





'''simple docstring'''

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			__lowerCamelCase : Any											=			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 							UpperCAmelCase_    (	lowerCAmelCase_							):

							"""simple docstring"""






							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)

			__lowerCamelCase : List[Any]											=			parser.parse_args()

			__lowerCamelCase : int											=			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)











'''simple docstring'''

import functools







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if not isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for day in days							):
								raise ValueError("The parameter days should be a list of integers"							)

				if len(lowerCAmelCase_							) != 3 or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for cost in costs							):
								raise ValueError("The parameter costs should be a list of three integers"							)

				if len(lowerCAmelCase_							) == 0:
								return 0

				if min(lowerCAmelCase_							) <= 0:
								raise ValueError("All days elements should be greater than 0"							)

				if max(lowerCAmelCase_							) >= 366:
								raise ValueError("All days elements should be less than 366"							)

				lowercase         = set(lowerCAmelCase_							)

				@functools.cache
				def dynamic_programming(lowerCAmelCase_							) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1							)

								return min(
								    costs[0] + dynamic_programming(index + 1							)		,							costs[1] + dynamic_programming(index + 7							)		,							costs[2] + dynamic_programming(index + 30							)		,							)

				return dynamic_programming(1							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




