



"""simple docstring"""

lowercase__			   =  {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowercase__			   =  ["a", "b", "c", "d", "e"]





def       __magic_name__    (   _lowerCamelCase				: Optional[Any]  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: Dict					):
							__a		:		List[str]									=       start
							# add current to visited
							visited.append(lowerCamelCase__					)
							__a		:		Optional[int]									=       edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					__a		:		int									=       topological_sort(lowerCamelCase__  ,       lowerCamelCase__  ,       lowerCamelCase__					)
    # if all neighbors visited add current to sort
							sort.append(lowerCamelCase__					)
							# if all vertices haven't been visited select a new one to visit
							if len(lowerCamelCase__					) != len(lowerCamelCase__					):
														for vertice in vertices:
																					if vertice not in visited:
																												__a		:		Tuple									=       topological_sort(lowerCamelCase__  ,       lowerCamelCase__  ,       lowerCamelCase__					)
    # return sort
							return sort


if __name__ == "__main__":
	lowercase__			   =  topological_sort("a", [], [])
	print(sort)




"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    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(
	    "--image_size",
	    default=512,
	    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(
	    "--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.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

