


from math import ceil
from typing import List, Optional, Union

import numpy as np

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


snake_case							:	List[str]					      =				logging.get_logger(__name__)







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[str]   				=      ['''audio_values''', '''audio_mask''']
					def __init__(				self       :Any      ,__snake_case       :str=20_48      ,__snake_case       :Dict=1      ,__snake_case       :Dict=[16, 16]      ,__snake_case       :Dict=1_28      ,__snake_case       :Optional[int]=4_41_00      ,__snake_case       :Union[str, Any]=86      ,__snake_case       :Union[str, Any]=20_48      ,__snake_case       :Union[str, Any]=0.0      ,**__snake_case       :Dict      ,) ->   int:
												super().__init__(
												    feature_size=__snake_case      ,sampling_rate=__snake_case      ,padding_value=__snake_case      ,**__snake_case      ,)

												a__					=					spectrogram_length
												a__					=					num_channels
												a__					=					patch_size
												a__					=					feature_size // self.patch_size[1]
												a__					=					n_fft
												a__					=					sampling_rate // hop_length_to_sampling_rate
												a__					=					sampling_rate
												a__					=					padding_value
												a__					=					mel_filter_bank(
												    num_frequency_bins=1 + n_fft // 2      ,num_mel_filters=__snake_case      ,min_frequency=0.0      ,max_frequency=2_20_50.0      ,sampling_rate=__snake_case      ,norm='slaney'      ,mel_scale='slaney'      ,).T
					def 			lowerCamelCase__(				self       :Any      ,__snake_case       :np.array				) ->   np.ndarray:
												a__					=					spectrogram(
												    __snake_case      ,window_function(self.n_fft      ,'hann'				)      ,frame_length=self.n_fft      ,hop_length=self.hop_length      ,power=2.0      ,mel_filters=self.mel_filters.T      ,log_mel='dB'      ,db_range=80.0      ,)
												a__					=					log_spec[:, :-1]
												a__					=					log_spec - 20.0
												a__					=					np.clip(log_spec / 40.0      ,-2.0      ,0.0				) + 1.0
												return log_spec



					def __call__(				self       :List[str]      ,__snake_case       :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[bool] = True      ,__snake_case       :Optional[int] = None      ,__snake_case       :bool = False      ,__snake_case       :bool = False      ,**__snake_case       :Dict      ,) ->   BatchFeature:

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    'This feature extractor is set to support sampling rate'
																										    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
																										    F' 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(__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}'				)
												a__					=					is_batched_numpy or (
												    isinstance(__snake_case      ,(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(__snake_case      ,np.ndarray				):
																			a__					=					np.asarray(__snake_case      ,dtype=np.floataa				)
												elif isinstance(__snake_case      ,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]

												# Convert audio signals to log mel spectrograms, truncate by time axis
												a__					=					[
												    self._np_extract_fbank_features(waveform.squeeze()				).T[: self.spectrogram_length] for waveform in raw_speech
												]
												if isinstance(audio_features[0]      ,__snake_case				):
																			a__					=					[np.asarray(__snake_case      ,dtype=np.floataa				) for feature in audio_features]

												# Create audio attention mask
												a__					=					max(
												    [ceil(feature.shape[0] / self.patch_size[0]				) * self.freq_len for feature in audio_features]				)  # The maximum number of audio patches in a batch
												if return_attention_mask:
																			a__					=					[
																			    (ceil(feature.shape[0] / self.patch_size[0]				) * self.freq_len) * [1]
																			    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]				) * self.freq_len) * [0]
																			    for feature in audio_features
																			]
																			a__					=					np.array(__snake_case				).astype(np.floataa				)

												# convert into correct format for padding
												a__					=					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
												a__					=					np.ones([len(__snake_case				), 1, max_time_len, self.feature_size]				).astype(np.floataa				)
												a__					=					padded_audio_features * self.padding_value
												for i in range(len(__snake_case				)				):
																			a__					=					audio_features[i]
																			a__					=					feature

												# return as BatchFeature
												if return_attention_mask:
																			a__					=					{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
												else:
																			a__					=					{'audio_values': padded_audio_features}

												a__					=					BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)
												return encoded_inputs








def 							__lowercase       (			__lowerCAmelCase							:    Optional[Any]       ):
							return [
							    {
							        0: [1, 2],
							        1: [0, 2],
							        2: [0, 1, 3, 5],
							        3: [2, 4],
							        4: [3],
							        5: [2, 6, 8],
							        6: [5, 7],
							        7: [6, 8],
							        8: [5, 7],
							    },
							    {
							        0: [6],
							        1: [9],
							        2: [4, 5],
							        3: [4],
							        4: [2, 3],
							        5: [2],
							        6: [0, 7],
							        7: [6],
							        8: [],
							        9: [1],
							    },
							    {
							        0: [4],
							        1: [6],
							        2: [],
							        3: [5, 6, 7],
							        4: [0, 6],
							        5: [3, 8, 9],
							        6: [1, 3, 4, 7],
							        7: [3, 6, 8, 9],
							        8: [5, 7],
							        9: [5, 7],
							    },
							    {
							        0: [1, 3],
							        1: [0, 2, 4],
							        2: [1, 3, 4],
							        3: [0, 2, 4],
							        4: [1, 2, 3],
							    },
							][index]






def 							__lowercase       (			__lowerCAmelCase							:    dict[int, list[int]]       ):
							a__					=					0
							a__					=					len(__lowerCAmelCase       )  # No of vertices in graph
							a__					=					[0] * n
							a__					=					[False] * n

							def dfs(__lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    Tuple       ):
														a__					=					True
														a__					=					id_
														id_ += 1
														for to in graph[at]:
																					if to == parent:
																												pass
																					elif not visited[to]:
																												dfs(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   id_       )
																												a__					=					min(low[at] ,   low[to]       )
																												if id_ <= low[to]:
																																			bridges.append((at, to) if at < to else (to, at)       )
																					else:
																												# This edge is a back edge and cannot be a bridge
																												a__					=					min(low[at] ,   low[to]       )

							a__					=					[]
							for i in range(__lowerCAmelCase       ):
														if not visited[i]:
																					dfs(__lowerCAmelCase ,   -1 ,   __lowerCAmelCase ,   id_       )
							return bridges


if __name__ == "__main__":
						import doctest

						doctest.testmod()




