




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
  _snake_case										=       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.",
  )
  _snake_case										=       parser.parse_args()

  _snake_case										=       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 				__lowerCamelCase						( UpperCAmelCase_						:		dict ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a							,				a						:Optional[Any] 							=       set(UpperCAmelCase_						), [start]

      while stack:
            a						:Optional[int] 							=       stack.pop()
            explored.add(UpperCAmelCase_						)
            # Differences from BFS:
            # 1) pop last element instead of first one
            # 2) add adjacent elements to stack without exploring them
            for adj in reversed(graph[v]						):
                  if adj not in explored:
                        stack.append(UpperCAmelCase_						)
      return explored


snake_case				:    Optional[int]       				=     {
    '''A''': ['''B''', '''C''', '''D'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F'''],
    '''D''': ['''B''', '''D'''],
    '''E''': ['''B''', '''F'''],
    '''F''': ['''C''', '''E''', '''G'''],
    '''G''': ['''F'''],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, '''A'''))





