






'''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							:							Tuple  							=		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							:							Optional[int]  							=		parser.parse_args()

	__A							:							List[str]  							=		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)









'''simple docstring'''





def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

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

  lowerCamelCase__  =     update_area_of_max_square(lowercase__				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     col + 1)
  lowerCamelCase__  =     update_area_of_max_square(row + 1				,     lowercase__)

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

 lowerCamelCase__  =     [0]
 update_area_of_max_square(0				,     0)
 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):

 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]

  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(lowercase__				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     col + 1				,     lowercase__)
  lowerCamelCase__  =     update_area_of_max_square_using_dp_array(row + 1				,     lowercase__				,     lowercase__)

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

 lowerCamelCase__  =     [0]
 lowerCamelCase__  =     [[-1] * cols for _ in range(lowercase__)]
 update_area_of_max_square_using_dp_array(0				,     0				,     lowercase__)

 return largest_square_area[0]






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [[0] * (cols + 1) for _ in range(rows + 1)]
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     dp_array[row][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col + 1]
   lowerCamelCase__  =     dp_array[row + 1][col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(dp_array[row][col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0

 return largest_square_area






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     [0] * (cols + 1)
 lowerCamelCase__  =     0
 for row in range(rows - 1				,     -1				,     -1):
  for col in range(cols - 1				,     -1				,     -1):
   lowerCamelCase__  =     current_row[col + 1]
   lowerCamelCase__  =     next_row[col + 1]
   lowerCamelCase__  =     next_row[col]

   if mat[row][col] == 1:
    lowerCamelCase__  =     1 + min(lowercase__				,     lowercase__				,     lowercase__)
    lowerCamelCase__  =     max(current_row[col]				,     lowercase__)
   else:
    lowerCamelCase__  =     0
  lowerCamelCase__  =     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]]))

