


'''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    (    _A    :				List[Any]					,   _A    :				Any				)    ->      Any:




	"""simple docstring"""





	lowerCAmelCase					:    Dict      							= checkpoint

	lowerCAmelCase					:    Optional[Any]      							= {}

	lowerCAmelCase					:    Optional[Any]      							= vae_state_dict["""encoder.conv_in.weight"""]
	lowerCAmelCase					:    Any      							= vae_state_dict["""encoder.conv_in.bias"""]
	lowerCAmelCase					:    int      							= vae_state_dict["""encoder.conv_out.weight"""]
	lowerCAmelCase					:    List[Any]      							= vae_state_dict["""encoder.conv_out.bias"""]
	lowerCAmelCase					:    List[str]      							= vae_state_dict["""encoder.norm_out.weight"""]
	lowerCAmelCase					:    Any      							= vae_state_dict["""encoder.norm_out.bias"""]

	lowerCAmelCase					:    Any      							= vae_state_dict["""decoder.conv_in.weight"""]
	lowerCAmelCase					:    List[Any]      							= vae_state_dict["""decoder.conv_in.bias"""]
	lowerCAmelCase					:    Union[str, Any]      							= vae_state_dict["""decoder.conv_out.weight"""]
	lowerCAmelCase					:    int      							= vae_state_dict["""decoder.conv_out.bias"""]
	lowerCAmelCase					:    Any      							= vae_state_dict["""decoder.norm_out.weight"""]
	lowerCAmelCase					:    List[str]      							= vae_state_dict["""decoder.norm_out.bias"""]

	lowerCAmelCase					:    Any      							= vae_state_dict["""quant_conv.weight"""]
	lowerCAmelCase					:    str      							= vae_state_dict["""quant_conv.bias"""]
	lowerCAmelCase					:    List[Any]      							= vae_state_dict["""post_quant_conv.weight"""]
	lowerCAmelCase					:    Any      							= vae_state_dict["""post_quant_conv.bias"""]

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

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

	for i in range(snake_case_				):
		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					:    Dict      							= vae_state_dict.pop(
			    F"encoder.down.{i}.downsample.conv.weight"				)
			lowerCAmelCase					:    int      							= vae_state_dict.pop(
			    F"encoder.down.{i}.downsample.conv.bias"				)

		lowerCAmelCase					:    Dict      							= renew_vae_resnet_paths(snake_case_				)
		lowerCAmelCase					:    Tuple      							= {"""old""": F"down.{i}.block", """new""": F"down_blocks.{i}.resnets"}
		assign_to_checkpoint(snake_case_					,   snake_case_					,   snake_case_					,   additional_replacements=[meta_path]					,   config=snake_case_				)

	lowerCAmelCase					:    Tuple      							= [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					:    Dict      							= [key for key in mid_resnets if F"encoder.mid.block_{i}" in key]

		lowerCAmelCase					:    Any      							= renew_vae_resnet_paths(snake_case_				)
		lowerCAmelCase					:    Tuple      							= {"""old""": F"mid.block_{i}", """new""": F"mid_block.resnets.{i - 1}"}
		assign_to_checkpoint(snake_case_					,   snake_case_					,   snake_case_					,   additional_replacements=[meta_path]					,   config=snake_case_				)

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

	for i in range(snake_case_				):
		lowerCAmelCase					:    Dict      							= num_up_blocks - 1 - i
		lowerCAmelCase					:    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					:    Tuple      							= vae_state_dict[
			    F"decoder.up.{block_id}.upsample.conv.weight"
			]
			lowerCAmelCase					:    Union[str, Any]      							= vae_state_dict[
			    F"decoder.up.{block_id}.upsample.conv.bias"
			]

		lowerCAmelCase					:    List[str]      							= renew_vae_resnet_paths(snake_case_				)
		lowerCAmelCase					:    Union[str, Any]      							= {"""old""": F"up.{block_id}.block", """new""": F"up_blocks.{i}.resnets"}
		assign_to_checkpoint(snake_case_					,   snake_case_					,   snake_case_					,   additional_replacements=[meta_path]					,   config=snake_case_				)

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

		lowerCAmelCase					:    List[Any]      							= renew_vae_resnet_paths(snake_case_				)
		lowerCAmelCase					:    Any      							= {"""old""": F"mid.block_{i}", """new""": F"mid_block.resnets.{i - 1}"}
		assign_to_checkpoint(snake_case_					,   snake_case_					,   snake_case_					,   additional_replacements=[meta_path]					,   config=snake_case_				)

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

def 			__UpperCamelCase    (    _A    :				List[str]					,   _A    :				List[str]					,   )    ->      Any:




	"""simple docstring"""





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

	lowerCAmelCase					:    Optional[Any]      							= OmegaConf.load(snake_case_				)
	lowerCAmelCase					:    Tuple      							= 5_12
	lowerCAmelCase					:    str      							= """cuda""" if torch.cuda.is_available() else """cpu"""
	if checkpoint_path.endswith('safetensors'				):
		from safetensors import safe_open

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

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

	lowerCAmelCase					:    List[str]      							= AutoencoderKL(**snake_case_				)
	vae.load_state_dict(snake_case_				)
	vae.save_pretrained(snake_case_				)


if __name__ == "__main__":
	_lowerCAmelCase  :		Union[str, 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.')

	_lowerCAmelCase  :		Dict		     =	parser.parse_args()

	vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				int				)    ->      bool:




	"""simple docstring"""





	return number & 1 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
