






def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	int:

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

														lowerCamelCase__ :     Union[str, Any]	  =	update_area_of_max_square(UpperCamelCase    ,      col + 1				)
														lowerCamelCase__ :     Tuple	  =	update_area_of_max_square(row + 1    ,      col + 1				)
														lowerCamelCase__ :     str	  =	update_area_of_max_square(row + 1    ,      UpperCamelCase				)

														if mat[row][col]:
																					lowerCamelCase__ :     Optional[Any]	  =	1 + min([right, diagonal, down]				)
																					lowerCamelCase__ :     Optional[Any]	  =	max(largest_square_area[0]    ,      UpperCamelCase				)
																					return sub_problem_sol
														else:
																					return 0

							lowerCamelCase__ :     Optional[int]	  =	[0]
							update_area_of_max_square(0    ,      0				)
							return largest_square_area[0]





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	int:

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

														lowerCamelCase__ :     Union[str, Any]	  =	update_area_of_max_square_using_dp_array(UpperCamelCase    ,      col + 1    ,      UpperCamelCase				)
														lowerCamelCase__ :     List[Any]	  =	update_area_of_max_square_using_dp_array(row + 1    ,      col + 1    ,      UpperCamelCase				)
														lowerCamelCase__ :     List[Any]	  =	update_area_of_max_square_using_dp_array(row + 1    ,      UpperCamelCase    ,      UpperCamelCase				)

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

							lowerCamelCase__ :     Tuple	  =	[0]
							lowerCamelCase__ :     Optional[int]	  =	[[-1] * cols for _ in range(UpperCamelCase				)]
							update_area_of_max_square_using_dp_array(0    ,      0    ,      UpperCamelCase				)

							return largest_square_area[0]





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	int:
							lowerCamelCase__ :     str	  =	[[0] * (cols + 1) for _ in range(rows + 1				)]
							lowerCamelCase__ :     int	  =	0
							for row in range(rows - 1    ,      -1    ,      -1				):
														for col in range(cols - 1    ,      -1    ,      -1				):
																					lowerCamelCase__ :     Tuple	  =	dp_array[row][col + 1]
																					lowerCamelCase__ :     Optional[Any]	  =	dp_array[row + 1][col + 1]
																					lowerCamelCase__ :     int	  =	dp_array[row + 1][col]

																					if mat[row][col] == 1:
																												lowerCamelCase__ :     List[Any]	  =	1 + min(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												lowerCamelCase__ :     int	  =	max(dp_array[row][col]    ,      UpperCamelCase				)
																					else:
																												lowerCamelCase__ :     Union[str, Any]	  =	0

							return largest_square_area





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)				->	int:
							lowerCamelCase__ :     List[str]	  =	[0] * (cols + 1)
							lowerCamelCase__ :     Optional[int]	  =	[0] * (cols + 1)
							lowerCamelCase__ :     Dict	  =	0
							for row in range(rows - 1    ,      -1    ,      -1				):
														for col in range(cols - 1    ,      -1    ,      -1				):
																					lowerCamelCase__ :     List[str]	  =	current_row[col + 1]
																					lowerCamelCase__ :     Tuple	  =	next_row[col + 1]
																					lowerCamelCase__ :     Any	  =	next_row[col]

																					if mat[row][col] == 1:
																												lowerCamelCase__ :     Dict	  =	1 + min(UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase				)
																												lowerCamelCase__ :     str	  =	max(current_row[col]    ,      UpperCamelCase				)
																					else:
																												lowerCamelCase__ :     List[str]	  =	0
														lowerCamelCase__ :     Optional[Any]	  =	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 argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			_A     :      List[Any]   					=argparse.ArgumentParser()

			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

			parser.add_argument(
			    '''--txt2img_unclip''',
			    default='''kakaobrain/karlo-v1-alpha''',
			    type=str,
			    required=False,
			    help='''The pretrained txt2img unclip.''',
			)

			_A     :      Dict   					=parser.parse_args()

			_A     :      List[str]   					=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

			_A     :      Any   					=CLIPImageProcessor()
			_A     :      Union[str, Any]   					=CLIPVisionModelWithProjection.from_pretrained('''openai/clip-vit-large-patch14''')

			_A     :      Union[str, Any]   					=UnCLIPImageVariationPipeline(
			    decoder=txtaimg.decoder,
			    text_encoder=txtaimg.text_encoder,
			    tokenizer=txtaimg.tokenizer,
			    text_proj=txtaimg.text_proj,
			    feature_extractor=feature_extractor,
			    image_encoder=image_encoder,
			    super_res_first=txtaimg.super_res_first,
			    super_res_last=txtaimg.super_res_last,
			    decoder_scheduler=txtaimg.decoder_scheduler,
			    super_res_scheduler=txtaimg.super_res_scheduler,
			)

			imgaimg.save_pretrained(args.dump_path)

