

"""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


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





@require_sentencepiece
@require_tokenizers
class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   SpeechTaTokenizer
    _lowerCamelCase					=   False
    _lowerCamelCase					=   True




    def UpperCamelCase__		( self    )    ->      Any:
          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 UpperCamelCase__		( self ,lowerCamelCase_    )    ->      List[str]:
          A				  =		"""this is a test"""
          A				  =		"""this is a test"""
          return input_text, output_text




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_=False ,lowerCamelCase_=2_0 ,lowerCamelCase_=5    )    ->      str:
          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 UpperCamelCase__		( self    )    ->      Optional[Any]:
          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 UpperCamelCase__		( self    )    ->      Tuple:
          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_    ) ,8_1    )




    def UpperCamelCase__		( self    )    ->      Dict:
          self.assertEqual(self.get_tokenizer().vocab_size ,7_9    )




    def UpperCamelCase__		( self    )    ->      Optional[Any]:
          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 UpperCamelCase__		( self    )    ->      Union[str, Any]:
          pass




    def UpperCamelCase__		( self    )    ->      List[Any]:
          pass




    def UpperCamelCase__		( self    )    ->      Tuple:
          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, 3_2, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 7, 4, 6, 5, 1_2, 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, 3_0, 4, 2_0, 7, 1_2, 4, 2_5, 8, 1_3, 9, 4, 1_0, 9, 4, 3, 2_3, 4, 7, 9, 1_4, 4, 6, 1_1, 1_0, 1_2, 4, 1_0, 1_2, 4, 1_9, 7, 1_5, 1_2, 7_3, 2_6]    )
          # 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 UpperCamelCase__		( self    )    ->      Tuple:
          # Use custom sequence because this tokenizer does not handle numbers.
          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, 3_2, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 6_4, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_5, 2_2, 4, 2_8, 9, 8, 2_0, 9, 4, 7, 1_2, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 6, 1_3, 7, 9, 1_2, 1_9, 8, 1_3, 1_8, 5, 1_3, 1_2, 4, 7, 9, 1_4, 4, 2_4, 2_2, 6, 8, 1_3, 1_7, 1_1, 3_9, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 3_9, 2_5, 5, 1_3, 6, 6_3, 4, 2_4, 1_3, 8, 2_7, 1_0, 1_4, 5, 1_2, 4, 2_1, 5, 9, 5, 1_3, 7, 1_5, 3_9, 2_4, 1_6, 1_3, 2_4, 8, 1_2, 5, 4, 7, 1_3, 1_7, 1_1, 1_0, 6, 5, 1_7, 6, 1_6, 1_3, 5, 1_2, 4, 6_4, 4_0, 4_7, 5_4, 3_2, 2_3, 4, 5_3, 4_9, 3_2, 2_3, 4, 5_4, 8, 4_0, 4_7, 5_4, 3_2, 7, 2_3, 4, 6_9, 5_2, 4_3, 2_3, 4, 5_1, 1_0, 1_2, 6, 1_0, 1_5, 4_0, 5, 1_3, 6, 2_3, 4, 6_9, 5_2, 4_8, 5, 6, 2_6, 2_6, 2_6, 6_3, 4, 1_9, 8, 1_3, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 6_1, 9, 1_4, 5, 1_3, 1_2, 6, 7, 9, 1_4, 1_0, 9, 2_1, 4, 6_4, 4_8, 5_2, 6_1, 6_3, 4, 7, 9, 1_4, 4, 4_8, 7, 6, 1_6, 1_3, 7, 1_5, 4, 5_2, 7, 9, 2_1, 1_6, 7, 2_1, 5, 4, 5_3, 5, 9, 5, 1_3, 7, 6, 1_0, 8, 9, 4, 6_4, 4_8, 5_2, 5_3, 6_3, 4, 2_0, 1_0, 6, 1_1, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 1_0, 1_3, 6, 2_2, 3_9, 6, 2_0, 8, 4, 2_4, 1_3, 5, 6, 1_3, 7, 1_0, 9, 5, 1_4, 4, 1_8, 8, 1_4, 5, 1_5, 1_2, 4, 1_0, 9, 4, 8, 9, 5, 4, 1_1, 1_6, 9, 1_4, 1_3, 5, 1_4, 4, 2_4, 1_5, 1_6, 1_2, 4, 1_5, 7, 9, 2_1, 1_6, 7, 2_1, 5, 1_2, 4, 7, 9, 1_4, 4, 1_4, 5, 5, 2_4, 4, 1_0, 9, 6, 5, 1_3, 8, 2_4, 5, 1_3, 7, 2_5, 1_0, 1_5, 1_0, 6, 2_2, 4, 2_5, 5, 6, 2_0, 5, 5, 9, 4, 5_8, 7, 3_7, 2_3, 4, 4_9, 2_2, 3_2, 8, 1_3, 1_7, 1_1, 4, 7, 9, 1_4, 4, 3_2, 5, 9, 1_2, 8, 1_3, 5_5, 1_5, 8, 2_0, 2_6, 2],
                  [4, 4_0, 4_7, 5_4, 3_2, 4, 1_0, 1_2, 4, 1_4, 5, 1_2, 1_0, 2_1, 9, 5, 1_4, 4, 6, 8, 4, 2_4, 1_3, 5, 3_9, 6, 1_3, 7, 1_0, 9, 4, 1_4, 5, 5, 2_4, 4, 2_5, 1_0, 1_4, 1_0, 1_3, 5, 1_7, 6, 1_0, 8, 9, 7, 1_5, 4, 1_3, 5, 2_4, 1_3, 5, 1_2, 5, 9, 6, 7, 6, 1_0, 8, 9, 1_2, 4, 1_9, 1_3, 8, 1_8, 4, 1_6, 9, 1_5, 7, 2_5, 5, 1_5, 5, 1_4, 4, 6, 5, 3_7, 6, 4, 2_5, 2_2, 4, 4_6, 8, 1_0, 9, 6, 1_5, 2_2, 4, 1_7, 8, 9, 1_4, 1_0, 6, 1_0, 8, 9, 1_0, 9, 2_1, 4, 8, 9, 4, 2_5, 8, 6, 1_1, 4, 1_5, 5, 1_9, 6, 4, 7, 9, 1_4, 4, 1_3, 1_0, 2_1, 1_1, 6, 4, 1_7, 8, 9, 6, 5, 3_7, 6, 4, 1_0, 9, 4, 7, 1_5, 1_5, 4, 1_5, 7, 2_2, 5, 1_3, 1_2, 2_6, 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, 3_2, 1_1, 5, 4, 4_5, 1_6, 1_0, 1_7, 2_8, 4, 2_5, 1_3, 8, 2_0, 9, 4, 1_9, 8, 3_7, 4, 4_6, 1_6, 1_8, 2_4, 1_2, 4, 8, 2_7, 5, 1_3, 4, 6, 1_1, 5, 4, 1_5, 7, 5_7, 2_2, 4, 1_4, 8, 2_1, 2_6, 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"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece_bpe.model")





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BartphoTokenizer
    _lowerCamelCase					=   False
    _lowerCamelCase					=   True




    def UpperCamelCase__		( self    )    ->      str:
          super().setUp()

          A				  =		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
          A				  =		dict(zip(lowerCamelCase_ ,range(len(lowerCamelCase_    )    )    )    )
          A				  =		{"""unk_token""": """<unk>"""}

          A				  =		os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    )
          with open(self.monolingual_vocab_file ,"""w""" ,encoding="""utf-8"""    ) as fp:
                for token in vocab_tokens:
                      fp.write(f'{token} {vocab_tokens[token]}\n'    )

          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          tokenizer.save_pretrained(self.tmpdirname    )




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      Tuple:
          kwargs.update(self.special_tokens_map    )
          return BartphoTokenizer.from_pretrained(self.tmpdirname ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		"""This is a là test"""
          A				  =		"""This is a<unk><unk> test"""
          return input_text, output_text




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          A				  =		"""This is a là test"""
          A				  =		"""▁This ▁is ▁a ▁l à ▁t est""".split()
          A				  =		tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokens + [tokenizer.unk_token]
          A				  =		[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,lowerCamelCase_    )




