




import json
import os
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


__lowerCAmelCase	:       List[str]     =	logging.get_logger(__name__)

__lowerCAmelCase	:       Union[str, Any]     =	'▁'

__lowerCAmelCase	:       Union[str, Any]     =	{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

__lowerCAmelCase	:       Dict     =	{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

__lowerCAmelCase	:       List[str]     =	{
    'facebook/s2t-small-librispeech-asr': 1024,
}

__lowerCAmelCase	:       List[Any]     =	['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

__lowerCAmelCase	:       Tuple     =	{'mustc': MUSTC_LANGS}




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							List[str]											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							List[str]											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							List[str]											=      MAX_MODEL_INPUT_SIZES
				SCREAMING_SNAKE_CASE_ :							str											=      ["""input_ids""", """attention_mask"""]

				SCREAMING_SNAKE_CASE_ :							List[int]											=      []





				def __init__(   self       :  Optional[int]      ,   __lowerCamelCase       :  Dict      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Union[str, Any]="<s>"      ,   __lowerCamelCase       :  List[str]="</s>"      ,   __lowerCamelCase       :  Any="<pad>"      ,   __lowerCamelCase       :  Optional[int]="<unk>"      ,   __lowerCamelCase       :  int=False      ,   __lowerCamelCase       :  Union[str, Any]=False      ,   __lowerCamelCase       :  int=None      ,   __lowerCamelCase       :  Union[str, Any]=None      ,   __lowerCamelCase       :  Optional[Dict[str, Any]] = None      ,   **__lowerCamelCase       :  List[Any]      ,   )      ->  None:
							a					=    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   do_upper_case=__lowerCamelCase      ,   do_lower_case=__lowerCamelCase      ,   tgt_lang=__lowerCamelCase      ,   lang_codes=__lowerCamelCase      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **__lowerCamelCase      ,   )
							a					=    do_upper_case
							a					=    do_lower_case

							a					=    load_json(__lowerCamelCase							)
							a					=    {v: k for k, v in self.encoder.items()}
							a					=    spm_file
							a					=    load_spm(__lowerCamelCase      ,   self.sp_model_kwargs							)

							if lang_codes is not None:
										a					=    lang_codes
										a					=    LANGUAGES[lang_codes]
										a					=    [f"""<lang:{lang}>""" for lang in self.langs]
										a					=    {lang: self.sp_model.PieceToId(f"""<lang:{lang}>"""							) for lang in self.langs}

										a					=    self.lang_tokens
										a					=    tgt_lang if tgt_lang is not None else self.langs[0]

										self.set_tgt_lang_special_tokens(self._tgt_lang							)
							else:
										a					=    {}





				@property
				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							return len(self.encoder							)





				@property
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  str:
							return self._tgt_lang





				@tgt_lang.setter
				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  Union[str, Any]							)      ->  None:
							a					=    new_tgt_lang
							self.set_tgt_lang_special_tokens(__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  str							)      ->  None:
							a					=    self.lang_code_to_id[tgt_lang]
							a					=    [lang_code_id]





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  str							)      ->  List[str]:
							return self.sp_model.encode(__lowerCamelCase      ,   out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  int      ,   __lowerCamelCase       :  Tuple							)      ->  List[Any]:
							return self.encoder.get(__lowerCamelCase      ,   self.encoder[self.unk_token]							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  int							)      ->  str:
							return self.decoder.get(__lowerCamelCase      ,   self.unk_token							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[str]							)      ->  str:
							a					=    []
							a					=    ""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													a					=    self.sp_model.decode(__lowerCamelCase							)
													out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
													a					=    []
										else:
													current_sub_tokens.append(__lowerCamelCase							)
							a					=    self.sp_model.decode(__lowerCamelCase							)
							out_string += decoded.upper() if self.do_upper_case else decoded
							return out_string.strip()





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  List[Any]=None							)      ->  List[int]:
							if token_ids_a is None:
										return self.prefix_tokens + token_ids_a + [self.eos_token_id]
							# 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.eos_token_id]





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None      ,   __lowerCamelCase       :  bool = False							)      ->  List[int]:

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=__lowerCamelCase      ,   token_ids_a=__lowerCamelCase      ,   already_has_special_tokens=__lowerCamelCase							)

							a					=    [1] * len(self.prefix_tokens							)
							a					=    [1]
							if token_ids_a is None:
										return prefix_ones + ([0] * len(__lowerCamelCase							)) + suffix_ones
							return prefix_ones + ([0] * len(__lowerCamelCase							)) + ([0] * len(__lowerCamelCase							)) + suffix_ones





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Dict:
							a					=    self.encoder.copy()
							vocab.update(self.added_tokens_encoder							)
							return vocab





				def __getstate__(   self       :  int							)      ->  Dict:
							a					=    self.__dict__.copy()
							a					=    None
							return state





				def __setstate__(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  Dict							)      ->  None:
							a					=    d

							# for backward compatibility
							if not hasattr(self      ,   "sp_model_kwargs"							):
										a					=    {}

							a					=    load_spm(self.spm_file      ,   self.sp_model_kwargs							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							a					=    Path(__lowerCamelCase							)
							assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
							a					=    save_dir / (
							    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
							)
							a					=    save_dir / (
							    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
							)

							save_json(self.encoder      ,   __lowerCamelCase							)

							if os.path.abspath(self.spm_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.spm_file							):
										copyfile(self.spm_file      ,   __lowerCamelCase							)
							elif not os.path.isfile(self.spm_file							):
										with open(__lowerCamelCase      ,   "wb"							) as fi:
													a					=    self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (str(__lowerCamelCase							), str(__lowerCamelCase							))




def     __magic_name__       (				A	:      str,						A	:      Dict[str, Any]     ):






			'''simple docstring'''







			a					=    sentencepiece.SentencePieceProcessor(**A     )
			spm.Load(str(A     )     )
			return spm




def     __magic_name__       (				A	:      str     ):






			'''simple docstring'''







			with open(A,						"r"     ) as f:
						return json.load(A     )






def     __magic_name__       (				A	:      Dict,						A	:      str     ):






			'''simple docstring'''







			with open(A,						"w"     ) as f:
						json.dump(A,						A,						indent=2     )












def     __magic_name__       (				A	:      str,						A	:      str     ):






			'''simple docstring'''







			def get_matched_characters(A	:      str,						A	:      str     ) -> str:
						a					=    []
						a					=    min(len(_stra     ),						len(_stra     )     ) // 2
						for i, l in enumerate(_stra     ):
									a					=    int(max(0,						i - limit     )     )
									a					=    int(min(i + limit + 1,						len(_stra     )     )     )
									if l in _stra[left:right]:
												matched.append(A     )
												a					=    F"""{_stra[0:_stra.index(A     )]} {_stra[_stra.index(A     ) + 1:]}"""

						return "".join(A     )

			# matching characters
			a					=    get_matched_characters(A,						A     )
			a					=    get_matched_characters(A,						A     )
			a					=    len(A     )

			# transposition
			a					=    (
			    len([(ca, ca) for ca, ca in zip(A,						A     ) if ca != ca]     ) // 2
			)

			if not match_count:
						a					=    0.0
			else:
						a					=    (
						    1
						    / 3
						    * (
						        match_count / len(A     )
						        + match_count / len(A     )
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			a					=    0
			for ca, ca in zip(stra[:4],						stra[:4]     ):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(jaro_winkler('hello', 'world'))






