
'''simple docstring'''

import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       __snake_case:  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.",
       )
       __snake_case:  Optional[int]								=			parser.parse_args()

       __snake_case:  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 	_snake_case						(      A_       :    int     ,		A_       :    Optional[Any]     ,		A_       :    Optional[int]     ,		A_       :    Dict   ):
  """simple docstring"""







  a_      :	Optional[int]      =							[False] * len(A_   )
  a_      :	List[str]      =							[]
  queue.append(A_   )
  a_      :	Union[str, Any]      =							True

  while queue:
    a_      :	List[Any]      =							queue.pop(0   )
    for ind in range(len(graph[u]   )   ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(A_   )
        a_      :	Any      =							True
        a_      :	List[Any]      =							u

  return visited[t]






def 	_snake_case						(      A_       :    Union[str, Any]     ,		A_       :    Union[str, Any]     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	Dict      =							[-1] * (len(A_   ))
  a_      :	List[str]      =							0
  while bfs(A_     ,		A_     ,		A_     ,		A_   ):
    a_      :	Any      =							float("""Inf"""   )
    a_      :	Any      =							sink

    while s != source:
      # Find the minimum value in select path
      a_      :	Any      =							min(A_     ,		graph[parent[s]][s]   )
      a_      :	List[Any]      =							parent[s]

    max_flow += path_flow
    a_      :	Tuple      =							sink

    while v != source:
      a_      :	Tuple      =							parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      a_      :	List[Any]      =							parent[v]
  return max_flow


__snake_case:  List[str]								=			[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__snake_case							,__snake_case:  int								=			0, 5




print(ford_fulkerson(graph, source, sink))


