






"""simple docstring"""







import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
 __a       :	List[Any]             = {
     'linear': PIL.Image.Resampling.BILINEAR,
     'bilinear': PIL.Image.Resampling.BILINEAR,
     'bicubic': PIL.Image.Resampling.BICUBIC,
     'lanczos': PIL.Image.Resampling.LANCZOS,
     'nearest': PIL.Image.Resampling.NEAREST,
 }
else:
 __a       :	Optional[Any]             = {
     'linear': PIL.Image.LINEAR,
     'bilinear': PIL.Image.BILINEAR,
     'bicubic': PIL.Image.BICUBIC,
     'lanczos': PIL.Image.LANCZOS,
     'nearest': PIL.Image.NEAREST,
 }





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
     a__          =  (images / 2 + 0.5).clamp(0 ,     1)
     a__          =  images.cpu().permute(0 ,     2 ,     3 ,     1).float().numpy()
     a__          =  numpy_to_pil(lowerCamelCase_)
     return images





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
     if images.ndim == 3:
          a__          =  images[None, ...]
     a__          =  (images * 255).round().astype('''uint8''')
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__          =  [Image.fromarray(image.squeeze() ,     mode='''L''') for image in images]
     else:
          a__          =  [Image.fromarray(lowerCamelCase_) for image in images]

     return pil_images










"""simple docstring"""







import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  OmegaConf.load(lowerCamelCase_)
					a__          =  torch.load(lowerCamelCase_ ,     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(lowerCamelCase_):
															a__          =  state_dict[key]

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

					a__          =  config.model.params.first_stage_config.params
					a__          =  config.model.params.unet_config.params

					a__          =  VQModel(**lowerCamelCase_).eval()
					vqvae.load_state_dict(lowerCamelCase_)

					a__          =  UNetLDMModel(**lowerCamelCase_).eval()
					unet.load_state_dict(lowerCamelCase_)

					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=lowerCamelCase_ ,     )

					a__          =  LDMPipeline(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					pipeline.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	Tuple             = 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)


