
'''simple docstring'''







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser()

  parser.add_argument(
      """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
  )
  parser.add_argument(
      """--original_config_file""",
      type=str,
      required=True,
      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(
      """--image_size""",
      default=5_1_2,
      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(
      """--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.)""")





  def 					UpperCAmelCase_		(__a      :    int ):






     """simple docstring"""

     if string == "True":
        return True
     elif string == "False":
        return False
     else:
        raise ValueError(f"""could not parse string as bool {string}""" )

  parser.add_argument(
      """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
  )

  parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

  __lowerCAmelCase         =    parser.parse_args()

  __lowerCAmelCase         =    download_controlnet_from_original_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      extract_ema=args.extract_ema,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      use_linear_projection=args.use_linear_projection,
      cross_attention_dim=args.cross_attention_dim,
  )
  controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     print(self.vertex       )
     for i in self.vertex:
        print(_a							,' -> '							,' -> '.join([str(_a       ) for j in self.vertex[i]]       )       )


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

     # call the recursive helper function
     for i in range(len(self.vertex       )       ):
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

     # Recur for all the vertices that are adjacent to this node
     for i in self.vertex:
        if not visited[i]:
           self.dfs_recursive(_a							,_a       )


if __name__ == "__main__":
  __lowerCAmelCase         =    Graph()
  g.add_edge(0, 1)
  g.add_edge(0, 2)
  g.add_edge(1, 2)
  g.add_edge(2, 0)
  g.add_edge(2, 3)
  g.add_edge(3, 3)

  g.print_graph()
  print("""DFS:""")
  g.dfs()

  # OUTPUT:
  # 0  ->  1 -> 2
  # 1  ->  2
  # 2  ->  0 -> 3
  # 3  ->  3
  # DFS:
  #  0 1 2 3



