
"""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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				_lowercase ):



							'''simple docstring'''





							_snake_case						      =		['input_features', 'is_longer']




							def __init__( self    ,				snake_case_=64    ,				snake_case_=48_000    ,				snake_case_=480    ,				snake_case_=10    ,				snake_case_=1_024    ,				snake_case_=0.0    ,				snake_case_=False    ,				snake_case_ = 0    ,				snake_case_ = 14_000    ,				snake_case_ = None    ,				snake_case_ = "fusion"    ,				snake_case_ = "repeatpad"    ,				**snake_case_    ,				)       ->  Union[str, Any]:
													super().__init__(
													    feature_size=__SCREAMING_SNAKE_CASE    ,				sampling_rate=__SCREAMING_SNAKE_CASE    ,				padding_value=__SCREAMING_SNAKE_CASE    ,				return_attention_mask=__SCREAMING_SNAKE_CASE    ,				**__SCREAMING_SNAKE_CASE    ,				)
													__lowerCAmelCase     =  top_db
													__lowerCAmelCase     =  truncation
													__lowerCAmelCase     =  padding
													__lowerCAmelCase     =  fft_window_size
													__lowerCAmelCase     =  (fft_window_size >> 1) + 1
													__lowerCAmelCase     =  hop_length
													__lowerCAmelCase     =  max_length_s
													__lowerCAmelCase     =  max_length_s * sampling_rate
													__lowerCAmelCase     =  sampling_rate
													__lowerCAmelCase     =  frequency_min
													__lowerCAmelCase     =  frequency_max
													__lowerCAmelCase     =  mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=__SCREAMING_SNAKE_CASE    ,				min_frequency=__SCREAMING_SNAKE_CASE    ,				max_frequency=__SCREAMING_SNAKE_CASE    ,				sampling_rate=__SCREAMING_SNAKE_CASE    ,				norm=__SCREAMING_SNAKE_CASE    ,				mel_scale="""htk"""    ,				)
													__lowerCAmelCase     =  mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=__SCREAMING_SNAKE_CASE    ,				min_frequency=__SCREAMING_SNAKE_CASE    ,				max_frequency=__SCREAMING_SNAKE_CASE    ,				sampling_rate=__SCREAMING_SNAKE_CASE    ,				norm="""slaney"""    ,				mel_scale="""slaney"""    ,				)




							def    A__						( self				)       ->  Dict[str, Any]:
													__lowerCAmelCase     =  copy.deepcopy(self.__dict__				)
													__lowerCAmelCase     =  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    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  np.ndarray:
													__lowerCAmelCase     =  spectrogram(
													    __SCREAMING_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=__SCREAMING_SNAKE_CASE    ,				log_mel="""dB"""    ,				)
													return log_mel_spectrogram.T




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  List[Any]:
													__lowerCAmelCase     =  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
																			__lowerCAmelCase     =  [0]
													if len(ranges[2]				) == 0:
																			# if the audio is too short, we just use the first chunk
																			__lowerCAmelCase     =  [0]
													# randomly choose index for each part
													__lowerCAmelCase     =  np.random.choice(ranges[0]				)
													__lowerCAmelCase     =  np.random.choice(ranges[1]				)
													__lowerCAmelCase     =  np.random.choice(ranges[2]				)

													__lowerCAmelCase     =  mel[idx_front : idx_front + chunk_frames, :]
													__lowerCAmelCase     =  mel[idx_middle : idx_middle + chunk_frames, :]
													__lowerCAmelCase     =  mel[idx_back : idx_back + chunk_frames, :]

													__lowerCAmelCase     =  torch.tensor(mel[None, None, :]				)
													__lowerCAmelCase     =  torch.nn.functional.interpolate(
													    __SCREAMING_SNAKE_CASE    ,				size=[chunk_frames, 64]    ,				mode="""bilinear"""    ,				align_corners=__SCREAMING_SNAKE_CASE				)
													__lowerCAmelCase     =  mel_shrink[0][0].numpy()
													__lowerCAmelCase     =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,				axis=0				)
													return mel_fusion




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  np.array:



													if waveform.shape[0] > max_length:
																			if truncation == "rand_trunc":
																									__lowerCAmelCase     =  True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									__lowerCAmelCase     =  len(__SCREAMING_SNAKE_CASE				) - max_length
																									__lowerCAmelCase     =  np.random.randint(0    ,				overflow + 1				)
																									__lowerCAmelCase     =  waveform[idx : idx + max_length]
																									__lowerCAmelCase     =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE    ,				self.mel_filters_slaney				)[None, :]
																			elif truncation == "fusion":
																									__lowerCAmelCase     =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE    ,				self.mel_filters				)
																									__lowerCAmelCase     =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									__lowerCAmelCase     =  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.
																															__lowerCAmelCase     =  np.stack([mel, mel, mel, mel]    ,				axis=0				)
																															__lowerCAmelCase     =  False
																									else:
																															__lowerCAmelCase     =  self._random_mel_fusion(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE				)
																															__lowerCAmelCase     =  True
																			else:
																									raise NotImplementedError(f"""data_truncating {truncation} not implemented"""				)

													else:
																			__lowerCAmelCase     =  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":
																															__lowerCAmelCase     =  int(max_length / len(__SCREAMING_SNAKE_CASE				)				)
																															__lowerCAmelCase     =  np.stack(np.tile(__SCREAMING_SNAKE_CASE    ,				n_repeat + 1				)				)[:max_length]
																									if padding == "repeatpad":
																															__lowerCAmelCase     =  int(max_length / len(__SCREAMING_SNAKE_CASE				)				)
																															__lowerCAmelCase     =  np.stack(np.tile(__SCREAMING_SNAKE_CASE    ,				__SCREAMING_SNAKE_CASE				)				)
																									__lowerCAmelCase     =  np.pad(__SCREAMING_SNAKE_CASE    ,				(0, max_length - waveform.shape[0])    ,				mode="""constant"""    ,				constant_values=0				)

																			if truncation == "fusion":
																									__lowerCAmelCase     =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE    ,				self.mel_filters				)
																									__lowerCAmelCase     =  np.stack([input_mel, input_mel, input_mel, input_mel]    ,				axis=0				)
																			else:
																									__lowerCAmelCase     =  self._np_extract_fbank_features(__SCREAMING_SNAKE_CASE    ,				self.mel_filters_slaney				)[None, :]

													return input_mel, longer




							def __call__( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  BatchFeature:
													__lowerCAmelCase     =  truncation if truncation is not None else self.truncation
													__lowerCAmelCase     =  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."""				)

													__lowerCAmelCase     =  isinstance(__SCREAMING_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}"""				)
													__lowerCAmelCase     =  is_batched_numpy or (
													    isinstance(__SCREAMING_SNAKE_CASE    ,				(list, tuple)				) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)				))
													)

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

													# always return batch
													if not is_batched:
																			__lowerCAmelCase     =  [np.asarray(__SCREAMING_SNAKE_CASE				)]

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

													__lowerCAmelCase     =  []
													__lowerCAmelCase     =  []
													for mel, longer in padded_inputs:
																			input_mel.append(__SCREAMING_SNAKE_CASE				)
																			is_longer.append(__SCREAMING_SNAKE_CASE				)

													if truncation == "fusion" and sum(__SCREAMING_SNAKE_CASE				) == 0:
																			# if no audio is longer than 10s, then randomly select one audio to be longer
																			__lowerCAmelCase     =  np.random.randint(0    ,				len(__SCREAMING_SNAKE_CASE				)				)
																			__lowerCAmelCase     =  True

													if isinstance(input_mel[0]    ,				__SCREAMING_SNAKE_CASE				):
																			__lowerCAmelCase     =  [np.asarray(__SCREAMING_SNAKE_CASE    ,				dtype=np.floataa				) for feature in input_mel]

													# is_longer is a list of bool
													__lowerCAmelCase     =  [[longer] for longer in is_longer]

													__lowerCAmelCase     =  {"""input_features""": input_mel, """is_longer""": is_longer}
													__lowerCAmelCase     =  BatchFeature(__SCREAMING_SNAKE_CASE				)

													if return_tensors is not None:
																			__lowerCAmelCase     =  input_features.convert_to_tensors(__SCREAMING_SNAKE_CASE				)

													return input_features


"""simple docstring"""




import operator



def     __magic_name__		(					UpperCamelCase     :		list							,		UpperCamelCase     :		bool = False							,		UpperCamelCase     :		list | None = None     )     ->						list:
    a__		=			operator.lt if reverse else operator.gt
    a__		=			solution or []

    if not arr:
        return solution

    a__		=			[arr.pop(0     )]
    for i, item in enumerate(UpperCamelCase     ):
        if _operator(UpperCamelCase							,		sublist[-1]     ):
            sublist.append(UpperCamelCase     )
            arr.pop(UpperCamelCase     )

    #  merging sublist into solution list
    if not solution:
        solution.extend(UpperCamelCase     )
    else:
        while sublist:
            a__		=			sublist.pop(0     )
            for i, xx in enumerate(UpperCamelCase     ):
                if not _operator(UpperCamelCase							,		UpperCamelCase     ):
                    solution.insert(UpperCamelCase							,		UpperCamelCase     )
                    break
            else:
                solution.append(UpperCamelCase     )

    strand_sort(UpperCamelCase							,		UpperCamelCase							,		UpperCamelCase     )
    return solution


if __name__ == "__main__":
    assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
    assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]


