



def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						if len(__UpperCAmelCase	) < len(__UpperCAmelCase	):
												return levenshtein_distance(__UpperCAmelCase							,					__UpperCAmelCase	)

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

						UpperCAmelCase_     							=     list(range(len(__UpperCAmelCase	) + 1	)	)

						for i, ca in enumerate(__UpperCAmelCase	):
												UpperCAmelCase_     							=     [i + 1]

												for j, ca in enumerate(__UpperCAmelCase	):
																		# Calculate insertions, deletions and substitutions
																		UpperCAmelCase_     							=     previous_row[j + 1] + 1
																		UpperCAmelCase_     							=     current_row[j] + 1
																		UpperCAmelCase_     							=     previous_row[j] + (ca != ca)

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

												# Store the previous row
												UpperCAmelCase_     							=     current_row

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


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

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











import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_lowerCamelCase					:     Optional[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Dict					  =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCamelCase					:     List[str]					  =  {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCamelCase					:     Dict					  =  {
    "allenai/longformer-base-4096": 4_0_9_6,
    "allenai/longformer-large-4096": 4_0_9_6,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_0_9_6,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_0_9_6,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_0_9_6,
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   VOCAB_FILES_NAMES
				lowerCAmelCase__			=   PRETRAINED_VOCAB_FILES_MAP
				lowerCAmelCase__			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCAmelCase__			=   ["input_ids", "attention_mask"]
				lowerCAmelCase__			=   LongformerTokenizer



				def __init__(					self					:						Optional[Any]						,		_UpperCAmelCase					:						Tuple=None						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						List[str]=None						,		_UpperCAmelCase					:						Union[str, Any]="replace"						,		_UpperCAmelCase					:						str="<s>"						,		_UpperCAmelCase					:						str="</s>"						,		_UpperCAmelCase					:						int="</s>"						,		_UpperCAmelCase					:						str="<s>"						,		_UpperCAmelCase					:						Optional[Any]="<unk>"						,		_UpperCAmelCase					:						Optional[Any]="<pad>"						,		_UpperCAmelCase					:						Any="<mask>"						,		_UpperCAmelCase					:						Optional[int]=False						,		_UpperCAmelCase					:						List[str]=True						,		**_UpperCAmelCase					:						Optional[int]						,		)     ->			str:





								'''simple docstring'''




								super().__init__(
								    _UpperCAmelCase						,		_UpperCAmelCase						,		tokenizer_file=_UpperCAmelCase						,		errors=_UpperCAmelCase						,		bos_token=_UpperCAmelCase						,		eos_token=_UpperCAmelCase						,		sep_token=_UpperCAmelCase						,		cls_token=_UpperCAmelCase						,		unk_token=_UpperCAmelCase						,		pad_token=_UpperCAmelCase						,		mask_token=_UpperCAmelCase						,		add_prefix_space=_UpperCAmelCase						,		trim_offsets=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

								_lowerCAmelCase			:			Tuple        =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
								if pre_tok_state.get("""add_prefix_space"""						,		_UpperCAmelCase					) != add_prefix_space:
												_lowerCAmelCase			:			str        =					getattr(_UpperCAmelCase						,		pre_tok_state.pop("""type"""					)					)
												_lowerCAmelCase			:			Dict        =					add_prefix_space
												_lowerCAmelCase			:			Union[str, Any]        =					pre_tok_class(**_UpperCAmelCase					)

								_lowerCAmelCase			:			List[Any]        =					add_prefix_space

								_lowerCAmelCase			:			List[str]        =					"""post_processor"""
								_lowerCAmelCase			:			str        =					getattr(self.backend_tokenizer						,		_UpperCAmelCase						,		_UpperCAmelCase					)
								if tokenizer_component_instance:
												_lowerCAmelCase			:			Any        =					json.loads(tokenizer_component_instance.__getstate__()					)

												# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
												if "sep" in state:
																_lowerCAmelCase			:			str        =					tuple(state["""sep"""]					)
												if "cls" in state:
																_lowerCAmelCase			:			int        =					tuple(state["""cls"""]					)

												_lowerCAmelCase			:			Any        =					False

												if state.get("""add_prefix_space"""						,		_UpperCAmelCase					) != add_prefix_space:
																_lowerCAmelCase			:			int        =					add_prefix_space
																_lowerCAmelCase			:			List[str]        =					True

												if state.get("""trim_offsets"""						,		_UpperCAmelCase					) != trim_offsets:
																_lowerCAmelCase			:			List[str]        =					trim_offsets
																_lowerCAmelCase			:			List[str]        =					True

												if changes_to_apply:
																_lowerCAmelCase			:			List[str]        =					getattr(_UpperCAmelCase						,		state.pop("""type"""					)					)
																_lowerCAmelCase			:			Any        =					component_class(**_UpperCAmelCase					)
																setattr(self.backend_tokenizer						,		_UpperCAmelCase						,		_UpperCAmelCase					)



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			str:





								'''simple docstring'''




								if self._mask_token is None:
												if self.verbose:
																logger.error("""Using mask_token, but it is not set yet."""					)
												return None
								return str(self._mask_token					)



				@mask_token.setter
				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						Any					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					AddedToken(_UpperCAmelCase						,		lstrip=_UpperCAmelCase						,		rstrip=_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else value
								_lowerCAmelCase			:			List[Any]        =					value



				def 					SCREAMING_SNAKE_CASE				(					self					:						int						,		*_UpperCAmelCase					:						str						,		**_UpperCAmelCase					:						Union[str, Any]					)     ->			BatchEncoding:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					kwargs.get("""is_split_into_words"""						,		_UpperCAmelCase					)
								assert self.add_prefix_space or not is_split_into_words, (
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str						,		*_UpperCAmelCase					:						Any						,		**_UpperCAmelCase					:						Optional[Any]					)     ->			BatchEncoding:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					kwargs.get("""is_split_into_words"""						,		_UpperCAmelCase					)

								assert self.add_prefix_space or not is_split_into_words, (
								    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						Optional[str] = None					)     ->			Tuple[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					self._tokenizer.model.save(_UpperCAmelCase						,		name=_UpperCAmelCase					)
								return tuple(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						str=None					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
												return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						List[int]						,		_UpperCAmelCase					:						Optional[List[int]] = None					)     ->			List[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					[self.sep_token_id]
								_lowerCAmelCase			:			Optional[Any]        =					[self.cls_token_id]

								if token_ids_a is None:
												return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]

