
'''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  UpperCAmelCase				(	UpperCAmelCase__				:				Tuple							,				UpperCAmelCase__				:				Any):
				lowerCamelCase       :      List[Any]          =			checkpoint

				lowerCamelCase       :      Dict          =			{}

				lowerCamelCase       :      str          =			vae_state_dict['''encoder.conv_in.weight''']
				lowerCamelCase       :      Any          =			vae_state_dict['''encoder.conv_in.bias''']
				lowerCamelCase       :      Any          =			vae_state_dict['''encoder.conv_out.weight''']
				lowerCamelCase       :      Optional[Any]          =			vae_state_dict['''encoder.conv_out.bias''']
				lowerCamelCase       :      Any          =			vae_state_dict['''encoder.norm_out.weight''']
				lowerCamelCase       :      str          =			vae_state_dict['''encoder.norm_out.bias''']

				lowerCamelCase       :      Optional[int]          =			vae_state_dict['''decoder.conv_in.weight''']
				lowerCamelCase       :      Optional[int]          =			vae_state_dict['''decoder.conv_in.bias''']
				lowerCamelCase       :      Optional[Any]          =			vae_state_dict['''decoder.conv_out.weight''']
				lowerCamelCase       :      List[Any]          =			vae_state_dict['''decoder.conv_out.bias''']
				lowerCamelCase       :      List[Any]          =			vae_state_dict['''decoder.norm_out.weight''']
				lowerCamelCase       :      Union[str, Any]          =			vae_state_dict['''decoder.norm_out.bias''']

				lowerCamelCase       :      Dict          =			vae_state_dict['''quant_conv.weight''']
				lowerCamelCase       :      Dict          =			vae_state_dict['''quant_conv.bias''']
				lowerCamelCase       :      int          =			vae_state_dict['''post_quant_conv.weight''']
				lowerCamelCase       :      str          =			vae_state_dict['''post_quant_conv.bias''']

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

				# Retrieves the keys for the decoder up blocks only
				lowerCamelCase       :      Union[str, Any]          =			len({'.'.join(layer.split('.')[:3]) for layer in vae_state_dict if 'decoder.up' in layer})
				lowerCamelCase       :      str          =			{
				    layer_id: [key for key in vae_state_dict if F'''up.{layer_id}''' in key] for layer_id in range(_A)
				}

				for i in range(_A):
								lowerCamelCase       :      Optional[int]          =			[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       :      int          =			vae_state_dict.pop(
												    F'''encoder.down.{i}.downsample.conv.weight''')
												lowerCamelCase       :      List[Any]          =			vae_state_dict.pop(
												    F'''encoder.down.{i}.downsample.conv.bias''')

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

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

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

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

				for i in range(_A):
								lowerCamelCase       :      Tuple          =			num_up_blocks - 1 - i
								lowerCamelCase       :      Optional[Any]          =			[
								    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       :      int          =			vae_state_dict[
												    F'''decoder.up.{block_id}.upsample.conv.weight'''
												]
												lowerCamelCase       :      int          =			vae_state_dict[
												    F'''decoder.up.{block_id}.upsample.conv.bias'''
												]

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

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

								lowerCamelCase       :      List[str]          =			renew_vae_resnet_paths(_A)
								lowerCamelCase       :      Optional[Any]          =			{'''old''': F'''mid.block_{i}''', '''new''': F'''mid_block.resnets.{i - 1}'''}
								assign_to_checkpoint(_A							,				_A							,				_A							,				additional_replacements=[meta_path]							,				config=_A)

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





def  UpperCAmelCase				(	UpperCAmelCase__				:				str							,				UpperCAmelCase__				:				str							,				):
				lowerCamelCase       :      Dict          =			requests.get(
				    ' https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml')
				lowerCamelCase       :      str          =			io.BytesIO(r.content)

				lowerCamelCase       :      List[str]          =			OmegaConf.load(_A)
				lowerCamelCase       :      Optional[int]          =			5_12
				lowerCamelCase       :      str          =			'''cuda''' if torch.cuda.is_available() else '''cpu'''
				if checkpoint_path.endswith('safetensors'):
								from safetensors import safe_open

								lowerCamelCase       :      Any          =			{}
								with safe_open(_A							,				framework='pt'							,				device='cpu') as f:
												for key in f.keys():
																lowerCamelCase       :      Optional[Any]          =			f.get_tensor(_A)
				else:
								lowerCamelCase       :      Union[str, Any]          =			torch.load(_A							,				map_location=_A)['''state_dict''']

				# Convert the VAE model.
				lowerCamelCase       :      Optional[int]          =			create_vae_diffusers_config(_A							,				image_size=_A)
				lowerCamelCase       :      int          =			custom_convert_ldm_vae_checkpoint(_A							,				_A)

				lowerCamelCase       :      Optional[Any]          =			AutoencoderKL(**_A)
				vae.load_state_dict(_A)
				vae.save_pretrained(_A)


if __name__ == "__main__":
					A         =   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.')

					A         =   parser.parse_args()

					vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)

'''simple docstring'''

import warnings


warnings.warn(
    'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
    '`from accelerate import find_executable_batch_size` to avoid this warning.',
    FutureWarning,
)




