'''simple docstring'''




import os

__UpperCAmelCase     							={"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}






def __lowerCAmelCase (							UpperCamelCase__				)	->    int:
    __lowerCamelCase		=  0

    __lowerCamelCase		=  0
    while index < len(UpperCamelCase__				) - 1:
        __lowerCamelCase		=  SYMBOLS[numerals[index]]
        __lowerCamelCase		=  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 (							UpperCamelCase__				)	->    str:
    __lowerCamelCase		=  ''''''

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

    __lowerCamelCase		=  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

    __lowerCamelCase		=  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 (							UpperCamelCase__ = "/p089_roman.txt"				)	->    int:
    __lowerCamelCase		=  0

    with open(os.path.dirname(UpperCamelCase__				) + roman_numerals_filename				) as filea:
        __lowerCamelCase		=  filea.readlines()

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

    return savings


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








from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





