
"""simple docstring"""



import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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 				a			(					lowercase      ):
      UpperCamelCase							:   List[Any]           =     ["""input_features"""]



      def __init__(   self     ,	UpperCamelCase_=80     ,	UpperCamelCase_=16_000     ,	UpperCamelCase_=160     ,	UpperCamelCase_=30     ,	UpperCamelCase_=400     ,	UpperCamelCase_=0.0     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          super().__init__(
              feature_size=UpperCamelCase_     ,	sampling_rate=UpperCamelCase_     ,	padding_value=UpperCamelCase_     ,	return_attention_mask=UpperCamelCase_     ,	**UpperCamelCase_     ,	)
          UpperCAmelCase__	:      List[str]     							=					n_fft
          UpperCAmelCase__	:      Optional[int]     							=					hop_length
          UpperCAmelCase__	:      Dict     							=					chunk_length
          UpperCAmelCase__	:      List[str]     							=					chunk_length * sampling_rate
          UpperCAmelCase__	:      Optional[int]     							=					self.n_samples // hop_length
          UpperCAmelCase__	:      str     							=					sampling_rate
          UpperCAmelCase__	:      Tuple     							=					mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2     ,	num_mel_filters=UpperCamelCase_     ,	min_frequency=0.0     ,	max_frequency=8000.0     ,	sampling_rate=UpperCamelCase_     ,	norm='slaney'     ,	mel_scale='slaney'     ,	)



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Optional[Any]     							=					spectrogram(
              UpperCamelCase_     ,	window_function(self.n_fft     ,	'hann'      )     ,	frame_length=self.n_fft     ,	hop_length=self.hop_length     ,	power=2.0     ,	mel_filters=self.mel_filters     ,	log_mel='log10'     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					log_spec[:, :-1]
          UpperCAmelCase__	:      Union[str, Any]     							=					np.maximum(UpperCamelCase_     ,	log_spec.max() - 8.0      )
          UpperCAmelCase__	:      str     							=					(log_spec + 4.0) / 4.0
          return log_spec



      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def  __snake_case							(   UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = 0.0      ):
          if attention_mask is not None:
              UpperCAmelCase__	:      Union[str, Any]     							=					np.array(UpperCamelCase_     ,	np.intaa      )
              UpperCAmelCase__	:      str     							=					[]

              for vector, length in zip(UpperCamelCase_     ,	attention_mask.sum(-1      )      ):
                  UpperCAmelCase__	:      List[Any]     							=					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7      )
                  if length < normed_slice.shape[0]:
                      UpperCAmelCase__	:      str     							=					padding_value

                  normed_input_values.append(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Tuple     							=					[(x - x.mean()) / np.sqrt(x.var() + 1E-7      ) for x in input_values]

          return normed_input_values



      def __call__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = True     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = "max_length"     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	**UpperCamelCase_     ,	):

          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.'      )

          UpperCAmelCase__	:      Dict     							=					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}'''      )
          UpperCAmelCase__	:      List[Any]     							=					is_batched_numpy or (
              isinstance(UpperCamelCase_     ,	(list, tuple)      ) and (isinstance(raw_speech[0]     ,	(np.ndarray, tuple, list)      ))
          )

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

          # always return batch
          if not is_batched:
              UpperCAmelCase__	:      Dict     							=					[np.asarray([raw_speech]      ).T]

          UpperCAmelCase__	:      Any     							=					BatchFeature({'input_features': raw_speech}      )

          # convert into correct format for padding

          UpperCAmelCase__	:      List[Any]     							=					self.pad(
              UpperCamelCase_     ,	padding=UpperCamelCase_     ,	max_length=max_length if max_length else self.n_samples     ,	truncation=UpperCamelCase_     ,	pad_to_multiple_of=UpperCamelCase_     ,	return_attention_mask=return_attention_mask or do_normalize     ,	)

          # zero-mean and unit-variance normalization
          if do_normalize:
              UpperCAmelCase__	:      List[Any]     							=					self.zero_mean_unit_var_norm(
                  padded_inputs['input_features']     ,	attention_mask=padded_inputs['attention_mask']     ,	padding_value=self.padding_value     ,	)
              UpperCAmelCase__	:      Optional[int]     							=					np.stack(padded_inputs['input_features']     ,	axis=0      )

          # make sure list is in array format
          UpperCAmelCase__	:      int     							=					padded_inputs.get('input_features'      ).transpose(2     ,	0     ,	1      )

          UpperCAmelCase__	:      List[str]     							=					[self._np_extract_fbank_features(UpperCamelCase_      ) for waveform in input_features[0]]

          if isinstance(input_features[0]     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      Union[str, Any]     							=					[np.asarray(UpperCamelCase_     ,	dtype=np.floataa      ) for feature in input_features]
          else:
              UpperCAmelCase__	:      Union[str, Any]     							=					input_features

          if return_attention_mask:
              # rescale from sample (48000) to feature (3000)
              UpperCAmelCase__	:      Any     							=					padded_inputs['attention_mask'][:, :: self.hop_length]

          if return_tensors is not None:
              UpperCAmelCase__	:      Any     							=					padded_inputs.convert_to_tensors(UpperCamelCase_      )

          return padded_inputs



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					copy.deepcopy(self.__dict__      )
          UpperCAmelCase__	:      Union[str, Any]     							=					self.__class__.__name__
          if "mel_filters" in output:
              del output["mel_filters"]
          return output



"""simple docstring"""



from __future__ import annotations

import queue





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					data
          UpperCAmelCase__	:      Dict     							=					None
          UpperCAmelCase__	:      Optional[int]     							=					None




def 					lowerCamelCase   (     ):
    print('\n********Press N to stop entering at any point of time********\n' )
    UpperCAmelCase__	:      int     							=					input('Enter the value of the root node: ' ).strip().lower()
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    UpperCAmelCase__	:      Dict     							=					TreeNode(int(_snake_case ) )
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Tuple     							=					q.get()
        UpperCAmelCase__	:      List[Any]     							=					F'''Enter the left node of {node_found.data}: '''
        UpperCAmelCase__	:      Dict     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      Optional[int]     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Tuple     							=					left_node
        q.put(_snake_case )
        UpperCAmelCase__	:      int     							=					F'''Enter the right node of {node_found.data}: '''
        UpperCAmelCase__	:      int     							=					input(_snake_case ).strip().lower() or 'n'
        if check == "n":
            return tree_node
        UpperCAmelCase__	:      int     							=					TreeNode(int(_snake_case ) )
        UpperCAmelCase__	:      Any     							=					right_node
        q.put(_snake_case )
    raise
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    print(node.data    ,end=',' )
    pre_order(node.left )
    pre_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    in_order(node.left )
    print(node.data    ,end=',' )
    in_order(node.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    post_order(node.left )
    post_order(node.right )
    print(node.data    ,end=',' )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      int     							=					q.get()
        print(node_dequeued.data    ,end=',' )
        if node_dequeued.left:
            q.put(node_dequeued.left )
        if node_dequeued.right:
            q.put(node_dequeued.right )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      queue.Queue     							=					queue.Queue()
    q.put(_snake_case )
    while not q.empty():
        UpperCAmelCase__	:      Optional[Any]     							=					[]
        while not q.empty():
            UpperCAmelCase__	:      Union[str, Any]     							=					q.get()
            print(node_dequeued.data    ,end=',' )
            if node_dequeued.left:
                list_.append(node_dequeued.left )
            if node_dequeued.right:
                list_.append(node_dequeued.right )
        print()
        for node in list_:
            q.put(_snake_case )
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[int]     							=					node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data    ,end=',' )
            stack.append(_snake_case )
            UpperCAmelCase__	:      Optional[int]     							=					n.left
        # end of while means current node doesn't have left child
        UpperCAmelCase__	:      Union[str, Any]     							=					stack.pop()
        # start to traverse its right child
        UpperCAmelCase__	:      Dict     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__	:      list[TreeNode]     							=					[]
    UpperCAmelCase__	:      Optional[Any]     							=					node
    while n or stack:
        while n:
            stack.append(_snake_case )
            UpperCAmelCase__	:      int     							=					n.left
        UpperCAmelCase__	:      Tuple     							=					stack.pop()
        print(n.data    ,end=',' )
        UpperCAmelCase__	:      Tuple     							=					n.right
def 					lowerCamelCase   (     _snake_case ):
    if not isinstance(_snake_case    ,_snake_case ) or not node:
        return
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					[], []
    UpperCAmelCase__	:      Union[str, Any]     							=					node
    stacka.append(_snake_case )
    while stacka:  # to find the reversed order of post order, store it in stack2
        UpperCAmelCase__	:      Tuple     							=					stacka.pop()
        if n.left:
            stacka.append(n.left )
        if n.right:
            stacka.append(n.right )
        stacka.append(_snake_case )
    while stacka:  # pop up from stack2 will be the post order
        print(stacka.pop().data    ,end=',' )
def 					lowerCamelCase   (     _snake_case = ""    ,_snake_case=50    ,_snake_case="*" ):
    if not s:
        return "\n" + width * char
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					divmod(width - len(_snake_case ) - 2    ,2 )
    return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(prompt('Binary Tree Traversals'))

    UpperCamelCase__          =	build_tree()
    print(prompt('Pre Order Traversal'))
    pre_order(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal'))
    in_order(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal'))
    post_order(node)
    print(prompt() + '\n')

    print(prompt('Level Order Traversal'))
    level_order(node)
    print(prompt() + '\n')

    print(prompt('Actual Level Order Traversal'))
    level_order_actual(node)
    print('*' * 50 + '\n')

    print(prompt('Pre Order Traversal - Iteration Version'))
    pre_order_iter(node)
    print(prompt() + '\n')

    print(prompt('In Order Traversal - Iteration Version'))
    in_order_iter(node)
    print(prompt() + '\n')

    print(prompt('Post Order Traversal - Iteration Version'))
    post_order_iter(node)
    print(prompt())

