




from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A						:							Any							=  logging.get_logger(__name__)

__A						:							List[str]							=  {
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     '''efficientformer'''





							def __init__(					self       :	List[str]       ,     a       :	List[int] = [3, 2, 6, 4]       ,     a       :	List[int] = [48, 96, 224, 448]       ,     a       :	List[bool] = [True, True, True, True]       ,     a       :	int = 448       ,     a       :	int = 32       ,     a       :	int = 4       ,     a       :	int = 7       ,     a       :	int = 5       ,     a       :	int = 8       ,     a       :	int = 4       ,     a       :	float = 0.0       ,     a       :	int = 16       ,     a       :	int = 3       ,     a       :	int = 3       ,     a       :	int = 3       ,     a       :	int = 2       ,     a       :	int = 1       ,     a       :	float = 0.0       ,     a       :	int = 1       ,     a       :	bool = True       ,     a       :	bool = True       ,     a       :	float = 1E-5       ,     a       :	str = "gelu"       ,     a       :	float = 0.02       ,     a       :	float = 1E-1_2       ,     a       :	int = 224       ,     a       :	float = 1E-0_5       ,     **a       :	List[Any]       ,     )			->					None:
										super().__init__(**a			)

										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					hidden_sizes
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					layer_norm_eps
										SCREAMING_SNAKE_CASE           =					patch_size
										SCREAMING_SNAKE_CASE           =					num_channels
										SCREAMING_SNAKE_CASE           =					depths
										SCREAMING_SNAKE_CASE           =					mlp_expansion_ratio
										SCREAMING_SNAKE_CASE           =					downsamples
										SCREAMING_SNAKE_CASE           =					dim
										SCREAMING_SNAKE_CASE           =					key_dim
										SCREAMING_SNAKE_CASE           =					attention_ratio
										SCREAMING_SNAKE_CASE           =					resolution
										SCREAMING_SNAKE_CASE           =					pool_size
										SCREAMING_SNAKE_CASE           =					downsample_patch_size
										SCREAMING_SNAKE_CASE           =					downsample_stride
										SCREAMING_SNAKE_CASE           =					downsample_pad
										SCREAMING_SNAKE_CASE           =					drop_path_rate
										SCREAMING_SNAKE_CASE           =					num_metaad_blocks
										SCREAMING_SNAKE_CASE           =					distillation
										SCREAMING_SNAKE_CASE           =					use_layer_scale
										SCREAMING_SNAKE_CASE           =					layer_scale_init_value
										SCREAMING_SNAKE_CASE           =					image_size
										SCREAMING_SNAKE_CASE           =					batch_norm_eps












import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							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_1_2,
		    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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''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						:							Union[str, Any]							=  parser.parse_args()

		__A						:							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)






