


import torch

from diffusers import StableDiffusionPipeline


__snake_case   							=						'''path-to-your-trained-model'''
__snake_case   							=						StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

__snake_case   							=						'''A photo of sks dog in a bucket'''
__snake_case   							=						pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')








def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

       return int((input_a, input_a).count(1		) != 0		)







def SCREAMING_SNAKE_CASE							(			)  ->			None:



       """simple docstring"""

       assert or_gate(0					,							0		) == 0
       assert or_gate(0					,							1		) == 1
       assert or_gate(1					,							0		) == 1
       assert or_gate(1					,							1		) == 1


if __name__ == "__main__":
  print(or_gate(0, 1))
  print(or_gate(1, 0))
  print(or_gate(0, 0))
  print(or_gate(1, 1))


