
"""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		       =							logging.get_logger(__name__)

_a		       =							'▁'

_a		       =							{'vocab_file': 'sentencepiece.bpe.model'}

_a		       =							{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_a		       =							{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}


class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[Any]       =		VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__			:						Tuple       =		PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__			:						int       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__			:						Dict       =		["""input_ids""", """attention_mask"""]







  def __init__(		self				,   lowercase_				,   lowercase_="<s>"				,   lowercase_="</s>"				,   lowercase_="</s>"				,   lowercase_="<s>"				,   lowercase_="<unk>"				,   lowercase_="<pad>"				,   lowercase_="<mask>"				,   lowercase_ = None				,   **lowercase_				,   ):

     """simple docstring"""
     # Mask token behave like a normal word, i.e. include the space before it
     UpperCAmelCase_					:					Any			     =    AddedToken(lowercase_				,   lstrip=lowercase_				,   rstrip=lowercase_  ) if isinstance(lowercase_				,   lowercase_  ) else mask_token

     UpperCAmelCase_					:					List[str]			     =    {} if sp_model_kwargs is None else sp_model_kwargs

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

     UpperCAmelCase_					:					str			     =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
     self.sp_model.Load(str(lowercase_  )  )
     UpperCAmelCase_					:					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'

     # Mimic fairseq token-to-id alignment for the first 4 token
     UpperCAmelCase_					:					Any			     =    {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

     # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
     UpperCAmelCase_					:					int			     =    1

     UpperCAmelCase_					:					str			     =    len(self.sp_model  ) + self.fairseq_offset
     UpperCAmelCase_					:					Dict			     =    {v: k for k, v in self.fairseq_tokens_to_ids.items()}







  def __getstate__(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[Any]			     =    self.__dict__.copy()
     UpperCAmelCase_					:					List[str]			     =    None
     UpperCAmelCase_					:					int			     =    self.sp_model.serialized_model_proto()
     return state







  def __setstate__(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					int			     =    d

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

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None				,   lowercase_ = False  ):

     """simple docstring"""

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

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







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    [self.sep_token_id]
     UpperCAmelCase_					:					Dict			     =    [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  ):

     """simple docstring"""
     return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    {self.convert_ids_to_tokens(lowercase_  ): i for i in range(self.vocab_size  )}
     vocab.update(self.added_tokens_encoder  )
     return vocab







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     return self.sp_model.encode(lowercase_				,   out_type=lowercase_  )







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     UpperCAmelCase_					:					str			     =    self.sp_model.PieceToId(lowercase_  )

     # 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        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     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        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					Dict			     =    "".join(lowercase_  ).replace(lowercase_				,   " "  ).strip()
     return out_string







  def        UpperCamelCase__					(		self				,   lowercase_				,   lowercase_ = None  ):

     """simple docstring"""
     if not os.path.isdir(lowercase_  ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
        return
     UpperCAmelCase_					:					List[Any]			     =    os.path.join(
         lowercase_				,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

     if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase_  ) and os.path.isfile(self.vocab_file  ):
        copyfile(self.vocab_file				,   lowercase_  )
     elif not os.path.isfile(self.vocab_file  ):
        with open(lowercase_				,   "wb"  ) as fi:
           UpperCAmelCase_					:					List[str]			     =    self.sp_model.serialized_model_proto()
           fi.write(lowercase_  )

     return (out_vocab_file,)




"""simple docstring"""





import os

_a		       =							{'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}







def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					Union[str, Any]			     =    0

   UpperCAmelCase_					:					List[str]			     =    0
   while index < len(__lowerCamelCase  ) - 1:
      UpperCAmelCase_					:					Tuple			     =    SYMBOLS[numerals[index]]
      UpperCAmelCase_					:					List[str]			     =    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  ):
   UpperCAmelCase_					:					List[str]			     =    ""

   UpperCAmelCase_					:					Any			     =    num // 1000
   numerals += m_count * "M"
   num %= 1000

   UpperCAmelCase_					:					Any			     =    num // 100
   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 %= 100

   UpperCAmelCase_					:					str			     =    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 = "/p089_roman.txt"  ):
   UpperCAmelCase_					:					int			     =    0

   with open(os.path.dirname(__lowerCamelCase  ) + roman_numerals_filename  ) as filea:
      UpperCAmelCase_					:					Optional[Any]			     =    filea.readlines()

   for line in lines:
      UpperCAmelCase_					:					Tuple			     =    line.strip()
      UpperCAmelCase_					:					Optional[Any]			     =    parse_roman_numerals(__lowerCamelCase  )
      UpperCAmelCase_					:					Tuple			     =    generate_roman_numerals(__lowerCamelCase  )
      savings += len(__lowerCamelCase  ) - len(__lowerCamelCase  )

   return savings


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


