

'''simple docstring'''
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def        _lowerCAmelCase					(						_UpperCamelCase						:     Any							, _UpperCamelCase						:     Optional[int]							, _UpperCamelCase						:     List[Any]				)      ->			Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =OmegaConf.load(_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =torch.load(_UpperCamelCase							, map_location='cpu'				)['model']
     _SCREAMING_SNAKE_CASE							    =list(state_dict.keys()				)

     # extract state_dict for VQVAE
     _SCREAMING_SNAKE_CASE							    ={}
     _SCREAMING_SNAKE_CASE							    ='first_stage_model.'
     for key in keys:
          if key.startswith(_UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =state_dict[key]

    # extract state_dict for UNetLDM
     _SCREAMING_SNAKE_CASE							    ={}
     _SCREAMING_SNAKE_CASE							    ='model.diffusion_model.'
     for key in keys:
          if key.startswith(_UpperCamelCase				):
               _SCREAMING_SNAKE_CASE							    =state_dict[key]

     _SCREAMING_SNAKE_CASE							    =config.model.params.first_stage_config.params
     _SCREAMING_SNAKE_CASE							    =config.model.params.unet_config.params

     _SCREAMING_SNAKE_CASE							    =VQModel(**_UpperCamelCase				).eval()
     vqvae.load_state_dict(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =UNetLDMModel(**_UpperCamelCase				).eval()
     unet.load_state_dict(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =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							, )

     _SCREAMING_SNAKE_CASE							    =LDMPipeline(_UpperCamelCase							, _UpperCamelCase							, _UpperCamelCase				)
     pipeline.save_pretrained(_UpperCamelCase				)


if __name__ == "__main__":
     lowerCamelCase :      Dict					 =     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)
     lowerCamelCase :      str					 =     parser.parse_args()

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





'''simple docstring'''





from collections.abc import Sequence

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  return sum(c * (x**i) for i, c in enumerate(_lowerCAmelCase       )       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      float:
  __lowerCamelCase       :       Any           = 0.0
  for coeff in reversed(_lowerCAmelCase       ):
    __lowerCamelCase       :       Tuple           = result * x + coeff
  return result


if __name__ == "__main__":
   _UpperCamelCase											=						(0.0, 0.0, 5.0, 9.3, 7.0)
   _UpperCamelCase											=						1_0.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))






