
"""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


lowercase_       :						Tuple     					=	logging.get_logger(__name__)
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
					A__      =			["""input_features"""]
					def __init__(     self  ,     snake_case__=80  ,     snake_case__=16000  ,     snake_case__=160  ,     snake_case__=30  ,     snake_case__=400  ,     snake_case__=0.0  ,     snake_case__=False  ,     **snake_case__  ,     ):




							"""simple docstring"""


							super().__init__(
							    feature_size=snake_case__  ,     sampling_rate=snake_case__  ,     padding_value=snake_case__  ,     return_attention_mask=snake_case__  ,     **snake_case__  ,     )
							_SCREAMING_SNAKE_CASE   :				str							    = n_fft
							_SCREAMING_SNAKE_CASE   :				int							    = hop_length
							_SCREAMING_SNAKE_CASE   :				Tuple							    = chunk_length
							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = chunk_length * sampling_rate
							_SCREAMING_SNAKE_CASE   :				Tuple							    = self.n_samples // hop_length
							_SCREAMING_SNAKE_CASE   :				Any							    = sampling_rate
							_SCREAMING_SNAKE_CASE   :				int							    = mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2  ,     num_mel_filters=snake_case__  ,     min_frequency=0.0  ,     max_frequency=8000.0  ,     sampling_rate=snake_case__  ,     norm="slaney"  ,     mel_scale="slaney"  ,     )
					def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = spectrogram(
							    snake_case__  ,     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"  ,     )
							_SCREAMING_SNAKE_CASE   :				str							    = log_spec[:, :-1]
							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.maximum(snake_case__  ,     log_spec.max() - 8.0				)
							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (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 				__SCREAMING_SNAKE_CASE							(     snake_case__  ,     snake_case__  ,     snake_case__ = 0.0				):




							"""simple docstring"""


							if attention_mask is not None:
									_SCREAMING_SNAKE_CASE   :				int							    = np.array(snake_case__  ,     np.intaa				)
									_SCREAMING_SNAKE_CASE   :				str							    = []

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

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

							return normed_input_values
					def __call__(     self  ,     snake_case__  ,     snake_case__ = True  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = "max_length"  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     **snake_case__  ,     ):




							"""simple docstring"""


							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."				)

							_SCREAMING_SNAKE_CASE   :				int							    = isinstance(snake_case__  ,     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}'''				)
							_SCREAMING_SNAKE_CASE   :				List[Any]							    = is_batched_numpy or (
							    isinstance(snake_case__  ,     (list, tuple)				) and (isinstance(raw_speech[0]  ,     (np.ndarray, tuple, list)				))
							)

							if is_batched:
									_SCREAMING_SNAKE_CASE   :				Any							    = [np.asarray([speech]  ,     dtype=np.floataa				).T for speech in raw_speech]
							elif not is_batched and not isinstance(snake_case__  ,     np.ndarray				):
									_SCREAMING_SNAKE_CASE   :				Dict							    = np.asarray(snake_case__  ,     dtype=np.floataa				)
							elif isinstance(snake_case__  ,     np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
									_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = raw_speech.astype(np.floataa				)

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

							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = BatchFeature({"input_features": raw_speech}				)

							# convert into correct format for padding

							_SCREAMING_SNAKE_CASE   :				List[str]							    = self.pad(
							    snake_case__  ,     padding=snake_case__  ,     max_length=max_length if max_length else self.n_samples  ,     truncation=snake_case__  ,     pad_to_multiple_of=snake_case__  ,     return_attention_mask=return_attention_mask or do_normalize  ,     )

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

							# make sure list is in array format
							_SCREAMING_SNAKE_CASE   :				str							    = padded_inputs.get("input_features"				).transpose(2  ,     0  ,     1				)

							_SCREAMING_SNAKE_CASE   :				List[Any]							    = [self._np_extract_fbank_features(snake_case__				) for waveform in input_features[0]]

							if isinstance(input_features[0]  ,     snake_case__				):
									_SCREAMING_SNAKE_CASE   :				Any							    = [np.asarray(snake_case__  ,     dtype=np.floataa				) for feature in input_features]
							else:
									_SCREAMING_SNAKE_CASE   :				int							    = input_features

							if return_attention_mask:
									# rescale from sample (48000) to feature (3000)
									_SCREAMING_SNAKE_CASE   :				Dict							    = padded_inputs["attention_mask"][:, :: self.hop_length]

							if return_tensors is not None:
									_SCREAMING_SNAKE_CASE   :				Optional[int]							    = padded_inputs.convert_to_tensors(snake_case__				)

							return padded_inputs




					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				int							    = copy.deepcopy(self.__dict__				)
							_SCREAMING_SNAKE_CASE   :				int							    = self.__class__.__name__
							if "mel_filters" in output:
									del output["mel_filters"]
							return output





"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = value
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None  # Added in order to delete a node easier
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None
       _SCREAMING_SNAKE_CASE   :				Node | None							    = None




     def __repr__(     self				):




       """simple docstring"""


       from pprint import pformat

       if self.left is None and self.right is None:
         return str(self.value				)
       return pformat({F'''{self.value}''': (self.left, self.right)}  ,     indent=1				)
class 			UpperCamelCase      :
     def __init__(     self  ,     snake_case__ = None				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[str]							    = root
     def __str__(     self				):




       """simple docstring"""


       return str(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if new_children is not None:  # reset its kids
         _SCREAMING_SNAKE_CASE   :				Dict							    = node.parent
       if node.parent is not None:  # reset its parent
         if self.is_right(snake_case__				):  # If it is the right children
           _SCREAMING_SNAKE_CASE   :				Any							    = new_children
         else:
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_children
       else:
         _SCREAMING_SNAKE_CASE   :				Any							    = new_children
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node.parent and node.parent.right:
         return node == node.parent.right
       return False
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return self.root is None
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Node(snake_case__				)  # create a new Node
       if self.empty():  # if Tree is empty
         _SCREAMING_SNAKE_CASE   :				str							    = new_node  # set its root
       else:  # Tree is not empty
         _SCREAMING_SNAKE_CASE   :				Dict							    = self.root  # from root
         if parent_node is None:
           return
         while True:  # While we don't get to a leaf
           if value < parent_node.value:  # We go left
             if parent_node.left is None:
               _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = new_node  # We insert the new node in a leaf
               break
             else:
               _SCREAMING_SNAKE_CASE   :				int							    = parent_node.left
           else:
             if parent_node.right is None:
               _SCREAMING_SNAKE_CASE   :				str							    = new_node
               break
             else:
               _SCREAMING_SNAKE_CASE   :				Optional[int]							    = parent_node.right
         _SCREAMING_SNAKE_CASE   :				Any							    = parent_node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__				):




       """simple docstring"""


       for value in values:
         self.__insert(snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if self.empty():
         raise IndexError("Warning: Tree is empty! please use another."				)
       else:
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.root
         # use lazy evaluation here to avoid NoneType Attribute error
         while node is not None and node.value is not value:
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = node.left if value < node.value else node.right
         return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         if self.root is None:
           return None
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.root

       if not self.empty():
         while node.right is not None:
           _SCREAMING_SNAKE_CASE   :				Dict							    = node.right
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__ = None				):




       """simple docstring"""


       if node is None:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = self.root
       if self.root is None:
         return None
       if not self.empty():
         _SCREAMING_SNAKE_CASE   :				Any							    = self.root
         while node.left is not None:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = node.left
       return node
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.search(snake_case__				)  # Look for the node with that label
       if node is not None:
         if node.left is None and node.right is None:  # If it has no children
           self.__reassign_nodes(snake_case__  ,     snake_case__				)
         elif node.left is None:  # Has only right children
           self.__reassign_nodes(snake_case__  ,     node.right				)
         elif node.right is None:  # Has only left children
           self.__reassign_nodes(snake_case__  ,     node.left				)
         else:
           _SCREAMING_SNAKE_CASE   :				Dict							    = self.get_max(
               node.left				)  # Gets the max value of the left branch
           self.remove(tmp_node.value				)  # type: ignore
           _SCREAMING_SNAKE_CASE   :				List[Any]							    = (
               tmp_node.value  # type: ignore
           )  # Assigns the value to the node to delete and keep tree structure
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       if node is not None:
         yield node  # Preorder Traversal
         yield from self.preorder_traverse(node.left				)
         yield from self.preorder_traverse(node.right				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__=None				):




       """simple docstring"""


       if traversal_function is None:
         return self.preorder_traverse(self.root				)
       else:
         return traversal_function(self.root				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       if node:
         self.inorder(snake_case__  ,     node.left				)
         arr.append(node.value				)
         self.inorder(snake_case__  ,     node.right				)




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				list[int]							    = []
       self.inorder(snake_case__  ,     snake_case__				)  # append all values to list using inorder traversal
       return arr[k - 1]





def    _lowerCAmelCase    (     lowerCamelCase__							:							Node | None				)  ->    list[Node]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = []
  if curr_node is not None:
    _SCREAMING_SNAKE_CASE   :				int							    = postorder(curr_node.left				) + postorder(curr_node.right				) + [curr_node]
  return node_list





def    _lowerCAmelCase    (     )  ->    None:
  _SCREAMING_SNAKE_CASE   :				Any							    = (8, 3, 6, 1, 1_0, 1_4, 1_3, 4, 7)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = BinarySearchTree()
  for i in testlist:
    t.insert(lowerCamelCase__				)

  # Prints all the elements of the list in order traversal
  print(lowerCamelCase__				)

  if t.search(6				) is not None:
    print("The value 6 exists"				)
  else:
    print("The value 6 doesn't exist"				)

  if t.search(-1				) is not None:
    print("The value -1 exists"				)
  else:
    print("The value -1 doesn't exist"				)

  if not t.empty():
    print("Max Value: ",						t.get_max().value				)  # type: ignore
    print("Min Value: ",						t.get_min().value				)  # type: ignore

  for i in testlist:
    t.remove(lowerCamelCase__				)
    print(lowerCamelCase__				)


if __name__ == "__main__":
      import doctest

      doctest.testmod(verbose=True)



