"""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


__lowerCamelCase											=  transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if isinstance(UpperCamelCase__       ,    torch.Tensor				):
														return image
							elif isinstance(UpperCamelCase__       ,    PIL.Image.Image				):
														A__	     =							[image]

							A__	     =							[trans(img.convert('RGB'				)				) for img in image]
							A__	     =							torch.stack(UpperCamelCase__				)
							return image


class     UpperCamelCase__(			__A ):





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[Any]:
													super().__init__()

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

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





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Any:
													if strength < 0 or strength > 1:
																				raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )					->				Optional[int]:
													# get the original timestep using init_timestep
													A__	     =							min(int(num_inference_steps * strength     )		,__UpperCAmelCase     )

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

													return timesteps, num_inference_steps - t_start





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase=None     )					->				Optional[int]:
													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     )}'''     )

													A__	     =							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.'''     )

													A__	     =							init_latents.shape
													A__	     =							randn_tensor(__UpperCAmelCase		,generator=__UpperCAmelCase		,device=__UpperCAmelCase		,dtype=__UpperCAmelCase     )

													# get latents
													print('add noise to latents at timestep'		,__UpperCAmelCase     )
													A__	     =							self.scheduler.add_noise(__UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase     )
													A__	     =							init_latents

													return latents





						@torch.no_grad()
						def __call__(       self		,__UpperCAmelCase = None		,__UpperCAmelCase = 0.8		,__UpperCAmelCase = 1		,__UpperCAmelCase = None		,__UpperCAmelCase = 0.0		,__UpperCAmelCase = 50		,__UpperCAmelCase = None		,__UpperCAmelCase = "pil"		,__UpperCAmelCase = True		,)					->				Union[ImagePipelineOutput, Tuple]:
													self.check_inputs(__UpperCAmelCase     )

													# 2. Preprocess image
													A__	     =							preprocess(__UpperCAmelCase     )

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

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

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

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

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

													return ImagePipelineOutput(images=__UpperCAmelCase     )






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							if len(UpperCamelCase__				) <= 1:
														return [tuple(UpperCamelCase__				)]

							A__	     =							[]

							def generate(UpperCamelCase__       ,    UpperCamelCase__				):
														if k == 1:
																					res.append(tuple(arr[:]				)				)
																					return

														generate(k - 1       ,    UpperCamelCase__				)

														for i in range(k - 1				):
																					if k % 2 == 0:  # k is even
																												A__     ,  A__	     =							arr[k - 1], arr[i]
																					else:  # k is odd
																												A__     ,  A__	     =							arr[k - 1], arr[0]
																					generate(k - 1       ,    UpperCamelCase__				)

							generate(len(UpperCamelCase__				)       ,    UpperCamelCase__				)
							return res


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(heaps(arr))





