




'''simple docstring'''





def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  str:
     if not isinstance(__UpperCamelCase	,      __UpperCamelCase):
          a											=   (
              "operation can not be conducted on a object of type "
              f'''{type(__UpperCamelCase).__name__}'''
          )
          raise TypeError(__UpperCamelCase)
     a											=   ""
     for _ in range(0	,      32):
          bit_string += str(number % 2)
          a											=   number >> 1
     return bit_string

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  str:
     if number < 0:
          raise ValueError("the value of input must be positive")
     elif isinstance(__UpperCamelCase	,      __UpperCamelCase):
          raise TypeError("Input value must be a 'int' type")
     elif isinstance(__UpperCamelCase	,      __UpperCamelCase):
          raise TypeError("'<' not supported between instances of 'str' and 'int'")
     a											=   0
     # iterator over [1 to 32],since we are dealing with 32 bit integer
     for _ in range(1	,      33):
          # left shift the bits by unity
          a											=   result << 1
          # get the end bit
          a											=   number % 2
          # right shift the bits by unity
          a											=   number >> 1
          # add that bit to our ans
          a											=   result | end_bit
     return get_reverse_bit_string(__UpperCamelCase)


if __name__ == "__main__":
   import doctest

   doctest.testmod()







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

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available




@dataclass
class     a__ (      UpperCamelCase__   ):
     a	:    Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
   from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
   from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

