
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    a      ( a		,						a	)     ->int:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= checkpoint

			SCREAMING_SNAKE_CASE     						= {}

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

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

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

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

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

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

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

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

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

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

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

			SCREAMING_SNAKE_CASE     						= [key for key in vae_state_dict if '''decoder.mid.attn''' in key]
			SCREAMING_SNAKE_CASE     						= renew_vae_attention_paths(a	)
			SCREAMING_SNAKE_CASE     						= {'''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    a      ( a		,						a		,						)     ->str:




			'''simple docstring'''


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

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

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

			# Convert the VAE model.
			SCREAMING_SNAKE_CASE     						= create_vae_diffusers_config(a		,						image_size=a	)
			SCREAMING_SNAKE_CASE     						= custom_convert_ldm_vae_checkpoint(a		,						a	)

			SCREAMING_SNAKE_CASE     						= AutoencoderKL(**a	)
			vae.load_state_dict(a	)
			vae.save_pretrained(a	)


if __name__ == "__main__":
							__lowerCAmelCase					   =       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					   =       parser.parse_args()

							vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)

from __future__ import annotations


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						if resistor <= 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative or zero value!"""
									raise ValueError(a	)
						first_sum += 1 / float(a	)
						index += 1
			return 1 / first_sum


def    a      ( a	)     ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= 0.00
			SCREAMING_SNAKE_CASE     						= 0
			for resistor in resistors:
						sum_r += resistor
						if resistor < 0:
									SCREAMING_SNAKE_CASE     						= F"""Resistor at index {index} has a negative value!"""
									raise ValueError(a	)
						index += 1
			return sum_r


if __name__ == "__main__":
							import doctest

							doctest.testmod()