



"""simple docstring"""




import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 _lowerCAmelCase    : Tuple							=							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_12,
     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 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[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    : Union[str, Any]							=							parser.parse_args()

 _lowerCAmelCase    : str							=							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"""




from collections.abc import Callable






class      UpperCAmelCase_  :



      def __init__(					self					:     Dict					,     A					:     Callable | None = None						):
           # Stores actual heap items.
           _UpperCAmelCase      :     list        =   []
           # Stores indexes of each item for supporting updates and deletion.
           _UpperCAmelCase      :     dict        =   {}
           # Stores current size of heap.
           _UpperCAmelCase      :     Tuple        =   0
           # Stores function used to evaluate the score of an item on which basis ordering
           # will be done.
           _UpperCAmelCase      :     Any        =   key or (lambda A						: x)



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           return int((i - 1) / 2						) if i > 0 else None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   int(2 * i + 1						)
           return left if 0 < left < self.size else None



      def 						snake_case_ (					self					:     List[Any]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   int(2 * i + 2						)
           return right if 0 < right < self.size else None



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     int					,     A					:     int						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   (
               self.pos_map[self.arr[j][0]],
               self.pos_map[self.arr[i][0]],
           )
           # Then swap the items in the list.
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Any        =   self.arr[j], self.arr[i]



      def 						snake_case_ (					self					:     List[str]					,     A					:     int					,     A					:     int						):
           return self.arr[i][1] < self.arr[j][1]



      def 						snake_case_ (					self					:     Dict					,     A					:     int						):
           _UpperCAmelCase      :     str        =   self._left(A						)
           _UpperCAmelCase      :     str        =   self._right(A						)
           _UpperCAmelCase      :     List[Any]        =   i

           if left is not None and not self._cmp(A					,     A						):
                _UpperCAmelCase      :     Optional[int]        =   left
           if right is not None and not self._cmp(A					,     A						):
                _UpperCAmelCase      :     Any        =   right

           return valid_parent



      def 						snake_case_ (					self					:     Tuple					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   self._parent(A						)
           while parent is not None and not self._cmp(A					,     A						):
                self._swap(A					,     A						)
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   parent, self._parent(A						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     int						):
           _UpperCAmelCase      :     Tuple        =   self._get_valid_parent(A						)
           while valid_parent != index:
                self._swap(A					,     A						)
                _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   valid_parent, self._get_valid_parent(A						)



      def 						snake_case_ (					self					:     Dict					,     A					:     int					,     A					:     int						):
           if item not in self.pos_map:
                return
           _UpperCAmelCase      :     Any        =   self.pos_map[item]
           _UpperCAmelCase      :     Optional[int]        =   [item, self.key(A						)]
           # Make sure heap is right in both up and down direction.
           # Ideally only one of them will make any change.
           self._heapify_up(A						)
           self._heapify_down(A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     int						):
           if item not in self.pos_map:
                return
           _UpperCAmelCase      :     str        =   self.pos_map[item]
           del self.pos_map[item]
           _UpperCAmelCase      :     Tuple        =   self.arr[self.size - 1]
           _UpperCAmelCase      :     List[Any]        =   index
           self.size -= 1
           # Make sure heap is right in both up and down direction. Ideally only one
           # of them will make any change- so no performance loss in calling both.
           if self.size > index:
                self._heapify_up(A						)
                self._heapify_down(A						)



      def 						snake_case_ (					self					:     Any					,     A					:     int					,     A					:     int						):
           _UpperCAmelCase      :     Any        =   len(self.arr						)
           if arr_len == self.size:
                self.arr.append([item, self.key(A						)]						)
           else:
                _UpperCAmelCase      :     Any        =   [item, self.key(A						)]
           _UpperCAmelCase      :     List[Any]        =   self.size
           self.size += 1
           self._heapify_up(self.size - 1						)



      def 						snake_case_ (					self					:     Tuple						):
           return self.arr[0] if self.size else None



      def 						snake_case_ (					self					:     Any						):
           _UpperCAmelCase      :     Dict        =   self.get_top()
           if top_item_tuple:
                self.delete_item(top_item_tuple[0]						)
           return top_item_tuple







def 						__snake_case						(				)  ->       None:





     '''simple docstring'''



if __name__ == "__main__":
 import doctest

 doctest.testmod()
