


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   list[list[int]]   ):

					def update_area_of_max_square(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int   ) -> int:
										# BASE CASE
										if row >= rows or col >= cols:
															return 0

										__a			:       Dict					    =  update_area_of_max_square(_SCREAMING_SNAKE_CASE						,      col + 1   )
										__a			:       Dict					    =  update_area_of_max_square(row + 1						,      col + 1   )
										__a			:       Optional[Any]					    =  update_area_of_max_square(row + 1						,      _SCREAMING_SNAKE_CASE   )

										if mat[row][col]:
															__a			:       str					    =  1 + min([right, diagonal, down]   )
															__a			:       int					    =  max(largest_square_area[0]						,      _SCREAMING_SNAKE_CASE   )
															return sub_problem_sol
										else:
															return 0

					__a			:       Any					    =  [0]
					update_area_of_max_square(0						,      0   )
					return largest_square_area[0]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   list[list[int]]   ):

					def update_area_of_max_square_using_dp_array(
					    _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   list[list[int]]   ) -> int:
										if row >= rows or col >= cols:
															return 0
										if dp_array[row][col] != -1:
															return dp_array[row][col]

										__a			:       Tuple					    =  update_area_of_max_square_using_dp_array(_SCREAMING_SNAKE_CASE						,      col + 1						,      _SCREAMING_SNAKE_CASE   )
										__a			:       Optional[int]					    =  update_area_of_max_square_using_dp_array(row + 1						,      col + 1						,      _SCREAMING_SNAKE_CASE   )
										__a			:       Any					    =  update_area_of_max_square_using_dp_array(row + 1						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )

										if mat[row][col]:
															__a			:       Union[str, Any]					    =  1 + min([right, diagonal, down]   )
															__a			:       Any					    =  max(largest_square_area[0]						,      _SCREAMING_SNAKE_CASE   )
															__a			:       Dict					    =  sub_problem_sol
															return sub_problem_sol
										else:
															return 0

					__a			:       List[str]					    =  [0]
					__a			:       List[Any]					    =  [[-1] * cols for _ in range(_SCREAMING_SNAKE_CASE   )]
					update_area_of_max_square_using_dp_array(0						,      0						,      _SCREAMING_SNAKE_CASE   )

					return largest_square_area[0]






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   list[list[int]]   ):
					__a			:       List[str]					    =  [[0] * (cols + 1) for _ in range(rows + 1   )]
					__a			:       List[Any]					    =  0
					for row in range(rows - 1						,      -1						,      -1   ):
										for col in range(cols - 1						,      -1						,      -1   ):
															__a			:       Optional[Any]					    =  dp_array[row][col + 1]
															__a			:       Union[str, Any]					    =  dp_array[row + 1][col + 1]
															__a			:       int					    =  dp_array[row + 1][col]

															if mat[row][col] == 1:
																				__a			:       Tuple					    =  1 + min(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																				__a			:       int					    =  max(dp_array[row][col]						,      _SCREAMING_SNAKE_CASE   )
															else:
																				__a			:       Union[str, Any]					    =  0

					return largest_square_area






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   int						,      _SCREAMING_SNAKE_CASE      :   list[list[int]]   ):
					__a			:       Tuple					    =  [0] * (cols + 1)
					__a			:       Dict					    =  [0] * (cols + 1)
					__a			:       Optional[int]					    =  0
					for row in range(rows - 1						,      -1						,      -1   ):
										for col in range(cols - 1						,      -1						,      -1   ):
															__a			:       Optional[Any]					    =  current_row[col + 1]
															__a			:       Union[str, Any]					    =  next_row[col + 1]
															__a			:       Dict					    =  next_row[col]

															if mat[row][col] == 1:
																				__a			:       int					    =  1 + min(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
																				__a			:       Union[str, Any]					    =  max(current_row[col]						,      _SCREAMING_SNAKE_CASE   )
															else:
																				__a			:       Union[str, Any]					    =  0
										__a			:       List[str]					    =  current_row

					return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))







'''simple docstring'''



import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort





@nightly
@require_onnxruntime
@require_torch_gpu
class 							__UpperCamelCase				(		unittest.TestCase					):




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Dict					    =  ort.SessionOptions()
						__a			:       Dict					    =  False
						return options





	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Tuple					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo.png'							)
						__a			:       int					    =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'							)
						__a			:       Dict					    =  load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
						    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'							)

						# using the PNDM scheduler by default
						__a			:       str					    =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    'CompVis/stable-diffusion-v1-4'						,				revision='onnx'						,				safety_checker=__a						,				feature_extractor=__a						,				provider=self.gpu_provider						,				sess_options=self.gpu_options						,				)
						pipe.set_progress_bar_config(disable=__a							)

						__a			:       Tuple					    =  'A red cat sitting on a park bench'

						__a			:       int					    =  np.random.RandomState(0							)
						__a			:       Tuple					    =  pipe(
						    prompt=__a						,				image=__a						,				mask_image=__a						,				strength=0.75						,				guidance_scale=7.5						,				num_inference_steps=15						,				generator=__a						,				output_type='np'						,				)

						__a			:       Tuple					    =  output.images[0]

						assert image.shape == (512, 512, 3)
						assert np.abs(expected_image - image							).max() < 1E-2



