





'''simple docstring'''


import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCAmelCase :					str 							=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 							UpperCAmelCase_  (						__lowerCamelCase : Dict			):
    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 :					List[str] 							=parser.parse_args()

 lowerCAmelCase :					Any 							=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 							UpperCAmelCase_  (						__lowerCamelCase : dict			):
   lowercase_      :set[int]         =			set()
   # To detect a back edge, keep track of vertices currently in the recursion stack
   lowercase_      :set[int]         =			set()
   return any(
       node not in visited and depth_first_search(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			)
       for node in graph			)
def 							UpperCAmelCase_  (						__lowerCamelCase : dict     ,__lowerCamelCase : int     ,__lowerCamelCase : set     ,__lowerCamelCase : set			):
   visited.add(__lowerCamelCase			)
   rec_stk.add(__lowerCamelCase			)

   for node in graph[vertex]:
      if node not in visited:
         if depth_first_search(__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase     ,__lowerCamelCase			):
            return True
      elif node in rec_stk:
         return True

    # The node needs to be removed from recursion stack before function ends
   rec_stk.remove(__lowerCamelCase			)
   return False


if __name__ == "__main__":
 from doctest import testmod

 testmod()


