
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


lowercase					:							int   =      logging.get_logger(__name__)


class 		__UpperCAmelCase   (      __SCREAMING_SNAKE_CASE		):
							__lowercase   	= ["""input_values""", """attention_mask"""]


							def __init__( self					,     lowerCAmelCase_ = 1					,     lowerCAmelCase_ = 1_60_00					,     lowerCAmelCase_ = 0.0					,     lowerCAmelCase_ = False					,     lowerCAmelCase_ = 80					,     lowerCAmelCase_ = 16					,     lowerCAmelCase_ = 64					,     lowerCAmelCase_ = "hann_window"					,     lowerCAmelCase_ = 1.0					,     lowerCAmelCase_ = 80					,     lowerCAmelCase_ = 76_00					,     lowerCAmelCase_ = 1E-10					,     lowerCAmelCase_ = 2					,     lowerCAmelCase_ = True					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






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

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

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

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

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


							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def 				lowerCamelCase       ( lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_ = 0.0						):





								"""simple docstring"""






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

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

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

								return normed_input_values


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	spectrogram(
								    _a					,     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					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = False					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = False					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     **lowerCAmelCase_					,     ):





								"""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:
									_snake_case     		=	self._process_audio(
									    _a					,     _a					,     _a					,     _a					,     _a					,     _a					,     _a					,     _a					,     **_a					,     )
								else:
									_snake_case     		=	None

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

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

								return inputs


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = False					,     lowerCAmelCase_ = False					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = False					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								_snake_case     		=	isinstance(_a					,     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}'						)
								_snake_case     		=	is_batched_numpy or (
								    isinstance(_a					,     (list, tuple)						) and (isinstance(speech[0]					,     (np.ndarray, tuple, list)						))
								)

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

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

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

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

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

								_snake_case     		=	feature_size_hack

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

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

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

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

								return padded_inputs




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	super().to_dict()

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

								return output



'''simple docstring'''







from ..utils import DummyObject, requires_backends


class 		__UpperCAmelCase   (      metaclass=_lowerCamelCase		):
							__lowercase   	= ["""torch""", """torchsde"""]


							def __init__( self					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(self					,     ['torch', 'torchsde']						)


							@classmethod
							def 				lowerCamelCase       ( cls					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['torch', 'torchsde']						)




							@classmethod
							def 				lowerCamelCase       ( cls					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								requires_backends(cls					,     ['torch', 'torchsde']						)

