

'''simple docstring'''


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

import numpy as np
import torch

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


lowerCAmelCase__						:       Optional[Any]										=						logging.get_logger(__name__)




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    ["""input_features""", """is_longer"""]







						def __init__(							self					:      Union[str, Any]			,			snake_case_					:      Dict=6_4			,			snake_case_					:      Dict=4_8_0_0_0			,			snake_case_					:      Union[str, Any]=4_8_0			,			snake_case_					:      List[str]=1_0			,			snake_case_					:      str=1_0_2_4			,			snake_case_					:      Union[str, Any]=0.0			,			snake_case_					:      int=False			,			snake_case_					:      float = 0			,			snake_case_					:      float = 1_4_0_0_0			,			snake_case_					:      int = None			,			snake_case_					:      str = "fusion"			,			snake_case_					:      str = "repeatpad"			,			**snake_case_					:      Any			,			):



								'''simple docstring'''



								super().__init__(
								    feature_size=snake_case_			,			sampling_rate=snake_case_			,			padding_value=snake_case_			,			return_attention_mask=snake_case_			,			**snake_case_			,			)
								snake_case__						:       Optional[Any]       						=  top_db
								snake_case__						:       Union[str, Any]       						=  truncation
								snake_case__						:       Optional[int]       						=  padding
								snake_case__						:       Optional[Any]       						=  fft_window_size
								snake_case__						:       Tuple       						=  (fft_window_size >> 1) + 1
								snake_case__						:       Dict       						=  hop_length
								snake_case__						:       int       						=  max_length_s
								snake_case__						:       Optional[Any]       						=  max_length_s * sampling_rate
								snake_case__						:       Union[str, Any]       						=  sampling_rate
								snake_case__						:       Union[str, Any]       						=  frequency_min
								snake_case__						:       List[str]       						=  frequency_max
								snake_case__						:       int       						=  mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins			,			num_mel_filters=snake_case_			,			min_frequency=snake_case_			,			max_frequency=snake_case_			,			sampling_rate=snake_case_			,			norm=snake_case_			,			mel_scale='''htk'''			,			)
								snake_case__						:       List[Any]       						=  mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins			,			num_mel_filters=snake_case_			,			min_frequency=snake_case_			,			max_frequency=snake_case_			,			sampling_rate=snake_case_			,			norm='''slaney'''			,			mel_scale='''slaney'''			,			)







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  copy.deepcopy(self.__dict__  )
								snake_case__						:       List[Any]       						=  self.__class__.__name__
								if "mel_filters" in output:
										del output["mel_filters"]
								if "mel_filters_slaney" in output:
										del output["mel_filters_slaney"]
								return output







						def       __magic_name__							(							self					:      Any			,			snake_case_					:      np.array			,			snake_case_					:      Optional[np.array] = None  ):



								'''simple docstring'''



								snake_case__						:       int       						=  spectrogram(
								    snake_case_			,			window_function(self.fft_window_size			,			'''hann'''  )			,			frame_length=self.fft_window_size			,			hop_length=self.hop_length			,			power=2.0			,			mel_filters=snake_case_			,			log_mel='''dB'''			,			)
								return log_mel_spectrogram.T







						def       __magic_name__							(							self					:      List[Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      int			,			snake_case_					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  np.array_split(list(range(0			,			total_frames - chunk_frames + 1  )  )			,			3  )
								if len(ranges[1]  ) == 0:
										# if the audio is too short, we just use the first chunk
										snake_case__						:       Any       						=  [0]
								if len(ranges[2]  ) == 0:
										# if the audio is too short, we just use the first chunk
										snake_case__						:       Optional[Any]       						=  [0]
								# randomly choose index for each part
								snake_case__						:       List[str]       						=  np.random.choice(ranges[0]  )
								snake_case__						:       int       						=  np.random.choice(ranges[1]  )
								snake_case__						:       Dict       						=  np.random.choice(ranges[2]  )

								snake_case__						:       List[Any]       						=  mel[idx_front : idx_front + chunk_frames, :]
								snake_case__						:       Any       						=  mel[idx_middle : idx_middle + chunk_frames, :]
								snake_case__						:       Tuple       						=  mel[idx_back : idx_back + chunk_frames, :]

								snake_case__						:       Tuple       						=  torch.tensor(mel[None, None, :]  )
								snake_case__						:       Dict       						=  torch.nn.functional.interpolate(
								    snake_case_			,			size=[chunk_frames, 6_4]			,			mode='''bilinear'''			,			align_corners=snake_case_  )
								snake_case__						:       List[Any]       						=  mel_shrink[0][0].numpy()
								snake_case__						:       str       						=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,			axis=0  )
								return mel_fusion







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      np.array			,			snake_case_					:      List[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      Dict  ):



								'''simple docstring'''



								if waveform.shape[0] > max_length:
										if truncation == "rand_trunc":
												snake_case__						:       Dict       						=  True
												# random crop to max_length (for compatibility) -> this should be handled by self.pad
												snake_case__						:       Dict       						=  len(snake_case_  ) - max_length
												snake_case__						:       Union[str, Any]       						=  np.random.randint(0			,			overflow + 1  )
												snake_case__						:       List[Any]       						=  waveform[idx : idx + max_length]
												snake_case__						:       Dict       						=  self._np_extract_fbank_features(snake_case_			,			self.mel_filters_slaney  )[None, :]
										elif truncation == "fusion":
												snake_case__						:       Union[str, Any]       						=  self._np_extract_fbank_features(snake_case_			,			self.mel_filters  )
												snake_case__						:       Optional[int]       						=  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
												snake_case__						:       str       						=  mel.shape[0]
												if chunk_frames == total_frames:
														# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
														# In this case, we just use the whole audio.
														snake_case__						:       Tuple       						=  np.stack([mel, mel, mel, mel]			,			axis=0  )
														snake_case__						:       Tuple       						=  False
												else:
														snake_case__						:       Any       						=  self._random_mel_fusion(snake_case_			,			snake_case_			,			snake_case_  )
														snake_case__						:       List[Any]       						=  True
										else:
												raise NotImplementedError(F"""data_truncating {truncation} not implemented"""  )

								else:
										snake_case__						:       List[Any]       						=  False
										# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
										if waveform.shape[0] < max_length:
												if padding == "repeat":
														snake_case__						:       List[str]       						=  int(max_length / len(snake_case_  )  )
														snake_case__						:       int       						=  np.stack(np.tile(snake_case_			,			n_repeat + 1  )  )[:max_length]
												if padding == "repeatpad":
														snake_case__						:       Optional[int]       						=  int(max_length / len(snake_case_  )  )
														snake_case__						:       List[Any]       						=  np.stack(np.tile(snake_case_			,			snake_case_  )  )
												snake_case__						:       str       						=  np.pad(snake_case_			,			(0, max_length - waveform.shape[0])			,			mode='''constant'''			,			constant_values=0  )

										if truncation == "fusion":
												snake_case__						:       Optional[Any]       						=  self._np_extract_fbank_features(snake_case_			,			self.mel_filters  )
												snake_case__						:       Dict       						=  np.stack([input_mel, input_mel, input_mel, input_mel]			,			axis=0  )
										else:
												snake_case__						:       Any       						=  self._np_extract_fbank_features(snake_case_			,			self.mel_filters_slaney  )[None, :]

								return input_mel, longer







						def __call__(							self					:      Tuple			,			snake_case_					:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,			snake_case_					:      str = None			,			snake_case_					:      Optional[str] = None			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      Optional[Union[str, TensorType]] = None			,			**snake_case_					:      Any			,			):



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  truncation if truncation is not None else self.truncation
								snake_case__						:       Optional[Any]       						=  padding if padding else self.padding

								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.'''  )

								snake_case__						:       Union[str, Any]       						=  isinstance(snake_case_			,			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}"""  )
								snake_case__						:       Dict       						=  is_batched_numpy or (
								    isinstance(snake_case_			,			(list, tuple)  ) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)  ))
								)

								if is_batched:
										snake_case__						:       Optional[Any]       						=  [np.asarray(snake_case_			,			dtype=np.floataa  ) for speech in raw_speech]
								elif not is_batched and not isinstance(snake_case_			,			np.ndarray  ):
										snake_case__						:       List[Any]       						=  np.asarray(snake_case_			,			dtype=np.floataa  )
								elif isinstance(snake_case_			,			np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
										snake_case__						:       Union[str, Any]       						=  raw_speech.astype(np.floataa  )

								# always return batch
								if not is_batched:
										snake_case__						:       Optional[Any]       						=  [np.asarray(snake_case_  )]

								# convert to mel spectrogram, truncate and pad if needed.
								snake_case__						:       Any       						=  [
								    self._get_input_mel(snake_case_			,			max_length if max_length else self.nb_max_samples			,			snake_case_			,			snake_case_  )
								    for waveform in raw_speech
								]

								snake_case__						:       List[Any]       						=  []
								snake_case__						:       Any       						=  []
								for mel, longer in padded_inputs:
										input_mel.append(snake_case_  )
										is_longer.append(snake_case_  )

								if truncation == "fusion" and sum(snake_case_  ) == 0:
										# if no audio is longer than 10s, then randomly select one audio to be longer
										snake_case__						:       Dict       						=  np.random.randint(0			,			len(snake_case_  )  )
										snake_case__						:       List[str]       						=  True

								if isinstance(input_mel[0]			,			snake_case_  ):
										snake_case__						:       Any       						=  [np.asarray(snake_case_			,			dtype=np.floataa  ) for feature in input_mel]

								# is_longer is a list of bool
								snake_case__						:       str       						=  [[longer] for longer in is_longer]

								snake_case__						:       Dict       						=  {'''input_features''': input_mel, '''is_longer''': is_longer}
								snake_case__						:       int       						=  BatchFeature(snake_case_  )

								if return_tensors is not None:
										snake_case__						:       int       						=  input_features.convert_to_tensors(snake_case_  )

								return input_features






'''simple docstring'''


from __future__ import annotations

from typing import Any




class     a			:






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      int			,			snake_case_					:      float = 0  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Optional[Any]       						=  row, column
								snake_case__						:       Dict       						=  [[default_value for c in range(snake_case_  )] for r in range(snake_case_  )]







						def __str__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

								# Make string identifier
								snake_case__						:       List[str]       						=  0
								for row_vector in self.array:
										for obj in row_vector:
												snake_case__						:       str       						=  max(snake_case_			,			len(str(snake_case_  )  )  )
								snake_case__						:       Dict       						=  F"""%{max_element_length}s"""

								# Make string and return
								def single_line(snake_case_					:      list[float]  ) -> str:
										nonlocal string_format_identifier
										snake_case__						:       str       						=  '''['''
										line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
										line += "]"
										return line

								s += "\n".join(single_line(snake_case_  ) for row_vector in self.array  )
								return s







						def __repr__(							self					:      Optional[int]  ):



								'''simple docstring'''



								return str(self  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								if not (isinstance(snake_case_			,			(list, tuple)  ) and len(snake_case_  ) == 2):
										return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
										return False
								else:
										return True







						def __getitem__(							self					:      List[str]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								return self.array[loc[0]][loc[1]]







						def __setitem__(							self					:      int			,			snake_case_					:      tuple[int, int]			,			snake_case_					:      float  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								snake_case__						:       Optional[Any]       						=  value







						def __add__(							self					:      Dict			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  )
								assert self.row == another.row and self.column == another.column

								# Add
								snake_case__						:       str       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       Optional[Any]       						=  self[r, c] + another[r, c]
								return result







						def __neg__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       str       						=  -self[r, c]
								return result







						def __sub__(							self					:      Optional[int]			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								return self + (-another)







						def __mul__(							self					:      int			,			snake_case_					:      int | float | Matrix  ):



								'''simple docstring'''



								if isinstance(snake_case_			,			(int, float)  ):  # Scalar multiplication
										snake_case__						:       Dict       						=  Matrix(self.row			,			self.column  )
										for r in range(self.row  ):
												for c in range(self.column  ):
														snake_case__						:       Tuple       						=  self[r, c] * another
										return result
								elif isinstance(snake_case_			,			snake_case_  ):  # Matrix multiplication
										assert self.column == another.row
										snake_case__						:       List[Any]       						=  Matrix(self.row			,			another.column  )
										for r in range(self.row  ):
												for c in range(another.column  ):
														for i in range(self.column  ):
																result[r, c] += self[r, i] * another[i, c]
										return result
								else:
										snake_case__						:       Union[str, Any]       						=  F"""Unsupported type given for another ({type(snake_case_  )})"""
										raise TypeError(snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  Matrix(self.column			,			self.row  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       List[Any]       						=  self[r, c]
								return result







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Matrix			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  ) and isinstance(snake_case_			,			snake_case_  )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								snake_case__						:       Tuple       						=  v.transpose()
								snake_case__						:       Optional[Any]       						=  (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
										return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




					def 	_a       (				):

							"""simple docstring"""

							snake_case__						:       Tuple       						=  Matrix(3   ,							3   ,							0     )
							for i in range(3     ):
									snake_case__						:       Any       						=  1
							print(F"""a^(-1) is {ainv}"""     )
							# u, v
							snake_case__						:       int       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  1, 2, -3
							snake_case__						:       Any       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       Optional[int]       						=  4, -2, 5
							print(F"""u is {u}"""     )
							print(F"""v is {v}"""     )
							print(F"""uv^T is {u * v.transpose()}"""     )
							# Sherman Morrison
							print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase   ,							__lowerCAmelCase     )}"""     )




					def 	_a       (				):

							"""simple docstring"""

							import doctest

							doctest.testmod()





					testa()



