






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 		_UpperCamelCase				(      UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   Dict						)					->				Dict:

						"""simple docstring"""



						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(UpperCamelCase_						)
						}

						# 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(UpperCamelCase_						)
						}

						for i in range(UpperCamelCase_						):
												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(UpperCamelCase_						)
												lowerCAmelCase__        =  {'old': F"down.{i}.block", 'new': F"down_blocks.{i}.resnets"}
												assign_to_checkpoint(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    additional_replacements=[meta_path]      ,    config=UpperCamelCase_						)

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

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

						for i in range(UpperCamelCase_						):
												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(UpperCamelCase_						)
												lowerCAmelCase__        =  {'old': F"up.{block_id}.block", 'new': F"up_blocks.{i}.resnets"}
												assign_to_checkpoint(UpperCamelCase_      ,    UpperCamelCase_      ,    UpperCamelCase_      ,    additional_replacements=[meta_path]      ,    config=UpperCamelCase_						)

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

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







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   str      ,    )					->				Optional[int]:

						"""simple docstring"""



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

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

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

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


if __name__ == "__main__":
					__snake_case      :      Dict             =       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      :      Dict             =       parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)













def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				str:

						"""simple docstring"""



						return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





