"""simple docstring"""
import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


SCREAMING_SNAKE_CASE__					  =     version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")





def       lowerCAmelCase__			(	_UpperCamelCase				: Tuple       ,	_UpperCamelCase				: tuple       ,	_UpperCamelCase				: Path       ,	_UpperCamelCase				: Optional[int]       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: Optional[Any]       ,	_UpperCamelCase				: Union[str, Any]       ,	_UpperCamelCase				: List[str]=False       ,	)    -> str:





						"""simple docstring"""






						output_path.parent.mkdir(parents=_SCREAMING_SNAKE_CASE       ,	exist_ok=_SCREAMING_SNAKE_CASE    )
						# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
						# so we check the torch version for backwards compatibility
						if is_torch_less_than_1_11:
												export(
												    _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	f=output_path.as_posix()       ,	input_names=_SCREAMING_SNAKE_CASE       ,	output_names=_SCREAMING_SNAKE_CASE       ,	dynamic_axes=_SCREAMING_SNAKE_CASE       ,	do_constant_folding=_SCREAMING_SNAKE_CASE       ,	use_external_data_format=_SCREAMING_SNAKE_CASE       ,	enable_onnx_checker=_SCREAMING_SNAKE_CASE       ,	opset_version=_SCREAMING_SNAKE_CASE       ,	)
						else:
												export(
												    _SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	f=output_path.as_posix()       ,	input_names=_SCREAMING_SNAKE_CASE       ,	output_names=_SCREAMING_SNAKE_CASE       ,	dynamic_axes=_SCREAMING_SNAKE_CASE       ,	do_constant_folding=_SCREAMING_SNAKE_CASE       ,	opset_version=_SCREAMING_SNAKE_CASE       ,	)





@torch.no_grad()
def       lowerCAmelCase__			(	_UpperCamelCase				: str       ,	_UpperCamelCase				: str       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: bool = False    )    -> Dict:





						"""simple docstring"""






						snake_case	    =	torch.floataa if fpaa else torch.floataa
						if fpaa and torch.cuda.is_available():
												snake_case	    =	'cuda'
						elif fpaa and not torch.cuda.is_available():
												raise ValueError('`float16` model export is only supported on GPUs with CUDA'    )
						else:
												snake_case	    =	'cpu'
						snake_case	    =	Path(_SCREAMING_SNAKE_CASE    )

						# VAE DECODER
						snake_case	    =	AutoencoderKL.from_pretrained(model_path + '/vae'    )
						snake_case	    =	vae_decoder.config.latent_channels
						# forward only through the decoder part
						snake_case	    =	vae_decoder.decode
						onnx_export(
						    _SCREAMING_SNAKE_CASE       ,	model_args=(
						        torch.randn(1       ,	_SCREAMING_SNAKE_CASE       ,	2_5       ,	2_5    ).to(device=_SCREAMING_SNAKE_CASE       ,	dtype=_SCREAMING_SNAKE_CASE    ),
						        False,
						    )       ,	output_path=output_path / 'vae_decoder' / 'model.onnx'       ,	ordered_input_names=['latent_sample', 'return_dict']       ,	output_names=['sample']       ,	dynamic_axes={
						        'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
						    }       ,	opset=_SCREAMING_SNAKE_CASE       ,	)
						del vae_decoder


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__					  =     argparse.ArgumentParser()

					parser.add_argument(
					    "--model_path",
					    type=str,
					    required=True,
					    help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
					)

					parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
					parser.add_argument(
					    "--opset",
					    default=14,
					    type=int,
					    help="The version of the ONNX operator set to use.",
					)
					parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

					SCREAMING_SNAKE_CASE__					  =     parser.parse_args()
					print(args.output_path)
					convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
					print("SD: Done: ONNX")






'''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''')



