


'''simple docstring'''






from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def    _snake_case  (	)		->       Tuple:
							lowerCAmelCase__			  =      HfArgumentParser(A			)
							lowerCAmelCase__			  =      parser.parse_args_into_dataclasses()[0]
							lowerCAmelCase__			  =      TensorFlowBenchmark(args=A			)
							try:
														lowerCAmelCase__			  =      parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowerCAmelCase__			  =      '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
														lowerCAmelCase__			  =      ''' '''.join(str(A			).split(''' '''			)[:-1]			)
														lowerCAmelCase__			  =      ''''''
														lowerCAmelCase__			  =      eval(str(A			).split(''' '''			)[-1]			)
														lowerCAmelCase__			  =      []
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in TensorFlowBenchmark.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]			)
																					else:
																												wrong_args.append(A			)
														if len(A			) > 0:
																					lowerCAmelCase__			  =      full_error_msg + begin_error_msg + str(A			)
														raise ValueError(A			)
							benchmark.run()


if __name__ == "__main__":
		main()



'''simple docstring'''






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def    _snake_case  (	A					,       A					,       A			)		->       Union[str, Any]:
							lowerCAmelCase__			  =      OmegaConf.load(A			)
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							lowerCAmelCase__			  =      list(state_dict.keys()			)

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

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

							lowerCAmelCase__			  =      config.model.params.first_stage_config.params
							lowerCAmelCase__			  =      config.model.params.unet_config.params

							lowerCAmelCase__			  =      VQModel(**A			).eval()
							vqvae.load_state_dict(A			)

							lowerCAmelCase__			  =      UNetLDMModel(**A			).eval()
							unet.load_state_dict(A			)

							lowerCAmelCase__			  =      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=A					,       )

							lowerCAmelCase__			  =      LDMPipeline(A					,       A					,       A			)
							pipeline.save_pretrained(A			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				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)
		__UpperCAmelCase       							=				parser.parse_args()

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