import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class __snake_case    (						unittest.TestCase		):




   @property
   def      __a						(   self      :    Any				):



     """simple docstring"""






     torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				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      __a						(   self      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				self.dummy_uncond_unet
     SCREAMING_SNAKE_CASE__      							=				PNDMScheduler()

     SCREAMING_SNAKE_CASE__      							=				PNDMPipeline(unet=_lowercase	,     scheduler=_lowercase				)
     pndm.to(_lowercase				)
     pndm.set_progress_bar_config(disable=_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pndm(generator=_lowercase	,     num_inference_steps=20	,     output_type="""numpy"""				).images

     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pndm(generator=_lowercase	,     num_inference_steps=20	,     output_type="""numpy"""	,     return_dict=_lowercase				)[0]

     SCREAMING_SNAKE_CASE__      							=				image[0, -3:, -3:, -1]
     SCREAMING_SNAKE_CASE__      							=				image_from_tuple[0, -3:, -3:, -1]

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




   def      __a						(   self      :    Optional[Any]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				"""google/ddpm-cifar10-32"""

     SCREAMING_SNAKE_CASE__      							=				UNetaDModel.from_pretrained(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				PNDMScheduler()

     SCREAMING_SNAKE_CASE__      							=				PNDMPipeline(unet=_lowercase	,     scheduler=_lowercase				)
     pndm.to(_lowercase				)
     pndm.set_progress_bar_config(disable=_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				torch.manual_seed(0				)
     SCREAMING_SNAKE_CASE__      							=				pndm(generator=_lowercase	,     output_type="""numpy"""				).images

     SCREAMING_SNAKE_CASE__      							=				image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__      							=				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]				)

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

from __future__ import annotations

__lowerCamelCase      :			Tuple							       =		list[list[int]]

# assigning initial values to the grid
__lowerCamelCase      :			Matrix							       =		[
    [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      :			Matrix							       =		[
    [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:







  """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      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> tuple[int, int] | None:







  """simple docstring"""





  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:







  """simple docstring"""





  if location := find_empty_location(__UpperCamelCase     ):
    SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				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     ):
      SCREAMING_SNAKE_CASE__      							=				digit

      if sudoku(__UpperCamelCase     ) is not None:
        return grid

      SCREAMING_SNAKE_CASE__      							=				0

  return None



def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Matrix     )							-> None:







  """simple docstring"""





  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      :			str							       =		sudoku(example_grid)
      if solution is not None:
         print_solution(solution)
      else:
         print('''Cannot find a solution.''')
