





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_albert import AlbertTokenizer
else:
       snake_case__					:							Tuple													=     None

snake_case__					:							str													=     logging.get_logger(__name__)
snake_case__					:							Dict													=     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case__					:							Any													=     {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json',
    },
}

snake_case__					:							Optional[Any]													=     {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

snake_case__					:							Tuple													=     '▁'





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = VOCAB_FILES_NAMES
   lowerCAmelCase__        = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase__        = AlbertTokenizer




   def __init__(self	:List[Any]    ,   _UpperCamelCase	:List[Any]=None    ,   _UpperCamelCase	:Tuple=None    ,   _UpperCamelCase	:Dict=True    ,   _UpperCamelCase	:List[Any]=True    ,   _UpperCamelCase	:int=False    ,   _UpperCamelCase	:List[str]="[CLS]"    ,   _UpperCamelCase	:Tuple="[SEP]"    ,   _UpperCamelCase	:Union[str, Any]="<unk>"    ,   _UpperCamelCase	:int="[SEP]"    ,   _UpperCamelCase	:List[str]="<pad>"    ,   _UpperCamelCase	:Optional[int]="[CLS]"    ,   _UpperCamelCase	:List[Any]="[MASK]"    ,   **_UpperCamelCase	:Any    ,   )->					int:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         __A          =				(
             AddedToken(_UpperCamelCase    ,   lstrip=_UpperCamelCase    ,   rstrip=_UpperCamelCase    ,   normalized=_UpperCamelCase )
             if isinstance(_UpperCamelCase    ,   _UpperCamelCase )
             else mask_token
         )

         super().__init__(
             _UpperCamelCase    ,   tokenizer_file=_UpperCamelCase    ,   do_lower_case=_UpperCamelCase    ,   remove_space=_UpperCamelCase    ,   keep_accents=_UpperCamelCase    ,   bos_token=_UpperCamelCase    ,   eos_token=_UpperCamelCase    ,   unk_token=_UpperCamelCase    ,   sep_token=_UpperCamelCase    ,   pad_token=_UpperCamelCase    ,   cls_token=_UpperCamelCase    ,   mask_token=_UpperCamelCase    ,   **_UpperCamelCase    ,   )

         __A          =				do_lower_case
         __A          =				remove_space
         __A          =				keep_accents
         __A          =				vocab_file
         __A          =				False if not self.vocab_file else True




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __A          =				[self.sep_token_id]
         __A          =				[self.cls_token_id]
         if token_ids_a is None:
               return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __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]






   def _lowerCAmelCase      (self	:Optional[int]    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[str] = None )->					Tuple[str]:
         if not self.can_save_slow_tokenizer:
               raise ValueError(
                   '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                   '''tokenizer.''' )

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

         if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCamelCase ):
               copyfile(self.vocab_file    ,   _UpperCamelCase )

         return (out_vocab_file,)












import os

snake_case__					:							Any													=     {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def _a				(	lowerCamelCase:   str )	->						int:







      '''simple docstring'''






      __A          =				0

      __A          =				0
      while index < len(lowerCamelCase ) - 1:
            __A          =				SYMBOLS[numerals[index]]
            __A          =				SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value



def _a				(	lowerCamelCase:   int )	->						str:







      '''simple docstring'''






      __A          =				''''''

      __A          =				num // 10_00
      numerals += m_count * "M"
      num %= 10_00

      __A          =				num // 1_00
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 1_00

      __A          =				num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals



def _a				(	lowerCamelCase:   str = "/p089_roman.txt" )	->						int:







      '''simple docstring'''






      __A          =				0

      with open(os.path.dirname(lowerCamelCase ) + roman_numerals_filename ) as filea:
            __A          =				filea.readlines()

      for line in lines:
            __A          =				line.strip()
            __A          =				parse_roman_numerals(lowerCamelCase )
            __A          =				generate_roman_numerals(lowerCamelCase )
            savings += len(lowerCamelCase ) - len(lowerCamelCase )

      return savings


if __name__ == "__main__":
       print(f'{solution() = }')





