



from dataclasses import dataclass
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
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 	__lowerCAmelCase							(__UpperCamelCase  ):


					'''simple docstring'''


					a__        =     42
					a__        =     42


if is_transformers_available() and is_torch_available():
				from .modeling_roberta_series import RobertaSeriesModelWithTransformation
				from .pipeline_alt_diffusion import AltDiffusionPipeline
				from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline






"""simple docstring"""






def      A      (			_A,     _A							):




					"""simple docstring"""
					if n == 0:
										return 1

					elif n % 2 == 1:
										return binary_exponentiation(_A,     n - 1							) * a

					else:
										snake_case_						:Optional[int]								=						binary_exponentiation(_A,     n / 2							)
										return b * b


if __name__ == "__main__":
				try:
								__UpperCAmelCase      :      str   					=					int(input('Enter Base : ').strip())
								__UpperCAmelCase      :      Any   					=					int(input('Enter Power : ').strip())
				except ValueError:
								print('Invalid literal for integer')

				__UpperCAmelCase      :      Optional[int]   					=					binary_exponentiation(BASE, POWER)
				print(F'''{BASE}^({POWER}) : {RESULT}''')

