
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__		:     Dict     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[str] 				=    ['''input_features''', '''is_longer''']


   def __init__(					self     :							Dict			,			lowerCAmelCase_     :							str=6_4			,			lowerCAmelCase_     :							Any=4_8_0_0_0			,			lowerCAmelCase_     :							Tuple=4_8_0			,			lowerCAmelCase_     :							str=1_0			,			lowerCAmelCase_     :							Union[str, Any]=1_0_2_4			,			lowerCAmelCase_     :							List[Any]=0.0			,			lowerCAmelCase_     :							str=False			,			lowerCAmelCase_     :							float = 0			,			lowerCAmelCase_     :							float = 1_4_0_0_0			,			lowerCAmelCase_     :							int = None			,			lowerCAmelCase_     :							str = "fusion"			,			lowerCAmelCase_     :							str = "repeatpad"			,			**lowerCAmelCase_     :							Any			,			):

      """simple docstring"""
      super().__init__(
          feature_size=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			padding_value=lowerCAmelCase_			,			return_attention_mask=lowerCAmelCase_			,			**lowerCAmelCase_			,			)
      _A:  List[Any]					=       top_db
      _A:  List[str]					=       truncation
      _A:  Any					=       padding
      _A:  List[str]					=       fft_window_size
      _A:  List[Any]					=       (fft_window_size >> 1) + 1
      _A:  Optional[Any]					=       hop_length
      _A:  Any					=       max_length_s
      _A:  Dict					=       max_length_s * sampling_rate
      _A:  Any					=       sampling_rate
      _A:  List[Any]					=       frequency_min
      _A:  int					=       frequency_max
      _A:  str					=       mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins			,			num_mel_filters=lowerCAmelCase_			,			min_frequency=lowerCAmelCase_			,			max_frequency=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			norm=lowerCAmelCase_			,			mel_scale='''htk'''			,			)
      _A:  Optional[int]					=       mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins			,			num_mel_filters=lowerCAmelCase_			,			min_frequency=lowerCAmelCase_			,			max_frequency=lowerCAmelCase_			,			sampling_rate=lowerCAmelCase_			,			norm='''slaney'''			,			mel_scale='''slaney'''			,			)


   def  __magic_name__				(					self     :							Union[str, Any]     ):

      """simple docstring"""
      _A:  Tuple					=       copy.deepcopy(self.__dict__     )
      _A:  List[Any]					=       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  __magic_name__				(					self     :							Dict			,			lowerCAmelCase_     :							np.array			,			lowerCAmelCase_     :							Optional[np.array] = None     ):

      """simple docstring"""
      _A:  Tuple					=       spectrogram(
          lowerCAmelCase_			,			window_function(self.fft_window_size			,			'''hann'''     )			,			frame_length=self.fft_window_size			,			hop_length=self.hop_length			,			power=2.0			,			mel_filters=lowerCAmelCase_			,			log_mel='''dB'''			,			)
      return log_mel_spectrogram.T


   def  __magic_name__				(					self     :							List[str]			,			lowerCAmelCase_     :							Tuple			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							List[Any]     ):

      """simple docstring"""
      _A:  Tuple					=       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
         _A:  List[str]					=       [0]
      if len(ranges[2]     ) == 0:
         # if the audio is too short, we just use the first chunk
         _A:  Tuple					=       [0]
      # randomly choose index for each part
      _A:  Union[str, Any]					=       np.random.choice(ranges[0]     )
      _A:  Optional[Any]					=       np.random.choice(ranges[1]     )
      _A:  List[Any]					=       np.random.choice(ranges[2]     )

      _A:  List[str]					=       mel[idx_front : idx_front + chunk_frames, :]
      _A:  List[Any]					=       mel[idx_middle : idx_middle + chunk_frames, :]
      _A:  Dict					=       mel[idx_back : idx_back + chunk_frames, :]

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


   def  __magic_name__				(					self     :							Optional[Any]			,			lowerCAmelCase_     :							np.array			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Union[str, Any]			,			lowerCAmelCase_     :							Any     ):

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

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

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

      return input_mel, longer


   def __call__(					self     :							Union[str, Any]			,			lowerCAmelCase_     :							Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,			lowerCAmelCase_     :							str = None			,			lowerCAmelCase_     :							Optional[str] = None			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							Optional[int] = None			,			lowerCAmelCase_     :							Optional[Union[str, TensorType]] = None			,			**lowerCAmelCase_     :							Optional[int]			,			):

      """simple docstring"""
      _A:  List[str]					=       truncation if truncation is not None else self.truncation
      _A:  Optional[Any]					=       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.'''     )

      _A:  str					=       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:  Union[str, Any]					=       is_batched_numpy or (
          isinstance(lowerCAmelCase_			,			(list, tuple)     ) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)     ))
      )

      if is_batched:
         _A:  int					=       [np.asarray(lowerCAmelCase_			,			dtype=np.floataa     ) for speech in raw_speech]
      elif not is_batched and not isinstance(lowerCAmelCase_			,			np.ndarray     ):
         _A:  str					=       np.asarray(lowerCAmelCase_			,			dtype=np.floataa     )
      elif isinstance(lowerCAmelCase_			,			np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
         _A:  List[str]					=       raw_speech.astype(np.floataa     )

      # always return batch
      if not is_batched:
         _A:  List[str]					=       [np.asarray(lowerCAmelCase_     )]

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

      _A:  Any					=       []
      _A:  Optional[int]					=       []
      for mel, longer in padded_inputs:
         input_mel.append(lowerCAmelCase_     )
         is_longer.append(lowerCAmelCase_     )

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

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

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

      _A:  Optional[int]					=       {'''input_features''': input_mel, '''is_longer''': is_longer}
      _A:  str					=       BatchFeature(lowerCAmelCase_     )

      if return_tensors is not None:
         _A:  Union[str, Any]					=       input_features.convert_to_tensors(lowerCAmelCase_     )

      return input_features





from __future__ import annotations






class UpperCAmelCase   :





   '''simple docstring'''


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int     ):

      """simple docstring"""
      _A:  List[str]					=       data
      _A:  Node | None					=       None
      _A:  Node | None					=       None






def   lowerCamelCase__							(					a      )      ->   None:  # In Order traversal of the tree
   if tree:
      display(tree.left      )
      print(tree.data      )
      display(tree.right      )






def   lowerCamelCase__							(					a      )      ->   int:
   return 1 + max(depth_of_tree(tree.left      )		,		depth_of_tree(tree.right      )      ) if tree else 0






def   lowerCamelCase__							(					a      )      ->   bool:
   if not tree:
      return True
   if tree.left and tree.right:
      return is_full_binary_tree(tree.left      ) and is_full_binary_tree(tree.right      )
   else:
      return not tree.left and not tree.right






def   lowerCamelCase__							(					)      ->   None:  # Main function for testing.
   _A:  Optional[int]					=       Node(1      )
   _A:  int					=       Node(2      )
   _A:  str					=       Node(3      )
   _A:  Union[str, Any]					=       Node(4      )
   _A:  Dict					=       Node(5      )
   _A:  int					=       Node(6      )
   _A:  Optional[Any]					=       Node(7      )
   _A:  List[str]					=       Node(8      )
   _A:  int					=       Node(9      )

   print(is_full_binary_tree(a      )      )
   print(depth_of_tree(a      )      )
   print('''Tree is: '''      )
   display(a      )


if __name__ == "__main__":
   main()



