





'''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   	=    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   	=    parser.parse_args()

    a   	=    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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->Optional[Any]:






	'''simple docstring'''






	a :						Any      =							[]
	a :						List[str]      =							set({"(", "[", "{"}   )
	a :						int      =							set({")", "]", "}"}   )
	a :						int      =							{"{": "}", "[": "]", "(": ")"}

	for i in range(len(_lowercase   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(_lowercase   ) == 0 or (len(_lowercase   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(_lowercase   ) == 0






def  _SCREAMING_SNAKE_CASE						(      )					->Tuple:






	'''simple docstring'''






	a :						Any      =							input("Enter sequence of brackets: "   )
	if is_balanced(_lowercase   ):
		print(_lowercase       ,			"is balanced"   )
	else:
		print(_lowercase       ,			"is not balanced"   )


if __name__ == "__main__":
		main()


