'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         super().__init__()

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

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




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



         '''simple docstring'''



         if isinstance(self.unet.config.sample_size					,lowerCamelCase_							):
            UpperCAmelCase__							:				Union[str, Any]   				=  (
                batch_size,
                self.unet.config.in_channels,
                self.unet.config.sample_size,
                self.unet.config.sample_size,
            )
         else:
            UpperCAmelCase__							:				Any   				=  (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

         UpperCAmelCase__							:				Optional[int]   				=  randn_tensor(lowerCamelCase_					,generator=lowerCamelCase_					,device=self.device					,dtype=self.unet.dtype							)

         # set step values
         self.scheduler.set_timesteps(lowerCamelCase_							)

         for t in self.progress_bar(self.scheduler.timesteps							):
            # 1. predict noise model_output
            UpperCAmelCase__							:				Optional[int]   				=  self.unet(lowerCamelCase_					,lowerCamelCase_							).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__							:				int   				=  self.scheduler.step(
                lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,eta=lowerCamelCase_					,use_clipped_model_output=lowerCamelCase_					,generator=lowerCamelCase_							).prev_sample

         UpperCAmelCase__							:				Union[str, Any]   				=  (image / 2 + 0.5).clamp(0					,1							)
         UpperCAmelCase__							:				Optional[int]   				=  image.cpu().permute(0					,2					,3					,1							).numpy()
         if output_type == "pil":
            UpperCAmelCase__							:				str   				=  self.numpy_to_pil(lowerCamelCase_							)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=lowerCamelCase_							)





'''simple docstring'''

def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''

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


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




