





'''simple docstring'''







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Dict   ) ->	int:
						UpperCAmelCase_			:      str     						=  OmegaConf.load(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[str]     						=  torch.load(SCREAMING_SNAKE_CASE__,			map_location='''cpu'''   )['''model''']
						UpperCAmelCase_			:      str     						=  list(state_dict.keys()   )

						# extract state_dict for VQVAE
						UpperCAmelCase_			:      List[str]     						=  {}
						UpperCAmelCase_			:      Tuple     						=  '''first_stage_model.'''
						for key in keys:
												if key.startswith(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      Dict     						=  state_dict[key]

    # extract state_dict for UNetLDM
						UpperCAmelCase_			:      Any     						=  {}
						UpperCAmelCase_			:      Optional[Any]     						=  '''model.diffusion_model.'''
						for key in keys:
												if key.startswith(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      List[Any]     						=  state_dict[key]

						UpperCAmelCase_			:      int     						=  config.model.params.first_stage_config.params
						UpperCAmelCase_			:      int     						=  config.model.params.unet_config.params

						UpperCAmelCase_			:      List[str]     						=  VQModel(**SCREAMING_SNAKE_CASE__   ).eval()
						vqvae.load_state_dict(SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Dict     						=  UNetLDMModel(**SCREAMING_SNAKE_CASE__   ).eval()
						unet.load_state_dict(SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Union[str, 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=SCREAMING_SNAKE_CASE__,			)

						UpperCAmelCase_			:      Optional[int]     						=  LDMPipeline(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						pipeline.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       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)
					snake_case_     :       Union[str, Any]						=  parser.parse_args()

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












'''simple docstring'''







snake_case_     :       int						=  {
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}





