
"""simple docstring"""
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()

def       lowercase			(_lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase		):
						__lowerCAmelCase   					=   LxmertConfig.from_json_file(_UpperCamelCase		)
						print(f"""Building PyTorch model from configuration: {config}"""		)
						__lowerCAmelCase   					=   LxmertForPreTraining(_UpperCamelCase		)

						# Load weights from tf checkpoint
						load_tf_weights_in_lxmert(_UpperCamelCase      ,  _UpperCamelCase      ,  _UpperCamelCase		)

						# Save pytorch-model
						print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
						torch.save(model.state_dict()      ,  _UpperCamelCase		)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE_ 			=     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE_ 			=     parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







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


lowerCAmelCase_    =				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')



@require_sentencepiece
@require_tokenizers
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[int]       			=      SpeechTaTokenizer
			lowerCamelCase_     :     int       			=      False
			lowerCamelCase_     :     Dict       			=      True






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


							'''simple docstring'''



							super().setUp()

							# We have a SentencePiece fixture for testing
							snake_case_						:    Tuple				=  SpeechTaTokenizer(__magic_name__    )

							snake_case_						:    Any				=  AddedToken('''<mask>'''					,							lstrip=__magic_name__					,							rstrip=__magic_name__    )
							snake_case_						:    int				=  mask_token
							tokenizer.add_special_tokens({'''mask_token''': mask_token}    )
							tokenizer.add_tokens(['''<ctc_blank>''']    )

							tokenizer.save_pretrained(self.tmpdirname    )






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Dict				=  '''this is a test'''
							snake_case_						:    int				=  '''this is a test'''
							return input_text, output_text






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__=False					,							__magic_name__=20					,							__magic_name__=5    )     ->   List[Any]:


							'''simple docstring'''



							snake_case_			,							snake_case_						:    int				=  self.get_input_output_texts(__magic_name__    )
							snake_case_						:    Optional[Any]				=  tokenizer.encode(__magic_name__					,							add_special_tokens=__magic_name__    )
							snake_case_						:    Any				=  tokenizer.decode(__magic_name__					,							clean_up_tokenization_spaces=__magic_name__    )
							return text, ids






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


							'''simple docstring'''



							snake_case_						:    List[str]				=  '''<pad>'''
							snake_case_						:    Any				=  1

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






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Any				=  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(__magic_name__    )					,							81    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							self.assertEqual(self.get_tokenizer().vocab_size					,							79    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    int				=  self.get_tokenizers(do_lower_case=__magic_name__    )
							for tokenizer in tokenizers:
											with self.subTest(F'''{tokenizer.__class__.__name__}'''    ):
															snake_case_						:    int				=  tokenizer.vocab_size
															snake_case_						:    Optional[Any]				=  len(__magic_name__    )

															self.assertNotEqual(__magic_name__					,							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)

															snake_case_						:    List[Any]				=  ['''aaaaa bbbbbb''', '''cccccccccdddddddd''']
															snake_case_						:    List[Any]				=  tokenizer.add_tokens(__magic_name__    )
															snake_case_						:    Dict				=  tokenizer.vocab_size
															snake_case_						:    Optional[Any]				=  len(__magic_name__    )

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

															snake_case_						:    Union[str, Any]				=  tokenizer.encode('''aaaaa bbbbbb low cccccccccdddddddd l'''					,							add_special_tokens=__magic_name__    )

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

															snake_case_						:    Union[str, Any]				=  {'''eos_token''': '''>>>>|||<||<<|<<''', '''pad_token''': '''<<<<<|||>|>>>>|>'''}
															snake_case_						:    List[str]				=  tokenizer.add_special_tokens(__magic_name__    )
															snake_case_						:    Dict				=  tokenizer.vocab_size
															snake_case_						:    Dict				=  len(__magic_name__    )

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

															snake_case_						:    Tuple				=  tokenizer.encode(
															    '''>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l'''					,							add_special_tokens=__magic_name__    )

															self.assertGreaterEqual(len(__magic_name__    )					,							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 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.get_tokenizer()

							snake_case_						:    Optional[Any]				=  tokenizer.tokenize('''This is a test'''    )
							# fmt: off
							self.assertListEqual(__magic_name__					,							[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(__magic_name__    )					,							[4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6]					,							)

							snake_case_						:    List[Any]				=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''    )
							self.assertListEqual(
							    __magic_name__					,							[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''', '''.''']    )

							snake_case_						:    List[str]				=  tokenizer.convert_tokens_to_ids(__magic_name__    )
							# fmt: off
							self.assertListEqual(__magic_name__					,							[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

							snake_case_						:    int				=  tokenizer.convert_ids_to_tokens(__magic_name__    )
							self.assertListEqual(
							    __magic_name__					,							[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 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Tuple				=  [
							    '''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
							snake_case_						:    List[Any]				=  {
							    '''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=__magic_name__					,							model_name='''microsoft/speecht5_asr'''					,							revision='''c5ef64c71905caeccde0e4462ef3f9077224c524'''					,							sequences=__magic_name__					,							)
