def        lowerCamelCase_       (    _lowercase   ,      _lowercase   ,      _lowercase							)				->			int:

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

				__A				:       Tuple			    = update_area_of_max_square(__UpperCAmelCase   ,      col + 1							)
				__A				:       str			    = update_area_of_max_square(row + 1   ,      col + 1							)
				__A				:       Tuple			    = update_area_of_max_square(row + 1   ,      __UpperCAmelCase							)

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

		__A				:       Tuple			    = [0]
		update_area_of_max_square(0   ,      0							)
		return largest_square_area[0]



def        lowerCamelCase_       (    _lowercase   ,      _lowercase   ,      _lowercase							)				->			int:

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

				__A				:       List[Any]			    = update_area_of_max_square_using_dp_array(__UpperCAmelCase   ,      col + 1   ,      __UpperCAmelCase							)
				__A				:       Any			    = update_area_of_max_square_using_dp_array(row + 1   ,      col + 1   ,      __UpperCAmelCase							)
				__A				:       Tuple			    = update_area_of_max_square_using_dp_array(row + 1   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)

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

		__A				:       Optional[Any]			    = [0]
		__A				:       int			    = [[-1] * cols for _ in range(__UpperCAmelCase							)]
		update_area_of_max_square_using_dp_array(0   ,      0   ,      __UpperCAmelCase							)

		return largest_square_area[0]



def        lowerCamelCase_       (    _lowercase   ,      _lowercase   ,      _lowercase							)				->			int:
		__A				:       Optional[Any]			    = [[0] * (cols + 1) for _ in range(rows + 1							)]
		__A				:       Tuple			    = 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				:       Dict			    = dp_array[row + 1][col + 1]
						__A				:       Union[str, Any]			    = dp_array[row + 1][col]

						if mat[row][col] == 1:
								__A				:       Optional[Any]			    = 1 + min(__UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)
								__A				:       List[Any]			    = max(dp_array[row][col]   ,      __UpperCAmelCase							)
						else:
								__A				:       List[Any]			    = 0

		return largest_square_area



def        lowerCamelCase_       (    _lowercase   ,      _lowercase   ,      _lowercase							)				->			int:
		__A				:       str			    = [0] * (cols + 1)
		__A				:       Optional[int]			    = [0] * (cols + 1)
		__A				:       int			    = 0
		for row in range(rows - 1   ,      -1   ,      -1							):
				for col in range(cols - 1   ,      -1   ,      -1							):
						__A				:       Optional[int]			    = current_row[col + 1]
						__A				:       str			    = next_row[col + 1]
						__A				:       Optional[Any]			    = next_row[col]

						if mat[row][col] == 1:
								__A				:       Union[str, Any]			    = 1 + min(__UpperCAmelCase   ,      __UpperCAmelCase   ,      __UpperCAmelCase							)
								__A				:       List[Any]			    = max(current_row[col]   ,      __UpperCAmelCase							)
						else:
								__A				:       List[str]			    = 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 argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
							a    :							List[str]										=    argparse.ArgumentParser()

							parser.add_argument(
							    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
							)
							# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
							parser.add_argument(
							    '--original_config_file',
							    default=None,
							    type=str,
							    help='The YAML config file corresponding to the original architecture.',
							)
							parser.add_argument(
							    '--num_in_channels',
							    default=None,
							    type=int,
							    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
							)
							parser.add_argument(
							    '--scheduler_type',
							    default='pndm',
							    type=str,
							    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
							)
							parser.add_argument(
							    '--pipeline_type',
							    default=None,
							    type=str,
							    help=(
							        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
							        '. If `None` pipeline will be automatically inferred.'
							    ),
							)
							parser.add_argument(
							    '--image_size',
							    default=None,
							    type=int,
							    help=(
							        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
							        ' Base. Use 768 for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--prediction_type',
							    default=None,
							    type=str,
							    help=(
							        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
							        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
							    ),
							)
							parser.add_argument(
							    '--extract_ema',
							    action='store_true',
							    help=(
							        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
							        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
							        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
							    ),
							)
							parser.add_argument(
							    '--upcast_attention',
							    action='store_true',
							    help=(
							        'Whether the attention computation should always be upcasted. This is necessary when running stable'
							        ' diffusion 2.1.'
							    ),
							)
							parser.add_argument(
							    '--from_safetensors',
							    action='store_true',
							    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
							)
							parser.add_argument(
							    '--to_safetensors',
							    action='store_true',
							    help='Whether to store pipeline in safetensors format or not.',
							)
							parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
							parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
							parser.add_argument(
							    '--stable_unclip',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
							)
							parser.add_argument(
							    '--stable_unclip_prior',
							    type=str,
							    default=None,
							    required=False,
							    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
							)
							parser.add_argument(
							    '--clip_stats_path',
							    type=str,
							    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
							    required=False,
							)
							parser.add_argument(
							    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
							)
							parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
							parser.add_argument(
							    '--vae_path',
							    type=str,
							    default=None,
							    required=False,
							    help='Set to a path, hub id to an already converted vae to not convert it again.',
							)
							a    :							str										=    parser.parse_args()

							a    :							List[Any]										=    download_from_original_stable_diffusion_ckpt(
							    checkpoint_path=args.checkpoint_path,
							    original_config_file=args.original_config_file,
							    image_size=args.image_size,
							    prediction_type=args.prediction_type,
							    model_type=args.pipeline_type,
							    extract_ema=args.extract_ema,
							    scheduler_type=args.scheduler_type,
							    num_in_channels=args.num_in_channels,
							    upcast_attention=args.upcast_attention,
							    from_safetensors=args.from_safetensors,
							    device=args.device,
							    stable_unclip=args.stable_unclip,
							    stable_unclip_prior=args.stable_unclip_prior,
							    clip_stats_path=args.clip_stats_path,
							    controlnet=args.controlnet,
							    vae_path=args.vae_path,
							)

							if args.half:
														pipe.to(torch_dtype=torch.floataa)

							if args.controlnet:
														# only save the controlnet model
														pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
							else:
														pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

