






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__magic_name__:					List[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						(	_A			):






								"""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)

		__magic_name__:					Any							=parser.parse_args()

		__magic_name__:					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)








from decimal import Decimal, getcontext
from math import ceil, factorial

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						if not isinstance(_A,   _A			):
												raise TypeError("""Undefined for non-integers"""			)
						elif precision < 1:
												raise ValueError("""Undefined for non-natural numbers"""			)

						__magic_name__      :       Dict       						= precision
						__magic_name__      :       str       						= ceil(precision / 14			)
						__magic_name__      :       List[str]       						= 426880 * Decimal(10005			).sqrt()
						__magic_name__      :       List[Any]       						= 1
						__magic_name__      :       Dict       						= 13591409
						__magic_name__      :       Tuple       						= Decimal(_A			)
						for k in range(1,   _A			):
												__magic_name__      :       List[Any]       						= factorial(6 * k			) // (factorial(3 * k			) * factorial(_A			) ** 3)
												linear_term += 545140134
												exponential_term *= -262537412640768000
												partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
						return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
		__magic_name__:					Tuple							=     50
		print(F"""The first {n} digits of pi is: {pi(n)}""")
