
import numpy as np


def 						UpperCAmelCase			(	a_		,   a_		,   a_ = 1E-12		,   a_ = 1_0_0		,   )		->	tuple[float, np.ndarray]:







				"""simple docstring"""



				assert np.shape(a_						)[0] == np.shape(a_						)[1]
				# Ensure proper dimensionality.
				assert np.shape(a_						)[0] == np.shape(a_						)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(a_						) == np.iscomplexobj(a_						)
				__A   			= np.iscomplexobj(a_						)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(a_		,   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   			= False
				__A   			= 0
				__A   			= 0
				__A   			= 1E12

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

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

								if error <= error_tol or iterations >= max_iterations:
												__A   			= True

								__A   			= lambda_

				if is_complex:
								__A   			= np.real(lambda_						)

				return lambda_, vector


def 						UpperCAmelCase			(	)		->	None:







				"""simple docstring"""



				__A   			= np.array([[4_1, 4, 2_0], [4, 2_6, 3_0], [2_0, 3_0, 5_0]]						)
				__A   			= np.array([4_1, 4, 2_0]						)
				__A   			= real_input_matrix.astype(np.complexaaa						)
				__A   			= np.triu(1J * complex_input_matrix		,   1						)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				__A   			= np.array([4_1, 4, 2_0]						).astype(np.complexaaa						)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												__A   			= real_input_matrix
												__A   			= real_vector
								elif problem_type == "complex":
												__A   			= complex_input_matrix
												__A   			= complex_vector

								# Our implementation.
								__A			,							__A   			= power_iteration(a_		,   a_						)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								__A			,							__A   			= np.linalg.eigh(a_						)
								# Last eigenvalue is the maximum one.
								__A   			= eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								__A   			= 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(a_						) - np.abs(a_						)						) <= 1E-6


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					test_power_iteration()


import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--repo_path',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the architecture.',
					)

					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    'image_size': 'sample_size',
					    'num_res_blocks': 'layers_per_block',
					    'block_channels': 'block_out_channels',
					    'down_blocks': 'down_block_types',
					    'up_blocks': 'up_block_types',
					    'downscale_freq_shift': 'freq_shift',
					    'resnet_num_groups': 'norm_num_groups',
					    'resnet_act_fn': 'act_fn',
					    'resnet_eps': 'norm_eps',
					    'num_head_channels': 'attention_head_dim',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					SCREAMING_SNAKE_CASE							:int							 =				'' if has_file(args.repo_path, 'config.json') else 'unet'

					with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
										SCREAMING_SNAKE_CASE							:Dict							 =				reader.read()
										SCREAMING_SNAKE_CASE							:List[str]							 =				json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, 'config.json'):
										SCREAMING_SNAKE_CASE							:Optional[int]							 =				UNetaDModel(**config)
					else:
										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										SCREAMING_SNAKE_CASE							:List[str]							 =				class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				SCREAMING_SNAKE_CASE							:Optional[Any]							 =				config[key]
																				del config[key]

										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[k.replace('UNetRes', '') for k in config['down_block_types']]
										SCREAMING_SNAKE_CASE							:List[Any]							 =				[k.replace('UNetRes', '') for k in config['up_block_types']]

					if do_only_weights:
										SCREAMING_SNAKE_CASE							:Tuple							 =				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

										SCREAMING_SNAKE_CASE							:Any							 =				{}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																				continue
															SCREAMING_SNAKE_CASE							:List[str]							 =				False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('.')[0] == key:
																									SCREAMING_SNAKE_CASE							:List[Any]							 =				param_value
																									SCREAMING_SNAKE_CASE							:str							 =				True
															if not has_changed:
																				SCREAMING_SNAKE_CASE							:List[str]							 =				param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))
