
import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					_UpperCAmelCase					: int      =       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.",
					)
					_UpperCAmelCase					: Optional[int]      =       parser.parse_args()

					_UpperCAmelCase					: Union[str, 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)


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
