



"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_mbartaa import MBartaaTokenizer
else:
					A_	:						Any				=			None


A_	:						Optional[int]				=			logging.get_logger(__name__)

A_	:						Tuple				=			{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A_	:						Optional[Any]				=			{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

A_	:						str				=			{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
A_	:						Optional[int]				=			['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']
class 						lowerCAmelCase__	(  __lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		List[str]     	=   VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE  :		Tuple     	=   PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE  :		List[Any]     	=   ['input_ids', 'attention_mask']
	_SCREAMING_SNAKE_CASE  :		List[str]     	=   MBartaaTokenizer

	_SCREAMING_SNAKE_CASE  :		List[int]     	=   []
	_SCREAMING_SNAKE_CASE  :		List[int]     	=   []





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    AddedToken(_SCREAMING_SNAKE_CASE    ,					lstrip=_SCREAMING_SNAKE_CASE    ,					rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						) else mask_token

					SCREAMING_SNAKE_CASE	:       List[str] 							=    kwargs.get('additional_special_tokens'    ,					[]						)
					kwargs["additional_special_tokens"] += [
					    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    _SCREAMING_SNAKE_CASE    ,					src_lang=_SCREAMING_SNAKE_CASE    ,					tgt_lang=_SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    vocab_file
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    False if not self.vocab_file else True

					SCREAMING_SNAKE_CASE	:       Any 							=    {
					    lang_code: self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						) for lang_code in FAIRSEQ_LANGUAGE_CODES
					}

					SCREAMING_SNAKE_CASE	:       Dict 							=    src_lang if src_lang is not None else 'en_XX'
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    tgt_lang
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.lang_code_to_id[self._src_lang]
					self.set_src_lang_special_tokens(self._src_lang						)





	@property
	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					return self._src_lang





	@src_lang.setter
	def  _lowerCAmelCase    (			self							:					List[Any]    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    new_src_lang
					self.set_src_lang_special_tokens(self._src_lang						)





	def  _lowerCAmelCase    (			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[int]    ,					_SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None						)	->  List[int]:





					"""simple docstring"""


					if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + self.suffix_tokens
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





	def  _lowerCAmelCase    (			self							:					Dict    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					str = "en_XX"    ,					_SCREAMING_SNAKE_CASE							:					Optional[List[str]] = None    ,					_SCREAMING_SNAKE_CASE							:					str = "ro_RO"    ,					**_SCREAMING_SNAKE_CASE							:					int    ,					)	->  BatchEncoding:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    src_lang
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    tgt_lang
					return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					int						)	->  Dict:





					"""simple docstring"""


					return self.set_src_lang_special_tokens(self.src_lang						)





	def  _lowerCAmelCase    (			self							:					str						)	->  Any:





					"""simple docstring"""


					return self.set_tgt_lang_special_tokens(self.tgt_lang						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.cur_lang_code_id]
					SCREAMING_SNAKE_CASE	:       int 							=    [self.eos_token_id]

					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.convert_ids_to_tokens(self.prefix_tokens						)
					SCREAMING_SNAKE_CASE	:       str 							=    self.convert_ids_to_tokens(self.suffix_tokens						)

					SCREAMING_SNAKE_CASE	:       List[str] 							=    processors.TemplateProcessing(
					    single=prefix_tokens_str + ['$A'] + suffix_tokens_str    ,					pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str    ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,					self.prefix_tokens + self.suffix_tokens						)						)    ,					)





	def  _lowerCAmelCase    (			self							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    [self.cur_lang_code_id]
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    [self.eos_token_id]

					SCREAMING_SNAKE_CASE	:       str 							=    self.convert_ids_to_tokens(self.prefix_tokens						)
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.convert_ids_to_tokens(self.suffix_tokens						)

					SCREAMING_SNAKE_CASE	:       Any 							=    processors.TemplateProcessing(
					    single=prefix_tokens_str + ['$A'] + suffix_tokens_str    ,					pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str    ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,					self.prefix_tokens + self.suffix_tokens						)						)    ,					)





	def  _lowerCAmelCase    (			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					str    ,					_SCREAMING_SNAKE_CASE							:					Optional[str]    ,					_SCREAMING_SNAKE_CASE							:					Optional[str]    ,					**_SCREAMING_SNAKE_CASE							:					List[Any]						)	->  Any:





					"""simple docstring"""


					if src_lang is None or tgt_lang is None:
									raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'						)
					SCREAMING_SNAKE_CASE	:       List[str] 							=    src_lang
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self(_SCREAMING_SNAKE_CASE    ,					add_special_tokens=_SCREAMING_SNAKE_CASE    ,					return_tensors=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Any 							=    tgt_lang_id
					return inputs







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





					"""simple docstring"""


					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
									    'tokenizer.'						)

					if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
									return
					SCREAMING_SNAKE_CASE	:       Any 							=    os.path.join(
					    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_SCREAMING_SNAKE_CASE						):
									copyfile(self.vocab_file    ,					_SCREAMING_SNAKE_CASE						)

					return (out_vocab_file,)










"""simple docstring"""

def    __snake_case       (  __A			:	str       ,		__A			:	str    )    ->					int:






				'''simple docstring'''
				# The longer word should come first
				if len(__A    ) < len(__A    ):
								return levenshtein_distance(__A       ,		__A    )

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

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    list(range(len(__A    ) + 1    )    )

				for i, ca in enumerate(__A    ):
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [i + 1]

								for j, ca in enumerate(__A    ):
												# Calculate insertions, deletions and substitutions
												SCREAMING_SNAKE_CASE	:       str 							=    previous_row[j + 1] + 1
												SCREAMING_SNAKE_CASE	:       List[str] 							=    current_row[j] + 1
												SCREAMING_SNAKE_CASE	:       Any 							=    previous_row[j] + (ca != ca)

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

								# Store the previous row
								SCREAMING_SNAKE_CASE	:       Tuple 							=    current_row

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


if __name__ == "__main__":
							A_	:						Optional[int]				=			input('Enter the first word:\n').strip()
							A_	:						Optional[Any]				=			input('Enter the second word:\n').strip()

							A_	:						str				=			levenshtein_distance(first_word, second_word)
							print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')





