
"""simple docstring"""




import unittest

import numpy as np


def   __magic_name__	(      __snake_case				:       np.ndarray     , __snake_case				:       np.ndarray     , __snake_case				:       np.ndarray     , __snake_case				:       np.ndarray | None = None     , ) ->				np.ndarray:
       lowercase    :				List[str]       		=							np.shape(__snake_case  )
       lowercase    :				List[Any]       		=							np.shape(__snake_case  )
       lowercase    :				Optional[Any]       		=							np.shape(__snake_case  )

       if shape_a[0] != shape_b[0]:
              lowercase    :				Optional[int]       		=							(
                  "Expected the same number of rows for A and B. "
                  f"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__snake_case  )

       if shape_b[1] != shape_c[1]:
              lowercase    :				Union[str, Any]       		=							(
                  "Expected the same number of columns for B and C. "
                  f"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__snake_case  )

       lowercase    :				Union[str, Any]       		=							pseudo_inv
       if a_inv is None:
              try:
                     lowercase    :				str       		=							np.linalg.inv(__snake_case  )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         "Input matrix A is not invertible. Cannot compute Schur complement."  )

       return mat_c - mat_b.T @ a_inv @ mat_b

class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
          lowercase    :				Union[str, Any]       		=							np.array([[0, 3], [3, 0], [2, 3]]				)
          lowercase    :				str       		=							np.array([[2, 1], [6, 3]]				)

          lowercase    :				int       		=							schur_complement(_a	,    _a	,    _a				)

          lowercase    :				Union[str, Any]       		=							np.block([[a, b], [b.T, c]]				)

          lowercase    :				Optional[int]       		=							np.linalg.det(_a				)
          lowercase    :				Any       		=							np.linalg.det(_a				)
          lowercase    :				List[str]       		=							np.linalg.det(_a				)

          self.assertAlmostEqual(_a	,    det_a * det_s				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
          lowercase    :				List[Any]       		=							np.array([[0, 3], [3, 0], [2, 3]]				)
          lowercase    :				Union[str, Any]       		=							np.array([[2, 1], [6, 3]]				)

          with self.assertRaises(_a				):
                 schur_complement(_a	,    _a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]				)
          lowercase    :				int       		=							np.array([[0, 3], [3, 0], [2, 3]]				)
          lowercase    :				Tuple       		=							np.array([[2, 1, 3], [6, 3, 5]]				)

          with self.assertRaises(_a				):
                 schur_complement(_a	,    _a	,    _a				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()




"""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)


