





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin





class 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Tuple      =  DebertaTokenizer
       SCREAMING_SNAKE_CASE_      : str      =  True
       SCREAMING_SNAKE_CASE_      : Any      =  DebertaTokenizerFast







       def    __UpperCAmelCase (    self	:	int  )						->					Union[str, Any]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         _lowercase							= [
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '[UNK]',
         ]
         _lowercase							= dict(zip(__A						,range(len(__A  )  )  )  )
         _lowercase							= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         _lowercase							= {'unk_token': '[UNK]'}

         _lowercase							= os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES['vocab_file']  )
         _lowercase							= os.path.join(self.tmpdirname						,VOCAB_FILES_NAMES['merges_file']  )
         with open(self.vocab_file						,'w'						,encoding='utf-8'  ) as fp:
           fp.write(json.dumps(__A  ) + '\n'  )
         with open(self.merges_file						,'w'						,encoding='utf-8'  ) as fp:
           fp.write('\n'.join(__A  )  )







       def    __UpperCAmelCase (    self	:	List[str]						,**__A	:	Dict  )						->					Tuple:
         kwargs.update(self.special_tokens_map  )
         return self.tokenizer_class.from_pretrained(self.tmpdirname						,**__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	Any  )						->					Dict:
         _lowercase							= 'lower newer'
         _lowercase							= 'lower newer'
         return input_text, output_text







       def    __UpperCAmelCase (    self	:	Any  )						->					List[str]:
         _lowercase							= self.get_tokenizer()
         _lowercase							= 'lower newer'
         _lowercase							= ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
         _lowercase							= tokenizer.tokenize(__A  )
         self.assertListEqual(__A						,__A  )

         _lowercase							= tokens + [tokenizer.unk_token]
         _lowercase							= [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(__A  )						,__A  )







       def    __UpperCAmelCase (    self	:	Optional[Any]  )						->					Any:
         _lowercase							= self.get_tokenizer()
         _lowercase							= tokenizer('Hello'						,'World'  )
         _lowercase							= [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['token_type_ids']						,__A  )







       @slow
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Tuple:
         _lowercase							= self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

         _lowercase							= tokenizer.encode('sequence builders'						,add_special_tokens=__A  )
         _lowercase							= tokenizer.encode('multi-sequence build'						,add_special_tokens=__A  )

         _lowercase							= tokenizer.encode(
             'sequence builders'						,add_special_tokens=__A						,add_prefix_space=__A  )
         _lowercase							= tokenizer.encode(
             'sequence builders'						,'multi-sequence build'						,add_special_tokens=__A						,add_prefix_space=__A  )

         _lowercase							= tokenizer.build_inputs_with_special_tokens(__A  )
         _lowercase							= tokenizer.build_inputs_with_special_tokens(__A						,__A  )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







       @slow
       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
         _lowercase							= [self.tokenizer_class]
         if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class  )

         for tokenizer_class in tokenizer_classes:
           _lowercase							= tokenizer_class.from_pretrained('microsoft/deberta-base'  )

           _lowercase							= [
               'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
               'ALBERT incorporates two parameter reduction techniques',
               'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
               ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
               ' vocabulary embedding.',
           ]

           _lowercase							= tokenizer(__A						,padding=__A  )
           _lowercase							= [tokenizer.decode(__A						,skip_special_tokens=__A  ) for seq in encoding['input_ids']]

           # fmt: off
           _lowercase							= {
               'input_ids': [
                   [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
               ],
               'token_type_ids': [
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
               ],
               'attention_mask': [
                   [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],
                   [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],
                   [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
               ]
           }
           # fmt: on

           _lowercase							= [
               'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
               'ALBERT incorporates two parameter reduction techniques',
               'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
               ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
               ' vocabulary embedding.',
           ]

           self.assertDictEqual(encoding.data						,__A  )

           for expected, decoded in zip(__A						,__A  ):
             self.assertEqual(__A						,__A  )












from __future__ import annotations

from collections.abc import Generator

def 							SCREAMING_SNAKE_CASE__       (  )      ->						Generator[int, None, None]:
  _lowercase							= {}
  _lowercase							= 2
  while True:
    _lowercase							= factor_map.pop(snake_case__			,							snake_case__     )
    if factor:
      _lowercase							= factor + prime
      while x in factor_map:
        x += factor
      _lowercase							= factor
    else:
      _lowercase							= prime
      yield prime
    prime += 1

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :float = 1E10     )      ->						int:
  _lowercase							= sieve()

  _lowercase							= 1
  while True:
    _lowercase							= next(snake_case__     )
    if (2 * prime * n) > limit:
      return n
    # Ignore the next prime as the reminder will be 2.
    next(snake_case__     )
    n += 2


if __name__ == "__main__":
      print(solution())