
from __future__ import annotations

import math

def a__    (       A_,     A_,     A_,     A_,     A_					):






  '''simple docstring'''





  if depth < 0:
    raise ValueError("""Depth cannot be less than 0"""					)

  if not scores:
    raise ValueError("""Scores cannot be empty"""					)

  if depth == height:
    return scores[node_index]

  return (
      max(
          minimax(depth + 1,     node_index * 2,     A_,     A_,     A_					),     minimax(depth + 1,     node_index * 2 + 1,     A_,     A_,     A_					),     )
      if is_max
      else min(
          minimax(depth + 1,     node_index * 2,     A_,     A_,     A_					),     minimax(depth + 1,     node_index * 2 + 1,     A_,     A_,     A_					),     )
  )

def a__    (       ):






  '''simple docstring'''





  __magic_name__				     =       [90, 23, 6, 33, 21, 65, 123, 34423]
  __magic_name__				     =       math.log(len(A_					),     2					)
  print(f'''Optimal value : {minimax(0,     0,     A_,     A_,     A_					)}'''					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()



"""simple docstring"""





import unittest

from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken

from ...test_tokenization_common import TokenizerTesterMixin


_a														=		get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")






@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase(    lowerCamelCase      ,     unittest.TestCase	):
		lowercase__						=      SpeechTaTokenizer
		lowercase__						=      False
		lowercase__						=      True



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCamelCase							 =  SpeechTaTokenizer(__a)

							_UpperCamelCase							 =  AddedToken('''<mask>'''				,		lstrip=__a				,		rstrip=__a)
							_UpperCamelCase							 =  mask_token
							tokenizer.add_special_tokens({'''mask_token''': mask_token})
							tokenizer.add_tokens(['''<ctc_blank>'''])

							tokenizer.save_pretrained(self.tmpdirname)



		def 						UpperCAmelCase							(					self				,		__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  '''this is a test'''
							_UpperCamelCase							 =  '''this is a test'''
							return input_text, output_text



		def 						UpperCAmelCase							(					self				,		__a				,		__a=False				,		__a=20				,		__a=5)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self.get_input_output_texts(__a)
							_UpperCamelCase							 =  tokenizer.encode(__a				,		add_special_tokens=__a)
							_UpperCamelCase							 =  tokenizer.decode(__a				,		clean_up_tokenization_spaces=__a)
							return text, ids



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase							 =  '''<pad>'''
							_UpperCamelCase							 =  1

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a)				,		__a)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a)				,		__a)



		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							_UpperCamelCase							 =  list(self.get_tokenizer().get_vocab().keys())

							self.assertEqual(vocab_keys[0]				,		'''<s>''')
							self.assertEqual(vocab_keys[1]				,		'''<pad>''')
							self.assertEqual(vocab_keys[-4]				,		'''œ''')
							self.assertEqual(vocab_keys[-2]				,		'''<mask>''')
							self.assertEqual(vocab_keys[-1]				,		'''<ctc_blank>''')
							self.assertEqual(len(__a)				,		81)



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							self.assertEqual(self.get_tokenizer().vocab_size				,		79)



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  self.get_tokenizers(do_lower_case=__a)
							for tokenizer in tokenizers:
												with self.subTest(F'''{tokenizer.__class__.__name__}'''):
																	_UpperCamelCase							 =  tokenizer.vocab_size
																	_UpperCamelCase							 =  len(__a)

																	self.assertNotEqual(__a				,		0)

																	# We usually have added tokens from the start in tests because our vocab fixtures are
																	# smaller than the original vocabs - let's not assert this
																	# self.assertEqual(vocab_size, all_size)

																	_UpperCamelCase							 =  ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
																	_UpperCamelCase							 =  tokenizer.add_tokens(__a)
																	_UpperCamelCase							 =  tokenizer.vocab_size
																	_UpperCamelCase							 =  len(__a)

																	self.assertNotEqual(__a				,		0)
																	self.assertEqual(__a				,		__a)
																	self.assertEqual(__a				,		len(__a))
																	self.assertEqual(__a				,		all_size + len(__a))

																	_UpperCamelCase							 =  tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''				,		add_special_tokens=__a)

																	self.assertGreaterEqual(len(__a)				,		4)
																	self.assertGreater(tokens[0]				,		tokenizer.vocab_size - 1)
																	self.assertGreater(tokens[-3]				,		tokenizer.vocab_size - 1)

																	_UpperCamelCase							 =  {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
																	_UpperCamelCase							 =  tokenizer.add_special_tokens(__a)
																	_UpperCamelCase							 =  tokenizer.vocab_size
																	_UpperCamelCase							 =  len(__a)

																	self.assertNotEqual(__a				,		0)
																	self.assertEqual(__a				,		__a)
																	self.assertEqual(__a				,		len(__a))
																	self.assertEqual(__a				,		all_size_a + len(__a))

																	_UpperCamelCase							 =  tokenizer.encode(
																	    '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''				,		add_special_tokens=__a)

																	self.assertGreaterEqual(len(__a)				,		6)
																	self.assertGreater(tokens[0]				,		tokenizer.vocab_size - 1)
																	self.assertGreater(tokens[0]				,		tokens[1])
																	self.assertGreater(tokens[-3]				,		tokenizer.vocab_size - 1)
																	self.assertGreater(tokens[-3]				,		tokens[-4])
																	self.assertEqual(tokens[0]				,		tokenizer.eos_token_id)
																	self.assertEqual(tokens[-3]				,		tokenizer.pad_token_id)



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							pass



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  self.get_tokenizer()

							_UpperCamelCase							 =  tokenizer.tokenize('''This is a test''')
							# fmt: off
							self.assertListEqual(__a				,		[SPIECE_UNDERLINE, '''T''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''a''', SPIECE_UNDERLINE, '''t''', '''e''', '''s''', '''t'''])
							# fmt: on

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(__a)				,		[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]				,		)

							_UpperCamelCase							 =  tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
							self.assertListEqual(
							    __a				,		[SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''92000''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''])

							_UpperCamelCase							 =  tokenizer.convert_tokens_to_ids(__a)
							# fmt: off
							self.assertListEqual(__a				,		[4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26])
							# fmt: on

							_UpperCamelCase							 =  tokenizer.convert_ids_to_tokens(__a)
							self.assertListEqual(
							    __a				,		[SPIECE_UNDERLINE, '''I''', SPIECE_UNDERLINE, '''w''', '''a''', '''s''', SPIECE_UNDERLINE, '''b''', '''o''', '''r''', '''n''', SPIECE_UNDERLINE, '''i''', '''n''', SPIECE_UNDERLINE, '''<unk>''', ''',''', SPIECE_UNDERLINE, '''a''', '''n''', '''d''', SPIECE_UNDERLINE, '''t''', '''h''', '''i''', '''s''', SPIECE_UNDERLINE, '''i''', '''s''', SPIECE_UNDERLINE, '''f''', '''a''', '''l''', '''s''', '''é''', '''.'''])




		@slow
		def 						UpperCAmelCase							(					self)       ->       Optional[int]:






							'''simple docstring'''
							# Use custom sequence because this tokenizer does not handle numbers.
							_UpperCamelCase							 =  [
							    '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
							    '''general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
							    '''Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained '''
							    '''models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
							    '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
							    '''conditioning on both left and right context in all layers.''',
							    '''The quick brown fox jumps over the lazy dog.''',
							]

							# fmt: off
							_UpperCamelCase							 =  {
							    '''input_ids''': [
							        [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
							        [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
							        [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
							    ],
							    '''attention_mask''': [
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
							        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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							    ]
							}
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=__a				,		model_name='''microsoft/speecht5_asr'''				,		revision='''c5ef64c71905caeccde0e4462ef3f9077224c524'''				,		sequences=__a				,		)






