
"""simple docstring"""




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

from packaging import version


def   __magic_name__	(      *__snake_case				:       Optional[Any]     , __snake_case				:       Optional[Union[Dict, Any]] = None     , __snake_case				:       Optional[Any]=True     , __snake_case				:       List[Any]=2  ) ->				Optional[Any]:
       from .. import __version__

       lowercase    :				Tuple       		=							take_from
       lowercase    :				int       		=							()
       if not isinstance(args[0]     , __snake_case  ):
              lowercase    :				Optional[int]       		=							(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    :				List[Any]       		=							None
              if isinstance(__snake_case     , __snake_case  ) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(__snake_case  ),)
                     lowercase    :				Optional[int]       		=							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    :				List[str]       		=							f"""The `{attribute}` attribute is deprecated and will be removed in version {version_name}."""
              elif deprecated_kwargs is None:
                     lowercase    :				str       		=							f"""`{attribute}` is deprecated and will be removed in version {version_name}."""

              if warning is not None:
                     lowercase    :				Optional[Any]       		=							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    :				int       		=							inspect.getouterframes(inspect.currentframe()  )[1]
              lowercase    :				Dict       		=							call_frame.filename
              lowercase    :				str       		=							call_frame.lineno
              lowercase    :				List[Any]       		=							call_frame.function
              lowercase		,    lowercase    :				Optional[Any]       		=							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)


