




import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


UpperCAmelCase_		:					Dict          =  datasets.logging.get_logger(__name__)


UpperCAmelCase_		:					Optional[int]          =  "\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

UpperCAmelCase_		:					Union[str, Any]          =  "\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

UpperCAmelCase_		:					Dict          =  "\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase=False						,  lowerCamelCase=False						,  lowerCamelCase=True						,  lowerCamelCase=False						,  lowerCamelCase="dummy_doc" ):
				__magic_name__						:						Optional[int]										={doc: key_lines}
				__magic_name__						:						Optional[int]										={doc: sys_lines}

				__magic_name__						:						Tuple										={}

				__magic_name__						:						Optional[Any]										=0
				__magic_name__						:						str										=0
				__magic_name__						:						str										=0
				__magic_name__						:						Optional[Any]										=0
				__magic_name__						:						str										=0
				__magic_name__						:						Tuple										=0

				__magic_name__					,			__magic_name__						:						str										=reader.get_doc_mentions(lowerCamelCase						,  key_doc_lines[doc]						,  lowerCamelCase )
				key_singletons_num += singletons_num

				if NP_only or min_span:
								__magic_name__						:						List[Any]										=reader.set_annotated_parse_trees(lowerCamelCase						,  key_doc_lines[doc]						,  lowerCamelCase						,  lowerCamelCase )

				__magic_name__					,			__magic_name__						:						Dict										=reader.get_doc_mentions(lowerCamelCase						,  sys_doc_lines[doc]						,  lowerCamelCase )
				sys_singletons_num += singletons_num

				if NP_only or min_span:
								__magic_name__						:						str										=reader.set_annotated_parse_trees(lowerCamelCase						,  key_doc_lines[doc]						,  lowerCamelCase						,  lowerCamelCase )

				if remove_nested:
								__magic_name__					,			__magic_name__						:						List[str]										=reader.remove_nested_coref_mentions(lowerCamelCase						,  lowerCamelCase )
								key_nested_coref_num += nested_mentions
								key_removed_nested_clusters += removed_clusters

								__magic_name__					,			__magic_name__						:						int										=reader.remove_nested_coref_mentions(lowerCamelCase						,  lowerCamelCase )
								sys_nested_coref_num += nested_mentions
								sys_removed_nested_clusters += removed_clusters

				__magic_name__						:						Optional[Any]										=reader.get_mention_assignments(lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						str										=reader.get_mention_assignments(lowerCamelCase						,  lowerCamelCase )

				__magic_name__						:						List[str]										=(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

				if remove_nested:
								logger.info(
								    """Number of removed nested coreferring mentions in the key """
								    F"annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}" )
								logger.info(
								    """Number of resulting singleton clusters in the key """
								    F"annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}" )

				if not keep_singletons:
								logger.info(
								    F"{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system "
								    """files, respectively""" )

				return doc_coref_infos




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						Dict										=get_coref_infos(lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase						,  lowerCamelCase )

				__magic_name__						:						Union[str, Any]										={}
				__magic_name__						:						Union[str, Any]										=0
				__magic_name__						:						Optional[Any]										=0

				for name, metric in metrics:
								__magic_name__					,			__magic_name__					,			__magic_name__						:						Any										=evaluator.evaluate_documents(lowerCamelCase						,  lowerCamelCase						,  beta=1 )
								if name in ["muc", "bcub", "ceafe"]:
												conll += fa
												conll_subparts_num += 1
								output_scores.update({F"{name}/recall": recall, F"{name}/precision": precision, F"{name}/f1": fa} )

								logger.info(
								    name.ljust(10 )						,  F"Recall: {recall * 100:.2f}"						,  F" Precision: {precision * 100:.2f}"						,  F" F1: {fa * 100:.2f}"						,  )

				if conll_subparts_num == 3:
								__magic_name__						:						Union[str, Any]										=(conll / 3) * 100
								logger.info(F"CoNLL score: {conll:.2f}" )
								output_scores.update({"""conll_score""": conll} )

				return output_scores




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Any										=False
				for line in key_lines:
								if not line.startswith("""#""" ):
												if len(line.split() ) > 6:
																__magic_name__						:						Dict										=line.split()[5]
																if not parse_col == "-":
																				__magic_name__						:						int										=True
																				break
																else:
																				break
				return has_gold_parse




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

						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION     ,			citation=_CITATION     ,			inputs_description=_KWARGS_DESCRIPTION     ,			features=datasets.Features(
										        {
										            """predictions""": datasets.Sequence(datasets.Value("""string"""			)			),
										            """references""": datasets.Sequence(datasets.Value("""string"""			)			),
										        }			)     ,			codebase_urls=["""https://github.com/ns-moosavi/coval"""]     ,			reference_urls=[
										        """https://github.com/ns-moosavi/coval""",
										        """https://www.aclweb.org/anthology/P16-1060""",
										        """http://www.conll.cemantix.org/2012/data.html""",
										    ]     ,			)


						def      A__    (		self :List[str]     ,			__snake_case :Union[str, Any]     ,			__snake_case :Optional[int]     ,			__snake_case :Any=True     ,			__snake_case :Union[str, Any]=False     ,			__snake_case :str=False     ,			__snake_case :Optional[Any]=False			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=[
										    ("""mentions""", evaluator.mentions),
										    ("""muc""", evaluator.muc),
										    ("""bcub""", evaluator.b_cubed),
										    ("""ceafe""", evaluator.ceafe),
										    ("""lea""", evaluator.lea),
										]

										if min_span:
														__magic_name__						:						List[str]										=util.check_gold_parse_annotation(__snake_case			)
														if not has_gold_parse:
																		raise NotImplementedError("""References should have gold parse annotation to use 'min_span'."""			)
																		# util.parse_key_file(key_file)
																		# key_file = key_file + ".parsed"

										__magic_name__						:						str										=evaluate(
										    key_lines=__snake_case     ,			sys_lines=__snake_case     ,			metrics=__snake_case     ,			NP_only=__snake_case     ,			remove_nested=__snake_case     ,			keep_singletons=__snake_case     ,			min_span=__snake_case     ,			)

										return score






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


UpperCamelCase_						:     Union[str, Any]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   ["input_values", "attention_mask"]






	def __init__(self				: int      ,	snake_case				: int = 1      ,	snake_case				: int = 1_6000      ,	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 = 7600      ,	snake_case				: float = 1e-10      ,	snake_case				: int = 2      ,	snake_case				: bool = True      ,	**snake_case				: Any      ,	) ->			str:
							super().__init__(feature_size=snake_case      ,	sampling_rate=snake_case      ,	padding_value=snake_case      ,	**snake_case    )
							_lowercase       :	int           =   do_normalize
							_lowercase       :	Any           =   return_attention_mask

							_lowercase       :	str           =   num_mel_bins
							_lowercase       :	str           =   hop_length
							_lowercase       :	List[Any]           =   win_length
							_lowercase       :	Tuple           =   win_function
							_lowercase       :	Any           =   frame_signal_scale
							_lowercase       :	Any           =   fmin
							_lowercase       :	Any           =   fmax
							_lowercase       :	List[str]           =   mel_floor
							_lowercase       :	str           =   reduction_factor

							_lowercase       :	str           =   win_length * sampling_rate // 1000
							_lowercase       :	Optional[int]           =   hop_length * sampling_rate // 1000
							_lowercase       :	Tuple           =   optimal_fft_length(self.sample_size    )
							_lowercase       :	int           =   (self.n_fft // 2) + 1

							_lowercase       :	Tuple           =   window_function(window_length=self.sample_size      ,	name=self.win_function      ,	periodic=snake_case    )

							_lowercase       :	Optional[int]           =   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    ) ->			List[np.ndarray]:
							if attention_mask is not None:
													_lowercase       :	List[str]           =   np.array(snake_case      ,	np.intaa    )
													_lowercase       :	Dict           =   []

													for vector, length in zip(snake_case      ,	attention_mask.sum(-1    )    ):
																			_lowercase       :	Tuple           =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
																			if length < normed_slice.shape[0]:
																									_lowercase       :	List[str]           =   padding_value

																			normed_input_values.append(snake_case    )
							else:
													_lowercase       :	Dict           =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

							return normed_input_values






	def       _a(self				: Optional[Any]      ,	snake_case				: np.ndarray      ,	) ->			np.ndarray:
							_lowercase       :	Tuple           =   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				: Tuple      ,	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				: Union[str, Any]      ,	) ->			BatchFeature:
							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:
													_lowercase       :	Union[str, Any]           =   self._process_audio(
													    snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	snake_case      ,	**snake_case      ,	)
							else:
													_lowercase       :	str           =   None

							if audio_target is not None:
													_lowercase       :	Union[str, Any]           =   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:
																			_lowercase       :	int           =   inputs_target["input_values"]
																			_lowercase       :	Optional[int]           =   inputs_target.get("attention_mask"    )
																			if decoder_attention_mask is not None:
																									_lowercase       :	List[str]           =   decoder_attention_mask

							return inputs






	def       _a(self				: Optional[Any]      ,	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				: int      ,	) ->			BatchFeature:
							_lowercase       :	int           =   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}"""    )
							_lowercase       :	List[str]           =   is_batched_numpy or (
							    isinstance(snake_case      ,	(list, tuple)    ) and (isinstance(speech[0]      ,	(np.ndarray, tuple, list)    ))
							)

							if is_batched:
													_lowercase       :	List[Any]           =   [np.asarray(snake_case      ,	dtype=np.floataa    ) for speech in speech]
							elif not is_batched and not isinstance(snake_case      ,	np.ndarray    ):
													_lowercase       :	Optional[Any]           =   np.asarray(snake_case      ,	dtype=np.floataa    )
							elif isinstance(snake_case      ,	np.ndarray    ) and speech.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Optional[int]           =   speech.astype(np.floataa    )

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

							# needed to make pad() work on spectrogram inputs
							_lowercase       :	Tuple           =   self.feature_size

							# convert into correct format for padding
							if is_target:
													_lowercase       :	List[str]           =   [self._extract_mel_features(snake_case    ) for waveform in speech]
													_lowercase       :	int           =   BatchFeature({"input_values": features}    )
													_lowercase       :	str           =   self.num_mel_bins
							else:
													_lowercase       :	Optional[int]           =   BatchFeature({"input_values": speech}    )

							_lowercase       :	List[Any]           =   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      ,	)

							_lowercase       :	Optional[Any]           =   feature_size_hack

							# convert input values to correct format
							_lowercase       :	int           =   padded_inputs["input_values"]
							if not isinstance(input_values[0]      ,	np.ndarray    ):
													_lowercase       :	Any           =   [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    )
							):
													_lowercase       :	Union[str, Any]           =   [array.astype(np.floataa    ) for array in input_values]
							elif isinstance(snake_case      ,	np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
													_lowercase       :	Tuple           =   input_values.astype(np.floataa    )

							# convert attention_mask to correct format
							_lowercase       :	Union[str, Any]           =   padded_inputs.get("attention_mask"    )
							if attention_mask is not None:
													_lowercase       :	str           =   [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:
													_lowercase       :	Dict           =   (
													    attention_mask
													    if self._get_padding_strategies(snake_case      ,	max_length=snake_case    ) is not PaddingStrategy.DO_NOT_PAD
													    else None
													)
													_lowercase       :	List[Any]           =   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:
													_lowercase       :	Any           =   padded_inputs.convert_to_tensors(snake_case    )

							return padded_inputs






	def       _a(self				: Dict    ) ->			Dict[str, Any]:
							_lowercase       :	str           =   super().to_dict()

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

							return output




