"""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 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	List[str]  				=				SpeechTaTokenizer
    _snake_case  :	str  				=				False
    _snake_case  :	Optional[Any]  				=				True


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      super().setUp()

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

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

      tokenizer.save_pretrained(self.tmpdirname       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	List[str]       ):
      _UpperCAmelCase    			=   """this is a test"""
      _UpperCAmelCase    			=   """this is a test"""
      return input_text, output_text


    def   lowerCAmelCase_	(       self						:	Union[str, Any] ,					__lowerCAmelCase						:	str ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Optional[int]=20 ,					__lowerCAmelCase						:	List[str]=5       ):
      _UpperCAmelCase			,					_UpperCAmelCase    			=   self.get_input_output_texts(__lowerCAmelCase       )
      _UpperCAmelCase    			=   tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )
      _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase ,					clean_up_tokenization_spaces=__lowerCAmelCase       )
      return text, ids


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   """<pad>"""
      _UpperCAmelCase    			=   1

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


    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      _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(__lowerCAmelCase       ) ,					81       )


    def   lowerCAmelCase_	(       self						:	Tuple       ):
      self.assertEqual(self.get_tokenizer().vocab_size ,					79       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      _UpperCAmelCase    			=   self.get_tokenizers(do_lower_case=__lowerCAmelCase       )
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase    			=   tokenizer.vocab_size
          _UpperCAmelCase    			=   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    			=   ["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
          _UpperCAmelCase    			=   tokenizer.add_tokens(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.vocab_size
          _UpperCAmelCase    			=   len(__lowerCAmelCase       )

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

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

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

          _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      _UpperCAmelCase    			=   self.get_tokenizer()

      _UpperCAmelCase    			=   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    			=   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    			=   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    			=   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   lowerCAmelCase_	(       self						:	Dict       ):
      # 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],
              [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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 ,					)

"""simple docstring"""


from __future__ import annotations

import typing
from collections import Counter






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   Counter()
  for base in range(1 ,max_perimeter + 1		):
    for perpendicular in range(lowercase ,max_perimeter + 1		):
      _UpperCAmelCase    			=   (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(lowercase		):
        _UpperCAmelCase    			=   int(base + perpendicular + hypotenuse		)
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets






def      __UpperCAmelCase       (      lowercase = 10_00		):







  """simple docstring"""
  _UpperCAmelCase    			=   pythagorean_triple(lowercase		)
  return triplets.most_common(1		)[0][0]


if __name__ == "__main__":
    print(F'''Perimeter {solution()} has maximum solutions''')
