






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      List[str]      ,					__lowercase :      Optional[int]     )    ->   List[str]:





    """simple docstring"""

    __A       =						OmegaConf.load(UpperCamelCase_     )
    __A       =						torch.load(UpperCamelCase_      ,					map_location="""cpu"""     )["""model"""]
    __A       =						list(state_dict.keys()     )

    # extract state_dict for VQVAE
    __A       =						{}
    __A       =						"""first_stage_model."""
    for key in keys:
        if key.startswith(UpperCamelCase_     ):
            __A       =						state_dict[key]

    # extract state_dict for UNetLDM
    __A       =						{}
    __A       =						"""model.diffusion_model."""
    for key in keys:
        if key.startswith(UpperCamelCase_     ):
            __A       =						state_dict[key]

    __A       =						config.model.params.first_stage_config.params
    __A       =						config.model.params.unet_config.params

    __A       =						VQModel(**UpperCamelCase_     ).eval()
    vqvae.load_state_dict(UpperCamelCase_     )

    __A       =						UNetLDMModel(**UpperCamelCase_     ).eval()
    unet.load_state_dict(UpperCamelCase_     )

    __A       =						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=UpperCamelCase_      ,					)

    __A       =						LDMPipeline(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_     )
    pipeline.save_pretrained(UpperCamelCase_     )


if __name__ == "__main__":
  __a   :							str						= 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   :							Optional[int]						= parser.parse_args()

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









from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_mctct": ["MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP", "MCTCTConfig"],
    "feature_extraction_mctct": ["MCTCTFeatureExtractor"],
    "processing_mctct": ["MCTCTProcessor"],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MCTCTForCTC",
       "MCTCTModel",
       "MCTCTPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
   from .feature_extraction_mctct import MCTCTFeatureExtractor
   from .processing_mctct import MCTCTProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
