
'''simple docstring'''

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  _UpperCAmelCase ( a__	,unittest.TestCase    ):

     """simple docstring"""

     a_    	=					DebertaTokenizer
     a_    	=					True
     a_    	=					DebertaTokenizerFast


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       super().setUp()

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

       a_      :	List[Any]      =							os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""] )
       a_      :	List[str]      =							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 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       kwargs.update(self.special_tokens_map )
       return self.tokenizer_class.from_pretrained(self.tmpdirname							,		**_A )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Tuple      =							'lower newer'
       a_      :	Dict      =							'lower newer'
       return input_text, output_text


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							self.get_tokenizer()
       a_      :	Optional[Any]      =							'lower newer'
       a_      :	Dict      =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       a_      :	Optional[int]      =							tokenizer.tokenize(_A )
       self.assertListEqual(_A							,		_A )

       a_      :	List[str]      =							tokens + [tokenizer.unk_token]
       a_      :	Dict      =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_A )							,		_A )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							self.get_tokenizer()
       a_      :	Any      =							tokenizer("""Hello"""							,		"""World""" )
       a_      :	Optional[int]      =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd["""token_type_ids"""]							,		_A )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[str]      =							self.tokenizer_class.from_pretrained("""microsoft/deberta-base""" )

       a_      :	Optional[Any]      =							tokenizer.encode("""sequence builders"""							,		add_special_tokens=_A )
       a_      :	Optional[Any]      =							tokenizer.encode("""multi-sequence build"""							,		add_special_tokens=_A )

       a_      :	Union[str, Any]      =							tokenizer.encode(
           """sequence builders"""							,		add_special_tokens=_A							,		add_prefix_space=_A )
       a_      :	Optional[Any]      =							tokenizer.encode(
           """sequence builders"""							,		"""multi-sequence build"""							,		add_special_tokens=_A							,		add_prefix_space=_A )

       a_      :	int      =							tokenizer.build_inputs_with_special_tokens(_A )
       a_      :	List[Any]      =							tokenizer.build_inputs_with_special_tokens(_A							,		_A )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode






     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							[self.tokenizer_class]
       if self.test_rust_tokenizer:
         tokenizer_classes.append(self.rust_tokenizer_class )

       for tokenizer_class in tokenizer_classes:
         a_      :	Dict      =							tokenizer_class.from_pretrained("""microsoft/deberta-base""" )

         a_      :	Tuple      =							[
             '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.',
         ]

         a_      :	List[str]      =							tokenizer(_A							,		padding=_A )
         a_      :	Dict      =							[tokenizer.decode(_A							,		skip_special_tokens=_A ) for seq in encoding['input_ids']]

         # fmt: off
         a_      :	Tuple      =							{
             'input_ids': [
                 [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

         a_      :	List[str]      =							[
             '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 )




'''simple docstring'''

from __future__ import annotations






def 	_snake_case						(      A_       :    int   ):
  """simple docstring"""







  a_      :	Optional[Any]      =							2
  a_      :	int      =							[]
  while i * i <= n:
    if n % i:
      i += 1
    else:
      n //= i
      factors.append(A_   )
  if n > 1:
    factors.append(A_   )
  return factors


if __name__ == "__main__":
    import doctest

    doctest.testmod()


