
'''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


UpperCamelCase_   	=				logging.get_logger(__name__)

UpperCamelCase_   	=				'▁'

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

UpperCamelCase_   	=				{
    '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',
    },
}

UpperCamelCase_   	=				{'vinai/bartpho-syllable': 10_24}


class      a_     (UpperCamelCase__			):
       __lowerCAmelCase			: Union[str, Any]			=					VOCAB_FILES_NAMES
       __lowerCAmelCase			: Tuple			=					PRETRAINED_VOCAB_FILES_MAP
       __lowerCAmelCase			: List[Any]			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowerCAmelCase			: Any			=					["""input_ids""", """attention_mask"""]




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

         _lowerCAmelCase				:     Optional[int]       =    {} 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      ,      )

         _lowerCAmelCase				:     Optional[int]       =    vocab_file
         _lowerCAmelCase				:     Union[str, Any]       =    monolingual_vocab_file
         _lowerCAmelCase				:     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
         _lowerCAmelCase				:     List[Any]       =    {}
         _lowerCAmelCase				:     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:
             _lowerCAmelCase				:     Optional[Any]       =    cnt
             cnt += 1
         with open(__lowerCamelCase      ,      """r"""      ,      encoding="""utf-8"""     ) as f:
           for line in f.readlines():
             _lowerCAmelCase				:     int       =    line.strip().split()[0]
             _lowerCAmelCase				:     List[str]       =    len(self.fairseq_tokens_to_ids     )
         if str(__lowerCamelCase     ) not in self.fairseq_tokens_to_ids:
           _lowerCAmelCase				:     List[str]       =    len(self.fairseq_tokens_to_ids     )

         _lowerCAmelCase				:     Optional[int]       =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}




       def __getstate__(   self     ):
         _lowerCAmelCase				:     Union[str, Any]       =    self.__dict__.copy()
         _lowerCAmelCase				:     Optional[Any]       =    None
         _lowerCAmelCase				:     Tuple       =    self.sp_model.serialized_model_proto()
         return state




       def __setstate__(   self      ,      snake_case_     ):
         _lowerCAmelCase				:     Optional[int]       =    d

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

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




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_ = None     ):

         if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _lowerCAmelCase				:     Union[str, Any]       =    [self.cls_token_id]
         _lowerCAmelCase				:     str       =    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_ = None      ,      snake_case_ = 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 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_ = None     ):
         _lowerCAmelCase				:     List[str]       =    [self.sep_token_id]
         _lowerCAmelCase				:     Union[str, Any]       =    [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     ):
         return len(self.fairseq_ids_to_tokens     )




       def 							__UpperCamelCase				(   self     ):
         _lowerCAmelCase				:     Dict       =    {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      ,      snake_case_     ):
         return self.sp_model.encode(__lowerCamelCase      ,      out_type=__lowerCamelCase     )




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




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         return self.fairseq_ids_to_tokens[index]




       def 							__UpperCamelCase				(   self      ,      snake_case_     ):
         _lowerCAmelCase				:     Dict       =    """""".join(__lowerCamelCase     ).replace(__lowerCamelCase      ,      """ """     ).strip()
         return out_string







       def 							__UpperCamelCase				(   self      ,      snake_case_      ,      snake_case_ = None     ):
         if not os.path.isdir(__lowerCamelCase     ):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
           return
         _lowerCAmelCase				:     Tuple       =    os.path.join(
             __lowerCamelCase      ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )
         _lowerCAmelCase				:     Any       =    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:
             _lowerCAmelCase				:     List[str]       =    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
import re
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__:      Union[str, Any]									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{'vocab_file': 'spiece.model'}

a__:      Tuple									=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

a__:      Any									=		{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase="<unk>",__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="<pad>",__lowerCamelCase="[SEP]",__lowerCamelCase="[MASK]",__lowerCamelCase="[CLS]",__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else bos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else eos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else unk_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else pad_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else cls_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

        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,sep_token=__lowerCamelCase,mask_token=__lowerCamelCase,cls_token=__lowerCamelCase,sp_model_kwargs=self.sp_model_kwargs,**__lowerCamelCase,)

        A__											=						vocab_file

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(__lowerCamelCase )



 @property
 def 				UpperCamelCase			(							self ):
        return self.sp_model.get_piece_size()



 def 				UpperCamelCase			(							self ):
        A__											=						{self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab



 def __getstate__(							self ):
        A__											=						self.__dict__.copy()
        A__											=						None
        return state



 def __setstate__(							self,__lowerCamelCase ):
        A__											=						d

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

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )



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



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.piece_to_id(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.sp_model.IdToPiece(__lowerCamelCase )
        return token



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[]
        A__											=						''''''
        A__											=						False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(__lowerCamelCase ) + token
                      A__											=						True
                      A__											=						[]
               else:
                      current_sub_tokens.append(__lowerCamelCase )
                      A__											=						False
        out_string += self.sp_model.decode(__lowerCamelCase )
        return out_string.strip()



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        A__											=						kwargs.pop('''use_source_tokenizer''',__lowerCamelCase )

        A__											=						self.convert_ids_to_tokens(__lowerCamelCase,skip_special_tokens=__lowerCamelCase )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A__											=						[]
        A__											=						[]
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )
                             A__											=						[]
                      sub_texts.append(__lowerCamelCase )
               else:
                      current_sub_text.append(__lowerCamelCase )
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               A__											=						re.sub(r''' (\[(MASK|SEP)\])''',r'''\1''',''' '''.join(__lowerCamelCase ) )
        else:
               A__											=						''''''.join(__lowerCamelCase )

        A__											=						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               A__											=						self.clean_up_tokenization(__lowerCamelCase )
               return clean_text
        else:
               return text



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not os.path.isdir(__lowerCamelCase ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
               return
        A__											=						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 ) 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:
                      A__											=						self.sp_model.serialized_model_proto()
                      fi.write(__lowerCamelCase )

        return (out_vocab_file,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 + token_ids_a + sep



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = 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] + ([0] * len(__lowerCamelCase )) + [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        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 ) * [0] + len(token_ids_a + sep ) * [1]





