





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
			__UpperCamelCase:	int = 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       SCREAMING_SNAKE_CASE__  (       _lowercase     : List[Any]			)   ->							Dict:




					'''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)

			__UpperCamelCase:	Dict = parser.parse_args()

			__UpperCamelCase:	Dict = 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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)

__UpperCamelCase:	Optional[int] = {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				"mobilenet_v1"

	def __init__(						self:						Dict,					lowerCamelCase_:						Any=3,					lowerCamelCase_:						Dict=224,					lowerCamelCase_:						int=1.0,					lowerCamelCase_:						List[Any]=8,					lowerCamelCase_:						int="relu6",					lowerCamelCase_:						Optional[int]=True,					lowerCamelCase_:						Any=0.9_9_9,					lowerCamelCase_:						List[str]=0.0_2,					lowerCamelCase_:						Optional[int]=0.0_0_1,					**lowerCamelCase_:						Tuple,					):
			super().__init__(**lowerCamelCase_			)

			if depth_multiplier <= 0:
					raise ValueError('depth_multiplier must be greater than zero.'			)

			lowercase__					:      Optional[int]      =		num_channels
			lowercase__					:      List[Any]      =		image_size
			lowercase__					:      str      =		depth_multiplier
			lowercase__					:      Optional[Any]      =		min_depth
			lowercase__					:      int      =		hidden_act
			lowercase__					:      Dict      =		tf_padding
			lowercase__					:      Optional[Any]      =		classifier_dropout_prob
			lowercase__					:      int      =		initializer_range
			lowercase__					:      Any      =		layer_norm_eps


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				version.parse("1.11"      )

	@property
	def    snake_case__(						self:						Optional[int]			):
			return OrderedDict([('pixel_values', {0: 'batch'})]			)

	@property
	def    snake_case__(						self:						Dict			):
			if self.task == "image-classification":
					return OrderedDict([('logits', {0: 'batch'})]			)
			else:
					return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]			)





	@property
	def    snake_case__(						self:						List[Any]			):
			return 1E-4


