


import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
  import torch

if is_datasets_available():
  from datasets import load_dataset

lowerCamelCase						=		random.Random()






def 					lowerCamelCase_   (	_a     ,       _a=1.0     ,       _a=None     ,       _a=None	):




  """simple docstring"""






  if rng is None:
    lowerCAmelCase__       :      Optional[int]   					= global_rng

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

  return values





class  _a							(    unittest.TestCase):




 def __init__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=7		, _SCREAMING_SNAKE_CASE						:		str=400		, _SCREAMING_SNAKE_CASE						:		Tuple=2000		, _SCREAMING_SNAKE_CASE						:		List[str]=2048		, _SCREAMING_SNAKE_CASE						:		Any=128		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=512		, _SCREAMING_SNAKE_CASE						:		List[Any]=30		, _SCREAMING_SNAKE_CASE						:		Tuple=4_4100		, )->	Any:
   lowerCAmelCase__       :      int   					= parent
   lowerCAmelCase__       :      Dict   					= batch_size
   lowerCAmelCase__       :      Optional[int]   					= min_seq_length
   lowerCAmelCase__       :      Optional[int]   					= max_seq_length
   lowerCAmelCase__       :      Optional[Any]   					= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   lowerCAmelCase__       :      Union[str, Any]   					= spectrogram_length
   lowerCAmelCase__       :      Dict   					= feature_size
   lowerCAmelCase__       :      Optional[int]   					= num_audio_channels
   lowerCAmelCase__       :      Union[str, Any]   					= hop_length
   lowerCAmelCase__       :      Dict   					= chunk_length
   lowerCAmelCase__       :      Union[str, Any]   					= sampling_rate




 def 							UpperCAmelCase__( self						:		List[str]		)->	int:
   return {
       "spectrogram_length": self.spectrogram_length,
       "feature_size": self.feature_size,
       "num_audio_channels": self.num_audio_channels,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "sampling_rate": self.sampling_rate,
   }




 def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Tuple=False		, _SCREAMING_SNAKE_CASE						:		Any=False		)->	Tuple:




   def _flatten(_SCREAMING_SNAKE_CASE						:		Optional[int]		):
     return list(itertools.chain(*_SCREAMING_SNAKE_CASE		)		)

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





@require_torch
@require_torchaudio
class  _a							(    UpperCAmelCase__					,						unittest.TestCase):
 _a  :  Dict				   =							TvltFeatureExtractor




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	str:
   lowerCAmelCase__       :      str   					= TvltFeatureExtractionTester(self		)




 def 							UpperCAmelCase__( self						:		List[str]		)->	Optional[int]:
   lowerCAmelCase__       :      Optional[Any]   					= self.feature_extraction_class(**self.feat_extract_dict		)
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		, '''spectrogram_length'''		)		)
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		, '''feature_size'''		)		)
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		, '''num_audio_channels'''		)		)
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		, '''hop_length'''		)		)
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		, '''chunk_length'''		)		)
   self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE		, '''sampling_rate'''		)		)




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	str:
   lowerCAmelCase__       :      List[str]   					= self.feature_extraction_class(**self.feat_extract_dict		)

   with tempfile.TemporaryDirectory() as tmpdirname:
     lowerCAmelCase__       :      Tuple   					= feat_extract_first.save_pretrained(_SCREAMING_SNAKE_CASE		)[0]
     check_json_file_has_correct_format(_SCREAMING_SNAKE_CASE		)
     lowerCAmelCase__       :      int   					= self.feature_extraction_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      List[Any]   					= feat_extract_first.to_dict()
   lowerCAmelCase__       :      List[str]   					= feat_extract_second.to_dict()
   lowerCAmelCase__       :      List[str]   					= dict_first.pop('''mel_filters'''		)
   lowerCAmelCase__       :      Tuple   					= dict_second.pop('''mel_filters'''		)
   self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)
   self.assertEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		List[Any]		)->	str:
   lowerCAmelCase__       :      Tuple   					= self.feature_extraction_class(**self.feat_extract_dict		)

   with tempfile.TemporaryDirectory() as tmpdirname:
     lowerCAmelCase__       :      str   					= os.path.join(_SCREAMING_SNAKE_CASE		, '''feat_extract.json'''		)
     feat_extract_first.to_json_file(_SCREAMING_SNAKE_CASE		)
     lowerCAmelCase__       :      Any   					= self.feature_extraction_class.from_json_file(_SCREAMING_SNAKE_CASE		)

   lowerCAmelCase__       :      Any   					= feat_extract_first.to_dict()
   lowerCAmelCase__       :      Any   					= feat_extract_second.to_dict()
   lowerCAmelCase__       :      str   					= dict_first.pop('''mel_filters'''		)
   lowerCAmelCase__       :      Optional[Any]   					= dict_second.pop('''mel_filters'''		)
   self.assertTrue(np.allclose(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)		)
   self.assertEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




 def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Dict:
   lowerCAmelCase__       :      List[Any]   					= self.feature_extraction_class(**self.feat_extract_dict		)

   # create three inputs of length 800, 1000, and 1200
   lowerCAmelCase__       :      Dict   					= [floats_list((1, x)		)[0] for x in range(800		, 1400		, 200		)]
   lowerCAmelCase__       :      List[str]   					= [np.asarray(_SCREAMING_SNAKE_CASE		) for speech_input in speech_inputs]

   # Test not batched input
   lowerCAmelCase__       :      str   					= feature_extractor(np_speech_inputs[0]		, return_tensors='''np'''		, sampling_rate=4_4100		).audio_values

   self.assertTrue(encoded_audios.ndim == 4		)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

   # Test batched
   lowerCAmelCase__       :      Dict   					= feature_extractor(_SCREAMING_SNAKE_CASE		, return_tensors='''np'''		, sampling_rate=4_4100		).audio_values

   self.assertTrue(encoded_audios.ndim == 4		)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

   # Test audio masking
   lowerCAmelCase__       :      int   					= feature_extractor(
       _SCREAMING_SNAKE_CASE		, return_tensors='''np'''		, sampling_rate=4_4100		, mask_audio=_SCREAMING_SNAKE_CASE		).audio_values

   self.assertTrue(encoded_audios.ndim == 4		)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

   # Test 2-D numpy arrays are batched.
   lowerCAmelCase__       :      int   					= [floats_list((1, x)		)[0] for x in (800, 800, 800)]
   lowerCAmelCase__       :      int   					= np.asarray(_SCREAMING_SNAKE_CASE		)
   lowerCAmelCase__       :      str   					= feature_extractor(_SCREAMING_SNAKE_CASE		, return_tensors='''np'''		, sampling_rate=4_4100		).audio_values
   self.assertTrue(encoded_audios.ndim == 4		)
   self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
   self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
   self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)




 def 							UpperCAmelCase__( self						:		int		, _SCREAMING_SNAKE_CASE						:		int		)->	Union[str, Any]:
   lowerCAmelCase__       :      List[str]   					= load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		, '''clean'''		, split='''validation'''		)
   # automatic decoding with librispeech
   lowerCAmelCase__       :      Union[str, Any]   					= ds.sort('''id'''		).select(range(_SCREAMING_SNAKE_CASE		)		)[:num_samples]["""audio"""]

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




 def 							UpperCAmelCase__( self						:		str		)->	List[str]:
   lowerCAmelCase__       :      Dict   					= self._load_datasamples(1		)
   lowerCAmelCase__       :      List[Any]   					= TvltFeatureExtractor()
   lowerCAmelCase__       :      Union[str, Any]   					= feature_extractor(_SCREAMING_SNAKE_CASE		, return_tensors='''pt'''		).audio_values

   self.assertEquals(audio_values.shape		, (1, 1, 192, 128)		)

   lowerCAmelCase__       :      Union[str, Any]   					= torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
   self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]		, _SCREAMING_SNAKE_CASE		, atol=1E-4		)		)








from collections.abc import Iterable
from typing import Any





class  _a							:




	def __init__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		int | None = None		)->	Tuple:
			lowerCAmelCase__       :      Union[str, Any]   					= value
			lowerCAmelCase__       :      Node | None   					= None  # Added in order to delete a node easier
			lowerCAmelCase__       :      Node | None   					= None
			lowerCAmelCase__       :      Node | None   					= None




	def __repr__( self						:		List[Any]		)->	str:
			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  _a							:




	def __init__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	int:
			lowerCAmelCase__       :      Dict   					= root




	def __str__( self						:		Tuple		)->	str:
			return str(self.root		)




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if new_children is not None:  # reset its kids
					lowerCAmelCase__       :      Union[str, Any]   					= node.parent
			if node.parent is not None:  # reset its parent
					if self.is_right(_SCREAMING_SNAKE_CASE		):  # If it is the right children
							lowerCAmelCase__       :      List[str]   					= new_children
					else:
							lowerCAmelCase__       :      Any   					= new_children
			else:
					lowerCAmelCase__       :      Optional[Any]   					= new_children




	def 							UpperCAmelCase__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		Node		)->	bool:
			if node.parent and node.parent.right:
					return node == node.parent.right
			return False




	def 							UpperCAmelCase__( self						:		int		)->	bool:
			return self.root is None




	def 							UpperCAmelCase__( self						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	None:
			lowerCAmelCase__       :      Any   					= Node(_SCREAMING_SNAKE_CASE		)  # create a new Node
			if self.empty():  # if Tree is empty
					lowerCAmelCase__       :      Optional[Any]   					= new_node  # set its root
			else:  # Tree is not empty
					lowerCAmelCase__       :      str   					= 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:
											lowerCAmelCase__       :      Dict   					= new_node  # We insert the new node in a leaf
											break
									else:
											lowerCAmelCase__       :      List[Any]   					= parent_node.left
							else:
									if parent_node.right is None:
											lowerCAmelCase__       :      Tuple   					= new_node
											break
									else:
											lowerCAmelCase__       :      Union[str, Any]   					= parent_node.right
					lowerCAmelCase__       :      Dict   					= parent_node




	def 							UpperCAmelCase__( self						:		str		, *_SCREAMING_SNAKE_CASE						:		List[Any]		)->	None:
			for value in values:
					self.__insert(_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Node | None:
			if self.empty():
					raise IndexError('''Warning: Tree is empty! please use another.'''		)
			else:
					lowerCAmelCase__       :      Union[str, Any]   					= self.root
					# use lazy evaluation here to avoid NoneType Attribute error
					while node is not None and node.value is not value:
							lowerCAmelCase__       :      List[str]   					= node.left if value < node.value else node.right
					return node




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					if self.root is None:
							return None
					lowerCAmelCase__       :      List[Any]   					= self.root

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




	def 							UpperCAmelCase__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		Node | None = None		)->	Node | None:
			if node is None:
					lowerCAmelCase__       :      Any   					= self.root
			if self.root is None:
					return None
			if not self.empty():
					lowerCAmelCase__       :      Dict   					= self.root
					while node.left is not None:
							lowerCAmelCase__       :      Tuple   					= node.left
			return node




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		int		)->	None:
			lowerCAmelCase__       :      Optional[Any]   					= self.search(_SCREAMING_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(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)
					elif node.left is None:  # Has only right children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.right		)
					elif node.right is None:  # Has only left children
							self.__reassign_nodes(_SCREAMING_SNAKE_CASE		, node.left		)
					else:
							lowerCAmelCase__       :      Tuple   					= self.get_max(
							    node.left		)  # Gets the max value of the left branch
							self.remove(tmp_node.value		)  # type: ignore
							lowerCAmelCase__       :      Optional[Any]   					= (
							    tmp_node.value  # type: ignore
							)  # Assigns the value to the node to delete and keep tree structure




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	Iterable:
			if node is not None:
					yield node  # Preorder Traversal
					yield from self.preorder_traverse(node.left		)
					yield from self.preorder_traverse(node.right		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Tuple=None		)->	Any:
			if traversal_function is None:
					return self.preorder_traverse(self.root		)
			else:
					return traversal_function(self.root		)




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		list		, _SCREAMING_SNAKE_CASE						:		Node | None		)->	None:
			if node:
					self.inorder(_SCREAMING_SNAKE_CASE		, node.left		)
					arr.append(node.value		)
					self.inorder(_SCREAMING_SNAKE_CASE		, node.right		)




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Node		)->	int:
			lowerCAmelCase__       :      list[int]   					= []
			self.inorder(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)  # append all values to list using inorder traversal
			return arr[k - 1]






def 					lowerCamelCase_   (	_a	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= []
		if curr_node is not None:
				lowerCAmelCase__       :      Optional[int]   					= postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
		return node_list






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[Any]   					= (8, 3, 6, 1, 10, 14, 13, 4, 7)
		lowerCAmelCase__       :      str   					= 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)




