"""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


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE    	='''▁'''

__SCREAMING_SNAKE_CASE    	={'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

__SCREAMING_SNAKE_CASE    	={
    '''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''',
    },
}

__SCREAMING_SNAKE_CASE    	={'''vinai/bartpho-syllable''': 1024}







class        UpperCamelCase		(			_snake_case  ):
   lowercase							=  VOCAB_FILES_NAMES
   lowercase							=  PRETRAINED_VOCAB_FILES_MAP
   lowercase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=  ['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     ,)	->      str:


        '''simple docstring'''


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

        lowercase_	:				Tuple													=	{} 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_	:				Optional[int]													=	vocab_file
        lowercase_	:				List[Any]													=	monolingual_vocab_file
        lowercase_	:				Tuple													=	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[str]													=	{}
        lowercase_	:				Optional[int]													=	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_	:				List[str]													=	cnt
                  cnt += 1
        with open(_lowerCamelCase     ,'r'     ,encoding='utf-8'			) as f:
             for line in f.readlines():
                  lowercase_	:				Union[str, Any]													=	line.strip().split()[0]
                  lowercase_	:				Union[str, Any]													=	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			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.__dict__.copy()
        lowercase_	:				List[Any]													=	None
        lowercase_	:				Dict													=	self.sp_model.serialized_model_proto()
        return state
   def __setstate__(			self     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	d

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

        lowercase_	:				str													=	spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      Dict:


        '''simple docstring'''


        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        lowercase_	:				Union[str, Any]													=	[self.cls_token_id]
        lowercase_	:				Dict													=	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = False			)	->      str:


        '''simple docstring'''


        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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      int:


        '''simple docstring'''


        lowercase_	:				Tuple													=	[self.sep_token_id]
        lowercase_	:				Optional[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 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        return len(self.fairseq_ids_to_tokens			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        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 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.sp_model.encode(_lowerCamelCase     ,out_type=_lowerCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      List[str]:


        '''simple docstring'''


        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        else:
             return self.unk_token_id
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        return self.fairseq_ids_to_tokens[index]
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	''''''.join(_lowerCamelCase			).replace(_lowerCamelCase     ,' '			).strip()
        return out_string







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


        '''simple docstring'''


        if not os.path.isdir(_lowerCamelCase			):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
             return
        lowercase_	:				int													=	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_	:				List[Any]													=	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





snake_case				:    str       				=     '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

snake_case				:    List[Any]       				=     [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
snake_case				:    int       				=     {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}





