import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_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.'''
   )
   # !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.''',
   )
   __lowerCamelCase    :				Any							     =	parser.parse_args()

   __lowerCamelCase    :				List[str]							     =	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)

from __future__ import annotations



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list			)							->      float:







  """simple docstring"""





  if not nums:
    raise ValueError("""List is empty"""			)
  return sum(__UpperCamelCase			) / len(__UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
