




'''simple docstring'''





import copy

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


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




class  _snake_case		(	lowercase_							):
			lowerCAmelCase_     :      Any						       =    "upernet"




			def __init__(					self				,							a__=None				,							a__=512				,							a__=0.0_2				,							a__=[1, 2, 3, 6]				,							a__=True				,							a__=0.4				,							a__=384				,							a__=256				,							a__=1				,							a__=False				,							a__=255				,							**a__				,							)				->				Union[str, Any]:

										'''simple docstring'''




										super().__init__(**a__							)

										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=["stage1", "stage2", "stage3", "stage4"]							)
										elif isinstance(a__				,							a__							):
																	snake_case_						=      backbone_config.get("model_type"							)
																	snake_case_						=      CONFIG_MAPPING[backbone_model_type]
																	snake_case_						=      config_class.from_dict(a__							)

										snake_case_						=      backbone_config
										snake_case_						=      hidden_size
										snake_case_						=      initializer_range
										snake_case_						=      pool_scales
										snake_case_						=      use_auxiliary_head
										snake_case_						=      auxiliary_loss_weight
										snake_case_						=      auxiliary_in_channels
										snake_case_						=      auxiliary_channels
										snake_case_						=      auxiliary_num_convs
										snake_case_						=      auxiliary_concat_input
										snake_case_						=      loss_ignore_index




			def    lowerCAmelCase__				(					self							)				->				Optional[Any]:

										'''simple docstring'''




										snake_case_						=      copy.deepcopy(self.__dict__							)
										snake_case_						=      self.backbone_config.to_dict()
										snake_case_						=      self.__class__.model_type
										return output






"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

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

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							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=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       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			:       Dict   		=       parser.parse_args()

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


