



"""simple docstring"""
import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
						__a      =				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    A_  (			_lowercase	):







												'''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)

						__a      =				parser.parse_args()

						__a      =				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"""
import re
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			re.compile(
						    r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$"""	)

						return bool(re.search(_lowercase,					_lowercase	)	)


if __name__ == "__main__":
						__a      =				"0094702343221"

						print(is_sri_lankan_phone_number(phone))
