






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       @property
       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            torch.manual_seed(0    )
            __snake_case			:List[Any]          =						UNetaDModel(
                block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
            return model



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						self.dummy_uncond_unet
            __snake_case			:Optional[Any]          =						ScoreSdeVeScheduler()

            __snake_case			:Dict          =						ScoreSdeVePipeline(unet=a__			,						scheduler=a__    )
            sde_ve.to(a__    )
            sde_ve.set_progress_bar_config(disable=a__    )

            __snake_case			:Any          =						torch.manual_seed(0    )
            __snake_case			:int          =						sde_ve(num_inference_steps=2			,						output_type="""numpy"""			,						generator=a__    ).images

            __snake_case			:str          =						torch.manual_seed(0    )
            __snake_case			:Any          =						sde_ve(num_inference_steps=2			,						output_type="""numpy"""			,						generator=a__			,						return_dict=a__    )[
                0
            ]

            __snake_case			:Union[str, Any]          =						image[0, -3:, -3:, -1]
            __snake_case			:Dict          =						image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            __snake_case			:Tuple          =						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2




@slow
@require_torch
class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						"""google/ncsnpp-church-256"""
            __snake_case			:Optional[Any]          =						UNetaDModel.from_pretrained(a__    )

            __snake_case			:Any          =						ScoreSdeVeScheduler.from_pretrained(a__    )

            __snake_case			:int          =						ScoreSdeVePipeline(unet=a__			,						scheduler=a__    )
            sde_ve.to(a__    )
            sde_ve.set_progress_bar_config(disable=a__    )

            __snake_case			:List[str]          =						torch.manual_seed(0    )
            __snake_case			:Optional[Any]          =						sde_ve(num_inference_steps=10			,						output_type="""numpy"""			,						generator=a__    ).images

            __snake_case			:Any          =						image[0, -3:, -3:, -1]

            assert image.shape == (1, 2_56, 2_56, 3)

            __snake_case			:List[str]          =						np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

            assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2










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   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     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   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for i in range(9  ):
          for j in range(9  ):
               if grid[i][j] == 0:
                    return i, j
     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,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.""")


