import os

__lowercase									=   {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}



def lowerCamelCase    (  SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:List[Any]         =						0

    __UpperCamelCase	:List[Any]         =						0
    while index < len(SCREAMING_SNAKE_CASE      ) - 1:
        __UpperCamelCase	:Optional[int]         =						SYMBOLS[numerals[index]]
        __UpperCamelCase	:List[Any]         =						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    (  SCREAMING_SNAKE_CASE      ):
    '''simple docstring'''







    __UpperCamelCase	:List[str]         =						''''''

    __UpperCamelCase	:List[Any]         =						num // 1_000
    numerals += m_count * "M"
    num %= 1_000

    __UpperCamelCase	:Union[str, Any]         =						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

    __UpperCamelCase	:Tuple         =						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    (  SCREAMING_SNAKE_CASE = "/p089_roman.txt"      ):
    '''simple docstring'''







    __UpperCamelCase	:List[str]         =						0

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

    for line in lines:
        __UpperCamelCase	:Optional[int]         =						line.strip()
        __UpperCamelCase	:int         =						parse_roman_numerals(SCREAMING_SNAKE_CASE      )
        __UpperCamelCase	:Dict         =						generate_roman_numerals(SCREAMING_SNAKE_CASE      )
        savings += len(SCREAMING_SNAKE_CASE      ) - len(SCREAMING_SNAKE_CASE      )

    return savings


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







'''simple docstring'''
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DonutImageProcessor


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def __init__(       self						,				A_						,				A_=7						,				A_=3						,				A_=18						,				A_=30						,				A_=400						,				A_=True						,				A_=None						,				A_=True						,				A_=False						,				A_=True						,				A_=True						,				A_=[0.5, 0.5, 0.5]						,				A_=[0.5, 0.5, 0.5]						,				)->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     parent
											UpperCamelCase   			=     batch_size
											UpperCamelCase   			=     num_channels
											UpperCamelCase   			=     image_size
											UpperCamelCase   			=     min_resolution
											UpperCamelCase   			=     max_resolution
											UpperCamelCase   			=     do_resize
											UpperCamelCase   			=     size if size is not None else {'height': 18, 'width': 20}
											UpperCamelCase   			=     do_thumbnail
											UpperCamelCase   			=     do_align_axis
											UpperCamelCase   			=     do_pad
											UpperCamelCase   			=     do_normalize
											UpperCamelCase   			=     image_mean
											UpperCamelCase   			=     image_std







				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_thumbnail": self.do_thumbnail,
											    "do_align_long_axis": self.do_align_axis,
											    "do_pad": self.do_pad,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}


@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							DonutImageProcessor if is_vision_available() else None







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     DonutImageProcessingTester(self     )







				@property
				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											return self.image_processor_tester.prepare_image_processor_dict()







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(A_						,				'do_resize'     )     )
											self.assertTrue(hasattr(A_						,				'size'     )     )
											self.assertTrue(hasattr(A_						,				'do_thumbnail'     )     )
											self.assertTrue(hasattr(A_						,				'do_align_long_axis'     )     )
											self.assertTrue(hasattr(A_						,				'do_pad'     )     )
											self.assertTrue(hasattr(A_						,				'do_normalize'     )     )
											self.assertTrue(hasattr(A_						,				'image_mean'     )     )
											self.assertTrue(hasattr(A_						,				'image_std'     )     )







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size						,				{'height': 18, 'width': 20}     )

											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=42     )
											self.assertEqual(image_processor.size						,				{'height': 42, 'width': 42}     )

											# Previous config had dimensions in (width, height) order
											UpperCamelCase   			=     self.image_processing_class.from_dict(self.image_processor_dict						,				size=(42, 84)     )
											self.assertEqual(image_processor.size						,				{'height': 84, 'width': 42}     )







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											pass







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				Image.Image     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				numpify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				np.ndarray     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)







				@is_flaky()
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											UpperCamelCase   			=     prepare_image_inputs(self.image_processor_tester						,				equal_resolution=A_						,				torchify=A_     )
											for image in image_inputs:
																		self.assertIsInstance(A_						,				torch.Tensor     )

											# Test not batched input
											UpperCamelCase   			=     image_processing(image_inputs[0]						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)

											# Test batched
											UpperCamelCase   			=     image_processing(A_						,				return_tensors='pt'     ).pixel_values
											self.assertEqual(
											    encoded_images.shape						,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size['height'],
											        self.image_processor_tester.size['width'],
											    )						,				)




