


'''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    :Any				       =     transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     if isinstance(_lowercase    ,      torch.Tensor						):
          return image
     elif isinstance(_lowercase    ,      PIL.Image.Image						):
          __UpperCAmelCase			:				List[str]													=    [image]

     __UpperCAmelCase			:				Tuple													=    [trans(img.convert('''RGB'''						)						) for img in image]
     __UpperCAmelCase			:				List[Any]													=    torch.stack(_lowercase						)
     return image



class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	List[str]							,				snake_case  :	int							,				snake_case  :	Optional[Any]    )		->							List[str]:
          super().__init__()

          # make sure scheduler can always be converted to DDIM
          __UpperCAmelCase			:				List[Any]													=    DDIMScheduler.from_config(scheduler.config    )

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

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	Dict    )		->							List[Any]:
          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  :	Optional[int]							,				snake_case  :	List[Any]							,				snake_case  :	List[str]    )		->							Optional[Any]:
          # get the original timestep using init_timestep
          __UpperCAmelCase			:				Optional[int]													=    min(int(num_inference_steps * strength    )							,				snake_case    )

          __UpperCAmelCase			:				List[Any]													=    max(num_inference_steps - init_timestep							,				0    )
          __UpperCAmelCase			:				str													=    self.scheduler.timesteps[t_start:]

          return timesteps, num_inference_steps - t_start

     def 				lowerCamelCase__				(       self  :	Dict							,				snake_case  :	str							,				snake_case  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	str							,				snake_case  :	Optional[int]							,				snake_case  :	Tuple=None    )		->							List[str]:
          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    )}'    )

          __UpperCAmelCase			:				List[str]													=    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.'    )

          __UpperCAmelCase			:				Dict													=    init_latents.shape
          __UpperCAmelCase			:				Any													=    randn_tensor(snake_case							,				generator=snake_case							,				device=snake_case							,				dtype=snake_case    )

          # get latents
          print('''add noise to latents at timestep'''							,				snake_case    )
          __UpperCAmelCase			:				List[Any]													=    self.scheduler.add_noise(snake_case							,				snake_case							,				snake_case    )
          __UpperCAmelCase			:				Dict													=    init_latents

          return latents






     @torch.no_grad()
     def __call__(       self  :	List[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
          __UpperCAmelCase			:				Optional[Any]													=    preprocess(snake_case    )

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

          # 4. Prepare latent variables
          __UpperCAmelCase			:				List[str]													=    self.prepare_latents(snake_case							,				snake_case							,				snake_case							,				self.unet.dtype							,				self.device							,				snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    latents

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

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

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

          return ImagePipelineOutput(images=snake_case    )



'''simple docstring'''



def 			_a			(		_lowercase      :			list[list[int]]    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			set						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    len(_lowercase						), len(grid[0]						)
     if (
         min(_lowercase    ,      _lowercase						) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)						)

     __UpperCAmelCase			:				Optional[Any]													=    0
     count += depth_first_search(_lowercase    ,      row + 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      row - 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col + 1    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col - 1    ,      _lowercase						)

     visit.remove((row, col)						)
     return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()