'''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:			Tuple	       =       logging.get_logger(__name__)

__a:			Union[str, Any]	       =       """▁"""

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

__a:			int	       =       {
    """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:			List[Any]	       =       {"""vinai/bartpho-syllable""": 10_24}



class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       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"]

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

        lowercase__    :   List[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__    :   Optional[int]						= monolingual_vocab_file
        lowercase__    :   int						= 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__    :   int						= {}
        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__    :   Optional[int]						= cnt
          cnt += 1
        with open(__lowerCAmelCase     ,       '''r'''     ,       encoding='''utf-8'''			) as f:
         for line in f.readlines():
          lowercase__    :   str						= line.strip().split()[0]
          lowercase__    :   Tuple						= len(self.fairseq_tokens_to_ids			)
        if str(__lowerCAmelCase			) not in self.fairseq_tokens_to_ids:
         lowercase__    :   str						= len(self.fairseq_tokens_to_ids			)

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

       def __getstate__(						self			)  ->  Tuple:
        lowercase__    :   Union[str, Any]						= self.__dict__.copy()
        lowercase__    :   Optional[int]						= None
        lowercase__    :   List[str]						= self.sp_model.serialized_model_proto()
        return state

       def __setstate__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Optional[Any]						= d

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

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 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			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   Dict						= [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 _lowerCAmelCase(						self			)  ->  Tuple:
        return len(self.fairseq_ids_to_tokens			)

       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   str						= {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  List[str]:
        return self.sp_model.encode(__lowerCAmelCase     ,       out_type=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Dict:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        else:
         return self.unk_token_id

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        return self.fairseq_ids_to_tokens[index]

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   int						= ''''''.join(__lowerCAmelCase			).replace(__lowerCAmelCase     ,       ''' '''			).strip()
        return out_string


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        if not os.path.isdir(__lowerCAmelCase			):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
         return
        lowercase__    :   List[Any]						= os.path.join(
            __lowerCAmelCase     ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
        lowercase__    :   Optional[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:
          lowercase__    :   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


'''simple docstring'''

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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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

__a:			int	       =       """▁"""

__a:			Optional[int]	       =       {"""vocab_file""": """prophetnet.tokenizer"""}

__a:			Optional[int]	       =       {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

__a:			List[str]	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

__a:			Tuple	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= collections.OrderedDict()
 with open(UpperCAmelCase		,					'''r'''		,					encoding='''utf-8'''					) as reader:
  lowercase__    :   List[Any]						= reader.readlines()
 for index, token in enumerate(UpperCAmelCase					):
  lowercase__    :   List[Any]						= token.rstrip('''\n'''					)
  lowercase__    :   Tuple						= index
 return vocab





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       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"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[UNK]"     ,       __lowerCAmelCase="[PAD]"     ,       __lowerCAmelCase="[CLS]"     ,       __lowerCAmelCase="[MASK]"     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  None:
        lowercase__    :   Dict						= {} if sp_model_kwargs is None else sp_model_kwargs

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

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        lowercase__    :   str						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(__lowerCAmelCase			)			)
        lowercase__    :   Optional[int]						= vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        lowercase__    :   str						= {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

        for i in range(10			):
         lowercase__    :   Tuple						= F"""[unused{i}]"""
         lowercase__    :   List[Any]						= 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase__    :   Optional[Any]						= 12
        lowercase__    :   Tuple						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(__lowerCAmelCase			)

       def __getstate__(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= self.__dict__.copy()
        lowercase__    :   Optional[Any]						= None
        return state

       def __setstate__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Any						= d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

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

        lowercase__    :   int						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(self.vocab_file			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = 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			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   List[str]						= [self.sep_token_id]

        if token_ids_a is None:
         return len(token_ids_a + sep			) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep			) * [0]

       @property
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        return len(self.sp_model			) + self.fairseq_offset

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self.sp_model.encode(__lowerCAmelCase     ,       out_type=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowercase__    :   Tuple						= self.sp_model.PieceToId(__lowerCAmelCase			)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= ''''''.join(__lowerCAmelCase			).replace(__lowerCAmelCase     ,       ''' '''			).strip()
        return out_string

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        if not os.path.isdir(__lowerCAmelCase			):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
         return
        lowercase__    :   str						= 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:
          lowercase__    :   List[Any]						= self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase			)

        return (out_vocab_file,)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:

        if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
        lowercase__    :   str						= [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep

