





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
     from ...tokenization_utils_base import TextInput
from ...utils import logging


__lowercase  :   Dict						      =logging.get_logger(__name__)

__lowercase  :   List[str]						      ={"""vocab_file""": """spiece.model"""}

__lowercase  :   Tuple						      ={
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
__lowercase  :   Tuple						      ={
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}

__lowercase  :   Tuple						      ="""▁"""

class      A       (	__a						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case							     =["input_ids", "attention_mask"]



      def __init__(	self:						Optional[Any]		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						str="</s>"		,  _lowerCAmelCase:						str="<unk>"		,  _lowerCAmelCase:						Tuple="<pad>"		,  _lowerCAmelCase:						List[str]=100		,  _lowerCAmelCase:						Any=None		,  _lowerCAmelCase:						Optional[int] = None		,  _lowerCAmelCase:						Dict=True		,  **_lowerCAmelCase:						Optional[Any]		,  )						->    None:
         '''simple docstring'''

         if extra_ids > 0 and additional_special_tokens is None:
            UpperCAmelCase_											=[F'<extra_id_{i}>' for i in range(lowerCAmelCase_       )]
         elif extra_ids > 0 and additional_special_tokens is not None:
            # Check that we have the right number of extra_id special tokens
            UpperCAmelCase_											=len(set(filter(lambda _lowerCAmelCase       : bool("extra_id" in str(lowerCAmelCase_       )       )		,  lowerCAmelCase_       )       )       )
            if extra_tokens != extra_ids:
               raise ValueError(
                   F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   " provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
                   " tokens"       )
         if legacy:
            logger.warning_once(
                F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
                " read the related pull request available at https://github.com/huggingface/transformers/pull/24565"       )

         UpperCAmelCase_											=legacy
         UpperCAmelCase_											={} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=lowerCAmelCase_		,  unk_token=lowerCAmelCase_		,  pad_token=lowerCAmelCase_		,  extra_ids=lowerCAmelCase_		,  additional_special_tokens=lowerCAmelCase_		,  sp_model_kwargs=self.sp_model_kwargs		,  legacy=lowerCAmelCase_		,  **lowerCAmelCase_		,  )

         UpperCAmelCase_											=vocab_file
         UpperCAmelCase_											=extra_ids

         UpperCAmelCase_											=spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(lowerCAmelCase_       )



      @staticmethod
      def 	lowerCAmelCase__    (	_lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Dict       )						->    int:
         '''simple docstring'''

         if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
            UpperCAmelCase_											=TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
            if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
            elif init_max_model_length is None:
               warnings.warn(
                   "This tokenizer was incorrectly instantiated with a model max length of"
                   F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   " behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
                   " `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
                   F' {pretrained_model_name_or_path} automatically truncating your input to'
                   F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   " `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
                   " instantiate this tokenizer with `model_max_length` set to your preferred value."		,  lowerCAmelCase_		,  )

         return max_model_length



      @property
      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Tuple:
         '''simple docstring'''

         return self.sp_model.get_piece_size() + self._extra_ids



      def 	lowerCAmelCase__    (	self:						int       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											={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:						str		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Union[str, Any] = None		,  _lowerCAmelCase:						Union[str, Any] = False       )						->    List[int]:
         '''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_       )

         # normal case: some special tokens
         if token_ids_a is None:
            return ([0] * len(lowerCAmelCase_       )) + [1]
         return ([0] * len(lowerCAmelCase_       )) + [1] + ([0] * len(lowerCAmelCase_       )) + [1]



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Union[str, Any]:
         '''simple docstring'''

         return list(
             set(filter(lambda _lowerCAmelCase       : bool(re.search(r"<extra_id_\d+>"		,  lowerCAmelCase_       )       ) is not None		,  self.additional_special_tokens       )       )       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         return [self._convert_token_to_id(lowerCAmelCase_       ) for token in self.get_sentinel_tokens()]



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Any       )						->    List[int]:
         '''simple docstring'''

         if len(lowerCAmelCase_       ) > 0 and token_ids[-1] == self.eos_token_id:
            warnings.warn(
                F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
                " eos tokens being added."       )
            return token_ids
         else:
            return token_ids + [self.eos_token_id]



      def 	lowerCAmelCase__    (	self:						Dict		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						int = None       )						->    List[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.eos_token_id]

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



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Tuple = None       )						->    List[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self._add_eos_if_not_present(lowerCAmelCase_       )
         if token_ids_a is None:
            return token_ids_a
         else:
            UpperCAmelCase_											=self._add_eos_if_not_present(lowerCAmelCase_       )
            return token_ids_a + token_ids_a



      def __getstate__(	self:						Optional[int]       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=self.__dict__.copy()
         UpperCAmelCase_											=None
         return state



      def __setstate__(	self:						Tuple		,  _lowerCAmelCase:						str       )						->    Any:
         '''simple docstring'''

         UpperCAmelCase_											=d

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

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



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Dict		,  **_lowerCAmelCase:						List[str]       )						->    List[str]:
         '''simple docstring'''

         if not self.legacy:
            UpperCAmelCase_											=SPIECE_UNDERLINE + text.replace(lowerCAmelCase_		,  " "       )
         return super().tokenize(lowerCAmelCase_		,  **lowerCAmelCase_       )



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[str]		,  **_lowerCAmelCase:						Dict       )						->    int:
         '''simple docstring'''

         if not self.legacy:
            UpperCAmelCase_											=text.startswith(lowerCAmelCase_       )
            if is_first:
               UpperCAmelCase_											=text[1:]

         UpperCAmelCase_											=self.sp_model.encode(lowerCAmelCase_		,  out_type=lowerCAmelCase_       )

         if not self.legacy and not is_first and not text.startswith(" "       ) and tokens[0].startswith(lowerCAmelCase_       ):
            UpperCAmelCase_											=([tokens[0][1:]] if len(tokens[0]       ) > 1 else []) + tokens[1:]
         return tokens



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Optional[Any]       )						->    Dict:
         '''simple docstring'''

         if token.startswith("<extra_id_"       ):
            UpperCAmelCase_											=re.match(r"<extra_id_(\d+)>"		,  lowerCAmelCase_       )
            UpperCAmelCase_											=int(match.group(1       )       )
            return self.vocab_size - num - 1
         return self.sp_model.piece_to_id(lowerCAmelCase_       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Dict       )						->    int:
         '''simple docstring'''

         if index < self.sp_model.get_piece_size():
            UpperCAmelCase_											=self.sp_model.IdToPiece(lowerCAmelCase_       )
         else:
            UpperCAmelCase_											=F'<extra_id_{self.vocab_size - 1 - index}>'
         return token



      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						List[Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=[]
         UpperCAmelCase_											=""
         UpperCAmelCase_											=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
               UpperCAmelCase_											=True
               UpperCAmelCase_											=[]
            else:
               current_sub_tokens.append(lowerCAmelCase_       )
               UpperCAmelCase_											=False
         out_string += self.sp_model.decode(lowerCAmelCase_       )
         return out_string.strip()






      def 	lowerCAmelCase__    (	self:						int		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						Tuple = None       )						->    Tuple[str]:
         '''simple docstring'''

         if not os.path.isdir(lowerCAmelCase_       ):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
            return
         UpperCAmelCase_											=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:
               UpperCAmelCase_											=self.sp_model.serialized_model_proto()
               fi.write(lowerCAmelCase_       )

         return (out_vocab_file,)











__lowercase  :   Optional[Any]						      =[
    (1000, """M"""),
    (900, """CM"""),
    (500, """D"""),
    (400, """CD"""),
    (100, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
   UpperCAmelCase_											=0
   UpperCAmelCase_											=0
   while place < len(lowercase__  ):
      if (place + 1 < len(lowercase__  )) and (vals[roman[place]] < vals[roman[place + 1]]):
         total += vals[roman[place + 1]] - vals[roman[place]]
         place += 2
      else:
         total += vals[roman[place]]
         place += 1
   return total




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=[]
   for arabic, roman in ROMAN:
      ((UpperCAmelCase_)						,       (UpperCAmelCase_))											=divmod(lowercase__  ,    lowercase__  )
      result.append(roman * factor  )
      if number == 0:
         break
   return "".join(lowercase__  )


if __name__ == "__main__":
     import doctest

     doctest.testmod()




