
'''simple docstring'''







import os

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







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	int:
	_snake_case   						=		0

	_snake_case   						=		0
	while index < len(__A	) - 1:
		_snake_case   						=		SYMBOLS[numerals[index]]
		_snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	str:
	_snake_case   						=		''

	_snake_case   						=		num // 1_000
	numerals += m_count * "M"
	num %= 1_000

	_snake_case   						=		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

	_snake_case   						=		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 				SCREAMING_SNAKE_CASE__	(   __A = "/p089_roman.txt"	)       ->	int:
	_snake_case   						=		0

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

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

	return savings


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








'''simple docstring'''
from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.25.0''')):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import (
        VersatileDiffusionDualGuidedPipeline,
        VersatileDiffusionImageVariationPipeline,
        VersatileDiffusionPipeline,
        VersatileDiffusionTextToImagePipeline,
    )
else:
    from .modeling_text_unet import UNetFlatConditionModel
    from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
    from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
    from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
    from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
