


"""simple docstring"""
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__SCREAMING_SNAKE_CASE							:      Any						=      False
__SCREAMING_SNAKE_CASE							:      int						=      True
__SCREAMING_SNAKE_CASE							:      int						=      False


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Dict						=      argparse.ArgumentParser()

				parser.add_argument(
				    '--repo_path',
				    default=None,
				    type=str,
				    required=True,
				    help='The config json file corresponding to the architecture.',
				)

				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

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

				__SCREAMING_SNAKE_CASE							:      List[str]						=      {
				    'image_size': 'sample_size',
				    'num_res_blocks': 'layers_per_block',
				    'block_channels': 'block_out_channels',
				    'down_blocks': 'down_block_types',
				    'up_blocks': 'up_block_types',
				    'downscale_freq_shift': 'freq_shift',
				    'resnet_num_groups': 'norm_num_groups',
				    'resnet_act_fn': 'act_fn',
				    'resnet_eps': 'norm_eps',
				    'num_head_channels': 'attention_head_dim',
				}

				__SCREAMING_SNAKE_CASE							:      List[str]						=      {
				    'time_steps': 'time_proj',
				    'mid': 'mid_block',
				    'downsample_blocks': 'down_blocks',
				    'upsample_blocks': 'up_blocks',
				}

				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      '' if has_file(args.repo_path, 'config.json') else 'unet'

				with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
								__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      reader.read()
								__SCREAMING_SNAKE_CASE							:      List[Any]						=      json.loads(text)

				if do_only_config:
								for key in config_parameters_to_change.keys():
												config.pop(key, None)

				if has_file(args.repo_path, 'config.json'):
								__SCREAMING_SNAKE_CASE							:      List[str]						=      UNetaDModel(**config)
				else:
								__SCREAMING_SNAKE_CASE							:      str						=      UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
								__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      class_name(**config)

				if do_only_config:
								model.save_config(os.path.join(args.repo_path, subfolder))

				__SCREAMING_SNAKE_CASE							:      List[Any]						=      dict(model.config)

				if do_only_renaming:
								for key, value in config_parameters_to_change.items():
												if key in config:
																__SCREAMING_SNAKE_CASE							:      List[str]						=      config[key]
																del config[key]

								__SCREAMING_SNAKE_CASE							:      Dict						=      [k.replace('UNetRes', '') for k in config['down_block_types']]
								__SCREAMING_SNAKE_CASE							:      Tuple						=      [k.replace('UNetRes', '') for k in config['up_block_types']]

				if do_only_weights:
								__SCREAMING_SNAKE_CASE							:      Dict						=      torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

								__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      {}
								for param_key, param_value in state_dict.items():
												if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																continue
												__SCREAMING_SNAKE_CASE							:      str						=      False
												for key, new_key in key_parameters_to_change.items():
																if not has_changed and param_key.split('.')[0] == key:
																				__SCREAMING_SNAKE_CASE							:      Union[str, Any]						=      param_value
																				__SCREAMING_SNAKE_CASE							:      int						=      True
												if not has_changed:
																__SCREAMING_SNAKE_CASE							:      Dict						=      param_value

								model.load_state_dict(new_state_dict)
								model.save_pretrained(os.path.join(args.repo_path, subfolder))







"""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()))



