
'''simple docstring'''
import requests
from bsa import BeautifulSoup

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE = "https://www.worldometers.info/coronavirus"       ):
    """simple docstring"""






    snake_case_ :    int     			=						BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE       ).text,		"html.parser"       )
    snake_case_ :    List[str]     			=						soup.findAll("h1"       )
    snake_case_ :    List[Any]     			=						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""")






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




