


def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				if not isinstance(lowerCamelCase_	,     lowerCamelCase_       ):
								raise TypeError('''only integers accepted as input'''       )
				else:
								lowercase__     				=    str(abs(lowerCamelCase_       )       )
								lowercase__     				=    [list(lowerCamelCase_       ) for char in range(len(lowerCamelCase_       )       )]
								for index in range(len(lowerCamelCase_       )       ):
												num_transpositions[index].pop(lowerCamelCase_       )
								return max(
								    int(''''''.join(list(lowerCamelCase_       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
							__import__('doctest').testmod()












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= DistilBertTokenizer
				lowercase__       							= DistilBertTokenizerFast
				lowercase__       							= True




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''						)

								lowercase__     				=    tokenizer.encode('''sequence builders''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''multi-sequence build''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase,						lowerCamelCase						)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]




