



'''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 __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Tuple		=			DebertaTokenizer
 __lowerCamelCase       :			Dict		=			True
 __lowerCamelCase       :			str		=			DebertaTokenizerFast



 def    a__    (self			)       ->	str:


      """simple docstring"""



      super().setUp()

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

      _a           =    os.path.join(self.tmpdirname					,  VOCAB_FILES_NAMES['''vocab_file''']			)
      _a           =    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    a__    (self					,  **A			)       ->	int:


      """simple docstring"""



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



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    '''lower newer'''
      _a           =    '''lower newer'''
      return input_text, output_text



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      _a           =    self.get_tokenizer()
      _a           =    '''lower newer'''
      _a           =    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      _a           =    tokenizer.tokenize(A			)
      self.assertListEqual(A					,  A			)

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



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.get_tokenizer()
      _a           =    tokenizer('''Hello'''					,  '''World'''			)
      _a           =    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['''token_type_ids''']					,  A			)



 @slow
 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''			)

      _a           =    tokenizer.encode('''sequence builders'''					,  add_special_tokens=A			)
      _a           =    tokenizer.encode('''multi-sequence build'''					,  add_special_tokens=A			)

      _a           =    tokenizer.encode(
          '''sequence builders'''					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.encode(
          '''sequence builders'''					,  '''multi-sequence build'''					,  add_special_tokens=A					,  add_prefix_space=A			)

      _a           =    tokenizer.build_inputs_with_special_tokens(A			)
      _a           =    tokenizer.build_inputs_with_special_tokens(A					,  A			)

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode



 @slow
 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



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

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

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

           # fmt: off
           _a           =    {
               '''input_ids''': [
                   [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 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, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 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           =    [
               '''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'''





def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    1
          for i in range(0    ,      len(__A)):
               total *= numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps




def lowerCAmelCase    (__A):



     """simple docstring"""



     if not isinstance(__A    ,      __A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a           =    0
     _a           =    str(__A)

     while len(__A) != 1:
          _a           =    [int(__A) for i in num_string]

          _a           =    0
          for i in range(0    ,      len(__A)):
               total += numbers[i]

          _a           =    str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



