


'''simple docstring'''




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_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."""
       )
       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=512,
           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 	a_					( lowerCamelCase	:     str				):
          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)

       __snake_case    						=parser.parse_args()

       __snake_case    						=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'''




def 	a_					( lowerCamelCase	:     Optional[Any]				):
   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]





def 	a_					( lowerCamelCase	:     dict[int, list[int]]				):
   lowerCAmelCase     =			0
   lowerCAmelCase     =			len(lowerCamelCase				)  # No of vertices in graph
   lowerCAmelCase     =			[0] * n
   lowerCAmelCase     =			[False] * n

   def dfs(lowerCamelCase	:     Tuple	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str				):
      lowerCAmelCase     =			True
      lowerCAmelCase     =			id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, id_				)
            lowerCAmelCase     =			min(low[at]	, low[to]				)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)				)
         else:
            # This edge is a back edge and cannot be a bridge
            lowerCAmelCase     =			min(low[at]	, low[to]				)

   lowerCAmelCase     =			[]
   for i in range(lowerCamelCase				):
      if not visited[i]:
         dfs(lowerCamelCase	, -1	, lowerCamelCase	, id_				)
   return bridges


if __name__ == "__main__":
       import doctest

       doctest.testmod()





