



'''simple docstring'''

def   __lowerCamelCase  (   lowerCAmelCase_		,    lowerCAmelCase_						)    ->   int:
		# The longer word should come first
		if len(lowerCAmelCase_						) < len(lowerCAmelCase_						):
				return levenshtein_distance(lowerCAmelCase_		,    lowerCAmelCase_						)

		if len(lowerCAmelCase_						) == 0:
				return len(lowerCAmelCase_						)

		_a  :   Tuple				 =							list(range(len(lowerCAmelCase_						) + 1						)						)

		for i, ca in enumerate(lowerCAmelCase_						):
				_a  :   Dict				 =							[i + 1]

				for j, ca in enumerate(lowerCAmelCase_						):
						# Calculate insertions, deletions and substitutions
						_a  :   Any				 =							previous_row[j + 1] + 1
						_a  :   Optional[Any]				 =							current_row[j] + 1
						_a  :   int				 =							previous_row[j] + (ca != ca)

						# Get the minimum to append to the current row
						current_row.append(min(lowerCAmelCase_		,    lowerCAmelCase_		,    lowerCAmelCase_						)						)

				# Store the previous row
				_a  :   List[str]				 =							current_row

		# Returns the last element (distance)
		return previous_row[-1]


if __name__ == "__main__":
							__lowerCAmelCase		    =     input('''Enter the first word:\n''').strip()
							__lowerCAmelCase		    =     input('''Enter the second word:\n''').strip()

							__lowerCAmelCase		    =     levenshtein_distance(first_word, second_word)
							print(f"""Levenshtein distance between {first_word} and {second_word} is {result}""")









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									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
									]




