

'''simple docstring'''
from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      a__						(							UpperCamelCase__						):
			_SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							DPRContextEncoderTokenizer
			_SCREAMING_SNAKE_CASE					:  str			 =							DPRContextEncoderTokenizerFast
			_SCREAMING_SNAKE_CASE					:  Optional[int]			 =							True




@require_tokenizers
class      a__						(							UpperCamelCase__						):
			_SCREAMING_SNAKE_CASE					:  int			 =							DPRQuestionEncoderTokenizer
			_SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							DPRQuestionEncoderTokenizerFast
			_SCREAMING_SNAKE_CASE					:  Optional[int]			 =							True




@require_tokenizers
class      a__						(							UpperCamelCase__						):
			_SCREAMING_SNAKE_CASE					:  List[Any]			 =							DPRReaderTokenizer
			_SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							DPRReaderTokenizerFast
			_SCREAMING_SNAKE_CASE					:  Dict			 =							True


			@slow
			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   List[Any]      =     self.tokenizer_class.from_pretrained("bert-base-uncased"						)

						_lowercase    :   List[Any]      =     tokenizer.encode("question sequence"    ,       add_special_tokens=snake_case_						)
						_lowercase    :   str      =     tokenizer.encode("title sequence"    ,       add_special_tokens=snake_case_						)
						_lowercase    :   Optional[int]      =     tokenizer.encode("text sequence " * 4    ,       add_special_tokens=snake_case_						)
						_lowercase    :   List[Any]      =     [[101] + text_a + [102] + text_a + [102] + text_a]
						_lowercase    :   Union[str, Any]      =     BatchEncoding({"input_ids": input_ids}						)

						_lowercase    :   List[Any]      =     [[0] * len(input_ids[0]						)]
						_lowercase    :   List[Any]      =     [[0] * len(input_ids[0]						)]
						_lowercase    :   List[Any]      =     [0]
						_lowercase    :   Optional[Any]      =     DPRReaderOutput(snake_case_    ,       snake_case_    ,       snake_case_						)

						_lowercase    :   int      =     8, 9
						_lowercase    :   Optional[int]      =     10
						_lowercase    :   Any      =     10
						_lowercase    :   Optional[Any]      =     tokenizer.decode_best_spans(snake_case_    ,       snake_case_						)
						self.assertEqual(predicted_spans[0].start_index    ,       snake_case_						)
						self.assertEqual(predicted_spans[0].end_index    ,       snake_case_						)
						self.assertEqual(predicted_spans[0].doc_id    ,       0						)






			@slow
			def 		_lowerCamelCase     (				self						):





						"""simple docstring"""





						_lowercase    :   List[str]      =     self.tokenizer_class.from_pretrained("bert-base-uncased"						)

						_lowercase    :   Optional[Any]      =     tokenizer.encode("question sequence"    ,       add_special_tokens=snake_case_						)
						_lowercase    :   Dict      =     tokenizer.encode("title sequence"    ,       add_special_tokens=snake_case_						)
						_lowercase    :   str      =     tokenizer.encode("text sequence"    ,       add_special_tokens=snake_case_						)
						_lowercase    :   List[Any]      =     [101] + text_a + [102] + text_a + [102] + text_a
						_lowercase    :   Tuple      =     tokenizer(questions=["question sequence"]    ,       titles=["title sequence"]    ,       texts=["text sequence"]						)
						self.assertIn("input_ids"    ,       snake_case_						)
						self.assertIn("attention_mask"    ,       snake_case_						)
						self.assertListEqual(encoded_input["input_ids"][0]    ,       snake_case_						)









'''simple docstring'''
class      a__						:


   def __init__(				self    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     data
      _lowercase    :   int
      _lowercase    :   Node







def       _A	(			snake_case  )    ->			None:
   _lowercase    :   Any      =     0
   _lowercase    :   str      =     x







def       _A	(			snake_case	,							snake_case  )    ->			None:
   _lowercase  ,   _lowercase    :   Any      =     find_set(snake_case  ), find_set(snake_case  )
   if x == y:
      return

   elif x.rank > y.rank:
      _lowercase    :   Dict      =     x
   else:
      _lowercase    :   Optional[int]      =     y
      if x.rank == y.rank:
         y.rank += 1







def       _A	(			snake_case  )    ->			Node:
   if x != x.parent:
      _lowercase    :   Optional[int]      =     find_set(x.parent  )
   return x.parent







def       _A	(			snake_case  )    ->			set:
   _lowercase    :   Any      =     ({0, 1, 2}, {3, 4, 5})
   for s in sets:
      if node.data in s:
         return s
   _lowercase    :   List[Any]      =     F'''{node.data} is not in {sets}'''
   raise ValueError(snake_case  )







def       _A	(			)    ->			None:
   _lowercase    :   Union[str, Any]      =     [Node(snake_case  ) for i in range(6  )]
   for v in vertex:
      make_set(snake_case  )

   union_set(vertex[0]	,							vertex[1]  )
   union_set(vertex[1]	,							vertex[2]  )
   union_set(vertex[3]	,							vertex[4]  )
   union_set(vertex[3]	,							vertex[5]  )

   for nodea in vertex:
      for nodea in vertex:
         if find_python_set(snake_case  ).isdisjoint(find_python_set(snake_case  )  ):
            assert find_set(snake_case  ) != find_set(snake_case  )
         else:
            assert find_set(snake_case  ) == find_set(snake_case  )


if __name__ == "__main__":
    test_disjoint_set()






