



'''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


__a  			=						logging.get_logger(__name__)

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







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"conditional_detr"
    lowercase   =						["past_key_values"]
    lowercase   =						{
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }




    def __init__(       self   :	Union[str, Any]					,	snake_case_   :	Optional[int]=True					,	snake_case_   :	Any=None					,	snake_case_   :	str=3					,	snake_case_   :	Union[str, Any]=300					,	snake_case_   :	List[str]=6					,	snake_case_   :	Dict=2_048					,	snake_case_   :	Optional[Any]=8					,	snake_case_   :	List[Any]=6					,	snake_case_   :	int=2_048					,	snake_case_   :	Optional[int]=8					,	snake_case_   :	int=0.0					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	Dict=True					,	snake_case_   :	List[Any]="relu"					,	snake_case_   :	Union[str, Any]=256					,	snake_case_   :	Dict=0.1					,	snake_case_   :	Optional[int]=0.0					,	snake_case_   :	List[str]=0.0					,	snake_case_   :	Union[str, Any]=0.02					,	snake_case_   :	Dict=1.0					,	snake_case_   :	Tuple=False					,	snake_case_   :	Optional[Any]="sine"					,	snake_case_   :	List[str]="resnet50"					,	snake_case_   :	Optional[Any]=True					,	snake_case_   :	str=False					,	snake_case_   :	List[str]=2					,	snake_case_   :	str=5					,	snake_case_   :	Any=2					,	snake_case_   :	Optional[Any]=1					,	snake_case_   :	Union[str, Any]=1					,	snake_case_   :	Any=2					,	snake_case_   :	Any=5					,	snake_case_   :	Union[str, Any]=2					,	snake_case_   :	Optional[Any]=0.25					,	**snake_case_   :	Any					,	):
          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__    :       int       		= CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]		)
                elif isinstance(snake_case_					,	snake_case_		):
                      snake_case__    :       Union[str, Any]       		= backbone_config.get("""model_type"""		)
                      snake_case__    :       int       		= CONFIG_MAPPING[backbone_model_type]
                      snake_case__    :       Dict       		= config_class.from_dict(snake_case_		)

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




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return self.encoder_attention_heads




    @property
    def lowerCamelCase			(       self   :	Optional[int]		):
          return self.d_model




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       str       		= copy.deepcopy(self.__dict__		)
          if self.backbone_config is not None:
                snake_case__    :       Any       		= self.backbone_config.to_dict()
          snake_case__    :       Any       		= self.__class__.model_type
          return output







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.11"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                  ("""pixel_mask""", {0: """batch"""}),
              ]		)




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	Optional[Any]		):
          return 12





'''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)
