



import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase						  =		get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class     _UpperCAmelCase			(       A__			,      unittest.TestCase     ):
     UpperCamelCase__		    =				ReformerTokenizer
     UpperCamelCase__		    =				ReformerTokenizerFast
     UpperCamelCase__		    =				True
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				True




     def 		snake_case_		(				self):
         super().setUp()

         A__		 =					ReformerTokenizer(a__ ,						keep_accents=a__)
         tokenizer.save_pretrained(self.tmpdirname)




     def 		snake_case_		(				self):
         A__		 =					'''<s>'''
         A__		 =					1

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




     def 		snake_case_		(				self):
         A__		 =					list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0] ,						'''<unk>''')
         self.assertEqual(vocab_keys[1] ,						'''<s>''')
         self.assertEqual(vocab_keys[-1] ,						'''j''')
         self.assertEqual(len(a__) ,						1_0_0_0)




     def 		snake_case_		(				self):
         self.assertEqual(self.get_tokenizer().vocab_size ,						1_0_0_0)




     def 		snake_case_		(				self):
         if not self.test_rust_tokenizer:
             return

         A__		 =					self.get_tokenizer()
         A__		 =					self.get_rust_tokenizer()

         A__		 =					'''I was born in 92000, and this is falsé.'''

         A__		 =					tokenizer.tokenize(a__)
         A__		 =					rust_tokenizer.tokenize(a__)
         self.assertListEqual(a__ ,						a__)

         A__		 =					tokenizer.encode(a__ ,						add_special_tokens=a__)
         A__		 =					rust_tokenizer.encode(a__ ,						add_special_tokens=a__)
         self.assertListEqual(a__ ,						a__)

         A__		 =					self.get_rust_tokenizer()
         A__		 =					tokenizer.encode(a__)
         A__		 =					rust_tokenizer.encode(a__)
         self.assertListEqual(a__ ,						a__)




     def 		snake_case_		(				self ,						a__=1_5):
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
                 A__		 =					self.rust_tokenizer_class.from_pretrained(a__ ,						**a__)

                 # Simple input
                 A__		 =					'''This is a simple input'''
                 A__		 =					['''This is a simple input 1''', '''This is a simple input 2''']
                 A__		 =					('''This is a simple input''', '''This is a pair''')
                 A__		 =					[
                     ('''This is a simple input 1''', '''This is a simple input 2'''),
                     ('''This is a simple pair 1''', '''This is a simple pair 2'''),
                 ]

                 # Simple input tests
                 self.assertRaises(a__ ,						tokenizer_r.encode ,						a__ ,						max_length=a__ ,						padding='''max_length''')

                 # Simple input
                 self.assertRaises(a__ ,						tokenizer_r.encode_plus ,						a__ ,						max_length=a__ ,						padding='''max_length''')

                 # Simple input
                 self.assertRaises(
                     a__ ,						tokenizer_r.batch_encode_plus ,						a__ ,						max_length=a__ ,						padding='''max_length''' ,						)

                 # Pair input
                 self.assertRaises(a__ ,						tokenizer_r.encode ,						a__ ,						max_length=a__ ,						padding='''max_length''')

                 # Pair input
                 self.assertRaises(a__ ,						tokenizer_r.encode_plus ,						a__ ,						max_length=a__ ,						padding='''max_length''')

                 # Pair input
                 self.assertRaises(
                     a__ ,						tokenizer_r.batch_encode_plus ,						a__ ,						max_length=a__ ,						padding='''max_length''' ,						)




     def 		snake_case_		(				self):
         pass




     def 		snake_case_		(				self):
         A__		 =					ReformerTokenizer(a__ ,						keep_accents=a__)

         A__		 =					tokenizer.tokenize('''This is a test''')
         self.assertListEqual(a__ ,						['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(a__) ,						[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2] ,						)

         A__		 =					tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
         self.assertListEqual(
             a__ ,						[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''9''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''é''',
                 '''.''',
             ] ,						)
         A__		 =					tokenizer.convert_tokens_to_ids(a__)
         self.assertListEqual(
             a__ ,						[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4] ,						)

         A__		 =					tokenizer.convert_ids_to_tokens(a__)
         self.assertListEqual(
             a__ ,						[
                 SPIECE_UNDERLINE + '''I''',
                 SPIECE_UNDERLINE + '''was''',
                 SPIECE_UNDERLINE + '''b''',
                 '''or''',
                 '''n''',
                 SPIECE_UNDERLINE + '''in''',
                 SPIECE_UNDERLINE + '''''',
                 '''<unk>''',
                 '''2''',
                 '''0''',
                 '''0''',
                 '''0''',
                 ''',''',
                 SPIECE_UNDERLINE + '''and''',
                 SPIECE_UNDERLINE + '''this''',
                 SPIECE_UNDERLINE + '''is''',
                 SPIECE_UNDERLINE + '''f''',
                 '''al''',
                 '''s''',
                 '''<unk>''',
                 '''.''',
             ] ,						)




     @cached_property
     def 		snake_case_		(				self):
         return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment''')




     @slow
     def 		snake_case_		(				self):
         A__		 =					'''Hello World!'''
         A__		 =					[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

         self.assertListEqual(a__ ,						self.big_tokenizer.encode(a__))




     @slow
     def 		snake_case_		(				self):
         A__		 =					(
             '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
             ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
         )
         A__		 =					[
             1_0_8,
             2_6_5,
             2_4,
             1_1_1,
             4,
             2_5_8,
             1_5_6,
             3_5,
             2_8,
             2_7_5,
             3,
             2_5_9,
             2_9_7,
             2_6_0,
             8_4,
             4,
             3_5,
             1_1_0,
             4_4,
             8,
             2_5_9,
             9_1,
             2_6_8,
             2_1,
             1_1,
             2_0_9,
             2_7_4,
             1_0_9,
             2_6_6,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             3_1_5,
             2_5_8,
             2_7_8,
             2_5_8,
             2_7_7,
             2_5_8,
             0,
             2_5_8,
             2_8_8,
             2_5_8,
             3_1_9,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             0,
             2_5_8,
             2_8_7,
             2_5_8,
             3_1_5,
             2_5_8,
             2_8_9,
             2_5_8,
             2_7_8,
             9_9,
             2_6_9,
             2_6_6,
             2_6_2,
             8,
             2_5_9,
             2_4_1,
             4,
             2_1_7,
             2_3_0,
             2_6_8,
             2_6_6,
             5_5,
             1_6_8,
             1_0_6,
             7_5,
             1_9_3,
             2_6_6,
             2_2_3,
             2_7,
             4_9,
             2_6,
             2_8_2,
             2_5,
             2_6_4,
             2_9_9,
             1_9,
             2_6,
             0,
             2_5_8,
             2_7_7,
             1_1_7,
             8_6,
             9_3,
             1_7_6,
             1_8_3,
             2_7_0,
             1_1,
             2_6_2,
             4_2,
             6_1,
             2_6_5,
         ]

         self.assertListEqual(a__ ,						self.big_tokenizer.encode(a__))




     @require_torch
     @slow
     def 		snake_case_		(				self):
         import torch

         from transformers import ReformerConfig, ReformerModel

         # Build sequence
         A__		 =					list(self.big_tokenizer.get_vocab().keys())[:1_0]
         A__		 =					''' '''.join(a__)
         A__		 =					self.big_tokenizer.encode_plus(a__ ,						return_tensors='''pt''')
         A__		 =					self.big_tokenizer.batch_encode_plus([sequence, sequence] ,						return_tensors='''pt''')

         A__		 =					ReformerConfig()
         # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
         A__		 =					encoded_sequence['''input_ids'''].shape
         A__		 =					ReformerModel(a__)

         # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
         assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

         with torch.no_grad():
             model(**a__)
             model(**a__)




     @slow
     def 		snake_case_		(				self):
         # fmt: off
         A__		 =					{'''input_ids''': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '''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]]}  # noqa: E501
         # fmt: on

         # This tokenizer does not know some characters like ")".
         # That is the reason why we use very simple texts here.
         # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
         A__		 =					[
             '''This is a very simple sentence.''',
             '''The quick brown fox jumps over the lazy dog.''',
         ]

         self.tokenizer_integration_test_util(
             expected_encoding=a__ ,						model_name='''google/reformer-crime-and-punishment''' ,						revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a''' ,						padding=a__ ,						sequences=a__ ,						)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       str	)->  float:

    def get_matched_characters(UpperCamelCase_				:       str ,       UpperCamelCase_				:       str	) -> str:
        A__		 =					[]
        A__		 =					min(len(_stra	) ,       len(_stra	)	) // 2
        for i, l in enumerate(_stra	):
            A__		 =					int(max(0 ,       i - limit	)	)
            A__		 =					int(min(i + limit + 1 ,       len(_stra	)	)	)
            if l in _stra[left:right]:
                matched.append(UpperCamelCase_	)
                A__		 =					f"{_stra[0:_stra.index(UpperCamelCase_	)]} {_stra[_stra.index(UpperCamelCase_	) + 1:]}"

        return "".join(UpperCamelCase_	)

    # matching characters
    A__		 =					get_matched_characters(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					get_matched_characters(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					len(UpperCamelCase_	)

    # transposition
    A__		 =					(
        len([(ca, ca) for ca, ca in zip(UpperCamelCase_ ,       UpperCamelCase_	) if ca != ca]	) // 2
    )

    if not match_count:
        A__		 =					0.0
    else:
        A__		 =					(
            1
            / 3
            * (
                match_count / len(UpperCamelCase_	)
                + match_count / len(UpperCamelCase_	)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    A__		 =					0
    for ca, ca in zip(stra[:4] ,       stra[:4]	):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(jaro_winkler("hello", "world"))
