

import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       OmegaConf.load(_a)
	SCREAMING_SNAKE_CASE   :      Any				     =       torch.load(_a							,			map_location="cpu")["model"]
	SCREAMING_SNAKE_CASE   :      str				     =       list(state_dict.keys())

	# extract state_dict for VQVAE
	SCREAMING_SNAKE_CASE   :      Tuple				     =       {}
	SCREAMING_SNAKE_CASE   :      Tuple				     =       "first_stage_model."
	for key in keys:
		if key.startswith(_a):
			SCREAMING_SNAKE_CASE   :      str				     =       state_dict[key]

    # extract state_dict for UNetLDM
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       {}
	SCREAMING_SNAKE_CASE   :      int				     =       "model.diffusion_model."
	for key in keys:
		if key.startswith(_a):
			SCREAMING_SNAKE_CASE   :      str				     =       state_dict[key]

	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       config.model.params.first_stage_config.params
	SCREAMING_SNAKE_CASE   :      int				     =       config.model.params.unet_config.params

	SCREAMING_SNAKE_CASE   :      int				     =       VQModel(**_a).eval()
	vqvae.load_state_dict(_a)

	SCREAMING_SNAKE_CASE   :      Dict				     =       UNetLDMModel(**_a).eval()
	unet.load_state_dict(_a)

	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       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							,			)

	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       LDMPipeline(_a							,			_a							,			_a)
	pipeline.save_pretrained(_a)


if __name__ == "__main__":
			a_						     =    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_						     =    parser.parse_args()

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


import math

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * power_factor

def  lowerCamelCase__ (					_a							,			_a):
	if (
	    not isinstance(_a							,			(int, float))
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("power_factor must be a valid float value between -1 and 1.")
	return apparent_power * math.sqrt(1 - power_factor**2)


if __name__ == "__main__":
			import doctest

			doctest.testmod()