"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
       __SCREAMING_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     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
            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)

       __SCREAMING_SNAKE_CASE								=parser.parse_args()

       __SCREAMING_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"""







from random import randint, random



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  bool = False  ,    __SCREAMING_SNAKE_CASE							:  int = 5  ,    ):
     lowercase_			:       Union[str, Any]									=			[[-1] * number_of_cells]  # Create a highway without any car
     lowercase_			:       str									=			0
     lowercase_			:       List[Any]									=			max(__SCREAMING_SNAKE_CASE  ,    0   )
     while i < number_of_cells:
          lowercase_			:       Union[str, Any]									=			(
              randint(0  ,    __SCREAMING_SNAKE_CASE   ) if random_speed else initial_speed
          )  # Place the cars
          i += (
              randint(1  ,    max_speed * 2   ) if random_frequency else frequency
          )  # Arbitrary number, may need tuning
     return highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			0
     lowercase_			:       Dict									=			highway_now[car_index + 1 :]
     for cell in range(len(__SCREAMING_SNAKE_CASE   )   ):  # May need a better name for this
          if cells[cell] != -1:  # If the cell is not empty then
               return distance  # we have the distance we wanted
          distance += 1
     # Here if the car is near the end of the highway
     return distance + get_distance(__SCREAMING_SNAKE_CASE  ,    -1   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[Any]									=			len(__SCREAMING_SNAKE_CASE   )
     # Beforce calculations, the highway is empty
     lowercase_			:       Dict									=			[-1] * number_of_cells

     for car_index in range(__SCREAMING_SNAKE_CASE   ):
          if highway_now[car_index] != -1:
               # Add 1 to the current speed of the car and cap the speed
               lowercase_			:       Any									=			min(highway_now[car_index] + 1  ,    __SCREAMING_SNAKE_CASE   )
               # Number of empty cell before the next car
               lowercase_			:       List[Any]									=			get_distance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ) - 1
               # We can't have the car causing an accident
               lowercase_			:       Dict									=			min(next_highway[car_index]  ,    __SCREAMING_SNAKE_CASE   )
               if random() < probability:
                    # Randomly, a driver will slow down
                    lowercase_			:       Optional[Any]									=			max(next_highway[car_index] - 1  ,    0   )
     return next_highway



def     lowercase__( __SCREAMING_SNAKE_CASE							:  list  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  float  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       Union[str, Any]									=			len(highway[0]   )

     for i in range(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			update(highway[i]  ,    __SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
          lowercase_			:       List[str]									=			[-1] * number_of_cells

          for car_index in range(__SCREAMING_SNAKE_CASE   ):
               lowercase_			:       Optional[Any]									=			next_speeds_calculated[car_index]
               if speed != -1:
                    # Change the position based on the speed (with % to create the loop)
                    lowercase_			:       Any									=			(car_index + speed) % number_of_cells
                    # Commit the change of position
                    lowercase_			:       Optional[Any]									=			speed
          highway.append(__SCREAMING_SNAKE_CASE   )

     return highway


if __name__ == "__main__":
       import doctest

       doctest.testmod()

