



'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def      _A				(      *_lowerCAmelCase     ,      _lowerCAmelCase = None     ,      _lowerCAmelCase=True     ,      _lowerCAmelCase=2    ):



							"""simple docstring"""



							from .. import __version__

							__lowercase						=take_from
							__lowercase						=()
							if not isinstance(args[0]     ,      __snake_case    ):
														__lowercase						=(args,)

							for attribute, version_name, message in args:
														if version.parse(version.parse(__snake_case    ).base_version    ) >= version.parse(__snake_case    ):
																					raise ValueError(
																					    f"""The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"""
																					    f""" version {__version__} is >= {version_name}"""    )

														__lowercase						=None
														if isinstance(__snake_case     ,      __snake_case    ) and attribute in deprecated_kwargs:
																					values += (deprecated_kwargs.pop(__snake_case    ),)
																					__lowercase						=f"""The `{attribute}` argument is deprecated and will be removed in version {version_name}."""
														elif hasattr(__snake_case     ,      __snake_case    ):
																					values += (getattr(__snake_case     ,      __snake_case    ),)
																					__lowercase						=f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
														elif deprecated_kwargs is None:
																					__lowercase						=f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

														if warning is not None:
																					__lowercase						=warning + " " if standard_warn else ""
																					warnings.warn(warning + message     ,      __snake_case     ,      stacklevel=__snake_case    )

							if isinstance(__snake_case     ,      __snake_case    ) and len(__snake_case    ) > 0:
														__lowercase						=inspect.getouterframes(inspect.currentframe()    )[1]
														__lowercase						=call_frame.filename
														__lowercase						=call_frame.lineno
														__lowercase						=call_frame.function
														__lowercase						=next(iter(deprecated_kwargs.items()    )    )
														raise TypeError(f"""{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"""    )

							if len(__snake_case    ) == 0:
														return
							elif len(__snake_case    ) == 1:
														return values[0]
							return values





"""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)


