



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

import numpy as np

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


UpperCAmelCase_							:      Any      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   int				=	["""input_features"""]



	def __init__(       self			,	UpperCAmelCase__=80			,	UpperCAmelCase__=16_000			,	UpperCAmelCase__=160			,	UpperCAmelCase__=30			,	UpperCAmelCase__=400			,	UpperCAmelCase__=0.0			,	UpperCAmelCase__=False			,	**UpperCAmelCase__			,	):
							super().__init__(
							    feature_size=UpperCAmelCase__			,	sampling_rate=UpperCAmelCase__			,	padding_value=UpperCAmelCase__			,	return_attention_mask=UpperCAmelCase__			,	**UpperCAmelCase__			,	)
							A__							=       n_fft
							A__							=       hop_length
							A__							=       chunk_length
							A__							=       chunk_length * sampling_rate
							A__							=       self.n_samples // hop_length
							A__							=       sampling_rate
							A__							=       mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2			,	num_mel_filters=UpperCAmelCase__			,	min_frequency=0.0			,	max_frequency=8_000.0			,	sampling_rate=UpperCAmelCase__			,	norm="slaney"			,	mel_scale="slaney"			,	)



	def 			__A							(       self			,	UpperCAmelCase__		):
							A__							=       spectrogram(
							    UpperCAmelCase__			,	window_function(self.n_fft			,	"hann"		)			,	frame_length=self.n_fft			,	hop_length=self.hop_length			,	power=2.0			,	mel_filters=self.mel_filters			,	log_mel="log10"			,	)
							A__							=       log_spec[:, :-1]
							A__							=       np.maximum(UpperCAmelCase__			,	log_spec.max() - 8.0		)
							A__							=       (log_spec + 4.0) / 4.0
							return log_spec



	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def 			__A							(       UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = 0.0		):
							if attention_mask is not None:
													A__							=       np.array(UpperCAmelCase__			,	np.intaa		)
													A__							=       []

													for vector, length in zip(UpperCAmelCase__			,	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(UpperCAmelCase__		)
							else:
													A__							=       [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

							return normed_input_values



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = "max_length"			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = None			,	**UpperCAmelCase__			,	):

							if sampling_rate is not None:
													if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																			    F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																			    F""" was sampled with {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."		)

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

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

							# always return batch
							if not is_batched:
													A__							=       [np.asarray([raw_speech]		).T]

							A__							=       BatchFeature({"input_features": raw_speech}		)

							# convert into correct format for padding

							A__							=       self.pad(
							    UpperCAmelCase__			,	padding=UpperCAmelCase__			,	max_length=max_length if max_length else self.n_samples			,	truncation=UpperCAmelCase__			,	pad_to_multiple_of=UpperCAmelCase__			,	return_attention_mask=return_attention_mask or do_normalize			,	)

							# zero-mean and unit-variance normalization
							if do_normalize:
													A__							=       self.zero_mean_unit_var_norm(
													    padded_inputs["input_features"]			,	attention_mask=padded_inputs["attention_mask"]			,	padding_value=self.padding_value			,	)
													A__							=       np.stack(padded_inputs["input_features"]			,	axis=0		)

							# make sure list is in array format
							A__							=       padded_inputs.get("input_features"		).transpose(2			,	0			,	1		)

							A__							=       [self._np_extract_fbank_features(UpperCAmelCase__		) for waveform in input_features[0]]

							if isinstance(input_features[0]			,	UpperCAmelCase__		):
													A__							=       [np.asarray(UpperCAmelCase__			,	dtype=np.floataa		) for feature in input_features]
							else:
													A__							=       input_features

							if return_attention_mask:
													# rescale from sample (48000) to feature (3000)
													A__							=       padded_inputs["attention_mask"][:, :: self.hop_length]

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

							return padded_inputs



	def 			__A							(       self		):
							A__							=       copy.deepcopy(self.__dict__		)
							A__							=       self.__class__.__name__
							if "mel_filters" in output:
													del output["mel_filters"]
							return output










def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       str:





						"""simple docstring"""





						A__							=       len(_A    )
						A__							=       len(_A    )
						A__							=       (
						    first_str_length if first_str_length > second_str_length else second_str_length
						)
						A__							=       []
						for char_count in range(_A    ):
												if char_count < first_str_length:
																		output_list.append(first_str[char_count]    )
												if char_count < second_str_length:
																		output_list.append(second_str[char_count]    )
						return "".join(_A    )


if __name__ == "__main__":
						print(alternative_string_arrange("AB", "XYZ"), end=" ")





