


"""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   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			List[str]:
							snake_case_          =						checkpoint

							snake_case_          =						{}

							snake_case_          =						vae_state_dict["""encoder.conv_in.weight"""]
							snake_case_          =						vae_state_dict["""encoder.conv_in.bias"""]
							snake_case_          =						vae_state_dict["""encoder.conv_out.weight"""]
							snake_case_          =						vae_state_dict["""encoder.conv_out.bias"""]
							snake_case_          =						vae_state_dict["""encoder.norm_out.weight"""]
							snake_case_          =						vae_state_dict["""encoder.norm_out.bias"""]

							snake_case_          =						vae_state_dict["""decoder.conv_in.weight"""]
							snake_case_          =						vae_state_dict["""decoder.conv_in.bias"""]
							snake_case_          =						vae_state_dict["""decoder.conv_out.weight"""]
							snake_case_          =						vae_state_dict["""decoder.conv_out.bias"""]
							snake_case_          =						vae_state_dict["""decoder.norm_out.weight"""]
							snake_case_          =						vae_state_dict["""decoder.norm_out.bias"""]

							snake_case_          =						vae_state_dict["""quant_conv.weight"""]
							snake_case_          =						vae_state_dict["""quant_conv.bias"""]
							snake_case_          =						vae_state_dict["""post_quant_conv.weight"""]
							snake_case_          =						vae_state_dict["""post_quant_conv.bias"""]

							# Retrieves the keys for the encoder down blocks only
							snake_case_          =						len({""".""".join(layer.split("""."""			)[:3]			) for layer in vae_state_dict if """encoder.down""" in layer}			)
							snake_case_          =						{
							    layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(_lowerCamelCase			)
							}

							# Retrieves the keys for the decoder up blocks only
							snake_case_          =						len({""".""".join(layer.split("""."""			)[:3]			) for layer in vae_state_dict if """decoder.up""" in layer}			)
							snake_case_          =						{
							    layer_id: [key for key in vae_state_dict if f"""up.{layer_id}""" in key] for layer_id in range(_lowerCamelCase			)
							}

							for i in range(_lowerCamelCase			):
														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:
																					snake_case_          =						vae_state_dict.pop(
																					    f"""encoder.down.{i}.downsample.conv.weight"""			)
																					snake_case_          =						vae_state_dict.pop(
																					    f"""encoder.down.{i}.downsample.conv.bias"""			)

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

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

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

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

							for i in range(_lowerCamelCase			):
														snake_case_          =						num_up_blocks - 1 - i
														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:
																					snake_case_          =						vae_state_dict[
																					    f"""decoder.up.{block_id}.upsample.conv.weight"""
																					]
																					snake_case_          =						vae_state_dict[
																					    f"""decoder.up.{block_id}.upsample.conv.bias"""
																					]

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

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

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

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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	)      ->			List[Any]:
							snake_case_          =						requests.get(
							    """ https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"""			)
							snake_case_          =						io.BytesIO(r.content			)

							snake_case_          =						OmegaConf.load(_lowerCamelCase			)
							snake_case_          =						512
							snake_case_          =						"""cuda""" if torch.cuda.is_available() else """cpu"""
							if checkpoint_path.endswith("""safetensors"""			):
														from safetensors import safe_open

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

							# Convert the VAE model.
							snake_case_          =						create_vae_diffusers_config(_lowerCamelCase			,	image_size=_lowerCamelCase			)
							snake_case_          =						custom_convert_ldm_vae_checkpoint(_lowerCamelCase			,	_lowerCamelCase			)

							snake_case_          =						AutoencoderKL(**_lowerCamelCase			)
							vae.load_state_dict(_lowerCamelCase			)
							vae.save_pretrained(_lowerCamelCase			)


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

			__SCREAMING_SNAKE_CASE							:      Optional[int]						=      parser.parse_args()

			vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)







"""simple docstring"""
from collections import namedtuple

import requests
from lxml import html  # type: ignore

__SCREAMING_SNAKE_CASE							:      List[str]						=      namedtuple('covid_data', 'cases deaths recovered')







def   _a			( _SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus/"			)      ->			covid_data:
							snake_case_          =						"""//div[@class = \"maincounter-number\"]/span/text()"""
							return covid_data(*html.fromstring(requests.get(_SCREAMING_SNAKE_CASE			).content			).xpath(_SCREAMING_SNAKE_CASE			)			)


__SCREAMING_SNAKE_CASE							:      List[str]						=      'Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}'





print(fmt.format(*covid_stats()))



