



"""simple docstring"""
import argparse
import io

import requests
import torch
from omegaconf import OmegaConf

from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    assign_to_checkpoint,
    conv_attn_to_linear,
    create_vae_diffusers_config,
    renew_vae_attention_paths,
    renew_vae_resnet_paths,
)



def        lowerCAmelCase_(   lowercase_     :							Tuple						,   lowercase_     :							Optional[Any]							)	->       List[str]:
				_lowerCamelCase        =				checkpoint

				_lowerCamelCase        =				{}

				_lowerCamelCase        =				vae_state_dict['''encoder.conv_in.weight''']
				_lowerCamelCase        =				vae_state_dict['''encoder.conv_in.bias''']
				_lowerCamelCase        =				vae_state_dict['''encoder.conv_out.weight''']
				_lowerCamelCase        =				vae_state_dict['''encoder.conv_out.bias''']
				_lowerCamelCase        =				vae_state_dict['''encoder.norm_out.weight''']
				_lowerCamelCase        =				vae_state_dict['''encoder.norm_out.bias''']

				_lowerCamelCase        =				vae_state_dict['''decoder.conv_in.weight''']
				_lowerCamelCase        =				vae_state_dict['''decoder.conv_in.bias''']
				_lowerCamelCase        =				vae_state_dict['''decoder.conv_out.weight''']
				_lowerCamelCase        =				vae_state_dict['''decoder.conv_out.bias''']
				_lowerCamelCase        =				vae_state_dict['''decoder.norm_out.weight''']
				_lowerCamelCase        =				vae_state_dict['''decoder.norm_out.bias''']

				_lowerCamelCase        =				vae_state_dict['''quant_conv.weight''']
				_lowerCamelCase        =				vae_state_dict['''quant_conv.bias''']
				_lowerCamelCase        =				vae_state_dict['''post_quant_conv.weight''']
				_lowerCamelCase        =				vae_state_dict['''post_quant_conv.bias''']

				# Retrieves the keys for the encoder down blocks only
				_lowerCamelCase        =				len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in vae_state_dict if '''encoder.down''' in layer}							)
				_lowerCamelCase        =				{
				    layer_id: [key for key in vae_state_dict if F"""down.{layer_id}""" in key] for layer_id in range(lowercase_							)
				}

				# Retrieves the keys for the decoder up blocks only
				_lowerCamelCase        =				len({'''.'''.join(layer.split('''.'''							)[:3]							) for layer in vae_state_dict if '''decoder.up''' in layer}							)
				_lowerCamelCase        =				{
				    layer_id: [key for key in vae_state_dict if F"""up.{layer_id}""" in key] for layer_id in range(lowercase_							)
				}

				for i in range(lowercase_							):
								_lowerCamelCase        =				[key for key in down_blocks[i] if F"""down.{i}""" in key and F"""down.{i}.downsample""" not in key]

								if F"""encoder.down.{i}.downsample.conv.weight""" in vae_state_dict:
												_lowerCamelCase        =				vae_state_dict.pop(
												    F"""encoder.down.{i}.downsample.conv.weight"""							)
												_lowerCamelCase        =				vae_state_dict.pop(
												    F"""encoder.down.{i}.downsample.conv.bias"""							)

								_lowerCamelCase        =				renew_vae_resnet_paths(lowercase_							)
								_lowerCamelCase        =				{'''old''': F"""down.{i}.block""", '''new''': F"""down_blocks.{i}.resnets"""}
								assign_to_checkpoint(lowercase_						,   lowercase_						,   lowercase_						,   additional_replacements=[meta_path]						,   config=lowercase_							)

				_lowerCamelCase        =				[key for key in vae_state_dict if '''encoder.mid.block''' in key]
				_lowerCamelCase        =				2
				for i in range(1						,   num_mid_res_blocks + 1							):
								_lowerCamelCase        =				[key for key in mid_resnets if F"""encoder.mid.block_{i}""" in key]

								_lowerCamelCase        =				renew_vae_resnet_paths(lowercase_							)
								_lowerCamelCase        =				{'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
								assign_to_checkpoint(lowercase_						,   lowercase_						,   lowercase_						,   additional_replacements=[meta_path]						,   config=lowercase_							)

				_lowerCamelCase        =				[key for key in vae_state_dict if '''encoder.mid.attn''' in key]
				_lowerCamelCase        =				renew_vae_attention_paths(lowercase_							)
				_lowerCamelCase        =				{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
				assign_to_checkpoint(lowercase_						,   lowercase_						,   lowercase_						,   additional_replacements=[meta_path]						,   config=lowercase_							)
				conv_attn_to_linear(lowercase_							)

				for i in range(lowercase_							):
								_lowerCamelCase        =				num_up_blocks - 1 - i
								_lowerCamelCase        =				[
								    key for key in up_blocks[block_id] if F"""up.{block_id}""" in key and F"""up.{block_id}.upsample""" not in key
								]

								if F"""decoder.up.{block_id}.upsample.conv.weight""" in vae_state_dict:
												_lowerCamelCase        =				vae_state_dict[
												    F"""decoder.up.{block_id}.upsample.conv.weight"""
												]
												_lowerCamelCase        =				vae_state_dict[
												    F"""decoder.up.{block_id}.upsample.conv.bias"""
												]

								_lowerCamelCase        =				renew_vae_resnet_paths(lowercase_							)
								_lowerCamelCase        =				{'''old''': F"""up.{block_id}.block""", '''new''': F"""up_blocks.{i}.resnets"""}
								assign_to_checkpoint(lowercase_						,   lowercase_						,   lowercase_						,   additional_replacements=[meta_path]						,   config=lowercase_							)

				_lowerCamelCase        =				[key for key in vae_state_dict if '''decoder.mid.block''' in key]
				_lowerCamelCase        =				2
				for i in range(1						,   num_mid_res_blocks + 1							):
								_lowerCamelCase        =				[key for key in mid_resnets if F"""decoder.mid.block_{i}""" in key]

								_lowerCamelCase        =				renew_vae_resnet_paths(lowercase_							)
								_lowerCamelCase        =				{'''old''': F"""mid.block_{i}""", '''new''': F"""mid_block.resnets.{i - 1}"""}
								assign_to_checkpoint(lowercase_						,   lowercase_						,   lowercase_						,   additional_replacements=[meta_path]						,   config=lowercase_							)

				_lowerCamelCase        =				[key for key in vae_state_dict if '''decoder.mid.attn''' in key]
				_lowerCamelCase        =				renew_vae_attention_paths(lowercase_							)
				_lowerCamelCase        =				{'''old''': '''mid.attn_1''', '''new''': '''mid_block.attentions.0'''}
				assign_to_checkpoint(lowercase_						,   lowercase_						,   lowercase_						,   additional_replacements=[meta_path]						,   config=lowercase_							)
				conv_attn_to_linear(lowercase_							)
				return new_checkpoint



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							str						,   )	->       Any:
				# Only support V1
				_lowerCamelCase        =				requests.get(
				    ''' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'''							)
				_lowerCamelCase        =				io.BytesIO(r.content							)

				_lowerCamelCase        =				OmegaConf.load(lowercase_							)
				_lowerCamelCase        =				5_12
				_lowerCamelCase        =				'''cuda''' if torch.cuda.is_available() else '''cpu'''
				if checkpoint_path.endswith('''safetensors'''							):
								from safetensors import safe_open

								_lowerCamelCase        =				{}
								with safe_open(lowercase_						,   framework='''pt'''						,   device='''cpu'''							) as f:
												for key in f.keys():
																_lowerCamelCase        =				f.get_tensor(lowercase_							)
				else:
								_lowerCamelCase        =				torch.load(lowercase_						,   map_location=lowercase_							)['''state_dict''']

				# Convert the VAE model.
				_lowerCamelCase        =				create_vae_diffusers_config(lowercase_						,   image_size=lowercase_							)
				_lowerCamelCase        =				custom_convert_ldm_vae_checkpoint(lowercase_						,   lowercase_							)

				_lowerCamelCase        =				AutoencoderKL(**lowercase_							)
				vae.load_state_dict(lowercase_							)
				vae.save_pretrained(lowercase_							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						int    							=      argparse.ArgumentParser()

			parser.add_argument('''--vae_pt_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')
			parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the VAE.pt to convert.''')

			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)





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



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   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.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

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

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

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

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

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

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

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


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
