


'''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       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		DebertaTokenizer
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		DebertaTokenizerFast
 def 							__UpperCAmelCase						(	self  )   ->							Optional[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(__magic_name__ ,			range(len(__magic_name__  )  )  )  )
      _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(__magic_name__  ) + '\n'  )
      with open(self.merges_file ,			'w' ,			encoding='utf-8'  ) as fp:
           fp.write('\n'.join(__magic_name__  )  )
 def 							__UpperCAmelCase						(	self ,			**__magic_name__  )   ->							List[Any]:
      kwargs.update(self.special_tokens_map  )
      return self.tokenizer_class.from_pretrained(self.tmpdirname ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							List[str]:
      _a      =							'lower newer'
      _a      =							'lower newer'
      return input_text, output_text
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.get_tokenizer()
      _a      =							'lower newer'
      _a      =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      _a      =							tokenizer.tokenize(__magic_name__  )
      self.assertListEqual(__magic_name__ ,			__magic_name__  )

      _a      =							tokens + [tokenizer.unk_token]
      _a      =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__  ) ,			__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _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'] ,			__magic_name__  )
 @slow
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							self.tokenizer_class.from_pretrained('microsoft/deberta-base'  )

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

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

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

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, 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(__magic_name__ ,			padding=__magic_name__  )
           _a      =							[tokenizer.decode(__magic_name__ ,			skip_special_tokens=__magic_name__  ) for seq in encoding['input_ids']]

           # fmt: off
           _a      =							{
               '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      =							[
               '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 ,			__magic_name__  )

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





'''simple docstring'''






def 		_A			(lowerCAmelCase__					:list						)				->					float:




     '''simple docstring'''



     _a      =							0
     while len(lowerCAmelCase__						) > 1:
          _a      =							0
          # Consider two files with minimum cost to be merged
          for _ in range(2						):
               _a      =							files.index(min(lowerCAmelCase__						)						)
               temp += files[min_index]
               files.pop(lowerCAmelCase__						)
          files.append(lowerCAmelCase__						)
          optimal_merge_cost += temp
     return optimal_merge_cost


if __name__ == "__main__":
 import doctest

 doctest.testmod()

