


'''simple docstring'''


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


UpperCAmelCase_		:   str							   =		transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				if isinstance(SCREAMING_SNAKE_CASE__							,      torch.Tensor     ):
								return image
				elif isinstance(SCREAMING_SNAKE_CASE__							,      PIL.Image.Image     ):
								_SCREAMING_SNAKE_CASE :       Dict     		=      [image]

				_SCREAMING_SNAKE_CASE :       Any     		=      [trans(img.convert("""RGB"""     )     ) for img in image]
				_SCREAMING_SNAKE_CASE :       int     		=      torch.stack(SCREAMING_SNAKE_CASE__     )
				return image







class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()

											# make sure scheduler can always be converted to DDIM
											_SCREAMING_SNAKE_CASE :       int     		=      DDIMScheduler.from_config(scheduler.config  )

											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											if strength < 0 or strength > 1:
															raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}"""  )






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case  ):
											# get the original timestep using init_timestep
											_SCREAMING_SNAKE_CASE :       List[str]     		=      min(int(num_inference_steps * strength  )    ,					__snake_case  )

											_SCREAMING_SNAKE_CASE :       Tuple     		=      max(num_inference_steps - init_timestep    ,					0  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start






							def UpperCAmelCase_	(    self    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case    ,					__snake_case=None  ):
											if not isinstance(__snake_case    ,					(torch.Tensor, PIL.Image.Image, list)  ):
															raise ValueError(
															    f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case  )}"""  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image.to(device=__snake_case    ,					dtype=__snake_case  )

											if isinstance(__snake_case    ,					__snake_case  ) and len(__snake_case  ) != batch_size:
															raise ValueError(
															    f"""You have passed a list of generators of length {len(__snake_case  )}, but requested an effective batch"""
															    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      init_latents.shape
											_SCREAMING_SNAKE_CASE :       int     		=      randn_tensor(__snake_case    ,					generator=__snake_case    ,					device=__snake_case    ,					dtype=__snake_case  )

											# get latents
											print("""add noise to latents at timestep"""    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.add_noise(__snake_case    ,					__snake_case    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       Tuple     		=      init_latents

											return latents






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = None    ,					__snake_case = 0.8    ,					__snake_case = 1    ,					__snake_case = None    ,					__snake_case = 0.0    ,					__snake_case = 50    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					):
											self.check_inputs(__snake_case  )

											# 2. Preprocess image
											_SCREAMING_SNAKE_CASE :       Any     		=      preprocess(__snake_case  )

											# 3. set timesteps
											self.scheduler.set_timesteps(__snake_case    ,					device=self.device  )
											_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      self.get_timesteps(__snake_case    ,					__snake_case    ,					self.device  )
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      timesteps[:1].repeat(__snake_case  )

											# 4. Prepare latent variables
											_SCREAMING_SNAKE_CASE :       Any     		=      self.prepare_latents(__snake_case    ,					__snake_case    ,					__snake_case    ,					self.unet.dtype    ,					self.device    ,					__snake_case  )
											_SCREAMING_SNAKE_CASE :       str     		=      latents

											# 5. Denoising loop
											for t in self.progress_bar(__snake_case  ):
															# 1. predict noise model_output
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.unet(__snake_case    ,					__snake_case  ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step(
															    __snake_case    ,					__snake_case    ,					__snake_case    ,					eta=__snake_case    ,					use_clipped_model_output=__snake_case    ,					generator=__snake_case    ,					).prev_sample

											_SCREAMING_SNAKE_CASE :       Dict     		=      (image / 2 + 0.5).clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      image.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (image, latent_timestep.item())

											return ImagePipelineOutput(images=__snake_case  )








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





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







def 			snake_case_   (  ):







				"""simple docstring"""





				assert nand_gate(0							,      0     ) == 1
				assert nand_gate(0							,      1     ) == 1
				assert nand_gate(1							,      0     ) == 1
				assert nand_gate(1							,      1     ) == 0


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




