype(np.floataa       ) for array in input_values]
								elif isinstance(_snake_case					,       np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
											A__	  =     input_values.astype(np.floataa       )

								# convert attention_mask to correct format
								A__	  =     padded_inputs.get('attention_mask'       )
								if attention_mask is not None:
											A__	  =     [np.asarray(_snake_case					,       dtype=np.intaa       ) for array in attention_mask]

								# zero-mean and unit-variance normalization
								if not is_target and self.do_normalize:
											A__	  =     (
											    attention_mask
											    if self._get_padding_strategies(_snake_case					,       max_length=_snake_case       ) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											A__	  =     self.zero_mean_unit_var_norm(
											    padded_inputs['input_values']					,       attention_mask=_snake_case					,       padding_value=self.padding_value       )

								if return_tensors is not None:
											A__	  =     padded_inputs.convert_to_tensors(_snake_case       )

								return padded_inputs



					def      _a				(  self       :							Optional[Any]       ):






								"""simple docstring"""


								A__	  =     super().to_dict()

								# Don't serialize these as they are derived from the other properties.
								A__	  =     ['window', 'mel_filters', 'sample_size', 'sample_stride', 'n_fft', 'n_freqs']
								for name in names:
											if name in output:
														del output[name]

								return output




