


'''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

UpperCAmelCase_			:  Any										=   random.Random()


def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       int=1.0,					UpperCamelCase__							:       Any=None,					UpperCamelCase__							:       List[Any]=None      ):
 if rng is None:
  __A	:		List[str]	  =      global_rng

 __A	:		Union[str, Any]	  =      []
 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     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=7   ,   __lowercase=400   ,   __lowercase=2_000   ,   __lowercase=10   ,   __lowercase=160   ,   __lowercase=8   ,   __lowercase=0.0   ,   __lowercase=4_000   ,   __lowercase=False   ,   __lowercase=True   ,   ):

  """simple docstring"""







  __A	:		int	  =      parent
  __A	:		Optional[int]	  =      batch_size
  __A	:		Optional[Any]	  =      min_seq_length
  __A	:		Tuple	  =      max_seq_length
  __A	:		int	  =      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
  __A	:		Optional[int]	  =      padding_value
  __A	:		str	  =      sampling_rate
  __A	:		Tuple	  =      return_attention_mask
  __A	:		Dict	  =      do_normalize
  __A	:		int	  =      feature_size
  __A	:		List[str]	  =      chunk_length
  __A	:		Optional[Any]	  =      hop_length


 def 			snake_case__ (   self    ):

  """simple docstring"""







  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 			snake_case__ (   self   ,   __lowercase=False   ,   __lowercase=False    ):

  """simple docstring"""







  def _flatten(__lowercase    ):
   return list(itertools.chain(*__lowercase    )    )

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


@require_torch
@require_torchaudio
class     _lowerCamelCase       (     snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		str     				=    WhisperFeatureExtractor if is_speech_available() else None


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      WhisperFeatureExtractionTester(self    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.feature_extraction_class(**self.feat_extract_dict    )

  with tempfile.TemporaryDirectory() as tmpdirname:
   __A	:		Union[str, Any]	  =      feat_extract_first.save_pretrained(__lowercase    )[0]
   check_json_file_has_correct_format(__lowercase    )
   __A	:		Union[str, Any]	  =      self.feature_extraction_class.from_pretrained(__lowercase    )

  __A	:		Optional[int]	  =      feat_extract_first.to_dict()
  __A	:		List[Any]	  =      feat_extract_second.to_dict()
  __A	:		int	  =      feat_extract_first.mel_filters
  __A	:		Tuple	  =      feat_extract_second.mel_filters
  self.assertTrue(np.allclose(__lowercase   ,   __lowercase    )    )
  self.assertEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      self.feature_extraction_class(**self.feat_extract_dict    )

  with tempfile.TemporaryDirectory() as tmpdirname:
   __A	:		List[Any]	  =      os.path.join(__lowercase   ,   'feat_extract.json'    )
   feat_extract_first.to_json_file(__lowercase    )
   __A	:		Tuple	  =      self.feature_extraction_class.from_json_file(__lowercase    )

  __A	:		Optional[int]	  =      feat_extract_first.to_dict()
  __A	:		Any	  =      feat_extract_second.to_dict()
  __A	:		Optional[int]	  =      feat_extract_first.mel_filters
  __A	:		List[Any]	  =      feat_extract_second.mel_filters
  self.assertTrue(np.allclose(__lowercase   ,   __lowercase    )    )
  self.assertEqual(__lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  # create three inputs of length 800, 1000, and 1200
  __A	:		Union[str, Any]	  =      [floats_list((1, x)    )[0] for x in range(800   ,   1_400   ,   200    )]
  __A	:		Any	  =      [np.asarray(__lowercase    ) for speech_input in speech_inputs]

  # Test feature size
  __A	:		Union[str, Any]	  =      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
  __A	:		Union[str, Any]	  =      feature_extractor(speech_inputs[0]   ,   return_tensors='np'    ).input_features
  __A	:		str	  =      feature_extractor(np_speech_inputs[0]   ,   return_tensors='np'    ).input_features
  self.assertTrue(np.allclose(__lowercase   ,   __lowercase   ,   atol=1E-3    )    )

  # Test batched
  __A	:		Optional[int]	  =      feature_extractor(__lowercase   ,   return_tensors='np'    ).input_features
  __A	:		Union[str, Any]	  =      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.
  __A	:		Any	  =      [floats_list((1, x)    )[0] for x in (800, 800, 800)]
  __A	:		Dict	  =      np.asarray(__lowercase    )
  __A	:		Optional[int]	  =      feature_extractor(__lowercase   ,   return_tensors='np'    ).input_features
  __A	:		Dict	  =      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
  __A	:		Dict	  =      [floats_list((1, x)    )[0] for x in range(200   ,   (feature_extractor.n_samples + 500)   ,   200    )]
  __A	:		Union[str, Any]	  =      [np.asarray(__lowercase    ) for speech_input in speech_inputs]

  __A	:		Union[str, Any]	  =      [x[: feature_extractor.n_samples] for x in speech_inputs]
  __A	:		Tuple	  =      [np.asarray(__lowercase    ) for speech_input in speech_inputs_truncated]

  __A	:		str	  =      feature_extractor(__lowercase   ,   return_tensors='np'    ).input_features
  __A	:		Optional[Any]	  =      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 			snake_case__ (   self    ):

  """simple docstring"""







  import torch

  __A	:		Dict	  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  __A	:		Optional[Any]	  =      np.random.rand(100   ,   32    ).astype(np.floataa    )
  __A	:		Optional[Any]	  =      np_speech_inputs.tolist()

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


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







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

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


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[Any]	  =      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

  __A	:		List[Any]	  =      self._load_datasamples(1    )
  __A	:		Optional[int]	  =      WhisperFeatureExtractor()
  __A	:		Tuple	  =      feature_extractor(__lowercase   ,   return_tensors='pt'    ).input_features
  self.assertEqual(input_features.shape   ,   (1, 80, 3_000)    )
  self.assertTrue(torch.allclose(input_features[0, 0, :30]   ,   __lowercase   ,   atol=1E-4    )    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
  __A	:		Dict	  =      self._load_datasamples(1    )[0]
  __A	:		List[str]	  =      ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
  __A	:		Optional[Any]	  =      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    )    )










'''simple docstring'''
from __future__ import annotations

import math
import random
from typing import Any

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		list[Any]	  =      []
  __A	:		int	  =      0
  __A	:		int	  =      0


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.head == self.tail


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  self.data.append(__lowercase    )
  __A	:		Any	  =      self.tail + 1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.data[self.head]
  __A	:		Union[str, Any]	  =      self.head + 1
  return ret


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.tail - self.head







 def 			snake_case__ (   self    ):

  """simple docstring"""







  print(self.data    )
  print('**************'    )
  print(self.data[self.head : self.tail]    )


class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      data
  __A	:		MyNode | None	  =      None
  __A	:		MyNode | None	  =      None
  __A	:		int	  =      1


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.data


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.left


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.right


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return self.height


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      data


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      node


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      node







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[Any]	  =      height


def       _lowercase							(					UpperCamelCase__							:       MyNode | None      ):
 if node is None:
  return 0
 return node.get_height()


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int      ):
 if a > b:
  return a
 return b


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('left rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_left()
 assert ret is not None
 node.set_left(ret.get_right()      )
 ret.set_right(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Optional[Any]	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 print('right rotation node:',					node.get_data()      )
 __A	:		Optional[Any]	  =      node.get_right()
 assert ret is not None
 node.set_right(ret.get_left()      )
 ret.set_left(UpperCamelCase__      )
 __A	:		List[str]	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 __A	:		Any	  =      my_max(get_height(ret.get_right()      ),					get_height(ret.get_left()      )      ) + 1
 ret.set_height(UpperCamelCase__      )
 return ret


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Dict	  =      node.get_left()
 assert left_child is not None
 node.set_left(left_rotation(UpperCamelCase__      )      )
 return right_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 __A	:		Tuple	  =      node.get_right()
 assert right_child is not None
 node.set_right(right_rotation(UpperCamelCase__      )      )
 return left_rotation(UpperCamelCase__      )


def       _lowercase							(					UpperCamelCase__							:       MyNode | None,					UpperCamelCase__							:       Any      ):
 if node is None:
  return MyNode(UpperCamelCase__      )
 if data < node.get_data():
  node.set_left(insert_node(node.get_left(),					UpperCamelCase__      )      )
  if (
      get_height(node.get_left()      ) - get_height(node.get_right()      ) == 2
  ):  # an unbalance detected
   __A	:		Union[str, Any]	  =      node.get_left()
   assert left_child is not None
   if (
       data < left_child.get_data()
   ):  # new node is the left child of the left child
    __A	:		Optional[Any]	  =      right_rotation(UpperCamelCase__      )
   else:
    __A	:		Any	  =      lr_rotation(UpperCamelCase__      )
 else:
  node.set_right(insert_node(node.get_right(),					UpperCamelCase__      )      )
  if get_height(node.get_right()      ) - get_height(node.get_left()      ) == 2:
   __A	:		Union[str, Any]	  =      node.get_right()
   assert right_child is not None
   if data < right_child.get_data():
    __A	:		int	  =      rl_rotation(UpperCamelCase__      )
   else:
    __A	:		Optional[Any]	  =      left_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(node.get_right()      ),					get_height(node.get_left()      )      ) + 1
 node.set_height(UpperCamelCase__      )
 return node


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		str	  =      root.get_right()
  if right_child is None:
   break
  __A	:		Union[str, Any]	  =      right_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode      ):
 while True:
  __A	:		Union[str, Any]	  =      root.get_left()
  if left_child is None:
   break
  __A	:		int	  =      left_child
 return root.get_data()


def       _lowercase							(					UpperCamelCase__							:       MyNode,					UpperCamelCase__							:       Any      ):
 __A	:		List[Any]	  =      root.get_left()
 __A	:		int	  =      root.get_right()
 if root.get_data() == data:
  if left_child is not None and right_child is not None:
   __A	:		List[Any]	  =      get_left_most(UpperCamelCase__      )
   root.set_data(UpperCamelCase__      )
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
  elif left_child is not None:
   __A	:		Tuple	  =      left_child
  elif right_child is not None:
   __A	:		str	  =      right_child
  else:
   return None
 elif root.get_data() > data:
  if left_child is None:
   print('No such data'      )
   return root
  else:
   root.set_left(del_node(UpperCamelCase__,					UpperCamelCase__      )      )
 else:  # root.get_data() < data
  if right_child is None:
   return root
  else:
   root.set_right(del_node(UpperCamelCase__,					UpperCamelCase__      )      )

 if get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == 2:
  assert right_child is not None
  if get_height(right_child.get_right()      ) > get_height(right_child.get_left()      ):
   __A	:		List[str]	  =      left_rotation(UpperCamelCase__      )
  else:
   __A	:		Union[str, Any]	  =      rl_rotation(UpperCamelCase__      )
 elif get_height(UpperCamelCase__      ) - get_height(UpperCamelCase__      ) == -2:
  assert left_child is not None
  if get_height(left_child.get_left()      ) > get_height(left_child.get_right()      ):
   __A	:		str	  =      right_rotation(UpperCamelCase__      )
  else:
   __A	:		List[Any]	  =      lr_rotation(UpperCamelCase__      )
 __A	:		Tuple	  =      my_max(get_height(root.get_right()      ),					get_height(root.get_left()      )      ) + 1
 root.set_height(UpperCamelCase__      )
 return root

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self    ):

  """simple docstring"""







  __A	:		MyNode | None	  =      None


 def 			snake_case__ (   self    ):

  """simple docstring"""







  return get_height(self.root    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('insert:' + str(__lowercase    )    )
  __A	:		Optional[int]	  =      insert_node(self.root   ,   __lowercase    )


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  print('delete:' + str(__lowercase    )    )
  if self.root is None:
   print('Tree is empty!'    )
   return
  __A	:		List[Any]	  =      del_node(self.root   ,   __lowercase    )







 def __str__(   self   ,   ):  # a level traversale, gives a more intuitive look on the tree

  """simple docstring"""







  __A	:		Union[str, Any]	  =      ''
  __A	:		Optional[Any]	  =      MyQueue()
  q.push(self.root    )
  __A	:		Tuple	  =      self.get_height()
  if layer == 0:
   return output
  __A	:		List[Any]	  =      0
  while not q.is_empty():
   __A	:		Union[str, Any]	  =      q.pop()
   __A	:		Any	  =      ' ' * int(math.pow(2   ,   layer - 1    )    )
   output += space
   if node is None:
    output += "*"
    q.push(__lowercase    )
    q.push(__lowercase    )
   else:
    output += str(node.get_data()    )
    q.push(node.get_left()    )
    q.push(node.get_right()    )
   output += space
   __A	:		Optional[int]	  =      cnt + 1
   for i in range(100    ):
    if cnt == math.pow(2   ,   __lowercase    ) - 1:
     __A	:		Optional[int]	  =      layer - 1
     if layer == 0:
      output += "\n*************************************"
      return output
     output += "\n"
     break
  output += "\n*************************************"
  return output


def       _lowercase							(					):
 import doctest

 doctest.testmod()


if __name__ == "__main__":
 _test()
 UpperCAmelCase_			:  Optional[Any]										=   AVLtree()
 UpperCAmelCase_			:  Dict										=   list(range(1_0))
 random.shuffle(lst)
 for i in lst:
  t.insert(i)
  print(str(t))
 random.shuffle(lst)
 for i in lst:
  t.del_node(i)
  print(str(t))






