






"""simple docstring"""
from math import pi, sqrt






def 			__UpperCamelCase	(		snake_case__					):
	if num <= 0:
		raise ValueError("""math domain error"""					)
	if num > 171.5:
		raise OverflowError("""math range error"""					)
	elif num - int(snake_case__					) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""					)
	elif num == 0.5:
		return sqrt(snake_case__					)
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1					)






def 			__UpperCamelCase	(		):
	assert gamma(0.5					) == sqrt(snake_case__					)
	assert gamma(1					) == 1.0
	assert gamma(2					) == 1.0


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							_lowerCAmelCase         =							1.0
							while num:
														_lowerCAmelCase         =							float(input("Gamma of: "))
														print(F'gamma({num}) = {gamma(num)}')
														print("\nEnter 0 to exit...")









"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						str	    =   OmegaConf.load(snake_case__					)
	A_		:						List[str]	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						Any	    =   list(state_dict.keys()					)

	# extract state_dict for VQVAE
	A_		:						Any	    =   {}
	A_		:						Union[str, Any]	    =   """first_stage_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						List[Any]	    =   state_dict[key]

    # extract state_dict for UNetLDM
	A_		:						Tuple	    =   {}
	A_		:						Dict	    =   """model.diffusion_model."""
	for key in keys:
		if key.startswith(snake_case__					):
			A_		:						Optional[Any]	    =   state_dict[key]

	A_		:						List[Any]	    =   config.model.params.first_stage_config.params
	A_		:						Tuple	    =   config.model.params.unet_config.params

	A_		:						Any	    =   VQModel(**snake_case__					).eval()
	vqvae.load_state_dict(snake_case__					)

	A_		:						Optional[int]	    =   UNetLDMModel(**snake_case__					).eval()
	unet.load_state_dict(snake_case__					)

	A_		:						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=snake_case__   ,   )

	A_		:						str	    =   LDMPipeline(snake_case__   ,   snake_case__   ,   snake_case__					)
	pipeline.save_pretrained(snake_case__					)


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

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

