



'''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


snake_case_			:     str  	=  get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')






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





	def      UpperCamelCase_  (			self :    Tuple			):







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

							# We have a SentencePiece fixture for testing
							_UpperCamelCase :		Any			    =       SpeechTaTokenizer(lowerCamelCase__			)

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

							tokenizer.save_pretrained(self.tmpdirname			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       'this is a test'
							_UpperCamelCase :		Dict			    =       'this is a test'
							return input_text, output_text





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Union[str, Any]=False			,lowerCamelCase__ :    Tuple=20			,lowerCamelCase__ :    List[Any]=5			):







							'''simple docstring'''
							_UpperCamelCase    ,      _UpperCamelCase :		List[str]			    =       self.get_input_output_texts(lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       tokenizer.encode(lowerCamelCase__			,add_special_tokens=lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       tokenizer.decode(lowerCamelCase__			,clean_up_tokenization_spaces=lowerCamelCase__			)
							return text, ids





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       '<pad>'
							_UpperCamelCase :		Optional[int]			    =       1

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





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       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(lowerCamelCase__			)			,81			)





	def      UpperCamelCase_  (			self :    int			):







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





	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       self.get_tokenizers(do_lower_case=lowerCamelCase__			)
							for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'			):
																			_UpperCamelCase :		Union[str, Any]			    =       tokenizer.vocab_size
																			_UpperCamelCase :		Optional[Any]			    =       len(lowerCamelCase__			)

																			self.assertNotEqual(lowerCamelCase__			,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 :		Union[str, Any]			    =       ['aaaaa bbbbbb', 'cccccccccdddddddd']
																			_UpperCamelCase :		Dict			    =       tokenizer.add_tokens(lowerCamelCase__			)
																			_UpperCamelCase :		Union[str, Any]			    =       tokenizer.vocab_size
																			_UpperCamelCase :		List[Any]			    =       len(lowerCamelCase__			)

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

																			_UpperCamelCase :		Optional[Any]			    =       tokenizer.encode('aaaaa bbbbbb low cccccccccdddddddd l'			,add_special_tokens=lowerCamelCase__			)

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

																			_UpperCamelCase :		Optional[int]			    =       {'eos_token': '>>>>|||<||<<|<<', 'pad_token': '<<<<<|||>|>>>>|>'}
																			_UpperCamelCase :		Any			    =       tokenizer.add_special_tokens(lowerCamelCase__			)
																			_UpperCamelCase :		List[Any]			    =       tokenizer.vocab_size
																			_UpperCamelCase :		Any			    =       len(lowerCamelCase__			)

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

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

																			self.assertGreaterEqual(len(lowerCamelCase__			)			,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 :    List[str]			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       self.get_tokenizer()

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

							_UpperCamelCase :		Optional[int]			    =       tokenizer.tokenize('I was born in 92000, and this is falsé.'			)
							self.assertListEqual(
							    lowerCamelCase__			,[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 :		Optional[Any]			    =       tokenizer.convert_tokens_to_ids(lowerCamelCase__			)
							# fmt: off
							self.assertListEqual(lowerCamelCase__			,[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 :		int			    =       tokenizer.convert_ids_to_tokens(lowerCamelCase__			)
							self.assertListEqual(
							    lowerCamelCase__			,[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 :    int			):







							'''simple docstring'''
							# Use custom sequence because this tokenizer does not handle numbers.
							_UpperCamelCase :		str			    =       [
							    '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 :		str			    =       {
							    '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=lowerCamelCase__			,model_name='microsoft/speecht5_asr'			,revision='c5ef64c71905caeccde0e4462ef3f9077224c524'			,sequences=lowerCamelCase__			,)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_			:     Any  	=  {
    'configuration_mvp': ['MVP_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MvpConfig', 'MvpOnnxConfig'],
    'tokenization_mvp': ['MvpTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     Dict  	=  ['MvpTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MVP_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MvpForCausalLM',
					    'MvpForConditionalGeneration',
					    'MvpForQuestionAnswering',
					    'MvpForSequenceClassification',
					    'MvpModel',
					    'MvpPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					snake_case_			:     Optional[Any]  	=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


