'''simple docstring'''


import os

__snake_case						  =		{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:
  lowercase_     =       0

  lowercase_     =       0
  while index < len(SCREAMING_SNAKE_CASE_						) - 1:
    lowercase_     =       SYMBOLS[numerals[index]]
    lowercase_     =       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_      (       SCREAMING_SNAKE_CASE_						)			->str:
  lowercase_     =       """"""

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

  lowercase_     =       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

  lowercase_     =       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_      (       SCREAMING_SNAKE_CASE_ = "/p089_roman.txt"						)			->int:
  lowercase_     =       0

  with open(os.path.dirname(SCREAMING_SNAKE_CASE_						) + roman_numerals_filename						) as filea:
    lowercase_     =       filea.readlines()

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

  return savings


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


'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

