
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class 							UpperCAmelCase      (  A_			,			A_			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     IFInpaintingPipeline
	snake_case_					    =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
	snake_case_					    =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	snake_case_					    =     PipelineTesterMixin.required_optional_params - {"latents"}






	def 				UpperCamelCase_  (       self					: List[str] ):
					return self._get_dummy_components()






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Any							,A					: int=0 ):


					if str(snake_case__ ).startswith("mps" ):
									__A   			= torch.manual_seed(snake_case__ )
					else:
									__A   			= torch.Generator(device=snake_case__ ).manual_seed(snake_case__ )

					__A   			= floats_tensor((1, 3, 32, 32)							,rng=random.Random(snake_case__ ) ).to(snake_case__ )
					__A   			= floats_tensor((1, 3, 32, 32)							,rng=random.Random(snake_case__ ) ).to(snake_case__ )

					__A   			= {
					    "prompt": "A painting of a squirrel eating a burger",
					    "image": image,
					    "mask_image": mask_image,
					    "generator": generator,
					    "num_inference_steps": 2,
					    "output_type": "numpy",
					}

					return inputs






	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()							,reason="XFormers attention is only available with CUDA and `xformers` installed"							,)
	def 				UpperCamelCase_  (       self					: int ):
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )






	def 				UpperCamelCase_  (       self					: Dict ):
					self._test_save_load_optional_components()






	@unittest.skipIf(torch_device != "cuda"							,reason="float16 requires CUDA" )
	def 				UpperCamelCase_  (       self					: int ):
					super().test_save_load_floataa(expected_max_diff=1E-1 )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )






	def 				UpperCamelCase_  (       self					: Any ):
					self._test_save_load_local()







	def 				UpperCamelCase_  (       self					: str ):
					self._test_inference_batch_single_identical(
					    expected_max_diff=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					(    __lowerCamelCase     : Matrix				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int				,					__lowerCamelCase     : int    ):
       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					(    __lowerCamelCase     : Matrix    ):
       for i in range(9    ):
              for j in range(9    ):
                     if grid[i][j] == 0:
                            return i, j
       return None


def 			UpperCamelCase					(    __lowerCamelCase     : Matrix    ):
       if location := find_empty_location(__lowerCamelCase    ):
              snake_case				,					snake_case       :   Union[str, Any]							=      location
       else:
              # If the location is ``None``, then the grid is solved.
              return grid

       for digit in range(1				,					10    ):
              if is_safe(__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase				,					__lowerCamelCase    ):
                     snake_case       :   List[Any]							=      digit

                     if sudoku(__lowerCamelCase    ) is not None:
                            return grid

                     snake_case       :   Union[str, Any]							=      0

       return None


def 			UpperCamelCase					(    __lowerCamelCase     : Matrix    ):
       for row in grid:
              for cell in row:
                     print(__lowerCamelCase				,					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.""")





