




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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict				)  ->							Optional[Any]:
    UpperCAmelCase_										=					image.size
    UpperCAmelCase_										=					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    UpperCAmelCase_										=					image.resize((w, h)	,					resample=PIL_INTERPOLATION['''lanczos''']				)
    UpperCAmelCase_										=					np.array(_UpperCAmelCase				).astype(np.floataa				) / 255.0
    UpperCAmelCase_										=					image[None].transpose(0	,					3	,					1	,					2				)
    UpperCAmelCase_										=					torch.from_numpy(_UpperCAmelCase				)
    return 2.0 * image - 1.0



class       a					(       SCREAMING_SNAKE_CASE__      ):

     '''simple docstring'''





     def __init__(     self       :      Optional[int] ,   __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       :      List[Any] ,   __snake_case       :      Union[torch.Tensor, PIL.Image.Image] = None ,   __snake_case       :      Optional[int] = 1 ,   __snake_case       :      Optional[int] = 1_00 ,   __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    ):
             UpperCAmelCase_										=					1
         elif isinstance(__snake_case ,   torch.Tensor    ):
             UpperCAmelCase_										=					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    ):
             UpperCAmelCase_										=					preprocess(__snake_case    )

         UpperCAmelCase_										=					image.shape[-2:]

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

         UpperCAmelCase_										=					randn_tensor(__snake_case ,   generator=__snake_case ,   device=self.device ,   dtype=__snake_case    )

         UpperCAmelCase_										=					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    )
         UpperCAmelCase_										=					self.scheduler.timesteps

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_										=					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]
         UpperCAmelCase_										=					'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
         UpperCAmelCase_										=					{}
         if accepts_eta:
             UpperCAmelCase_										=					eta

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

         # decode the image latents with the VQVAE
         UpperCAmelCase_										=					self.vqvae.decode(__snake_case    ).sample
         UpperCAmelCase_										=					torch.clamp(__snake_case ,   -1.0 ,   1.0    )
         UpperCAmelCase_										=					image / 2 + 0.5
         UpperCAmelCase_										=					image.cpu().permute(0 ,   2 ,   3 ,   1    ).numpy()

         if output_type == "pil":
             UpperCAmelCase_										=					self.numpy_to_pil(__snake_case    )

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=__snake_case    )











from __future__ import annotations

_lowerCamelCase      			=						list[list[int]]

# assigning initial values to the grid
_lowerCamelCase      			=						[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
_lowerCamelCase      			=						[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Matrix	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							bool:
    for i in range(9				):
        if grid[row][i] == n or grid[i][column] == n:
            return False

    for i in range(3				):
        for j in range(3				):
            if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
                return False

    return True




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Matrix				)  ->							tuple[int, int] | None:
    for i in range(9				):
        for j in range(9				):
            if grid[i][j] == 0:
                return i, j
    return None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Matrix				)  ->							Matrix | None:
    if location := find_empty_location(__UpperCamelCase				):
        UpperCAmelCase_			,							UpperCAmelCase_										=					location
    else:
        # If the location is ``None``, then the grid is solved.
        return grid

    for digit in range(1	,					10				):
        if is_safe(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				):
            UpperCAmelCase_										=					digit

            if sudoku(__UpperCamelCase				) is not None:
                return grid

            UpperCAmelCase_										=					0

    return None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Matrix				)  ->							None:
    for row in grid:
        for cell in row:
            print(__UpperCamelCase	,					end=''' '''				)
        print()


if __name__ == "__main__":
 # make a copy of grid so that you can compare with the unmodified grid
 for example_grid in (initial_grid, no_solution):
  print('\nExample grid:\n' + '=' * 20)
  print_solution(example_grid)
  print('\nExample grid solution:')
  _lowerCamelCase      			=						sudoku(example_grid)
  if solution is not None:
   print_solution(solution)
  else:
   print('Cannot find a solution.')





