





"""simple docstring"""




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


a						: List[Any]     	=			logging.get_logger(__name__)


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Any      					=["""audio_values""", """audio_mask"""]






							def __init__(   self      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=1      ,					lowerCAmelCase__=[16, 16]      ,					lowerCAmelCase__=128      ,					lowerCAmelCase__=4_4100      ,					lowerCAmelCase__=86      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=0.0      ,					**lowerCAmelCase__      ,					)	-> Any:
								super().__init__(
								    feature_size=lowerCAmelCase__      ,					sampling_rate=lowerCAmelCase__      ,					padding_value=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)

								a      :		Tuple	=				spectrogram_length
								a      :		Any	=				num_channels
								a      :		Union[str, Any]	=				patch_size
								a      :		Dict	=				feature_size // self.patch_size[1]
								a      :		Union[str, Any]	=				n_fft
								a      :		Optional[int]	=				sampling_rate // hop_length_to_sampling_rate
								a      :		Dict	=				sampling_rate
								a      :		List[Any]	=				padding_value
								a      :		Optional[int]	=				mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2      ,					num_mel_filters=lowerCAmelCase__      ,					min_frequency=0.0      ,					max_frequency=22_050.0      ,					sampling_rate=lowerCAmelCase__      ,					norm="slaney"      ,					mel_scale="slaney"      ,					).T






							def     __a   (   self      ,					lowerCAmelCase__      )	-> np.ndarray:
								a      :		List[Any]	=				spectrogram(
								    lowerCAmelCase__      ,					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      :		Dict	=				log_spec[:, :-1]
								a      :		str	=				log_spec - 20.0
								a      :		Optional[int]	=				np.clip(log_spec / 40.0      ,					-2.0      ,					0.0      ) + 1.0
								return log_spec






							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = True      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = False      ,					lowerCAmelCase__ = False      ,					**lowerCAmelCase__      ,					)	-> 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      :		List[Any]	=				isinstance(lowerCAmelCase__      ,					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      :		Any	=				is_batched_numpy or (
								    isinstance(lowerCAmelCase__      ,					(list, tuple)      ) and (isinstance(raw_speech[0]      ,					(np.ndarray, tuple, list)      ))
								)
								if is_batched:
									a      :		str	=				[np.asarray([speech]      ,					dtype=np.floataa      ).T for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCAmelCase__      ,					np.ndarray      ):
									a      :		List[str]	=				np.asarray(lowerCAmelCase__      ,					dtype=np.floataa      )
								elif isinstance(lowerCAmelCase__      ,					np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
									a      :		int	=				raw_speech.astype(np.floataa      )
								# always return batch
								if not is_batched:
									a      :		List[str]	=				[np.asarray([raw_speech]      ).T]

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

								# Create audio attention mask
								a      :		Union[str, Any]	=				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      :		Any	=				[
									    (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      :		Tuple	=				np.array(lowerCAmelCase__      ).astype(np.floataa      )

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

								# return as BatchFeature
								if return_attention_mask:
									a      :		Union[str, Any]	=				{"audio_values": padded_audio_features, "audio_mask": audio_mask}
								else:
									a      :		Any	=				{"audio_values": padded_audio_features}

								a      :		Tuple	=				BatchFeature(data=lowerCAmelCase__      ,					tensor_type=lowerCAmelCase__      )
								return encoded_inputs






"""simple docstring"""





def    _snake_case  (  _snake_case :	list	):

      def merge(_snake_case :	list       ,      _snake_case :	list	) -> list:

            def _merge():
                  while left and right:
                        yield (left if left[0] <= right[0] else right).pop(0	)
                  yield from left
                  yield from right

            return list(_merge()	)

      if len(_snake_case	) <= 1:
            return collection
      lowerCAmelCase    :		Union[str, Any]				=      len(_snake_case	) // 2
      return merge(merge_sort(collection[:mid]	)       ,      merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 snake_case__						:   Optional[Any]			 = input('''Enter numbers separated by a comma:\n''').strip()
 snake_case__						:   Union[str, Any]			 = [int(item) for item in user_input.split(''',''')]
 print(*merge_sort(unsorted), sep=''',''')


