

"""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        _lowercase					(     __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       DebertaTokenizer
     _lowerCamelCase     =       True
     _lowerCamelCase     =       DebertaTokenizerFast







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

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

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







     def 	lowerCAmelCase__    (      self ,						**UpperCamelCase_       ):
      kwargs.update(self.special_tokens_map       )
      return self.tokenizer_class.from_pretrained(self.tmpdirname ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      '''lower newer'''
      __magic_name__     =      '''lower newer'''
      return input_text, output_text







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      '''lower newer'''
      __magic_name__     =      ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      __magic_name__     =      tokenizer.tokenize(UpperCamelCase_       )
      self.assertListEqual(UpperCamelCase_ ,						UpperCamelCase_       )

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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.get_tokenizer()
      __magic_name__     =      tokenizer('''Hello''' ,						'''World'''       )
      __magic_name__     =      [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['''token_type_ids'''] ,						UpperCamelCase_       )







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''       )

      __magic_name__     =      tokenizer.encode('''sequence builders''' ,						add_special_tokens=UpperCamelCase_       )
      __magic_name__     =      tokenizer.encode('''multi-sequence build''' ,						add_special_tokens=UpperCamelCase_       )

      __magic_name__     =      tokenizer.encode(
          '''sequence builders''' ,						add_special_tokens=UpperCamelCase_ ,						add_prefix_space=UpperCamelCase_       )
      __magic_name__     =      tokenizer.encode(
          '''sequence builders''' ,						'''multi-sequence build''' ,						add_special_tokens=UpperCamelCase_ ,						add_prefix_space=UpperCamelCase_       )

      __magic_name__     =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase_       )
      __magic_name__     =      tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ ,						UpperCamelCase_       )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode







     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [self.tokenizer_class]
      if self.test_rust_tokenizer:
       tokenizer_classes.append(self.rust_tokenizer_class       )

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

       __magic_name__     =      [
           '''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.''',
       ]

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

       # fmt: off
       __magic_name__     =      {
           '''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

       __magic_name__     =      [
           '''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 ,						UpperCamelCase_       )

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




"""simple docstring"""






class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      size
      __magic_name__     =      [0] * size
      __magic_name__     =      [0] * size







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return index | (index + 1)







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return (index & (index + 1)) - 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      value
      while index < self.size:
       __magic_name__     =      self.get_prev(UpperCamelCase_       ) + 1
       if current_left_border == index:
        __magic_name__     =      value
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_next(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      right -= 1  # Because of right is exclusive
      __magic_name__     =      0
      while left <= right:
       __magic_name__     =      self.get_prev(UpperCamelCase_       )
       if left <= current_left:
        __magic_name__     =      max(UpperCamelCase_ ,						self.tree[right]       )
        __magic_name__     =      current_left
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						self.arr[right]       )
        right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()

