


"""simple docstring"""




__SCREAMING_SNAKE_CASE      		=    [
    (10_00, """M"""),
    (9_00, """CM"""),
    (5_00, """D"""),
    (4_00, """CD"""),
    (1_00, """C"""),
    (90, """XC"""),
    (50, """L"""),
    (40, """XL"""),
    (10, """X"""),
    (9, """IX"""),
    (5, """V"""),
    (4, """IV"""),
    (1, """I"""),
]



def     __a   (	a       ):





       """simple docstring"""







       _a							 =					{"I": 1, "V": 5, "X": 1_0, "L": 5_0, "C": 1_0_0, "D": 5_0_0, "M": 1_0_0_0}
       _a							 =					0
       _a							 =					0
       while place < len(_snake_case       ):
              if (place + 1 < len(_snake_case       )) and (vals[roman[place]] < vals[roman[place + 1]]):
                     total += vals[roman[place + 1]] - vals[roman[place]]
                     place += 2
              else:
                     total += vals[roman[place]]
                     place += 1
       return total



def     __a   (	a       ):





       """simple docstring"""







       _a							 =					[]
       for arabic, roman in ROMAN:
              ((_a) ,			(_a))							 =					divmod(_snake_case,		_snake_case       )
              result.append(roman * factor       )
              if number == 0:
                     break
       return "".join(_snake_case       )


if __name__ == "__main__":
   import doctest

   doctest.testmod()








from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Union[List[PIL.Image.Image], np.ndarray]
       __lowerCAmelCase    :		Optional[List[bool]]


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


