

"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_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."
     )
     # !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.",
     )
     lowercase_			=  parser.parse_args()

     lowercase_			=  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 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


if __name__ == "__main__":
     print(f"""{solution() = }""")

