import os

SCREAMING_SNAKE_CASE :	Optional[int]							     =		{'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def   __A		(			_A					):



	"""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		(			_A					):



	"""simple docstring"""


	__a       =  ''''''

	__a       =  num // 1000
	numerals += m_count * "M"
	num %= 1000

	__a       =  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

	__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		(			_A = "/p089_roman.txt"					):



	"""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() = }''')



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

   __a       =  encoder_input_tokens.shape[1]
   __a       =  torch.arange(__SCREAMING_SNAKE_CASE     ,							device=encoder_input_tokens.device       )
   x += self.position_encoding(__SCREAMING_SNAKE_CASE       )

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


