






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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__ = 30					):

	if not isinstance(snake_case__   ,   (int, float)					):
		raise ValueError("""maclaurin_sin() requires either an int or float for theta"""					)

	if not isinstance(snake_case__   ,   snake_case__					) or accuracy <= 0:
		raise ValueError("""maclaurin_sin() requires a positive int for accuracy"""					)

	A_		:						List[Any]	    =   float(snake_case__					)
	A_		:						int	    =   theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1					) for r in range(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__ = 30					):

	if not isinstance(snake_case__   ,   (int, float)					):
		raise ValueError("""maclaurin_cos() requires either an int or float for theta"""					)

	if not isinstance(snake_case__   ,   snake_case__					) or accuracy <= 0:
		raise ValueError("""maclaurin_cos() requires a positive int for accuracy"""					)

	A_		:						Tuple	    =   float(snake_case__					)
	A_		:						List[Any]	    =   theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r					) for r in range(snake_case__					)					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							print(maclaurin_sin(10))
							print(maclaurin_sin(-10))
							print(maclaurin_sin(10, 15))
							print(maclaurin_sin(-10, 15))

							print(maclaurin_cos(5))
							print(maclaurin_cos(-5))
							print(maclaurin_cos(10, 15))
							print(maclaurin_cos(-10, 15))









"""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)

