

'''simple docstring'''






from collections.abc import Iterable
from typing import Any






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     int | None = None			):
   __lowercase							  = value
   __lowercase							  = None  # Added in order to delete a node easier
   __lowercase							  = None
   __lowercase							  = None






  def __repr__(      self   :     int			):
   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 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     int					,lowercase__   :     Node | None = None			):
   __lowercase							  = root






  def __str__(      self   :     List[str]			):
   return str(self.root			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Node					,lowercase__   :     Node | None			):
   if new_children is not None:  # reset its kids
    __lowercase							  = node.parent
   if node.parent is not None:  # reset its parent
    if self.is_right(lowercase__			):  # If it is the right children
     __lowercase							  = new_children
    else:
     __lowercase							  = new_children
   else:
    __lowercase							  = new_children






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Node			):
   if node.parent and node.parent.right:
    return node == node.parent.right
   return False






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   return self.root is None






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Any			):
   __lowercase							  = Node(lowercase__			)  # create a new Node
   if self.empty():  # if Tree is empty
    __lowercase							  = new_node  # set its root
   else:  # Tree is not empty
    __lowercase							  = 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:
       __lowercase							  = new_node  # We insert the new node in a leaf
       break
      else:
       __lowercase							  = parent_node.left
     else:
      if parent_node.right is None:
       __lowercase							  = new_node
       break
      else:
       __lowercase							  = parent_node.right
    __lowercase							  = parent_node






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,*lowercase__   :     List[str]			):
   for value in values:
    self.__insert(lowercase__			)






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     Node | None = None			):
   if node is None:
    if self.root is None:
     return None
    __lowercase							  = self.root

   if not self.empty():
    while node.right is not None:
     __lowercase							  = node.right
   return node






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Node | None = None			):
   if node is None:
    __lowercase							  = self.root
   if self.root is None:
    return None
   if not self.empty():
    __lowercase							  = self.root
    while node.left is not None:
     __lowercase							  = node.left
   return node






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   __lowercase							  = self.search(lowercase__			)  # 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(lowercase__					,lowercase__			)
    elif node.left is None:  # Has only right children
     self.__reassign_nodes(lowercase__					,node.right			)
    elif node.right is None:  # Has only left children
     self.__reassign_nodes(lowercase__					,node.left			)
    else:
     __lowercase							  = self.get_max(
         node.left			)  # Gets the max value of the left branch
     self.remove(tmp_node.value			)  # type: ignore
     __lowercase							  = (
         tmp_node.value  # type: ignore
     )  # Assigns the value to the node to delete and keep tree structure






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     Node | None			):
   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   :     Dict					,lowercase__   :     str=None			):
   if traversal_function is None:
    return self.preorder_traverse(self.root			)
   else:
    return traversal_function(self.root			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     list					,lowercase__   :     Node | None			):
   if node:
    self.inorder(lowercase__					,node.left			)
    arr.append(node.value			)
    self.inorder(lowercase__					,node.right			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     int					,lowercase__   :     Node			):
   __lowercase							  = []
   self.inorder(lowercase__					,lowercase__			)  # append all values to list using inorder traversal
   return arr[k - 1]


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = []
 if curr_node is not None:
  __lowercase							  = postorder(curr_node.left      ) + postorder(curr_node.right      ) + [curr_node]
 return node_list


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = (8, 3, 6, 1, 10, 14, 13, 4, 7)
 __lowercase							  = BinarySearchTree()
 for i in testlist:
  t.insert(A__      )

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

 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(A__      )
  print(A__      )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)




'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

lowerCAmelCase__											=	random.Random()


def 		_A     (    A__     ,				A__=1.0     ,				A__=None     ,				A__=None      ):






 """simple docstring"""



 if rng is None:
  __lowercase							  = global_rng

 __lowercase							  = []
 for batch_idx in range(shape[0]      ):
  values.append([]      )
  for _ in range(shape[1]      ):
   values[-1].append(rng.random() * scale      )

 return values






@require_torch
@require_torchaudio
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     List[Any]=7					,lowercase__   :     Dict=4_0_0					,lowercase__   :     Tuple=2_0_0_0					,lowercase__   :     Optional[int]=1_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=8					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=4_0_0_0					,lowercase__   :     Optional[int]=False					,lowercase__   :     List[str]=True					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = min_seq_length
   __lowercase							  = max_seq_length
   __lowercase							  = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase							  = padding_value
   __lowercase							  = sampling_rate
   __lowercase							  = return_attention_mask
   __lowercase							  = do_normalize
   __lowercase							  = feature_size
   __lowercase							  = chunk_length
   __lowercase							  = hop_length






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return {
       "feature_size": self.feature_size,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     List[str]=False					,lowercase__   :     Dict=False			):
   def _flatten(lowercase__   :     Optional[Any]			):
    return list(itertools.chain(*lowercase__			)			)

   if equal_length:
    __lowercase							  = [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
   else:
    # make sure that inputs increase in size
    __lowercase							  = [
        floats_list((x, self.feature_size)			)
        for x in range(self.min_seq_length					,self.max_seq_length					,self.seq_length_diff			)
    ]
   if numpify:
    __lowercase							  = [np.asarray(lowercase__			) for x in speech_inputs]
   return speech_inputs






@require_torch
@require_torchaudio
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[int]							=			WhisperFeatureExtractor if is_speech_available() else None






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = WhisperFeatureExtractionTester(self			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = feat_extract_first.save_pretrained(lowercase__			)[0]
    check_json_file_has_correct_format(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_pretrained(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = os.path.join(lowercase__					,'''feat_extract.json'''			)
    feat_extract_first.to_json_file(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_json_file(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   # create three inputs of length 800, 1000, and 1200
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(8_0_0					,1_4_0_0					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   # Test feature size
   __lowercase							  = feature_extractor(lowercase__					,padding='''max_length'''					,return_tensors='''np'''			).input_features
   self.assertTrue(input_features.ndim == 3			)
   self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
   self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

   # Test not batched input
   __lowercase							  = feature_extractor(speech_inputs[0]					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(np_speech_inputs[0]					,return_tensors='''np'''			).input_features
   self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test batched
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test 2-D numpy arrays are batched.
   __lowercase							  = [floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase							  = np.asarray(lowercase__			)
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test truncation required
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(2_0_0					,(feature_extractor.n_samples + 5_0_0)					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   __lowercase							  = [x[: feature_extractor.n_samples] for x in speech_inputs]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs_truncated]

   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   import torch

   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = np.random.rand(1_0_0					,3_2			).astype(np.floataa			)
   __lowercase							  = np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __lowercase							  = feature_extractor.pad([{'''input_features''': inputs}]					,return_tensors='''np'''			)
    self.assertTrue(np_processed.input_features.dtype == np.floataa			)
    __lowercase							  = feature_extractor.pad([{'''input_features''': inputs}]					,return_tensors='''pt'''			)
    self.assertTrue(pt_processed.input_features.dtype == torch.floataa			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   __lowercase							  = load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,'''clean'''					,split='''validation'''			)
   # automatic decoding with librispeech
   __lowercase							  = ds.sort('''id'''			).select(range(lowercase__			)			)[:num_samples]['''audio''']

   return [x["array"] for x in speech_samples]






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # fmt: off
   __lowercase							  = torch.tensor(
       [
           0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
           0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
           0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
           -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
       ]			)
   # fmt: on

   __lowercase							  = self._load_datasamples(1			)
   __lowercase							  = WhisperFeatureExtractor()
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''pt'''			).input_features
   self.assertEqual(input_features.shape					,(1, 8_0, 3_0_0_0)			)
   self.assertTrue(torch.allclose(input_features[0, 0, :3_0]					,lowercase__					,atol=1e-4			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = self._load_datasamples(1			)[0]
   __lowercase							  = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
   __lowercase							  = feat_extract.zero_mean_unit_var_norm([audio]					,attention_mask=lowercase__			)[0]

   self.assertTrue(np.all(np.mean(lowercase__			) < 1e-3			)			)
   self.assertTrue(np.all(np.abs(np.var(lowercase__			) - 1			) < 1e-3			)			)

