


'''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__				(	A       :      Dict				,       A       :      str					):




						'''simple docstring'''






						UpperCAmelCase					    =     checkpoint

						UpperCAmelCase					    =     {}

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

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

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

						# Retrieves the keys for the encoder down blocks only
						UpperCAmelCase					    =     len({'''.'''.join(layer.split('''.'''					)[:3]					) for layer in vae_state_dict if '''encoder.down''' in layer}					)
						UpperCAmelCase					    =     {
						    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
						UpperCAmelCase					    =     len({'''.'''.join(layer.split('''.'''					)[:3]					) for layer in vae_state_dict if '''decoder.up''' in layer}					)
						UpperCAmelCase					    =     {
						    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					):
												UpperCAmelCase					    =     [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:
																		UpperCAmelCase					    =     vae_state_dict.pop(
																		    f"""encoder.down.{i}.downsample.conv.weight"""					)
																		UpperCAmelCase					    =     vae_state_dict.pop(
																		    f"""encoder.down.{i}.downsample.conv.bias"""					)

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

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

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

						UpperCAmelCase					    =     [key for key in vae_state_dict if '''encoder.mid.attn''' in key]
						UpperCAmelCase					    =     renew_vae_attention_paths(A					)
						UpperCAmelCase					    =     {'''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					):
												UpperCAmelCase					    =     num_up_blocks - 1 - i
												UpperCAmelCase					    =     [
												    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:
																		UpperCAmelCase					    =     vae_state_dict[
																		    f"""decoder.up.{block_id}.upsample.conv.weight"""
																		]
																		UpperCAmelCase					    =     vae_state_dict[
																		    f"""decoder.up.{block_id}.upsample.conv.bias"""
																		]

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

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

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

						UpperCAmelCase					    =     [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
						UpperCAmelCase					    =     renew_vae_attention_paths(A					)
						UpperCAmelCase					    =     {'''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 				lowerCamelCase__				(	A       :      str				,       A       :      str				,       ):




						'''simple docstring'''






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

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

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

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

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


if __name__ == "__main__":
	_lowercase      :      Tuple            =					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.""")

	_lowercase      :      Dict            =					parser.parse_args()

	vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)









'''simple docstring'''



def 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						while a != 0:
												UpperCAmelCase    ,    UpperCAmelCase					    =     b % a, a
						return b




def 				lowerCamelCase__				(	A       :      int				,       A       :      int					):




						'''simple docstring'''






						if gcd(A				,       A					) != 1:
												UpperCAmelCase					    =     f"""mod inverse of {a!r} and {m!r} does not exist"""
												raise ValueError(A					)
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     1, 0, a
						UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     0, 1, m
						while va != 0:
												UpperCAmelCase					    =     ua // va
												UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase    ,    UpperCAmelCase					    =     (ua - q * va), (ua - q * va), (ua - q * va), va, va, va
						return ua % m





