'''simple docstring'''




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel




def 							lowerCamelCase	(      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					Optional[int] )			->      Optional[int]:
							lowercase_					:			int										=       OmegaConf.load(UpperCAmelCase__ )
							lowercase_					:			Optional[Any]										=       torch.load(UpperCAmelCase__      ,      map_location="""cpu""" )["""model"""]
							lowercase_					:			Optional[Any]										=       list(state_dict.keys() )

							# extract state_dict for VQVAE
							lowercase_					:			Any										=       {}
							lowercase_					:			Any										=       """first_stage_model."""
							for key in keys:
														if key.startswith(UpperCAmelCase__ ):
																					lowercase_					:			Dict										=       state_dict[key]

    # extract state_dict for UNetLDM
							lowercase_					:			List[Any]										=       {}
							lowercase_					:			List[str]										=       """model.diffusion_model."""
							for key in keys:
														if key.startswith(UpperCAmelCase__ ):
																					lowercase_					:			Tuple										=       state_dict[key]

							lowercase_					:			Any										=       config.model.params.first_stage_config.params
							lowercase_					:			Any										=       config.model.params.unet_config.params

							lowercase_					:			Optional[Any]										=       VQModel(**UpperCAmelCase__ ).eval()
							vqvae.load_state_dict(UpperCAmelCase__ )

							lowercase_					:			int										=       UNetLDMModel(**UpperCAmelCase__ ).eval()
							unet.load_state_dict(UpperCAmelCase__ )

							lowercase_					:			List[str]										=       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__      ,      )

							lowercase_					:			List[Any]										=       LDMPipeline(UpperCAmelCase__      ,      UpperCAmelCase__      ,      UpperCAmelCase__ )
							pipeline.save_pretrained(UpperCAmelCase__ )


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

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






'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

							with open(os.path.join(os.path.dirname(UpperCAmelCase__ )      ,      UpperCAmelCase__ ) ) as input_file:
														lowercase_					:			str										=       [
														    [int(UpperCAmelCase__ ) for element in line.split(""",""" )]
														    for line in input_file.readlines()
														]

							lowercase_					:			Optional[Any]										=       len(UpperCAmelCase__ )
							lowercase_					:			Any										=       len(matrix[0] )

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

							for j in range(1      ,      UpperCAmelCase__ ):
														for i in range(UpperCAmelCase__ ):
																					lowercase_					:			Union[str, Any]										=       minimal_path_sums[i][j - 1] + matrix[i][j]

														for i in range(1      ,      UpperCAmelCase__ ):
																					lowercase_					:			Tuple										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i - 1][j] + matrix[i][j] )

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       min(
																					    minimal_path_sums[i][j]      ,      minimal_path_sums[i + 1][j] + matrix[i][j] )

							return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums )


if __name__ == "__main__":
						print(f"""{solution() = }""")





