


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


UpperCAmelCase      =   logging.get_logger(__name__)







class lowerCAmelCase_						(			lowerCamelCase__	):

 '''simple docstring'''

 __snake_case												=  ["input_features", "is_longer"]



 def __init__(     self		,				_UpperCAmelCase=64		,				_UpperCAmelCase=4_80_00		,				_UpperCAmelCase=4_80		,				_UpperCAmelCase=10		,				_UpperCAmelCase=10_24		,				_UpperCAmelCase=0.0		,				_UpperCAmelCase=False		,				_UpperCAmelCase = 0		,				_UpperCAmelCase = 1_40_00		,				_UpperCAmelCase = None		,				_UpperCAmelCase = "fusion"		,				_UpperCAmelCase = "repeatpad"		,				**_UpperCAmelCase		,				):
       super().__init__(
           feature_size=_UpperCAmelCase		,				sampling_rate=_UpperCAmelCase		,				padding_value=_UpperCAmelCase		,				return_attention_mask=_UpperCAmelCase		,				**_UpperCAmelCase		,				)
       snake_case_	=				top_db
       snake_case_	=				truncation
       snake_case_	=				padding
       snake_case_	=				fft_window_size
       snake_case_	=				(fft_window_size >> 1) + 1
       snake_case_	=				hop_length
       snake_case_	=				max_length_s
       snake_case_	=				max_length_s * sampling_rate
       snake_case_	=				sampling_rate
       snake_case_	=				frequency_min
       snake_case_	=				frequency_max
       snake_case_	=				mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins		,				num_mel_filters=_UpperCAmelCase		,				min_frequency=_UpperCAmelCase		,				max_frequency=_UpperCAmelCase		,				sampling_rate=_UpperCAmelCase		,				norm=_UpperCAmelCase		,				mel_scale='''htk'''		,				)
       snake_case_	=				mel_filter_bank(
           num_frequency_bins=self.nb_frequency_bins		,				num_mel_filters=_UpperCAmelCase		,				min_frequency=_UpperCAmelCase		,				max_frequency=_UpperCAmelCase		,				sampling_rate=_UpperCAmelCase		,				norm='''slaney'''		,				mel_scale='''slaney'''		,				)



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				copy.deepcopy(self.__dict__ )
       snake_case_	=				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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None ):
       snake_case_	=				spectrogram(
           _UpperCAmelCase		,				window_function(self.fft_window_size		,				'''hann''' )		,				frame_length=self.fft_window_size		,				hop_length=self.hop_length		,				power=2.0		,				mel_filters=_UpperCAmelCase		,				log_mel='''dB'''		,				)
       return log_mel_spectrogram.T



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

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

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



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

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

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

       return input_mel, longer





 def __call__(     self		,				_UpperCAmelCase		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				_UpperCAmelCase = None		,				**_UpperCAmelCase		,				):
       snake_case_	=				truncation if truncation is not None else self.truncation
       snake_case_	=				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_	=				isinstance(_UpperCAmelCase		,				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_	=				is_batched_numpy or (
           isinstance(_UpperCAmelCase		,				(list, tuple) ) and (isinstance(raw_speech[0]		,				(np.ndarray, tuple, list) ))
       )

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

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

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

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

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

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

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

       snake_case_	=				{'''input_features''': input_mel, '''is_longer''': is_longer}
       snake_case_	=				BatchFeature(_UpperCAmelCase )

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

       return input_features



import os






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      snake_case_	=				os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
      with open(SCREAMING_SNAKE_CASE ) as file_hand:
            return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]


if __name__ == "__main__":
   print(solution())