"""simple docstring"""




import os

__lowerCamelCase											=  {"I": 1, "V": 5, "X": 10, "L": 50, "C": 1_00, "D": 5_00, "M": 10_00}






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0

							A__	     =							0
							while index < len(UpperCamelCase__				) - 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   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							''

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

							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   UpperCAmelCase					(	UpperCamelCase__ = "/p089_roman.txt"				):






							"""simple docstring"""





							A__	     =							0

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

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

							return savings


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






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase											=  {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  ["GLPNFeatureExtractor"]
				__lowerCamelCase											=  ["GLPNImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCamelCase											=  [
				    "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GLPNForDepthEstimation",
				    "GLPNLayer",
				    "GLPNModel",
				    "GLPNPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_glpn import GLPNFeatureExtractor
								from .image_processing_glpn import GLPNImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_glpn import (
								    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GLPNForDepthEstimation,
								    GLPNLayer,
								    GLPNModel,
								    GLPNPreTrainedModel,
								)


else:
				import sys

				__lowerCamelCase											=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





