



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


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

__snake_case						:str						=					'''▁'''

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

__snake_case						:Union[str, Any]						=					{
    '''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''',
    },
}

__snake_case						:str						=					{'''vinai/bartpho-syllable''': 1024}

class    _A	(     __UpperCAmelCase   ):
       UpperCamelCase__  :   List[str]						 =						VOCAB_FILES_NAMES
       UpperCamelCase__  :   str						 =						PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase__  :   Optional[Any]						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase__  :   int						 =						['''input_ids''', '''attention_mask''']



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


           '''simple docstring'''

           __a							=    AddedToken(__SCREAMING_SNAKE_CASE					,		lstrip=__SCREAMING_SNAKE_CASE					,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else mask_token

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

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

           __a							=    vocab_file
           __a							=    monolingual_vocab_file
           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(__SCREAMING_SNAKE_CASE))

           # Load the reduced vocab

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

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



       def __getstate__(		self							:					Optional[int]):


           '''simple docstring'''

           __a							=    self.__dict__.copy()
           __a							=    None
           __a							=    self.sp_model.serialized_model_proto()
           return state



       def __setstate__(		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    d

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

           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

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



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

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

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



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [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 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           return len(self.fairseq_ids_to_tokens)



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

           __a							=    {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab



       def 		_lowerCamelCase      (		self							:					List[Any]					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           return self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					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 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           return self.fairseq_ids_to_tokens[index]



       def 		_lowerCamelCase      (		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           __a							=    ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE					,		''' ''').strip()
           return out_string





       def 		_lowerCamelCase      (		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

           if not os.path.isdir(__SCREAMING_SNAKE_CASE):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
           __a							=    os.path.join(
               __SCREAMING_SNAKE_CASE					,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
           __a							=    os.path.join(
               __SCREAMING_SNAKE_CASE					,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']					,		)

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

           if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
               __SCREAMING_SNAKE_CASE) and os.path.isfile(self.monolingual_vocab_file):
               copyfile(self.monolingual_vocab_file					,		__SCREAMING_SNAKE_CASE)
           elif not os.path.isfile(self.monolingual_vocab_file):
               with open(__SCREAMING_SNAKE_CASE					,		'''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(__SCREAMING_SNAKE_CASE)} \n')

           return out_vocab_file, out_monolingual_vocab_file










def 					__snake_case     (      _UpperCAmelCase    ):
    if not isinstance(_UpperCAmelCase       ,			_UpperCAmelCase    ):
        raise TypeError('''Input value must be an \'int\' type'''    )

    __a							=    0
    while number:
        position += 1
        number >>= 1

    return position


if __name__ == "__main__":
 import doctest

 doctest.testmod()





