
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


__magic_name__            =  logging.get_logger(__name__)

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




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[str]    	=     'conditional_detr'
      _A						:		List[Any]    	=     ['past_key_values']
      _A						:		Dict    	=     {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }
      def __init__(			self		,	lowerCamelCase=True		,	lowerCamelCase=None		,	lowerCamelCase=3		,	lowerCamelCase=3_00		,	lowerCamelCase=6		,	lowerCamelCase=20_48		,	lowerCamelCase=8		,	lowerCamelCase=6		,	lowerCamelCase=20_48		,	lowerCamelCase=8		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0		,	lowerCamelCase=True		,	lowerCamelCase="relu"		,	lowerCamelCase=2_56		,	lowerCamelCase=0.1		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0		,	lowerCamelCase=0.0_2		,	lowerCamelCase=1.0		,	lowerCamelCase=False		,	lowerCamelCase="sine"		,	lowerCamelCase="resnet50"		,	lowerCamelCase=True		,	lowerCamelCase=False		,	lowerCamelCase=2		,	lowerCamelCase=5		,	lowerCamelCase=2		,	lowerCamelCase=1		,	lowerCamelCase=1		,	lowerCamelCase=2		,	lowerCamelCase=5		,	lowerCamelCase=2		,	lowerCamelCase=0.2_5		,	**lowerCamelCase		,	):
          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." )
                  snake_case__						=				CONFIG_MAPPING["resnet"](out_features=["stage4"] )
              elif isinstance(lowerCamelCase		,	lowerCamelCase ):
                  snake_case__						=				backbone_config.get("model_type" )
                  snake_case__						=				CONFIG_MAPPING[backbone_model_type]
                  snake_case__						=				config_class.from_dict(lowerCamelCase )

          snake_case__						=				use_timm_backbone
          snake_case__						=				backbone_config
          snake_case__						=				num_channels
          snake_case__						=				num_queries
          snake_case__						=				d_model
          snake_case__						=				encoder_ffn_dim
          snake_case__						=				encoder_layers
          snake_case__						=				encoder_attention_heads
          snake_case__						=				decoder_ffn_dim
          snake_case__						=				decoder_layers
          snake_case__						=				decoder_attention_heads
          snake_case__						=				dropout
          snake_case__						=				attention_dropout
          snake_case__						=				activation_dropout
          snake_case__						=				activation_function
          snake_case__						=				init_std
          snake_case__						=				init_xavier_std
          snake_case__						=				encoder_layerdrop
          snake_case__						=				decoder_layerdrop
          snake_case__						=				encoder_layers
          snake_case__						=				auxiliary_loss
          snake_case__						=				position_embedding_type
          snake_case__						=				backbone
          snake_case__						=				use_pretrained_backbone
          snake_case__						=				dilation
          # Hungarian matcher
          snake_case__						=				class_cost
          snake_case__						=				bbox_cost
          snake_case__						=				giou_cost
          # Loss coefficients
          snake_case__						=				mask_loss_coefficient
          snake_case__						=				dice_loss_coefficient
          snake_case__						=				cls_loss_coefficient
          snake_case__						=				bbox_loss_coefficient
          snake_case__						=				giou_loss_coefficient
          snake_case__						=				focal_alpha
          super().__init__(is_encoder_decoder=lowerCamelCase		,	**lowerCamelCase )
      @property
      def A_			(			self ):
          return self.encoder_attention_heads
      @property
      def A_			(			self ):
          return self.d_model



      def A_			(			self ):
          snake_case__						=				copy.deepcopy(self.__dict__ )
          if self.backbone_config is not None:
              snake_case__						=				self.backbone_config.to_dict()
          snake_case__						=				self.__class__.model_type
          return output





class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Tuple    	=     version.parse('1.11' )
      @property
      def A_			(			self ):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ] )
      @property
      def A_			(			self ):
          return 1e-5



      @property
      def A_			(			self ):
          return 12





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
     __magic_name__            =  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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
         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__            =  parser.parse_args()

     __magic_name__            =  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)



