




"""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_			(	__lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	List[str]     =      DebertaTokenizer
 UpperCAmelCase       :	Union[str, Any]     =      True
 UpperCAmelCase       :	str     =      DebertaTokenizerFast

 def      lowerCAmelCase_				(					self			:			int						):
       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(_UpperCAmelCase       ,				range(len(_UpperCAmelCase						)						)						)						)
       _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(_UpperCAmelCase						) + '\n'						)
       with open(self.merges_file       ,				'w'       ,				encoding='utf-8'						) as fp:
             fp.write('\n'.join(_UpperCAmelCase						)						)

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				**_UpperCAmelCase			:			Union[str, Any]						):
       kwargs.update(self.special_tokens_map						)
       return self.tokenizer_class.from_pretrained(self.tmpdirname       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Dict						):
       _A     =  'lower newer'
       _A     =  'lower newer'
       return input_text, output_text

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.get_tokenizer()
       _A     =  'lower newer'
       _A     =  ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       _A     =  tokenizer.tokenize(_UpperCAmelCase						)
       self.assertListEqual(_UpperCAmelCase       ,				_UpperCAmelCase						)

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

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _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']       ,				_UpperCAmelCase						)

 @slow
 def      lowerCAmelCase_				(					self			:			int						):
       _A     =  self.tokenizer_class.from_pretrained('microsoft/deberta-base'						)

       _A     =  tokenizer.encode('sequence builders'       ,				add_special_tokens=_UpperCAmelCase						)
       _A     =  tokenizer.encode('multi-sequence build'       ,				add_special_tokens=_UpperCAmelCase						)

       _A     =  tokenizer.encode(
           'sequence builders'       ,				add_special_tokens=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase						)
       _A     =  tokenizer.encode(
           'sequence builders'       ,				'multi-sequence build'       ,				add_special_tokens=_UpperCAmelCase       ,				add_prefix_space=_UpperCAmelCase						)

       _A     =  tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase						)
       _A     =  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			:			List[Any]						):
       _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(_UpperCAmelCase       ,				padding=_UpperCAmelCase						)
             _A     =  [tokenizer.decode(_UpperCAmelCase       ,				skip_special_tokens=_UpperCAmelCase						) 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       ,				_UpperCAmelCase						)

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







"""simple docstring"""

def 		_snake_case	(   _snake_case				:   int							,    _snake_case				:   int				)       ->   int:






      '''simple docstring'''


      return int((input_a, input_a).count(1				) != 0				)






def 		_snake_case	(   )       ->   None:






      '''simple docstring'''


      assert or_gate(0							,    0				) == 0
      assert or_gate(0							,    1				) == 1
      assert or_gate(1							,    0				) == 1
      assert or_gate(1							,    1				) == 1


if __name__ == "__main__":
       print(or_gate(0, 1))
       print(or_gate(1, 0))
       print(or_gate(0, 0))
       print(or_gate(1, 1))

