



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

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A__  :  Tuple								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = ['''input_values''', '''attention_mask''']




							def __init__(       self     :       Any				,				A_     :       int = 1				,				A_     :       int = 1_6_0_0_0				,				A_     :       float = 0.0				,				A_     :       bool = False				,				A_     :       int = 8_0				,				A_     :       int = 1_6				,				A_     :       int = 6_4				,				A_     :       str = "hann_window"				,				A_     :       float = 1.0				,				A_     :       float = 8_0				,				A_     :       float = 7_6_0_0				,				A_     :       float = 1e-10				,				A_     :       int = 2				,				A_     :       bool = True				,				**A_     :       Union[str, Any]				,				):
									super().__init__(feature_size=A_				,				sampling_rate=A_				,				padding_value=A_				,				**A_)
									lowerCAmelCase_			:     Union[str, Any]									= do_normalize
									lowerCAmelCase_			:     Dict									= return_attention_mask

									lowerCAmelCase_			:     Optional[Any]									= num_mel_bins
									lowerCAmelCase_			:     Union[str, Any]									= hop_length
									lowerCAmelCase_			:     List[str]									= win_length
									lowerCAmelCase_			:     Any									= win_function
									lowerCAmelCase_			:     Union[str, Any]									= frame_signal_scale
									lowerCAmelCase_			:     Optional[int]									= fmin
									lowerCAmelCase_			:     List[str]									= fmax
									lowerCAmelCase_			:     Optional[Any]									= mel_floor
									lowerCAmelCase_			:     List[Any]									= reduction_factor

									lowerCAmelCase_			:     Any									= win_length * sampling_rate // 1_0_0_0
									lowerCAmelCase_			:     Union[str, Any]									= hop_length * sampling_rate // 1_0_0_0
									lowerCAmelCase_			:     Optional[Any]									= optimal_fft_length(self.sample_size)
									lowerCAmelCase_			:     Tuple									= (self.n_fft // 2) + 1

									lowerCAmelCase_			:     Union[str, Any]									= window_function(window_length=self.sample_size				,				name=self.win_function				,				periodic=A_)

									lowerCAmelCase_			:     str									= mel_filter_bank(
									    num_frequency_bins=self.n_freqs				,				num_mel_filters=self.num_mel_bins				,				min_frequency=self.fmin				,				max_frequency=self.fmax				,				sampling_rate=self.sampling_rate				,				norm='''slaney'''				,				mel_scale='''slaney'''				,				)

									if frame_signal_scale != 1.0:
											warnings.warn(
											    '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''				,				A_				,				)
									if reduction_factor != 2.0:
											warnings.warn(
											    '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''				,				A_				,				)




							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def    UpperCAmelCase__			(       A_     :       List[np.ndarray]				,				A_     :       List[np.ndarray]				,				A_     :       float = 0.0):
									if attention_mask is not None:
											lowerCAmelCase_			:     int									= np.array(A_				,				np.intaa)
											lowerCAmelCase_			:     Tuple									= []

											for vector, length in zip(A_				,				attention_mask.sum(-1)):
													lowerCAmelCase_			:     Optional[Any]									= (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7)
													if length < normed_slice.shape[0]:
															lowerCAmelCase_			:     Any									= padding_value

													normed_input_values.append(A_)
									else:
											lowerCAmelCase_			:     Optional[int]									= [(x - x.mean()) / np.sqrt(x.var() + 1e-7) for x in input_values]

									return normed_input_values




							def    UpperCAmelCase__			(       self     :       str				,				A_     :       np.ndarray				,				):
									lowerCAmelCase_			:     Optional[int]									= spectrogram(
									    A_				,				window=self.window				,				frame_length=self.sample_size				,				hop_length=self.sample_stride				,				fft_length=self.n_fft				,				mel_filters=self.mel_filters				,				mel_floor=self.mel_floor				,				log_mel='''log10'''				,				)
									return log_mel_spec.T




							def __call__(       self     :       Tuple				,				A_     :       Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None				,				A_     :       Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None				,				A_     :       Union[bool, str, PaddingStrategy] = False				,				A_     :       Optional[int] = None				,				A_     :       bool = False				,				A_     :       Optional[int] = None				,				A_     :       Optional[bool] = None				,				A_     :       Optional[Union[str, TensorType]] = None				,				A_     :       Optional[int] = None				,				**A_     :       str				,				):
									if audio is None and audio_target is None:
											raise ValueError('''You must provide either `audio` or `audio_target` values.''')

									if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
													raise ValueError(
													    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
													    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
													    F""" {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.''')

									if audio is not None:
											lowerCAmelCase_			:     Optional[int]									= self._process_audio(
											    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				**A_				,				)
									else:
											lowerCAmelCase_			:     Tuple									= None

									if audio_target is not None:
											lowerCAmelCase_			:     Tuple									= self._process_audio(
											    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				**A_				,				)

											if inputs is None:
													return inputs_target
											else:
													lowerCAmelCase_			:     Tuple									= inputs_target['''input_values''']
													lowerCAmelCase_			:     List[Any]									= inputs_target.get('''attention_mask''')
													if decoder_attention_mask is not None:
															lowerCAmelCase_			:     int									= decoder_attention_mask

									return inputs




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				A_     :       bool = False				,				A_     :       Union[bool, str, PaddingStrategy] = False				,				A_     :       Optional[int] = None				,				A_     :       bool = False				,				A_     :       Optional[int] = None				,				A_     :       Optional[bool] = None				,				A_     :       Optional[Union[str, TensorType]] = None				,				**A_     :       Union[str, Any]				,				):
									lowerCAmelCase_			:     str									= isinstance(A_				,				np.ndarray) and len(speech.shape) > 1
									if is_batched_numpy and len(speech.shape) > 2:
											raise ValueError(F"""Only mono-channel audio is supported for input to {self}""")
									lowerCAmelCase_			:     List[str]									= is_batched_numpy or (
									    isinstance(A_				,				(list, tuple)) and (isinstance(speech[0]				,				(np.ndarray, tuple, list)))
									)

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

									# always return batch
									if not is_batched:
											lowerCAmelCase_			:     Union[str, Any]									= [speech]

									# needed to make pad() work on spectrogram inputs
									lowerCAmelCase_			:     str									= self.feature_size

									# convert into correct format for padding
									if is_target:
											lowerCAmelCase_			:     Optional[Any]									= [self._extract_mel_features(A_) for waveform in speech]
											lowerCAmelCase_			:     Dict									= BatchFeature({'''input_values''': features})
											lowerCAmelCase_			:     Union[str, Any]									= self.num_mel_bins
									else:
											lowerCAmelCase_			:     int									= BatchFeature({'''input_values''': speech})

									lowerCAmelCase_			:     Tuple									= self.pad(
									    A_				,				padding=A_				,				max_length=A_				,				truncation=A_				,				pad_to_multiple_of=A_				,				return_attention_mask=A_				,				**A_				,				)

									lowerCAmelCase_			:     Tuple									= feature_size_hack

									# convert input values to correct format
									lowerCAmelCase_			:     List[str]									= padded_inputs['''input_values''']
									if not isinstance(input_values[0]				,				np.ndarray):
											lowerCAmelCase_			:     List[str]									= [np.asarray(A_				,				dtype=np.floataa) for array in input_values]
									elif (
									    not isinstance(A_				,				np.ndarray)
									    and isinstance(input_values[0]				,				np.ndarray)
									    and input_values[0].dtype is np.dtype(np.floataa)
									):
											lowerCAmelCase_			:     Any									= [array.astype(np.floataa) for array in input_values]
									elif isinstance(A_				,				np.ndarray) and input_values.dtype is np.dtype(np.floataa):
											lowerCAmelCase_			:     Dict									= input_values.astype(np.floataa)

									# convert attention_mask to correct format
									lowerCAmelCase_			:     List[Any]									= padded_inputs.get('''attention_mask''')
									if attention_mask is not None:
											lowerCAmelCase_			:     Optional[Any]									= [np.asarray(A_				,				dtype=np.intaa) for array in attention_mask]

									# zero-mean and unit-variance normalization
									if not is_target and self.do_normalize:
											lowerCAmelCase_			:     Tuple									= (
											    attention_mask
											    if self._get_padding_strategies(A_				,				max_length=A_) is not PaddingStrategy.DO_NOT_PAD
											    else None
											)
											lowerCAmelCase_			:     str									= self.zero_mean_unit_var_norm(
											    padded_inputs['''input_values''']				,				attention_mask=A_				,				padding_value=self.padding_value)

									if return_tensors is not None:
											lowerCAmelCase_			:     Dict									= padded_inputs.convert_to_tensors(A_)

									return padded_inputs







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= super().to_dict()

									# Don't serialize these as they are derived from the other properties.
									lowerCAmelCase_			:     List[Any]									= ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
									for name in names:
											if name in output:
													del output[name]

									return output






'''simple docstring'''





from __future__ import annotations

import queue




class        __A      :



    def __init__(self    :    Optional[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   data
         UpperCAmelCase_           =   None
         UpperCAmelCase_           =   None





def 	lowerCAmelCase_				(					)    ->      TreeNode:


     '''simple docstring'''




     print("\n********Press N to stop entering at any point of time********\n"			)
     UpperCAmelCase_           =   input("Enter the value of the root node: "			).strip().lower()
     UpperCAmelCase_           =   queue.Queue()
     UpperCAmelCase_           =   TreeNode(int(snake_case_			)			)
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   q.get()
          UpperCAmelCase_           =   f"""Enter the left node of {node_found.data}: """
          UpperCAmelCase_           =   input(snake_case_			).strip().lower() or "n"
          if check == "n":
               return tree_node
          UpperCAmelCase_           =   TreeNode(int(snake_case_			)			)
          UpperCAmelCase_           =   left_node
          q.put(snake_case_			)
          UpperCAmelCase_           =   f"""Enter the right node of {node_found.data}: """
          UpperCAmelCase_           =   input(snake_case_			).strip().lower() or "n"
          if check == "n":
               return tree_node
          UpperCAmelCase_           =   TreeNode(int(snake_case_			)			)
          UpperCAmelCase_           =   right_node
          q.put(snake_case_			)
     raise





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     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_       : TreeNode			)    ->      None:


     '''simple docstring'''




     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_       : TreeNode			)    ->      None:


     '''simple docstring'''




     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_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   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_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   queue.Queue()
     q.put(snake_case_			)
     while not q.empty():
          UpperCAmelCase_           =   []
          while not q.empty():
               UpperCAmelCase_           =   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_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   node
     while n or stack:
          while n:  # start from root node, find its left child
               print(n.data					,    end=","			)
               stack.append(snake_case_			)
               UpperCAmelCase_           =   n.left
          # end of while means current node doesn't have left child
          UpperCAmelCase_           =   stack.pop()
          # start to traverse its right child
          UpperCAmelCase_           =   n.right





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_           =   []
     UpperCAmelCase_           =   node
     while n or stack:
          while n:
               stack.append(snake_case_			)
               UpperCAmelCase_           =   n.left
          UpperCAmelCase_           =   stack.pop()
          print(n.data					,    end=","			)
          UpperCAmelCase_           =   n.right





def 	lowerCAmelCase_				(					snake_case_       : TreeNode			)    ->      None:


     '''simple docstring'''




     if not isinstance(snake_case_					,    snake_case_			) or not node:
          return
     UpperCAmelCase_    , UpperCAmelCase_           =   [], []
     UpperCAmelCase_           =   node
     stacka.append(snake_case_			)
     while stacka:  # to find the reversed order of post order, store it in stack2
          UpperCAmelCase_           =   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_       : str = ""					,    snake_case_       : Any=50					,    snake_case_       : Union[str, Any]="*"			)    ->      str:


     '''simple docstring'''




     if not s:
          return "\n" + width * char
     UpperCAmelCase_    , UpperCAmelCase_           =   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'))

    SCREAMING_SNAKE_CASE_:     TreeNode  =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())




