





import requests
from bsa import BeautifulSoup





def      _lowercase					(						_UpperCAmelCase = "https://www.worldometers.info/coronavirus"				)						->  dict:
				lowerCamelCase	 =BeautifulSoup(requests.get(_UpperCAmelCase				).text			,      """html.parser"""				)
				lowerCamelCase	 =soup.findAll("""h1"""				)
				lowerCamelCase	 =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(_UpperCAmelCase			,      _UpperCAmelCase				)}


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")












import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=10_24			,      _UpperCAmelCase=False			,      **_UpperCAmelCase				)						->  Optional[Any]:
				lowerCamelCase	 =AutoTokenizer.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""train"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =tok.pad_token_id

				def get_lens(_UpperCAmelCase				):
								lowerCamelCase	 =tqdm(
								    DataLoader(_UpperCAmelCase			,      batch_size=5_12			,      num_workers=8			,      shuffle=_UpperCAmelCase			,      collate_fn=ds.collate_fn				)			,      desc=str(ds.len_file				)			,      )
								lowerCamelCase	 =[]
								for batch in dl:
												lowerCamelCase	 =batch["""input_ids"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												lowerCamelCase	 =batch["""labels"""].ne(_UpperCAmelCase				).sum(1				).tolist()
												if consider_target:
																for src, tgt in zip(_UpperCAmelCase			,      _UpperCAmelCase				):
																				max_lens.append(max(_UpperCAmelCase			,      _UpperCAmelCase				)				)
												else:
																max_lens.extend(_UpperCAmelCase				)
								return max_lens

				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				lowerCamelCase	 =SeqaSeqDataset(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      type_path="""val"""			,      **_UpperCAmelCase				)
				lowerCamelCase	 =get_lens(_UpperCAmelCase				)
				pickle_save(_UpperCAmelCase			,      train_ds.len_file				)
				pickle_save(_UpperCAmelCase			,      val_ds.len_file				)


if __name__ == "__main__":
							fire.Fire(save_len_file)





