






import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				OmegaConf.load(A_)
							snake_case_           =				torch.load(A_				,	map_location='cpu')['''model''']
							snake_case_           =				list(state_dict.keys())

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

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

							snake_case_           =				config.model.params.first_stage_config.params
							snake_case_           =				config.model.params.unet_config.params

							snake_case_           =				VQModel(**A_).eval()
							vqvae.load_state_dict(A_)

							snake_case_           =				UNetLDMModel(**A_).eval()
							unet.load_state_dict(A_)

							snake_case_           =				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_				,	)

							snake_case_           =				LDMPipeline(A_				,	A_				,	A_)
							pipeline.save_pretrained(A_)


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

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








"""simple docstring"""





def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number < 0 or shift_amount < 0:
		raise ValueError('''both inputs must be positive integers'''					)

	lowerCAmelCase__      :		int    							=      str(bin(A_					)					)
	binary_number += "0" * shift_amount
	return binary_number

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number < 0 or shift_amount < 0:
		raise ValueError('''both inputs must be positive integers'''					)

	lowerCAmelCase__      :		Any    							=      str(bin(A_					)					)[2:]
	if shift_amount >= len(A_					):
		return "0b0"
	lowerCAmelCase__      :		Tuple    							=      binary_number[: len(A_					) - shift_amount]
	return "0b" + shifted_binary_number

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	if number >= 0:  # Get binary representation of positive number
		lowerCAmelCase__      :		Union[str, Any]    							=      '''0''' + str(bin(A_					)					).strip('''-'''					)[2:]
	else:  # Get binary (2's complement) representation of negative number
		lowerCAmelCase__      :		Optional[Any]    							=      len(bin(A_					)[3:]					)  # Find 2's complement of number
		lowerCAmelCase__      :		Optional[int]    							=      bin(abs(A_					) - (1 << binary_number_length)					)[3:]
		lowerCAmelCase__      :		int    							=      (
		    '''1''' + '''0''' * (binary_number_length - len(A_					)) + binary_number
		)

	if shift_amount >= len(A_					):
		return "0b" + binary_number[0] * len(A_					)
	return (
	    "0b"
	    + binary_number[0] * shift_amount
	    + binary_number[: len(A_					) - shift_amount]
	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




