






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    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  int	,			__magic_name__					:  Any					)		->			Any:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	checkpoint

	UpperCamelCase     :Optional[int]          =	{}

	UpperCamelCase     :Any          =	vae_state_dict["""encoder.conv_in.weight"""]
	UpperCamelCase     :Union[str, Any]          =	vae_state_dict["""encoder.conv_in.bias"""]
	UpperCamelCase     :Optional[int]          =	vae_state_dict["""encoder.conv_out.weight"""]
	UpperCamelCase     :str          =	vae_state_dict["""encoder.conv_out.bias"""]
	UpperCamelCase     :str          =	vae_state_dict["""encoder.norm_out.weight"""]
	UpperCamelCase     :Optional[int]          =	vae_state_dict["""encoder.norm_out.bias"""]

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

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

	# Retrieves the keys for the encoder down blocks only
	UpperCamelCase     :List[str]          =	len({""".""".join(layer.split("""."""					)[:3]					) for layer in vae_state_dict if """encoder.down""" in layer}					)
	UpperCamelCase     :Optional[int]          =	{
	    layer_id: [key for key in vae_state_dict if f"""down.{layer_id}""" in key] for layer_id in range(__magic_name__					)
	}

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

	for i in range(__magic_name__					):
		UpperCamelCase     :Tuple          =	[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     :Any          =	vae_state_dict.pop(
			    f"""encoder.down.{i}.downsample.conv.weight"""					)
			UpperCamelCase     :Union[str, Any]          =	vae_state_dict.pop(
			    f"""encoder.down.{i}.downsample.conv.bias"""					)

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

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

		UpperCamelCase     :List[Any]          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :str          =	{"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

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

	for i in range(__magic_name__					):
		UpperCamelCase     :Optional[int]          =	num_up_blocks - 1 - i
		UpperCamelCase     :List[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:
			UpperCamelCase     :str          =	vae_state_dict[
			    f"""decoder.up.{block_id}.upsample.conv.weight"""
			]
			UpperCamelCase     :int          =	vae_state_dict[
			    f"""decoder.up.{block_id}.upsample.conv.bias"""
			]

		UpperCamelCase     :int          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :List[Any]          =	{"""old""": f"""up.{block_id}.block""", """new""": f"""up_blocks.{i}.resnets"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

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

		UpperCamelCase     :List[str]          =	renew_vae_resnet_paths(__magic_name__					)
		UpperCamelCase     :int          =	{"""old""": f"""mid.block_{i}""", """new""": f"""mid_block.resnets.{i - 1}"""}
		assign_to_checkpoint(__magic_name__	,			__magic_name__	,			__magic_name__	,			additional_replacements=[meta_path]	,			config=__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  str	,			)		->			Optional[int]:






	"""simple docstring"""



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

	UpperCamelCase     :str          =	OmegaConf.load(__magic_name__					)
	UpperCamelCase     :str          =	512
	UpperCamelCase     :List[Any]          =	"""cuda""" if torch.cuda.is_available() else """cpu"""
	if checkpoint_path.endswith("""safetensors"""					):
		from safetensors import safe_open

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

	# Convert the VAE model.
	UpperCamelCase     :Union[str, Any]          =	create_vae_diffusers_config(__magic_name__	,			image_size=__magic_name__					)
	UpperCamelCase     :Union[str, Any]          =	custom_convert_ldm_vae_checkpoint(__magic_name__	,			__magic_name__					)

	UpperCamelCase     :str          =	AutoencoderKL(**__magic_name__					)
	vae.load_state_dict(__magic_name__					)
	vae.save_pretrained(__magic_name__					)


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

	UpperCAmelCase_ :						Any      =      parser.parse_args()

	vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)




'''simple docstring'''



import requests
from bsa import BeautifulSoup






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   str = "https://www.worldometers.info/coronavirus"   ):
					__a			:       List[Any]					    =  BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE   ).text						,      'html.parser'   )
					__a			:       Union[str, Any]					    =  soup.findAll('h1'   )
					__a			:       int					    =  soup.findAll('div'						,      {'class': 'maincounter-number'}   )
					keys += soup.findAll('span'						,      {'class': 'panel-title'}   )
					values += soup.findAll('div'						,      {'class': 'number-table-main'}   )
					return {key.text.strip(): value.text.strip() for key, value in zip(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )}


if __name__ == "__main__":
	print('\033[1m' + 'COVID-19 Status of the World' + '\033[0m\n')
	for key, value in world_covidaa_stats().items():
		print(f'''{key}\n{value}\n''')



