






"""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    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Dict 						=DebertaTokenizer
   a      :				Optional[int] 						=True
   a      :				Optional[Any] 						=DebertaTokenizerFast

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       super().setUp()

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

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

   def 						SCREAMING_SNAKE_CASE__ (					self ,				**snake_case_      )		->       List[str]:
       kwargs.update(self.special_tokens_map      )
       return self.tokenizer_class.from_pretrained(self.tmpdirname ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       UpperCamelCase__         =    'lower newer'
       UpperCamelCase__         =    'lower newer'
       return input_text, output_text

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    self.get_tokenizer()
       UpperCamelCase__         =    'lower newer'
       UpperCamelCase__         =    ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
       UpperCamelCase__         =    tokenizer.tokenize(snake_case_      )
       self.assertListEqual(snake_case_ ,				snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.get_tokenizer()
       UpperCamelCase__         =    tokenizer('Hello' ,				'World'      )
       UpperCamelCase__         =    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
       self.assertListEqual(tokd['token_type_ids'] ,				snake_case_      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.tokenizer_class.from_pretrained('microsoft/deberta-base'      )

       UpperCamelCase__         =    tokenizer.encode('sequence builders' ,				add_special_tokens=snake_case_      )
       UpperCamelCase__         =    tokenizer.encode('multi-sequence build' ,				add_special_tokens=snake_case_      )

       UpperCamelCase__         =    tokenizer.encode(
           'sequence builders' ,				add_special_tokens=snake_case_ ,				add_prefix_space=snake_case_      )
       UpperCamelCase__         =    tokenizer.encode(
           'sequence builders' ,				'multi-sequence build' ,				add_special_tokens=snake_case_ ,				add_prefix_space=snake_case_      )

       UpperCamelCase__         =    tokenizer.build_inputs_with_special_tokens(snake_case_      )
       UpperCamelCase__         =    tokenizer.build_inputs_with_special_tokens(snake_case_ ,				snake_case_      )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Union[str, Any]:
       UpperCamelCase__         =    [self.tokenizer_class]
       if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class      )

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

           UpperCamelCase__         =    [
               '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.',
           ]

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

           # fmt: off
           UpperCamelCase__         =    {
               '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

           UpperCamelCase__         =    [
               '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 ,				snake_case_      )

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












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




