




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray


def    A						(__A   :    NDArray[floataa]   ,				__A   :    NDArray[floataa]   ,				__A   :    list[int]   ,				__A   :    int   ,				)   ->      list[float]:







     """simple docstring"""


     UpperCAmelCase_     ,       UpperCAmelCase_          =							coefficient_matrix.shape
     UpperCAmelCase_     ,       UpperCAmelCase_          =							constant_matrix.shape

     if rowsa != colsa:
          UpperCAmelCase_          =							F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
          raise ValueError(__A       )

     if colsa != 1:
          UpperCAmelCase_          =							F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
          raise ValueError(__A       )

     if rowsa != rowsa:
          UpperCAmelCase_          =							(
              '''Coefficient and constant matrices dimensions must be nxn and nx1 but '''
              F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
          )
          raise ValueError(__A       )

     if len(__A       ) != rowsa:
          UpperCAmelCase_          =							(
              '''Number of initial values must be equal to number of rows in coefficient '''
              F"""matrix but received {len(__A       )} and {rowsa}"""
          )
          raise ValueError(__A       )

     if iterations <= 0:
          raise ValueError('''Iterations must be at least 1'''       )

     UpperCAmelCase_          =							np.concatenate(
         (coefficient_matrix, constant_matrix)   ,				axis=1       )

     UpperCAmelCase_     ,       UpperCAmelCase_          =							table.shape

     strictly_diagonally_dominant(__A       )

     # Iterates the whole matrix for given number of times
     for _ in range(__A       ):
          UpperCAmelCase_          =							[]
          for row in range(__A       ):
               UpperCAmelCase_          =							0
               for col in range(__A       ):
                    if col == row:
                         UpperCAmelCase_          =							table[row][col]
                    elif col == cols - 1:
                         UpperCAmelCase_          =							table[row][col]
                    else:
                         temp += (-1) * table[row][col] * init_val[col]
               UpperCAmelCase_          =							(temp + val) / denom
               new_val.append(__A       )
          UpperCAmelCase_          =							new_val

     return [float(__A       ) for i in new_val]


def    A						(__A   :    NDArray[floataa]       )   ->      bool:







     """simple docstring"""


     UpperCAmelCase_     ,       UpperCAmelCase_          =							table.shape

     UpperCAmelCase_          =							True

     for i in range(0   ,				__A       ):
          UpperCAmelCase_          =							0
          for j in range(0   ,				cols - 1       ):
               if i == j:
                    continue
               else:
                    total += table[i][j]

          if table[i][i] <= total:
               raise ValueError('''Coefficient matrix is not strictly diagonally dominant'''       )

     return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
    import doctest

    doctest.testmod()











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


A__								=						logging.get_logger(__name__)


class       a							(			__lowerCamelCase				):
		__lowerCAmelCase	:    str											=					["""input_features""", """is_longer"""]



		def __init__(     self     :Union[str, Any]   ,__lowercase     :str=6_4   ,__lowercase     :Any=4_8_0_0_0   ,__lowercase     :List[Any]=4_8_0   ,__lowercase     :Optional[int]=1_0   ,__lowercase     :Optional[int]=1_0_2_4   ,__lowercase     :int=0.0   ,__lowercase     :List[Any]=False   ,__lowercase     :float = 0   ,__lowercase     :float = 1_4_0_0_0   ,__lowercase     :int = None   ,__lowercase     :str = "fusion"   ,__lowercase     :str = "repeatpad"   ,**__lowercase     :List[Any]   ,):
						super().__init__(
						    feature_size=__lowercase   ,sampling_rate=__lowercase   ,padding_value=__lowercase   ,return_attention_mask=__lowercase   ,**__lowercase   ,)
						snake_case__	:      Optional[Any]     		=	top_db
						snake_case__	:      Tuple     		=	truncation
						snake_case__	:      Tuple     		=	padding
						snake_case__	:      List[Any]     		=	fft_window_size
						snake_case__	:      List[Any]     		=	(fft_window_size >> 1) + 1
						snake_case__	:      str     		=	hop_length
						snake_case__	:      Dict     		=	max_length_s
						snake_case__	:      List[str]     		=	max_length_s * sampling_rate
						snake_case__	:      List[Any]     		=	sampling_rate
						snake_case__	:      Any     		=	frequency_min
						snake_case__	:      Dict     		=	frequency_max
						snake_case__	:      Tuple     		=	mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins   ,num_mel_filters=__lowercase   ,min_frequency=__lowercase   ,max_frequency=__lowercase   ,sampling_rate=__lowercase   ,norm=__lowercase   ,mel_scale='''htk'''   ,)
						snake_case__	:      Dict     		=	mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins   ,num_mel_filters=__lowercase   ,min_frequency=__lowercase   ,max_frequency=__lowercase   ,sampling_rate=__lowercase   ,norm='''slaney'''   ,mel_scale='''slaney'''   ,)



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Dict     		=	copy.deepcopy(self.__dict__  )
						snake_case__	:      Optional[int]     		=	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 	__lowerCamelCase			(     self     :Union[str, Any]   ,__lowercase     :np.array   ,__lowercase     :Optional[np.array] = None  ):
						snake_case__	:      List[Any]     		=	spectrogram(
						    __lowercase   ,window_function(self.fft_window_size   ,'''hann'''  )   ,frame_length=self.fft_window_size   ,hop_length=self.hop_length   ,power=2.0   ,mel_filters=__lowercase   ,log_mel='''dB'''   ,)
						return log_mel_spectrogram.T



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :List[str]   ,__lowercase     :Tuple   ,__lowercase     :List[str]  ):
						snake_case__	:      Dict     		=	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__	:      Tuple     		=	[0]
						if len(ranges[2]  ) == 0:
										# if the audio is too short, we just use the first chunk
										snake_case__	:      str     		=	[0]
						# randomly choose index for each part
						snake_case__	:      Dict     		=	np.random.choice(ranges[0]  )
						snake_case__	:      Any     		=	np.random.choice(ranges[1]  )
						snake_case__	:      Dict     		=	np.random.choice(ranges[2]  )

						snake_case__	:      Optional[int]     		=	mel[idx_front : idx_front + chunk_frames, :]
						snake_case__	:      Optional[Any]     		=	mel[idx_middle : idx_middle + chunk_frames, :]
						snake_case__	:      List[str]     		=	mel[idx_back : idx_back + chunk_frames, :]

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



		def 	__lowerCamelCase			(     self     :Any   ,__lowercase     :np.array   ,__lowercase     :str   ,__lowercase     :int   ,__lowercase     :List[str]  ):
						if waveform.shape[0] > max_length:
										if truncation == "rand_trunc":
														snake_case__	:      Tuple     		=	True
														# random crop to max_length (for compatibility) -> this should be handled by self.pad
														snake_case__	:      List[str]     		=	len(__lowercase  ) - max_length
														snake_case__	:      Any     		=	np.random.randint(0   ,overflow + 1  )
														snake_case__	:      Tuple     		=	waveform[idx : idx + max_length]
														snake_case__	:      Tuple     		=	self._np_extract_fbank_features(__lowercase   ,self.mel_filters_slaney  )[None, :]
										elif truncation == "fusion":
														snake_case__	:      str     		=	self._np_extract_fbank_features(__lowercase   ,self.mel_filters  )
														snake_case__	:      Union[str, Any]     		=	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
														snake_case__	:      Union[str, Any]     		=	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__	:      List[Any]     		=	False
														else:
																		snake_case__	:      List[Any]     		=	self._random_mel_fusion(__lowercase   ,__lowercase   ,__lowercase  )
																		snake_case__	:      Dict     		=	True
										else:
														raise NotImplementedError(F"""data_truncating {truncation} not implemented"""  )

						else:
										snake_case__	:      Optional[int]     		=	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(__lowercase  )  )
																		snake_case__	:      List[str]     		=	np.stack(np.tile(__lowercase   ,n_repeat + 1  )  )[:max_length]
														if padding == "repeatpad":
																		snake_case__	:      Union[str, Any]     		=	int(max_length / len(__lowercase  )  )
																		snake_case__	:      List[str]     		=	np.stack(np.tile(__lowercase   ,__lowercase  )  )
														snake_case__	:      int     		=	np.pad(__lowercase   ,(0, max_length - waveform.shape[0])   ,mode='''constant'''   ,constant_values=0  )

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

						return input_mel, longer






		def __call__(     self     :Dict   ,__lowercase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,__lowercase     :str = None   ,__lowercase     :Optional[str] = None   ,__lowercase     :Optional[int] = None   ,__lowercase     :Optional[int] = None   ,__lowercase     :Optional[Union[str, TensorType]] = None   ,**__lowercase     :Optional[int]   ,):
						snake_case__	:      Optional[int]     		=	truncation if truncation is not None else self.truncation
						snake_case__	:      Optional[int]     		=	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__	:      List[str]     		=	isinstance(__lowercase   ,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__	:      Optional[int]     		=	is_batched_numpy or (
						    isinstance(__lowercase   ,(list, tuple)  ) and (isinstance(raw_speech[0]   ,(np.ndarray, tuple, list)  ))
						)

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

						# always return batch
						if not is_batched:
										snake_case__	:      Dict     		=	[np.asarray(__lowercase  )]

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

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

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

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

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

						snake_case__	:      Dict     		=	{'''input_features''': input_mel, '''is_longer''': is_longer}
						snake_case__	:      str     		=	BatchFeature(__lowercase  )

						if return_tensors is not None:
										snake_case__	:      List[str]     		=	input_features.convert_to_tensors(__lowercase  )

						return input_features

