



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_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Tuple				)       ->							Any:

							'''simple docstring'''






							A      						=    checkpoint

							A      						=    {}

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

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

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

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

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

							for i in range(lowerCAmelCase__				):
														A      						=    [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:
																					A      						=    vae_state_dict.pop(
																					    F'''encoder.down.{i}.downsample.conv.weight'''				)
																					A      						=    vae_state_dict.pop(
																					    F'''encoder.down.{i}.downsample.conv.bias'''				)

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

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

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

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

							for i in range(lowerCAmelCase__				):
														A      						=    num_up_blocks - 1 - i
														A      						=    [
														    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:
																					A      						=    vae_state_dict[
																					    F'''decoder.up.{block_id}.upsample.conv.weight'''
																					]
																					A      						=    vae_state_dict[
																					    F'''decoder.up.{block_id}.upsample.conv.bias'''
																					]

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

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

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

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






def  lowerCamelCase_	(      lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	str			,	)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    requests.get(
							    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml'				)
							A      						=    io.BytesIO(r.content				)

							A      						=    OmegaConf.load(lowerCAmelCase__				)
							A      						=    512
							A      						=    'cuda' if torch.cuda.is_available() else 'cpu'
							if checkpoint_path.endswith('safetensors'				):
														from safetensors import safe_open

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

							# Convert the VAE model.
							A      						=    create_vae_diffusers_config(lowerCAmelCase__			,	image_size=lowerCAmelCase__				)
							A      						=    custom_convert_ldm_vae_checkpoint(lowerCAmelCase__			,	lowerCAmelCase__				)

							A      						=    AutoencoderKL(**lowerCAmelCase__				)
							vae.load_state_dict(lowerCAmelCase__				)
							vae.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :Any			       =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.')

				__snake_case     :Tuple			       =parser.parse_args()

				vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)





'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

