



'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__						,				snake_case__  :    Tuple    						=      image.size
      snake_case__						,				snake_case__  :    Union[str, Any]    						=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
      snake_case__  :    List[str]    						=      image.resize((w, h)     ,   resample=PIL_INTERPOLATION["""lanczos"""]		)
      snake_case__  :    Union[str, Any]    						=      np.array(_lowerCAmelCase		).astype(np.floataa		) / 255.0
      snake_case__  :    Optional[Any]    						=      image[None].transpose(0     ,   3     ,   1     ,   2		)
      snake_case__  :    Tuple    						=      torch.from_numpy(_lowerCAmelCase		)
      return 2.0 * image - 1.0







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def __init__(    self					:     List[str] ,     snake_case_					:     VQModel ,     snake_case_					:     UNetaDModel ,     snake_case_					:     Union[
            DDIMScheduler,
            PNDMScheduler,
            LMSDiscreteScheduler,
            EulerDiscreteScheduler,
            EulerAncestralDiscreteScheduler,
            DPMSolverMultistepScheduler,
        ] ,     ):
          super().__init__()
          self.register_modules(vqvae=snake_case_ ,     unet=snake_case_ ,     scheduler=snake_case_						)




    @torch.no_grad()
    def __call__(    self					:     Optional[Any] ,     snake_case_					:     Union[torch.Tensor, PIL.Image.Image] = None ,     snake_case_					:     Optional[int] = 1 ,     snake_case_					:     Optional[int] = 100 ,     snake_case_					:     Optional[float] = 0.0 ,     snake_case_					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None ,     snake_case_					:     Optional[str] = "pil" ,     snake_case_					:     bool = True ,     ):
          if isinstance(snake_case_ ,     PIL.Image.Image						):
                snake_case__  :    str    						=      1
          elif isinstance(snake_case_ ,     torch.Tensor						):
                snake_case__  :    List[Any]    						=      image.shape[0]
          else:
                raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case_						)}"						)

          if isinstance(snake_case_ ,     PIL.Image.Image						):
                snake_case__  :    Union[str, Any]    						=      preprocess(snake_case_						)

          snake_case__						,				snake_case__  :    Tuple    						=      image.shape[-2:]

          # in_channels should be 6: 3 for latents, 3 for low resolution image
          snake_case__  :    str    						=      (batch_size, self.unet.config.in_channels // 2, height, width)
          snake_case__  :    Dict    						=      next(self.unet.parameters()						).dtype

          snake_case__  :    int    						=      randn_tensor(snake_case_ ,     generator=snake_case_ ,     device=self.device ,     dtype=snake_case_						)

          snake_case__  :    Optional[Any]    						=      image.to(device=self.device ,     dtype=snake_case_						)

          # set timesteps and move to the correct device
          self.scheduler.set_timesteps(snake_case_ ,     device=self.device						)
          snake_case__  :    Optional[Any]    						=      self.scheduler.timesteps

          # scale the initial noise by the standard deviation required by the scheduler
          snake_case__  :    Optional[Any]    						=      latents * self.scheduler.init_noise_sigma

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
          # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
          # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
          # and should be between [0, 1]
          snake_case__  :    Union[str, Any]    						=      """eta""" in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
          snake_case__  :    Optional[int]    						=      {}
          if accepts_eta:
                snake_case__  :    Optional[Any]    						=      eta

          for t in self.progress_bar(snake_case_						):
                # concat latents and low resolution image in the channel dimension.
                snake_case__  :    int    						=      torch.cat([latents, image] ,     dim=1						)
                snake_case__  :    Optional[int]    						=      self.scheduler.scale_model_input(snake_case_ ,     snake_case_						)
                # predict the noise residual
                snake_case__  :    str    						=      self.unet(snake_case_ ,     snake_case_						).sample
                # compute the previous noisy sample x_t -> x_t-1
                snake_case__  :    Tuple    						=      self.scheduler.step(snake_case_ ,     snake_case_ ,     snake_case_ ,     **snake_case_						).prev_sample

          # decode the image latents with the VQVAE
          snake_case__  :    Optional[int]    						=      self.vqvae.decode(snake_case_						).sample
          snake_case__  :    Optional[int]    						=      torch.clamp(snake_case_ ,     -1.0 ,     1.0						)
          snake_case__  :    Dict    						=      image / 2 + 0.5
          snake_case__  :    Any    						=      image.cpu().permute(0 ,     2 ,     3 ,     1						).numpy()

          if output_type == "pil":
                snake_case__  :    Tuple    						=      self.numpy_to_pil(snake_case_						)

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=snake_case_						)







from __future__ import annotations

from PIL import Image

# Define glider example
A    :					Any =      [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
A    :					Optional[Any] =      [[0, 1, 0], [0, 1, 0], [0, 1, 0]]







def    __lowerCamelCase					(							__a   :list[list[int]]    )		->				list[list[int]]:






		"""simple docstring"""

		A__        =   []
		for i in range(len(__a    )    ):
				A__        =   []
				for j in range(len(cells[i]    )    ):
						# Get the number of live neighbours
						A__        =   0
						if i > 0 and j > 0:
								neighbour_count += cells[i - 1][j - 1]
						if i > 0:
								neighbour_count += cells[i - 1][j]
						if i > 0 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i - 1][j + 1]
						if j > 0:
								neighbour_count += cells[i][j - 1]
						if j < len(cells[i]    ) - 1:
								neighbour_count += cells[i][j + 1]
						if i < len(__a    ) - 1 and j > 0:
								neighbour_count += cells[i + 1][j - 1]
						if i < len(__a    ) - 1:
								neighbour_count += cells[i + 1][j]
						if i < len(__a    ) - 1 and j < len(cells[i]    ) - 1:
								neighbour_count += cells[i + 1][j + 1]

						# Rules of the game of life (excerpt from Wikipedia):
						# 1. Any live cell with two or three live neighbours survives.
						# 2. Any dead cell with three live neighbours becomes a live cell.
						# 3. All other live cells die in the next generation.
						#    Similarly, all other dead cells stay dead.
						A__        =   cells[i][j] == 1
						if (
						    (alive and 2 <= neighbour_count <= 3)
						    or not alive
						    and neighbour_count == 3
						):
								next_generation_row.append(1    )
						else:
								next_generation_row.append(0    )

				next_generation.append(__a    )
		return next_generation







def    __lowerCamelCase					(							__a   :list[list[int]]    ,     __a   :int    )		->				list[Image.Image]:






		"""simple docstring"""

		A__        =   []
		for _ in range(__a    ):
				# Create output image
				A__        =   Image.new("""RGB"""    ,     (len(cells[0]    ), len(__a    ))    )
				A__        =   img.load()

				# Save cells to image
				for x in range(len(__a    )    ):
						for y in range(len(cells[0]    )    ):
								A__        =   2_5_5 - cells[y][x] * 2_5_5
								A__        =   (colour, colour, colour)

        # Save image
				images.append(__a    )
				A__        =   new_generation(__a    )
		return images


if __name__ == "__main__":
					A    :					str =      generate_images(GLIDER, 1_6)
					images[0].save('''out.gif''', save_all=True, append_images=images[1:])



