


'''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_      (  __lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	Any		=     DebertaTokenizer
     lowerCamelCase				:	Union[str, Any]		=     True
     lowerCamelCase				:	List[Any]		=     DebertaTokenizerFast





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Dict:
        super().setUp()

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

        lowerCAmelCase     =			os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['vocab_file']    )
        lowerCAmelCase     =			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 							__UpperCAmelCase      ( self       :      List[Any]    ,      **UpperCAmelCase__       :      Any    )				->  Optional[Any]:
        kwargs.update(self.special_tokens_map    )
        return self.tokenizer_class.from_pretrained(self.tmpdirname    ,      **UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Tuple    ,      UpperCAmelCase__       :      Any    )				->  Tuple:
        lowerCAmelCase     =			'lower newer'
        lowerCAmelCase     =			'lower newer'
        return input_text, output_text





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.get_tokenizer()
        lowerCAmelCase     =			'lower newer'
        lowerCAmelCase     =			['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
        lowerCAmelCase     =			tokenizer.tokenize(UpperCAmelCase__    )
        self.assertListEqual(UpperCAmelCase__    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			tokens + [tokenizer.unk_token]
        lowerCAmelCase     =			[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__    )    ,      UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  List[str]:
        lowerCAmelCase     =			self.get_tokenizer()
        lowerCAmelCase     =			tokenizer('Hello'    ,      'World'    )
        lowerCAmelCase     =			[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['token_type_ids']    ,      UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[int]:
        lowerCAmelCase     =			self.tokenizer_class.from_pretrained('microsoft/deberta-base'    )

        lowerCAmelCase     =			tokenizer.encode('sequence builders'    ,      add_special_tokens=UpperCAmelCase__    )
        lowerCAmelCase     =			tokenizer.encode('multi-sequence build'    ,      add_special_tokens=UpperCAmelCase__    )

        lowerCAmelCase     =			tokenizer.encode(
            'sequence builders'    ,      add_special_tokens=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    )
        lowerCAmelCase     =			tokenizer.encode(
            'sequence builders'    ,      'multi-sequence build'    ,      add_special_tokens=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    )

        lowerCAmelCase     =			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__    )
        lowerCAmelCase     =			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__    ,      UpperCAmelCase__    )

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  List[str]:
        lowerCAmelCase     =			[self.tokenizer_class]
        if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class    )

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

           lowerCAmelCase     =			[
               '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.',
           ]

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

           # fmt: off
           lowerCAmelCase     =			{
               'input_ids': [
                   [1, 2_1_1_8, 1_1_1_2_6, 5_6_5, 3_5, 8_3, 2_5_1_9_1, 1_6_3, 1_8_8_5_4, 1_3, 1_2_1_5_6, 1_2, 1_6_1_0_1, 2_5_3_7_6, 1_3_8_0_7, 9, 2_2_2_0_5, 2_7_8_9_3, 1_6_3_5, 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_1_1_8, 1_1_1_2_6, 5_6_5, 2_4_5_3_6, 8_0, 4_3_7_9_7, 4_8_7_8, 7_3_7_3, 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_3_3, 7_8, 6_5, 1_6, 1_0, 3_7_2_4, 1_5_3_8, 3_3_1_8_3, 1_1_3_0_3, 4_3_7_9_7, 1_9_3_8, 4, 8_7_0, 2_4_1_6_5, 2_9_1_0_5, 5, 7_3_9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 3_6_1_7_3, 8_8, 8_0, 6_5_0, 7_8_2_1, 4_5_9_4_0, 6, 5_2, 2_5_5_9, 5, 1_8_3_6, 9, 5, 7_3_9_7, 1_3_1_7_1, 3_1, 5, 1_8_3_6, 9, 3_2_6_4_4, 3_3_1_8_3, 1_1_3_0_3, 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

           lowerCAmelCase     =			[
               '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 	a_					( lowerCamelCase	:     Optional[Any]				):
   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]





def 	a_					( lowerCamelCase	:     dict[int, list[int]]				):
   lowerCAmelCase     =			0
   lowerCAmelCase     =			len(lowerCamelCase				)  # No of vertices in graph
   lowerCAmelCase     =			[0] * n
   lowerCAmelCase     =			[False] * n

   def dfs(lowerCamelCase	:     Tuple	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str				):
      lowerCAmelCase     =			True
      lowerCAmelCase     =			id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, id_				)
            lowerCAmelCase     =			min(low[at]	, low[to]				)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)				)
         else:
            # This edge is a back edge and cannot be a bridge
            lowerCAmelCase     =			min(low[at]	, low[to]				)

   lowerCAmelCase     =			[]
   for i in range(lowerCamelCase				):
      if not visited[i]:
         dfs(lowerCamelCase	, -1	, lowerCamelCase	, id_				)
   return bridges


if __name__ == "__main__":
       import doctest

       doctest.testmod()





