



"""simple docstring"""

import copy
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
from ..auto import CONFIG_MAPPING


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}




class SCREAMING_SNAKE_CASE__					(		__lowercase      ):
						_lowerCAmelCase							=					'conditional_detr'
						_lowerCAmelCase							=					['past_key_values']
						_lowerCAmelCase							=					{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						}



						def __init__(self						,     _lowercase=True						,     _lowercase=None						,     _lowercase=3						,     _lowercase=300						,     _lowercase=6						,     _lowercase=2048						,     _lowercase=8						,     _lowercase=6						,     _lowercase=2048						,     _lowercase=8						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=True						,     _lowercase="relu"						,     _lowercase=256						,     _lowercase=0.1						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1.0						,     _lowercase=False						,     _lowercase="sine"						,     _lowercase="resnet50"						,     _lowercase=True						,     _lowercase=False						,     _lowercase=2						,     _lowercase=5						,     _lowercase=2						,     _lowercase=1						,     _lowercase=1						,     _lowercase=2						,     _lowercase=5						,     _lowercase=2						,     _lowercase=0.25						,     **_lowercase						,     ):





													'''simple docstring'''

													if backbone_config is not None and use_timm_backbone:
																				raise ValueError("""You can\'t specify both `backbone_config` and `use_timm_backbone`."""							)

													if not use_timm_backbone:
																				if backbone_config is None:
																											logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
																											__a		:		Tuple									=       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
																				elif isinstance(__A						,     __A							):
																											__a		:		Dict									=       backbone_config.get("""model_type"""							)
																											__a		:		Tuple									=       CONFIG_MAPPING[backbone_model_type]
																											__a		:		List[str]									=       config_class.from_dict(__A							)

													__a		:		Tuple									=       use_timm_backbone
													__a		:		Union[str, Any]									=       backbone_config
													__a		:		Dict									=       num_channels
													__a		:		Union[str, Any]									=       num_queries
													__a		:		Dict									=       d_model
													__a		:		int									=       encoder_ffn_dim
													__a		:		List[str]									=       encoder_layers
													__a		:		List[Any]									=       encoder_attention_heads
													__a		:		Tuple									=       decoder_ffn_dim
													__a		:		List[Any]									=       decoder_layers
													__a		:		List[Any]									=       decoder_attention_heads
													__a		:		Dict									=       dropout
													__a		:		Any									=       attention_dropout
													__a		:		Union[str, Any]									=       activation_dropout
													__a		:		List[str]									=       activation_function
													__a		:		Dict									=       init_std
													__a		:		Union[str, Any]									=       init_xavier_std
													__a		:		Dict									=       encoder_layerdrop
													__a		:		Optional[int]									=       decoder_layerdrop
													__a		:		int									=       encoder_layers
													__a		:		List[Any]									=       auxiliary_loss
													__a		:		int									=       position_embedding_type
													__a		:		Optional[int]									=       backbone
													__a		:		str									=       use_pretrained_backbone
													__a		:		List[Any]									=       dilation
													# Hungarian matcher
													__a		:		Optional[Any]									=       class_cost
													__a		:		Optional[Any]									=       bbox_cost
													__a		:		Tuple									=       giou_cost
													# Loss coefficients
													__a		:		Optional[Any]									=       mask_loss_coefficient
													__a		:		str									=       dice_loss_coefficient
													__a		:		Tuple									=       cls_loss_coefficient
													__a		:		Optional[Any]									=       bbox_loss_coefficient
													__a		:		Dict									=       giou_loss_coefficient
													__a		:		Any									=       focal_alpha
													super().__init__(is_encoder_decoder=__A						,     **__A							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.encoder_attention_heads



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return self.d_model





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       copy.deepcopy(self.__dict__							)
													if self.backbone_config is not None:
																				__a		:		Any									=       self.backbone_config.to_dict()
													__a		:		Tuple									=       self.__class__.model_type
													return output







class SCREAMING_SNAKE_CASE__					(		__lowercase      ):
						_lowerCAmelCase							=					version.parse("1.11"      )



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return OrderedDict(
													    [
													        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
													        ("""pixel_mask""", {0: """batch"""}),
													    ]							)



						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 1e-5





						@property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return 12




"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  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       __magic_name__    (   _lowerCamelCase				: 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)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  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)

