"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A			   = logging.get_logger(__name__)

__A			   = "▁"

__A			   = {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

__A			   = {
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

__A			   = {"vinai/bartpho-syllable": 1_0_2_4}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = VOCAB_FILES_NAMES
    _UpperCAmelCase	:Union[str, Any]						    = PRETRAINED_VOCAB_FILES_MAP
    _UpperCAmelCase	:Tuple						    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _UpperCAmelCase	:Union[str, Any]						    = ["input_ids", "attention_mask"]






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase="<s>"    ,			_UpperCAmelCase="</s>"    ,			_UpperCAmelCase="</s>"    ,			_UpperCAmelCase="<s>"    ,			_UpperCAmelCase="<unk>"    ,			_UpperCAmelCase="<pad>"    ,			_UpperCAmelCase="<mask>"    ,			_UpperCAmelCase = None    ,			**_UpperCAmelCase    ,			):
       # Mask token behave like a normal word, i.e. include the space before it
       lowercase__:   Optional[int]					=      AddedToken(__lowerCamelCase    ,			lstrip=__lowerCamelCase    ,			rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase    ,			__lowerCamelCase							) else mask_token

       lowercase__:   Optional[Any]					=      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__lowerCamelCase    ,			eos_token=__lowerCamelCase    ,			unk_token=__lowerCamelCase    ,			sep_token=__lowerCamelCase    ,			cls_token=__lowerCamelCase    ,			pad_token=__lowerCamelCase    ,			mask_token=__lowerCamelCase    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**__lowerCamelCase    ,			)

       lowercase__:   Tuple					=      vocab_file
       lowercase__:   Tuple					=      monolingual_vocab_file
       lowercase__:   List[Any]					=      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(str(__lowerCamelCase							)							)

       # Load the reduced vocab

       # Keep order of special tokens for backward compatibility
       lowercase__:   List[Any]					=      {}
       lowercase__:   Optional[Any]					=      0
       for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
          if str(__lowerCamelCase							) not in self.fairseq_tokens_to_ids:
             lowercase__:   Union[str, Any]					=      cnt
             cnt += 1
       with open(__lowerCamelCase    ,			'''r'''    ,			encoding='''utf-8'''							) as f:
          for line in f.readlines():
             lowercase__:   int					=      line.strip().split()[0]
             lowercase__:   str					=      len(self.fairseq_tokens_to_ids							)
       if str(__lowerCamelCase							) not in self.fairseq_tokens_to_ids:
          lowercase__:   Optional[int]					=      len(self.fairseq_tokens_to_ids							)

       lowercase__:   Dict					=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






    def __getstate__(   self							):
       lowercase__:   Dict					=      self.__dict__.copy()
       lowercase__:   int					=      None
       lowercase__:   Union[str, Any]					=      self.sp_model.serialized_model_proto()
       return state






    def __setstate__(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      d

       # for backward compatibility
       if not hasattr(self    ,			'''sp_model_kwargs'''							):
          lowercase__:   Optional[int]					=      {}

       lowercase__:   List[Any]					=      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):

       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowercase__:   int					=      [self.cls_token_id]
       lowercase__:   int					=      [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = False							):

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

       if token_ids_a is None:
          return [1] + ([0] * len(__lowerCamelCase							)) + [1]
       return [1] + ([0] * len(__lowerCamelCase							)) + [1, 1] + ([0] * len(__lowerCamelCase							)) + [1]






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       lowercase__:   str					=      [self.sep_token_id]
       lowercase__:   int					=      [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]






    @property
    def _snake_case			(   self							):
       return len(self.fairseq_ids_to_tokens							)






    def _snake_case			(   self							):
       lowercase__:   Optional[Any]					=      {self.convert_ids_to_tokens(__lowerCamelCase							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       return self.sp_model.encode(__lowerCamelCase    ,			out_type=__lowerCamelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
       else:
          return self.unk_token_id






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       return self.fairseq_ids_to_tokens[index]






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[Any]					=      "".join(__lowerCamelCase							).replace(__lowerCamelCase    ,			''' '''							).strip()
       return out_string






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None							):
       if not os.path.isdir(__lowerCamelCase							):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
          return
       lowercase__:   Union[str, Any]					=      os.path.join(
           __lowerCamelCase    ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
       lowercase__:   Optional[int]					=      os.path.join(
           __lowerCamelCase    ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']    ,			)

       if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.vocab_file							):
          copyfile(self.vocab_file    ,			__lowerCamelCase							)
       elif not os.path.isfile(self.vocab_file							):
          with open(__lowerCamelCase    ,			'''wb'''							) as fi:
             lowercase__:   int					=      self.sp_model.serialized_model_proto()
             fi.write(__lowerCamelCase							)

       if os.path.abspath(self.monolingual_vocab_file							) != os.path.abspath(
           __lowerCamelCase							) and os.path.isfile(self.monolingual_vocab_file							):
          copyfile(self.monolingual_vocab_file    ,			__lowerCamelCase							)
       elif not os.path.isfile(self.monolingual_vocab_file							):
          with open(__lowerCamelCase    ,			'''w'''    ,			encoding='''utf-8'''							) as fp:
             for token in self.fairseq_tokens_to_ids:
                if token not in self.all_special_tokens:
                   fp.write(F"""{str(__lowerCamelCase							)} \n"""							)

       return out_vocab_file, out_monolingual_vocab_file




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_mbart import MBartTokenizer
else:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


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

lowerCAmelCase__      		= {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['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']







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[int]:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       List[str]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

            super().__init__(
                vocab_file=__lowerCamelCase ,						tokenizer_file=__lowerCamelCase ,						bos_token=__lowerCamelCase ,						eos_token=__lowerCamelCase ,						sep_token=__lowerCamelCase ,						cls_token=__lowerCamelCase ,						unk_token=__lowerCamelCase ,						pad_token=__lowerCamelCase ,						mask_token=__lowerCamelCase ,						src_lang=__lowerCamelCase ,						tgt_lang=__lowerCamelCase ,						additional_special_tokens=__lowerCamelCase ,						**__lowerCamelCase ,						)

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = FAIRSEQ_LANGUAGE_CODES.copy()

            if additional_special_tokens is not None:
                   # Only add those special tokens if they are not already there.
                   _additional_special_tokens.extend(
                       [t for t in additional_special_tokens if t not in _additional_special_tokens])

            self.add_special_tokens({"additional_special_tokens": _additional_special_tokens})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            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 ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       List[str]				    = [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]


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


     def  _lowerCamelCase (						self)			->				List[str]:
            return self.set_src_lang_special_tokens(self.src_lang)


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

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

            _A      :       List[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 ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Optional[int]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       int				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       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 ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            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(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

            return (out_vocab_file,)
