def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			str:
							if not isinstance(snake_case__     ,						snake_case__							):
														lowerCAmelCase				=	(
														    '''operation can not be conducted on a object of type '''
														    f"{type(snake_case__							).__name__}"
														)
														raise TypeError(snake_case__							)
							lowerCAmelCase				=	''''''
							for _ in range(0     ,						3_2							):
														bit_string += str(number % 2							)
														lowerCAmelCase				=	number >> 1
							return bit_string

def      SCREAMING_SNAKE_CASE_  (		snake_case__							)  ->			str:
							if number < 0:
														raise ValueError('''the value of input must be positive'''							)
							elif isinstance(snake_case__     ,						snake_case__							):
														raise TypeError('''Input value must be a \'int\' type'''							)
							elif isinstance(snake_case__     ,						snake_case__							):
														raise TypeError('''\'<\' not supported between instances of \'str\' and \'int\''''							)
							lowerCAmelCase				=	0
							# iterator over [1 to 32],since we are dealing with 32 bit integer
							for _ in range(1     ,						3_3							):
														# left shift the bits by unity
														lowerCAmelCase				=	result << 1
														# get the end bit
														lowerCAmelCase				=	number % 2
														# right shift the bits by unity
														lowerCAmelCase				=	number >> 1
														# add that bit to our ans
														lowerCAmelCase				=	result | end_bit
							return get_reverse_bit_string(snake_case__							)


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      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	UpperCAmelCase_						:      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



