


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


snake_case							: Optional[int]      =   transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 							__lowercase     (				__lowerCAmelCase							:       Tuple     ):
							if isinstance(__lowerCAmelCase      ,					torch.Tensor     ):
														return image
							elif isinstance(__lowerCAmelCase      ,					PIL.Image.Image     ):
														a__    =  [image]

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







class  snake_case_     (lowerCamelCase_   ):
					def __init__(   self     :Optional[Any]    ,__snake_case     :Any    ,__snake_case     :List[Any]   )      ->					Optional[int]:
												super().__init__()

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

												self.register_modules(unet=__snake_case    ,scheduler=__snake_case   )
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :str   )      ->					Tuple:
												if strength < 0 or strength > 1:
																			raise ValueError(F'The value of strength should in [0.0, 1.0] but is {strength}'   )
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :int    ,__snake_case     :Optional[int]    ,__snake_case     :Tuple   )      ->					Any:
												# get the original timestep using init_timestep
												a__    =  min(int(num_inference_steps * strength   )    ,__snake_case   )

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

												return timesteps, num_inference_steps - t_start
					def 				lowerCamelCase__(   self     :Dict    ,__snake_case     :Tuple    ,__snake_case     :Optional[int]    ,__snake_case     :str    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Any=None   )      ->					Optional[int]:
												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   )}'   )

												a__    =  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.'   )

												a__    =  init_latents.shape
												a__    =  randn_tensor(__snake_case    ,generator=__snake_case    ,device=__snake_case    ,dtype=__snake_case   )

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

												return latents



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

												# 2. Preprocess image
												a__    =  preprocess(__snake_case   )

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

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

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

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

												return ImagePipelineOutput(images=__snake_case   )








def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if length <= 0 or not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise ValueError('Length must be a positive integer.'     )
							return [n * (2 * n - 1) for n in range(__lowerCAmelCase     )]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))




