





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       :   List[str]					= get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')


@require_sentencepiece
@require_tokenizers
class     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			Any							     =       SpeechTaTokenizer
			__lowerCamelCase      :			str							     =       False
			__lowerCamelCase      :			int							     =       True






			def   a_			(     self  :       Dict  )      ->      Any:







					"""simple docstring"""






					super().setUp()

					# We have a SentencePiece fixture for testing
					A__     =  SpeechTaTokenizer(__lowerCAmelCase  )

					A__     =  AddedToken("""<mask>"""				,      lstrip=__lowerCAmelCase				,      rstrip=__lowerCAmelCase  )
					A__     =  mask_token
					tokenizer.add_special_tokens({"""mask_token""": mask_token}  )
					tokenizer.add_tokens(["""<ctc_blank>"""]  )

					tokenizer.save_pretrained(self.tmpdirname  )






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]  )      ->      Any:







					"""simple docstring"""






					A__     =  """this is a test"""
					A__     =  """this is a test"""
					return input_text, output_text






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       List[Any]=False				,      __lowerCAmelCase  :       Optional[Any]=20				,      __lowerCAmelCase  :       List[Any]=5  )      ->      Any:







					"""simple docstring"""






					A__ ,     A__     =  self.get_input_output_texts(__lowerCAmelCase  )
					A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
					A__     =  tokenizer.decode(__lowerCAmelCase				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
					return text, ids






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







					"""simple docstring"""






					A__     =  """<pad>"""
					A__     =  1

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






			def   a_			(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  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   a_			(     self  :       Union[str, Any]  )      ->      Optional[Any]:







					"""simple docstring"""






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






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







					"""simple docstring"""






					A__     =  self.get_tokenizers(do_lower_case=__lowerCAmelCase  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  tokenizer.vocab_size
									A__     =  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)

									A__     =  ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
									A__     =  tokenizer.add_tokens(__lowerCAmelCase  )
									A__     =  tokenizer.vocab_size
									A__     =  len(__lowerCAmelCase  )

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

									A__     =  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  )

									A__     =  {"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
									A__     =  tokenizer.add_special_tokens(__lowerCAmelCase  )
									A__     =  tokenizer.vocab_size
									A__     =  len(__lowerCAmelCase  )

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

									A__     =  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   a_			(     self  :       str  )      ->      int:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      int:







					"""simple docstring"""






					pass






			def   a_			(     self  :       Union[str, Any]  )      ->      List[str]:







					"""simple docstring"""






					A__     =  self.get_tokenizer()

					A__     =  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]				,      )

					A__     =  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""", """."""]  )

					A__     =  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

					A__     =  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   a_			(     self  :       str  )      ->      int:







					"""simple docstring"""






					A__     =  [
					    """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
					A__     =  {
					    """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],
					        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
					    ]
					}
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=__lowerCAmelCase				,      model_name="""microsoft/speecht5_asr"""				,      revision="""c5ef64c71905caeccde0e4462ef3f9077224c524"""				,      sequences=__lowerCAmelCase				,      )










import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
					A       :   Optional[Any]					= '''pt'''
elif is_tf_available():
					A       :   List[Any]					= '''tf'''
else:
					A       :   Union[str, Any]					= '''jax'''


class     A     (SCREAMING_SNAKE_CASE  ,	unittest.TestCase       ):
			'''simple docstring'''




			__lowerCamelCase      :			str							     =       ByTaTokenizer
			__lowerCamelCase      :			Tuple							     =       False






			def   a_			(     self  :       Dict  )      ->      Dict:







					"""simple docstring"""






					super().setUp()
					A__     =  ByTaTokenizer()
					tokenizer.save_pretrained(self.tmpdirname  )






			@cached_property
			def   a_			(     self  :       Dict  )      ->      List[Any]:







					"""simple docstring"""






					return ByTaTokenizer.from_pretrained("""google/byt5-small"""  )






			def   a_			(     self  :       Union[str, Any]				,      **__lowerCAmelCase  :       int  )      ->      ByTaTokenizer:







					"""simple docstring"""






					return self.tokenizer_class.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Optional[Any]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       Union[str, Any]=False				,      __lowerCAmelCase  :       Dict=20				,      __lowerCAmelCase  :       List[str]=5  )      ->      Tuple[str, list]:







					"""simple docstring"""






					A__     =  []
					for i in range(len(__lowerCAmelCase  )  ):
							try:
									A__     =  tokenizer.decode([i]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							except UnicodeDecodeError:
									pass
							toks.append((i, tok)  )

					A__     =  list(filter(lambda __lowerCAmelCase  : re.match(R"""^[ a-zA-Z]+$"""				,      t[1]  )				,      __lowerCAmelCase  )  )
					A__     =  list(filter(lambda __lowerCAmelCase  : [t[0]] == tokenizer.encode(t[1]				,      add_special_tokens=__lowerCAmelCase  )				,      __lowerCAmelCase  )  )
					if max_length is not None and len(__lowerCAmelCase  ) > max_length:
							A__     =  toks[:max_length]
					if min_length is not None and len(__lowerCAmelCase  ) < min_length and len(__lowerCAmelCase  ) > 0:
							while len(__lowerCAmelCase  ) < min_length:
									A__     =  toks + toks
        # toks_str = [t[1] for t in toks]
					A__     =  [t[0] for t in toks]

					# Ensure consistency
					A__     =  tokenizer.decode(__lowerCAmelCase				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
					if " " not in output_txt and len(__lowerCAmelCase  ) > 1:
							A__     =  (
							    tokenizer.decode([toks_ids[0]]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							    + """ """
							    + tokenizer.decode(toks_ids[1:]				,      clean_up_tokenization_spaces=__lowerCAmelCase  )
							)
					if with_prefix_space:
							A__     =  """ """ + output_txt
					A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
					return output_txt, output_ids






			def   a_			(     self  :       List[str]  )      ->      int:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]  )
					A__     =  tokenizer(["""hi""", """I went to the gym""", """"""]  )
					self.assertListEqual(batch_with_eos_added["""input_ids"""]				,      batch_without_eos_added["""input_ids"""]  )






			def   a_			(     self  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  """Unicode €."""
					A__     =  tokenizer(__lowerCAmelCase  )
					A__     =  [88, 1_13, 1_08, 1_02, 1_14, 1_03, 1_04, 35, 2_29, 1_33, 1_75, 49, 1]
					self.assertEqual(encoded["""input_ids"""]				,      __lowerCAmelCase  )

					# decoding
					A__     =  tokenizer.decode(__lowerCAmelCase  )
					self.assertEqual(__lowerCAmelCase				,      """Unicode €.</s>"""  )

					A__     =  tokenizer("""e è é ê ë"""  )
					A__     =  [1_04, 35, 1_98, 1_71, 35, 1_98, 1_72, 35, 1_98, 1_73, 35, 1_98, 1_74, 1]
					self.assertEqual(encoded["""input_ids"""]				,      __lowerCAmelCase  )
					# decoding
					A__     =  tokenizer.decode(__lowerCAmelCase  )
					self.assertEqual(__lowerCAmelCase				,      """e è é ê ë</s>"""  )

					# encode/decode, but with `encode` instead of `__call__`
					self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""  )  )				,      """e è é ê ë</s>"""  )






			def   a_			(     self  :       Dict  )      ->      Optional[int]:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					# fmt: off
					A__     =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 1, 0]
					# fmt: on
					A__     =  tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					self.assertIsInstance(__lowerCAmelCase				,      __lowerCAmelCase  )

					if FRAMEWORK != "jax":
							A__     =  list(batch.input_ids.numpy()[0]  )
					else:
							A__     =  list(batch.input_ids.tolist()[0]  )

					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )

					self.assertEqual((2, 37)				,      batch.input_ids.shape  )
					self.assertEqual((2, 37)				,      batch.attention_mask.shape  )






			def   a_			(     self  :       Optional[Any]  )      ->      List[str]:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
					A__     =  tokenizer(__lowerCAmelCase				,      padding=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					# check if input_ids are returned and no decoder_input_ids
					self.assertIn("""input_ids"""				,      __lowerCAmelCase  )
					self.assertIn("""attention_mask"""				,      __lowerCAmelCase  )
					self.assertNotIn("""decoder_input_ids"""				,      __lowerCAmelCase  )
					self.assertNotIn("""decoder_attention_mask"""				,      __lowerCAmelCase  )






			def   a_			(     self  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  [
					    """Summary of the text.""",
					    """Another summary.""",
					]
					A__     =  tokenizer(
					    text_target=__lowerCAmelCase				,      max_length=32				,      padding="""max_length"""				,      truncation=__lowerCAmelCase				,      return_tensors=__lowerCAmelCase  )
					self.assertEqual(32				,      targets["""input_ids"""].shape[1]  )






			def   a_			(     self  :       List[str]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.ta_base_tokenizer
					A__     =  ["""A long paragraph for summarization. </s>"""]
					A__     =  ["""Summary of the text. </s>"""]
					# fmt: off
					A__     =  [68, 35, 1_11, 1_14, 1_13, 1_06, 35, 1_15, 1_00, 1_17, 1_00, 1_06, 1_17, 1_00, 1_15, 1_07, 35, 1_05, 1_14, 1_17, 35, 1_18, 1_20, 1_12, 1_12, 1_00, 1_17, 1_08, 1_25, 1_00, 1_19, 1_08, 1_14, 1_13, 49, 35, 1]
					A__     =  [86, 1_20, 1_12, 1_12, 1_00, 1_17, 1_24, 35, 1_14, 1_05, 35, 1_19, 1_07, 1_04, 35, 1_19, 1_04, 1_23, 1_19, 49, 35, 1]
					# fmt: on

					A__     =  tokenizer(__lowerCAmelCase				,      text_target=__lowerCAmelCase  )

					self.assertEqual(__lowerCAmelCase				,      batch["""input_ids"""][0]  )
					self.assertEqual(__lowerCAmelCase				,      batch["""labels"""][0]  )






			def   a_			(     self  :       str  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									self.assertNotEqual(tokenizer.model_max_length				,      42  )

        # Now let's start the test
					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									# Isolate this from the other tests because we save additional tokens/etc
									A__     =  tempfile.mkdtemp()

									A__     =  """ He is very happy, UNwant\u00E9d,running"""
									A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									tokenizer.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
									A__     =  after_tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )

									shutil.rmtree(__lowerCAmelCase  )

					A__     =  self.get_tokenizers(model_max_length=42  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									# Isolate this from the other tests because we save additional tokens/etc
									A__     =  tempfile.mkdtemp()

									A__     =  """ He is very happy, UNwant\u00E9d,running"""
									tokenizer.add_tokens(["""bim""", """bambam"""]  )
									A__     =  tokenizer.additional_special_tokens
									additional_special_tokens.append("""new_additional_special_token"""  )
									tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
									A__     =  tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									tokenizer.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase  )
									A__     =  after_tokenizer.encode(__lowerCAmelCase				,      add_special_tokens=__lowerCAmelCase  )
									self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )
									self.assertIn("""new_additional_special_token"""				,      after_tokenizer.additional_special_tokens  )
									self.assertEqual(after_tokenizer.model_max_length				,      42  )

									A__     =  tokenizer.__class__.from_pretrained(__lowerCAmelCase				,      model_max_length=43  )
									self.assertEqual(tokenizer.model_max_length				,      43  )

									shutil.rmtree(__lowerCAmelCase  )






			def   a_			(     self  :       Optional[int]  )      ->      str:







					"""simple docstring"""






					A__     =  []
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(__lowerCAmelCase  )

									with open(os.path.join(__lowerCAmelCase				,      """special_tokens_map.json"""  )				,      encoding="""utf-8"""  ) as json_file:
											A__     =  json.load(__lowerCAmelCase  )

									with open(os.path.join(__lowerCAmelCase				,      """tokenizer_config.json"""  )				,      encoding="""utf-8"""  ) as json_file:
											A__     =  json.load(__lowerCAmelCase  )

									A__     =  [f'<extra_id_{i}>' for i in range(1_25  )]

									A__     =  added_tokens_extra_ids + [
									    """an_additional_special_token"""
									]
									A__     =  added_tokens_extra_ids + [
									    """an_additional_special_token"""
									]

									with open(os.path.join(__lowerCAmelCase				,      """special_tokens_map.json"""  )				,      """w"""				,      encoding="""utf-8"""  ) as outfile:
											json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )
									with open(os.path.join(__lowerCAmelCase				,      """tokenizer_config.json"""  )				,      """w"""				,      encoding="""utf-8"""  ) as outfile:
											json.dump(__lowerCAmelCase				,      __lowerCAmelCase  )

									# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
									# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
									# "special_tokens_map.json" files
									A__     =  tokenizer_class.from_pretrained(
									    __lowerCAmelCase				,      )
									self.assertIn(
									    """an_additional_special_token"""				,      tokenizer_without_change_in_init.additional_special_tokens  )
									# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
									self.assertEqual(
									    ["""an_additional_special_token"""]				,      tokenizer_without_change_in_init.convert_ids_to_tokens(
									        tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]  )  )				,      )

									# Now we test that we can change the value of additional_special_tokens in the from_pretrained
									A__     =  added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,      lstrip=__lowerCAmelCase  )]
									A__     =  tokenizer_class.from_pretrained(
									    __lowerCAmelCase				,      additional_special_tokens=__lowerCAmelCase				,      )

									self.assertIn("""a_new_additional_special_token"""				,      tokenizer.additional_special_tokens  )
									self.assertEqual(
									    ["""a_new_additional_special_token"""]				,      tokenizer.convert_ids_to_tokens(
									        tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]  )  )				,      )






			def   a_			(     self  :       Tuple  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  []
					if self.test_slow_tokenizer:
							tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

					if self.test_rust_tokenizer:
							tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

					for tokenizer_class, tokenizer_utils in tokenizer_list:
							with tempfile.TemporaryDirectory() as tmp_dir:
									tokenizer_utils.save_pretrained(__lowerCAmelCase  )

									A__     =  tokenizer_class.from_pretrained(__lowerCAmelCase  )

									self.assertTrue(tokenizer.decode([2_55]  ) == """"""  )






			def   a_			(     self  :       List[Any]  )      ->      Tuple:







					"""simple docstring"""






					pass






			def   a_			(     self  :       Optional[int]  )      ->      Any:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      Optional[Any]:







					"""simple docstring"""






					pass






			def   a_			(     self  :       str  )      ->      Optional[int]:







					"""simple docstring"""






					pass






			def   a_			(     self  :       int  )      ->      Dict:







					"""simple docstring"""






					A__     =  self.get_tokenizers(fast=__lowerCAmelCase				,      do_lower_case=__lowerCAmelCase  )
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  ["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
									A__     =  tokenizer.convert_tokens_to_string(__lowerCAmelCase  )

									self.assertIsInstance(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[Any]  )      ->      int:







					"""simple docstring"""






					A__     =  self.get_tokenizers()
					for tokenizer in tokenizers:
							with self.subTest(f'{tokenizer.__class__.__name__}'  ):
									A__     =  [
									    """bos_token""",
									    """eos_token""",
									    """unk_token""",
									    """sep_token""",
									    """pad_token""",
									    """cls_token""",
									    """mask_token""",
									]

									A__     =  0
									A__     =  tokenizer.convert_ids_to_tokens(
									    __lowerCAmelCase				,      skip_special_tokens=__lowerCAmelCase  )

									for attr in attributes_list:
											setattr(__lowerCAmelCase				,      attr + """_id"""				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      __lowerCAmelCase  )				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      attr + """_id"""  )				,      __lowerCAmelCase  )

											setattr(__lowerCAmelCase				,      attr + """_id"""				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      __lowerCAmelCase  )				,      __lowerCAmelCase  )
											self.assertEqual(getattr(__lowerCAmelCase				,      attr + """_id"""  )				,      __lowerCAmelCase  )

									setattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""				,      []  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens"""  )				,      []  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""  )				,      []  )

									setattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""				,      [token_id_to_test_setters]  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens"""  )				,      [token_to_test_setters]  )
									self.assertListEqual(getattr(__lowerCAmelCase				,      """additional_special_tokens_ids"""  )				,      [token_id_to_test_setters]  )



