



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


__lowerCamelCase     :							Dict  							=				transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[Any]:
						if isinstance(lowerCamelCase_    ,   torch.Tensor    ):
												return image
						elif isinstance(lowerCamelCase_    ,   PIL.Image.Image    ):
												UpperCAmelCase         =     [image]

						UpperCAmelCase         =     [trans(img.convert("RGB"    )    ) for img in image]
						UpperCAmelCase         =     torch.stack(lowerCamelCase_    )
						return image





class  __magic_name__   (							A__						):






		def __init__(						self      :     Optional[int]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Dict      )				->					int:
								'''simple docstring'''







								super().__init__()

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

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






		def SCREAMING_SNAKE_CASE_			(						self      :     Dict				,		UpperCamelCase__      :     Optional[Any]      )				->					Any:
								'''simple docstring'''







								if strength < 0 or strength > 1:
														raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'      )






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Any      )				->					Optional[int]:
								'''simple docstring'''







								UpperCAmelCase         =     min(int(num_inference_steps * strength      )				,		UpperCamelCase__      )

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

								return timesteps, num_inference_steps - t_start






		def SCREAMING_SNAKE_CASE_			(						self      :     Union[str, Any]				,		UpperCamelCase__      :     Optional[int]				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     Any				,		UpperCamelCase__      :     List[str]				,		UpperCamelCase__      :     Tuple				,		UpperCamelCase__      :     str=None      )				->					Union[str, Any]:
								'''simple docstring'''







								if not isinstance(UpperCamelCase__				,		(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(UpperCamelCase__      )}'      )

								UpperCAmelCase         =     image.to(device=UpperCamelCase__				,		dtype=UpperCamelCase__      )

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

								UpperCAmelCase         =     init_latents.shape
								UpperCAmelCase         =     randn_tensor(UpperCamelCase__				,		generator=UpperCamelCase__				,		device=UpperCamelCase__				,		dtype=UpperCamelCase__      )

								# get latents
								print("add noise to latents at timestep"				,		UpperCamelCase__      )
								UpperCAmelCase         =     self.scheduler.add_noise(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     init_latents

								return latents







		@torch.no_grad()
		def __call__(						self      :     Optional[Any]				,		UpperCamelCase__      :     Union[torch.FloatTensor, PIL.Image.Image] = None				,		UpperCamelCase__      :     float = 0.8				,		UpperCamelCase__      :     int = 1				,		UpperCamelCase__      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,		UpperCamelCase__      :     float = 0.0				,		UpperCamelCase__      :     int = 50				,		UpperCamelCase__      :     Optional[bool] = None				,		UpperCamelCase__      :     Optional[str] = "pil"				,		UpperCamelCase__      :     bool = True				,		)				->					Union[ImagePipelineOutput, Tuple]:
								'''simple docstring'''







								self.check_inputs(UpperCamelCase__      )

								# 2. Preprocess image
								UpperCAmelCase         =     preprocess(UpperCamelCase__      )

								# 3. set timesteps
								self.scheduler.set_timesteps(UpperCamelCase__				,		device=self.device      )
								UpperCAmelCase       ,		UpperCAmelCase         =     self.get_timesteps(UpperCamelCase__				,		UpperCamelCase__				,		self.device      )
								UpperCAmelCase         =     timesteps[:1].repeat(UpperCamelCase__      )

								# 4. Prepare latent variables
								UpperCAmelCase         =     self.prepare_latents(UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		self.unet.dtype				,		self.device				,		UpperCamelCase__      )
								UpperCAmelCase         =     latents

								# 5. Denoising loop
								for t in self.progress_bar(UpperCamelCase__      ):
														# 1. predict noise model_output
														UpperCAmelCase         =     self.unet(UpperCamelCase__				,		UpperCamelCase__      ).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
														UpperCAmelCase         =     self.scheduler.step(
														    UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__				,		eta=UpperCamelCase__				,		use_clipped_model_output=UpperCamelCase__				,		generator=UpperCamelCase__				,		).prev_sample

								UpperCAmelCase         =     (image / 2 + 0.5).clamp(0				,		1      )
								UpperCAmelCase         =     image.cpu().permute(0				,		2				,		3				,		1      ).numpy()
								if output_type == "pil":
														UpperCAmelCase         =     self.numpy_to_pil(UpperCamelCase__      )

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

								return ImagePipelineOutput(images=UpperCamelCase__      )








def    lowerCamelCase_(lowerCamelCase_ = 600_851_475_143    ) ->       int:

						try:
												UpperCAmelCase         =     int(lowerCamelCase_    )
						except (TypeError, ValueError):
												raise TypeError("Parameter n must be int or castable to int."    )
						if n <= 0:
												raise ValueError("Parameter n must be greater than or equal to one."    )
						UpperCAmelCase         =     1
						UpperCAmelCase         =     2
						while i * i <= n:
												while n % i == 0:
																		UpperCAmelCase         =     i
																		n //= i
												i += 1
						if n > 1:
												UpperCAmelCase         =     n
						return int(lowerCamelCase_    )


if __name__ == "__main__":
		print(F'''{solution() = }''')



