import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     OmegaConf.load(__snake_case						)
					_UpperCamelCase			     =     torch.load(__snake_case  ,     map_location='''cpu'''						)['''model''']
					_UpperCamelCase			     =     list(state_dict.keys()						)

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

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

					_UpperCamelCase			     =     config.model.params.first_stage_config.params
					_UpperCamelCase			     =     config.model.params.unet_config.params

					_UpperCamelCase			     =     VQModel(**__snake_case						).eval()
					vqvae.load_state_dict(__snake_case						)

					_UpperCamelCase			     =     UNetLDMModel(**__snake_case						).eval()
					unet.load_state_dict(__snake_case						)

					_UpperCamelCase			     =     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  ,     )

					_UpperCamelCase			     =     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)








"""simple docstring"""
import numpy as np






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 1E-12 , snake_case__ = 100 , ):
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[1]
	# Ensure proper dimensionality.
	assert np.shape(snake_case__       )[0] == np.shape(snake_case__       )[0]
	# Ensure inputs are either both complex or both real
	assert np.iscomplexobj(snake_case__       ) == np.iscomplexobj(snake_case__       )
	A_		:    Union[str, Any]						     = np.iscomplexobj(snake_case__       )
	if is_complex:
		# Ensure complex input_matrix is Hermitian
		assert np.array_equal(snake_case__ , input_matrix.conj().T       )

	# Set convergence to False. Will define convergence when we exceed max_iterations
	# or when we have small changes from one iteration to next.

	A_		:    List[str]						     = False
	A_		:    int						     = 0
	A_		:    List[Any]						     = 0
	A_		:    Tuple						     = 1E12

	while not convergence:
		# Multiple matrix by the vector.
		A_		:    Dict						     = np.dot(snake_case__ , snake_case__       )
		# Normalize the resulting output vector.
		A_		:    str						     = w / np.linalg.norm(snake_case__       )
		# Find rayleigh quotient
		# (faster than usual b/c we know vector is normalized already)
		A_		:    str						     = vector.conj().T if is_complex else vector.T
		A_		:    Optional[Any]						     = np.dot(snake_case__ , np.dot(snake_case__ , snake_case__       )       )

		# Check convergence.
		A_		:    Any						     = np.abs(lambda_ - lambda_previous       ) / lambda_
		iterations += 1

		if error <= error_tol or iterations >= max_iterations:
			A_		:    Optional[Any]						     = True

		A_		:    Union[str, Any]						     = lambda_

	if is_complex:
		A_		:    str						     = np.real(lambda_       )

	return lambda_, vector






def       __UpperCamelCase						(		):
	A_		:    Optional[int]						     = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]       )
	A_		:    Tuple						     = np.array([41, 4, 20]       )
	A_		:    Tuple						     = real_input_matrix.astype(np.complexaaa       )
	A_		:    List[Any]						     = np.triu(1J * complex_input_matrix , 1       )
	complex_input_matrix += imag_matrix
	complex_input_matrix += -1 * imag_matrix.T
	A_		:    Any						     = np.array([41, 4, 20]       ).astype(np.complexaaa       )

	for problem_type in ["real", "complex"]:
		if problem_type == "real":
			A_		:    Optional[int]						     = real_input_matrix
			A_		:    List[str]						     = real_vector
		elif problem_type == "complex":
			A_		:    List[Any]						     = complex_input_matrix
			A_		:    str						     = complex_vector

		# Our implementation.
		A_     ,  A_		:    Dict						     = power_iteration(snake_case__ , snake_case__       )

		# Numpy implementation.

		# Get eigenvalues and eigenvectors using built-in numpy
		# eigh (eigh used for symmetric or hermetian matrices).
		A_     ,  A_		:    Optional[Any]						     = np.linalg.eigh(snake_case__       )
		# Last eigenvalue is the maximum one.
		A_		:    List[Any]						     = eigen_values[-1]
		# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
		A_		:    Optional[Any]						     = eigen_vectors[:, -1]

		# Check our implementation and numpy gives close answers.
		assert np.abs(eigen_value - eigen_value_max       ) <= 1E-6
		# Take absolute values element wise of each eigenvector.
		# as they are only unique to a minus sign.
		assert np.linalg.norm(np.abs(snake_case__       ) - np.abs(snake_case__       )       ) <= 1E-6


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							test_power_iteration()

