



'''simple docstring'''






import requests
from bsa import BeautifulSoup




def     snake_case_					(     lowerCAmelCase_ = "https://www.worldometers.info/coronavirus"		)->		dict:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = BeautifulSoup(requests.get(lowerCAmelCase_		).text       ,   """html.parser"""		)
       _UpperCAmelCase  :						List[str]			     = soup.findAll("""h1"""		)
       _UpperCAmelCase  :						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(lowerCAmelCase_       ,   lowerCAmelCase_		)}


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 shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = """laion/clap-htsat-unfused"""
        _UpperCAmelCase  :						int			     = tempfile.mkdtemp()







 def _snake_case (							self  ,**a_							)		->   str:
        return RobertaTokenizer.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self  ,**a_							)		->   Tuple:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self							)		->   int:
        shutil.rmtree(self.tmpdirname							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = self.get_tokenizer()
        _UpperCAmelCase  :						Any			     = self.get_feature_extractor()

        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        processor.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						List[Any]			     = ClapProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""							)
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor(do_normalize=a_  ,padding_value=1.0							)

        _UpperCAmelCase  :						Optional[Any]			     = ClapProcessor.from_pretrained(
            self.tmpdirname  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,do_normalize=a_  ,padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Tuple			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Dict			     = self.get_tokenizer()

        _UpperCAmelCase  :						str			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Tuple			     = floats_list((3, 1_000)							)

        _UpperCAmelCase  :						int			     = feature_extractor(a_  ,return_tensors="""np"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = processor(audios=a_  ,return_tensors="""np"""							)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1E-2							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Any			     = self.get_tokenizer()

        _UpperCAmelCase  :						Optional[int]			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = """This is a test string"""

        _UpperCAmelCase  :						Optional[Any]			     = processor(text=a_							)

        _UpperCAmelCase  :						Any			     = tokenizer(a_							)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						str			     = self.get_feature_extractor()
        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer()

        _UpperCAmelCase  :						Any			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						List[str]			     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase  :						Dict			     = processor.batch_decode(a_							)
        _UpperCAmelCase  :						Any			     = tokenizer.batch_decode(a_							)

        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						List[str]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						int			     = self.get_tokenizer()

        _UpperCAmelCase  :						Dict			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        self.assertListEqual(
            processor.model_input_names[2:]  ,feature_extractor.model_input_names  ,msg="""`processor` and `feature_extractor` model input names do not match"""  ,)



