





'''simple docstring'''







from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def  _lowerCamelCase				( lowercase						:      np.ndarray    ,							lowercase						:      np.ndarray    ,							lowercase						:      np.ndarray    ,							lowercase						:      int    ,							lowercase						:      int						)	->	np.ndarray:
				_a								= cva.getAffineTransform(lowercase    ,							lowercase						)
				return cva.warpAffine(lowercase    ,							lowercase    ,							(rows, cols)						)


if __name__ == "__main__":
					# read original image
					lowerCAmelCase_					:    Tuple      	=			cva.imread(
					    str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
					)
					# turn image in gray scale value
					lowerCAmelCase_					:    Optional[Any]      	=			cva.cvtColor(image, cva.COLOR_BGR2GRAY)
					# get image shape
					lowerCAmelCase_	,  lowerCAmelCase_					:    List[str]      	=			gray_img.shape

					# set different points to rotate image
					lowerCAmelCase_					:    List[str]      	=			np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
					lowerCAmelCase_					:    Any      	=			np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
					lowerCAmelCase_					:    Optional[Any]      	=			np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
					lowerCAmelCase_					:    Tuple      	=			np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

					# add all rotated images in a list
					lowerCAmelCase_					:    Dict      	=			[
					    gray_img,
					    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
					    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
					    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
					]

					# plot different image rotations
					lowerCAmelCase_					:    Optional[int]      	=			plt.figure(1)
					lowerCAmelCase_					:    int      	=			['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
					for i, image in enumerate(images):
										plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
										plt.title(titles[i])
										plt.axis('off')
										plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
					plt.show()


"""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)


