


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase       			=						logging.get_logger(__name__)

lowerCAmelCase       			=						{
    '''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}





class  A       (  _a					):
							UpperCamelCase_  :				Optional[int]			='''deformable_detr'''
							UpperCamelCase_  :				str			={
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}


							def __init__(self ,							lowerCAmelCase=True ,							lowerCAmelCase=None ,							lowerCAmelCase=3 ,							lowerCAmelCase=3_0_0 ,							lowerCAmelCase=1_0_2_4 ,							lowerCAmelCase=6 ,							lowerCAmelCase=1_0_2_4 ,							lowerCAmelCase=8 ,							lowerCAmelCase=6 ,							lowerCAmelCase=1_0_2_4 ,							lowerCAmelCase=8 ,							lowerCAmelCase=0.0 ,							lowerCAmelCase=True ,							lowerCAmelCase="relu" ,							lowerCAmelCase=2_5_6 ,							lowerCAmelCase=0.1 ,							lowerCAmelCase=0.0 ,							lowerCAmelCase=0.0 ,							lowerCAmelCase=0.02 ,							lowerCAmelCase=1.0 ,							lowerCAmelCase=True ,							lowerCAmelCase=False ,							lowerCAmelCase="sine" ,							lowerCAmelCase="resnet50" ,							lowerCAmelCase=True ,							lowerCAmelCase=False ,							lowerCAmelCase=4 ,							lowerCAmelCase=4 ,							lowerCAmelCase=4 ,							lowerCAmelCase=False ,							lowerCAmelCase=3_0_0 ,							lowerCAmelCase=False ,							lowerCAmelCase=1 ,							lowerCAmelCase=5 ,							lowerCAmelCase=2 ,							lowerCAmelCase=1 ,							lowerCAmelCase=1 ,							lowerCAmelCase=5 ,							lowerCAmelCase=2 ,							lowerCAmelCase=0.1 ,							lowerCAmelCase=0.25 ,							lowerCAmelCase=False ,							**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.'   )
													__lowercase=     CONFIG_MAPPING["""resnet"""](out_features=['stage4']   )
											elif isinstance(snake_case_ ,							snake_case_   ):
													__lowercase=     backbone_config.get('model_type'   )
													__lowercase=     CONFIG_MAPPING[backbone_model_type]
													__lowercase=     config_class.from_dict(snake_case_   )
									__lowercase=     use_timm_backbone
									__lowercase=     backbone_config
									__lowercase=     num_channels
									__lowercase=     num_queries
									__lowercase=     max_position_embeddings
									__lowercase=     d_model
									__lowercase=     encoder_ffn_dim
									__lowercase=     encoder_layers
									__lowercase=     encoder_attention_heads
									__lowercase=     decoder_ffn_dim
									__lowercase=     decoder_layers
									__lowercase=     decoder_attention_heads
									__lowercase=     dropout
									__lowercase=     attention_dropout
									__lowercase=     activation_dropout
									__lowercase=     activation_function
									__lowercase=     init_std
									__lowercase=     init_xavier_std
									__lowercase=     encoder_layerdrop
									__lowercase=     auxiliary_loss
									__lowercase=     position_embedding_type
									__lowercase=     backbone
									__lowercase=     use_pretrained_backbone
									__lowercase=     dilation
									# deformable attributes
									__lowercase=     num_feature_levels
									__lowercase=     encoder_n_points
									__lowercase=     decoder_n_points
									__lowercase=     two_stage
									__lowercase=     two_stage_num_proposals
									__lowercase=     with_box_refine
									if two_stage is True and with_box_refine is False:
											raise ValueError('If two_stage is True, with_box_refine must be True.'   )
									# Hungarian matcher
									__lowercase=     class_cost
									__lowercase=     bbox_cost
									__lowercase=     giou_cost
									# Loss coefficients
									__lowercase=     mask_loss_coefficient
									__lowercase=     dice_loss_coefficient
									__lowercase=     bbox_loss_coefficient
									__lowercase=     giou_loss_coefficient
									__lowercase=     eos_coefficient
									__lowercase=     focal_alpha
									__lowercase=     disable_custom_kernels
									super().__init__(is_encoder_decoder=snake_case_ ,							**snake_case_   )


							@property
							def      _A			(self   ):
									return self.encoder_attention_heads


							@property
							def      _A			(self   ):
									return self.d_model







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











'''simple docstring'''





import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					int:
      snake_case__    :       Tuple       		= OmegaConf.load(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= torch.load(_lowerCAmelCase	,  map_location="""cpu"""						)["""model"""]
      snake_case__    :       int       		= list(state_dict.keys()						)

      # extract state_dict for VQVAE
      snake_case__    :       Dict       		= {}
      snake_case__    :       Tuple       		= """first_stage_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

    # extract state_dict for UNetLDM
      snake_case__    :       Optional[Any]       		= {}
      snake_case__    :       Optional[int]       		= """model.diffusion_model."""
      for key in keys:
            if key.startswith(_lowerCAmelCase						):
                  snake_case__    :       Tuple       		= state_dict[key]

      snake_case__    :       Dict       		= config.model.params.first_stage_config.params
      snake_case__    :       Optional[Any]       		= config.model.params.unet_config.params

      snake_case__    :       Tuple       		= VQModel(**_lowerCAmelCase						).eval()
      vqvae.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= UNetLDMModel(**_lowerCAmelCase						).eval()
      unet.load_state_dict(_lowerCAmelCase						)

      snake_case__    :       List[Any]       		= DDIMScheduler(
          timesteps=config.model.params.timesteps	,  beta_schedule="""scaled_linear"""	,  beta_start=config.model.params.linear_start	,  beta_end=config.model.params.linear_end	,  clip_sample=_lowerCAmelCase	,  )

      snake_case__    :       List[str]       		= LDMPipeline(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)
      pipeline.save_pretrained(_lowerCAmelCase						)


if __name__ == "__main__":
       __a  			=						argparse.ArgumentParser()
       parser.add_argument("--checkpoint_path", type=str, required=True)
       parser.add_argument("--config_path", type=str, required=True)
       parser.add_argument("--output_path", type=str, required=True)
       __a  			=						parser.parse_args()

       convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
