

import requests
from bsa import BeautifulSoup




def 					_lowercase     (				a_				:					str = "https://www.worldometers.info/coronavirus"				)				->	dict:




					'''simple docstring'''







					__magic_name__							=    BeautifulSoup(requests.get(a_				).text					,'html.parser'				)
					__magic_name__							=    soup.findAll('h1'				)
					__magic_name__							=    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(a_					,a_				)}


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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




