



"""simple docstring"""

import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_	:						List[Any]				=			logging.get_logger(__name__)

A_	:						Optional[int]				=			{
    'source_spm': 'source.spm',
    'target_spm': 'target.spm',
    'vocab': 'vocab.json',
    'target_vocab_file': 'target_vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A_	:						List[str]				=			{
    'source_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm'
    },
    'target_spm': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm'
    },
    'vocab': {
        'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json'
    },
    'tokenizer_config_file': {
        'Helsinki-NLP/opus-mt-en-de': (
            'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json'
        )
    },
}

A_	:						str				=			{'Helsinki-NLP/opus-mt-en-de': 512}
A_	:						Any				=			{}
class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		str     	=   VOCAB_FILES_NAMES
	_SCREAMING_SNAKE_CASE  :		int     	=   PRETRAINED_VOCAB_FILES_MAP
	_SCREAMING_SNAKE_CASE  :		Tuple     	=   PRETRAINED_INIT_CONFIGURATION
	_SCREAMING_SNAKE_CASE  :		Union[str, Any]     	=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_SCREAMING_SNAKE_CASE  :		Optional[int]     	=   ['''input_ids''', '''attention_mask''']
	_SCREAMING_SNAKE_CASE  :		Any     	=   re.compile('''>>.+<<'''   )  # type: re.Pattern





	def __init__(			self							:					Optional[int]    ,					_SCREAMING_SNAKE_CASE							:					int    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					Any    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Union[str, Any]=None    ,					_SCREAMING_SNAKE_CASE							:					Tuple=None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]="<unk>"    ,					_SCREAMING_SNAKE_CASE							:					List[Any]="</s>"    ,					_SCREAMING_SNAKE_CASE							:					List[str]="<pad>"    ,					_SCREAMING_SNAKE_CASE							:					List[str]=512    ,					_SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None    ,					_SCREAMING_SNAKE_CASE							:					Optional[Any]=False    ,					**_SCREAMING_SNAKE_CASE							:					Dict    ,					)	->  None:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
					    source_lang=_SCREAMING_SNAKE_CASE    ,					target_lang=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					model_max_length=_SCREAMING_SNAKE_CASE    ,					sp_model_kwargs=self.sp_model_kwargs    ,					target_vocab_file=_SCREAMING_SNAKE_CASE    ,					separate_vocabs=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)
					assert Path(_SCREAMING_SNAKE_CASE						).exists(), f"""cannot find spm source {source_spm}"""

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    separate_vocabs
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    load_json(_SCREAMING_SNAKE_CASE						)
					if self.unk_token not in self.encoder:
									raise KeyError('<unk> token must be in vocab'						)
					assert self.pad_token in self.encoder

					if separate_vocabs:
									SCREAMING_SNAKE_CASE	:       str 							=    load_json(_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       str 							=    {v: k for k, v in self.target_encoder.items()}
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    []
					else:
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    {v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE	:       list 							=    [k for k in self.encoder if k.startswith('>>'						) and k.endswith('<<'						)]

					SCREAMING_SNAKE_CASE	:       List[Any] 							=    source_lang
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    target_lang
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    [source_spm, target_spm]

					# load SentencePiece model for pre-processing
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    load_spm(_SCREAMING_SNAKE_CASE    ,					self.sp_model_kwargs						)
					SCREAMING_SNAKE_CASE	:       Tuple 							=    load_spm(_SCREAMING_SNAKE_CASE    ,					self.sp_model_kwargs						)
					SCREAMING_SNAKE_CASE	:       int 							=    self.spm_source
					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.encoder

					# Multilingual target side: default to using first supported language code.

					self._setup_normalizer()





	def  _lowerCAmelCase    (			self							:					str						)	->  Tuple:





					"""simple docstring"""


					try:
									from sacremoses import MosesPunctNormalizer

									SCREAMING_SNAKE_CASE	:       str 							=    MosesPunctNormalizer(self.source_lang						).normalize
					except (ImportError, FileNotFoundError):
									warnings.warn('Recommended: pip install sacremoses.'						)
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    lambda _SCREAMING_SNAKE_CASE						: x





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  str:





					"""simple docstring"""


					return self.punc_normalizer(_SCREAMING_SNAKE_CASE						) if x else ""





	def  _lowerCAmelCase    (			self							:					Optional[Any]    ,					_SCREAMING_SNAKE_CASE							:					Tuple						)	->  Tuple:





					"""simple docstring"""


					return self.current_encoder.get(_SCREAMING_SNAKE_CASE    ,					self.current_encoder[self.unk_token]						)





	def  _lowerCAmelCase    (			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.language_code_re.match(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       list 							=    [match.group(0						)] if match else []
					return code, self.language_code_re.sub(''    ,					_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       int 							=    self.remove_language_code(_SCREAMING_SNAKE_CASE						)
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.current_spm.encode(_SCREAMING_SNAKE_CASE    ,					out_type=_SCREAMING_SNAKE_CASE						)
					return code + pieces





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





					"""simple docstring"""


					return self.decoder.get(_SCREAMING_SNAKE_CASE    ,					self.unk_token						)





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





					"""simple docstring"""


					return super().batch_decode(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





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





					"""simple docstring"""


					return super().decode(_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					List[str]						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self.spm_source if self._decode_use_source_tokenizer else self.spm_target
					SCREAMING_SNAKE_CASE	:       int 							=    []
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    ''
					for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
													out_string += sp_model.decode_pieces(_SCREAMING_SNAKE_CASE						) + token + " "
													SCREAMING_SNAKE_CASE	:       List[str] 							=    []
									else:
													current_sub_tokens.append(_SCREAMING_SNAKE_CASE						)
					out_string += sp_model.decode_pieces(_SCREAMING_SNAKE_CASE						)
					return out_string.strip()





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





					"""simple docstring"""


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





	def  _lowerCAmelCase    (			self							:					List[str]						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    self.spm_source
					SCREAMING_SNAKE_CASE	:       str 							=    self.encoder





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Dict 							=    self.spm_target
					if self.separate_vocabs:
									SCREAMING_SNAKE_CASE	:       List[Any] 							=    self.target_encoder





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





					"""simple docstring"""


					return len(self.encoder						)





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





					"""simple docstring"""


					if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
									return
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    []

					if self.separate_vocabs:
									SCREAMING_SNAKE_CASE	:       Optional[int] 							=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']    ,					)
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['target_vocab_file']    ,					)
									save_json(self.encoder    ,					_SCREAMING_SNAKE_CASE						)
									save_json(self.target_encoder    ,					_SCREAMING_SNAKE_CASE						)
									saved_files.append(_SCREAMING_SNAKE_CASE						)
									saved_files.append(_SCREAMING_SNAKE_CASE						)
					else:
									SCREAMING_SNAKE_CASE	:       Any 							=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab']						)
									save_json(self.encoder    ,					_SCREAMING_SNAKE_CASE						)
									saved_files.append(_SCREAMING_SNAKE_CASE						)

					for spm_save_filename, spm_orig_path, spm_model in zip(
					    [VOCAB_FILES_NAMES['source_spm'], VOCAB_FILES_NAMES['target_spm']]    ,					self.spm_files    ,					[self.spm_source, self.spm_target]    ,					):
									SCREAMING_SNAKE_CASE	:       List[str] 							=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + spm_save_filename						)
									if os.path.abspath(_SCREAMING_SNAKE_CASE						) != os.path.abspath(_SCREAMING_SNAKE_CASE						) and os.path.isfile(_SCREAMING_SNAKE_CASE						):
													copyfile(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE						)
													saved_files.append(_SCREAMING_SNAKE_CASE						)
									elif not os.path.isfile(_SCREAMING_SNAKE_CASE						):
													with open(_SCREAMING_SNAKE_CASE    ,					'wb'						) as fi:
																	SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    spm_model.serialized_model_proto()
																	fi.write(_SCREAMING_SNAKE_CASE						)
													saved_files.append(_SCREAMING_SNAKE_CASE						)

					return tuple(_SCREAMING_SNAKE_CASE						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					return self.get_src_vocab()





	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Any:





					"""simple docstring"""


					return dict(self.encoder    ,					**self.added_tokens_encoder						)





	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Optional[int]:





					"""simple docstring"""


					return dict(self.target_encoder    ,					**self.added_tokens_decoder						)





	def __getstate__(			self							:					Union[str, Any]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.__dict__.copy()
					state.update(
					    {k: None for k in ['spm_source', 'spm_target', 'current_spm', 'punc_normalizer', 'target_vocab_file']}						)
					return state





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    d

					# for backward compatibility
					if not hasattr(self    ,					'sp_model_kwargs'						):
									SCREAMING_SNAKE_CASE	:       int 							=    {}

					SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE	:       Dict 							=    (load_spm(_SCREAMING_SNAKE_CASE    ,					self.sp_model_kwargs						) for f in self.spm_files)
					SCREAMING_SNAKE_CASE	:       Dict 							=    self.spm_source
					self._setup_normalizer()





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





					"""simple docstring"""


					return 1





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Tuple 							=    set(self.all_special_ids						)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special
					return [1 if x in all_special_ids else 0 for x in seq]







	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					List    ,					_SCREAMING_SNAKE_CASE							:					Optional[List] = None    ,					_SCREAMING_SNAKE_CASE							:					bool = False						)	->  List[int]:





					"""simple docstring"""


					if already_has_special_tokens:
									return self._special_token_mask(_SCREAMING_SNAKE_CASE						)
					elif token_ids_a is None:
									return self._special_token_mask(_SCREAMING_SNAKE_CASE						) + [1]
					else:
									return self._special_token_mask(token_ids_a + token_ids_a						) + [1]




def    __snake_case       (  __A			:	str       ,		__A			:	Dict[str, Any]    )    ->					sentencepiece.SentencePieceProcessor:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    sentencepiece.SentencePieceProcessor(**__A    )
				spm.Load(__A    )
				return spm
def    __snake_case       (  __A			:	Any       ,		__A			:	str    )    ->					None:






				'''simple docstring'''
				with open(__A       ,		'w'    ) as f:
								json.dump(__A       ,		__A       ,		indent=2    )







def    __snake_case       (  __A			:	str    )    ->					Union[Dict, List]:






				'''simple docstring'''
				with open(__A       ,		'r'    ) as f:
								return json.load(__A    )










"""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}''')





