






_SCREAMING_SNAKE_CASE   =  {
    0: "0",
    1: "1",
    2: "2",
    3: "3",
    4: "4",
    5: "5",
    6: "6",
    7: "7",
    8: "8",
    9: "9",
    10: "a",
    11: "b",
    12: "c",
    13: "d",
    14: "e",
    15: "f",
}





def        _snake_case   (_snake_case				:       float)					->	str:
   assert type(_snake_case) in (int, float) and decimal == int(_snake_case)
   _lowercase              =int(_snake_case)
   _lowercase              =''
   _lowercase              =False
   if decimal < 0:
      _lowercase              =True
      decimal *= -1
   while decimal > 0:
      _lowercase       ,       _lowercase              =divmod(_snake_case      ,    16)
      _lowercase              =values[remainder] + hexadecimal
   _lowercase              ='0x' + hexadecimal
   if negative:
      _lowercase              ='-' + hexadecimal
   return hexadecimal


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


