





'''simple docstring'''




import os

lowercase__   :				Any      =   {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 1_00, '''D''': 5_00, '''M''': 10_00}






def 	_lowerCAmelCase      (  __snake_case  :			str      )					->					int:
     __A						:			str								=	0

     __A						:			int								=	0
     while index < len(__snake_case      ) - 1:
          __A						:			Tuple								=	SYMBOLS[numerals[index]]
          __A						:			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 	_lowerCAmelCase      (  __snake_case  :			int      )					->					str:
     __A						:			str								=	''

     __A						:			Optional[int]								=	num // 10_00
     numerals += m_count * "M"
     num %= 10_00

     __A						:			Union[str, Any]								=	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						:			Union[str, Any]								=	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 	_lowerCAmelCase      (  __snake_case  :			str = "/p089_roman.txt"      )					->					int:
     __A						:			Optional[Any]								=	0

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

     for line in lines:
          __A						:			Any								=	line.strip()
          __A						:			int								=	parse_roman_numerals(__snake_case      )
          __A						:			Tuple								=	generate_roman_numerals(__snake_case      )
          savings += len(__snake_case      ) - len(__snake_case      )

     return savings


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






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

          __A						:			Union[str, Any]								=	vocab_size
          __A						:			List[str]								=	hidden_size
          __A						:			Tuple								=	num_hidden_layers
          __A						:			List[str]								=	num_attention_heads
          __A						:			Tuple								=	hidden_act
          __A						:			Optional[Any]								=	intermediate_size
          __A						:			List[Any]								=	hidden_dropout_prob
          __A						:			Union[str, Any]								=	attention_probs_dropout_prob
          __A						:			Dict								=	max_position_embeddings
          __A						:			Any								=	type_vocab_size
          __A						:			Optional[int]								=	initializer_range
          __A						:			List[str]								=	layer_norm_eps
          __A						:			Optional[int]								=	position_embedding_type
          __A						:			Dict								=	use_cache
          __A						:			List[str]								=	classifier_dropout




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						:			List[str]								=	{0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						:			Tuple								=	{0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])