
"""simple docstring"""

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      UpperCamelCase     :    List[str]       =			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     :    Tuple       =			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)





"""simple docstring"""

def 							A						(    snake_case				:list[list[int]]    , snake_case				:int    , snake_case				:int    , snake_case				:list[int]		)				->     bool:

 # 1. Validate that path exists between current and next vertices
 if graph[path[curr_ind - 1]][next_ver] == 0:
  return False

 # 2. Validate that next vertex is not already in path
 return not any(vertex == next_ver for vertex in path		)






def 							A						(    snake_case				:list[list[int]]    , snake_case				:list[int]    , snake_case				:int		)				->     bool:

 # Base Case
 if curr_ind == len(snake_case		):
  # return whether path exists between current and starting vertices
  return graph[path[curr_ind - 1]][path[0]] == 1

 # Recursive Step
 for next_ver in range(0    , len(snake_case		)		):
  if valid_connection(snake_case    , snake_case    , snake_case    , snake_case		):
   # Insert current vertex  into path as next transition
   __UpperCamelCase      =	next_ver
   # Validate created path
   if util_hamilton_cycle(snake_case    , snake_case    , curr_ind + 1		):
    return True
   # Backtrack
   __UpperCamelCase      =	-1
 return False






def 							A						(    snake_case				:list[list[int]]    , snake_case				:int = 0		)				->     list[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		) + 1)
 # initialize start and end of path with starting index
 __UpperCamelCase      =	__UpperCamelCase      =	start_index
 # evaluate and if we find answer return path either return empty array
 return path if util_hamilton_cycle(snake_case    , snake_case    , 1		) else []



