


'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     ['image_processor', 'tokenizer']
    lowerCamelCase    :   Union[str, Any]					=     'CLIPImageProcessor'
    lowerCamelCase    :   Union[str, Any]					=     ('CLIPTokenizer', 'CLIPTokenizerFast')







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      List[Any]														=						None
         if "feature_extractor" in kwargs:
              warnings.warn(
                  'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                  ' instead.'     ,	SCREAMING_SNAKE_CASE_     ,	)
              __lowerCamelCase  :      Dict														=						kwargs.pop('feature_extractor'      )

         __lowerCamelCase  :      List[Any]														=						image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
              raise ValueError('You need to specify an `image_processor`.'      )
         if tokenizer is None:
              raise ValueError('You need to specify a `tokenizer`.'      )

         super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	Dict:

         if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'      )

         if text is not None:
              __lowerCamelCase  :      Dict														=						self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         if images is not None:
              __lowerCamelCase  :      List[str]														=						self.image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         if text is not None and images is not None:
              __lowerCamelCase  :      str														=						image_features.pixel_values
              return encoding
         elif text is not None:
              return encoding
         else:
              return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_      )     ,	tensor_type=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	List[str]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Dict:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    @property
    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						self.tokenizer.model_input_names
         __lowerCamelCase  :      List[Any]														=						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







    @property
    def  lowercase_							(				self      )							->	Dict:
         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,	SCREAMING_SNAKE_CASE_     ,	)
         return self.image_processor_class







    @property
    def  lowercase_							(				self      )							->	List[str]:
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,	SCREAMING_SNAKE_CASE_     ,	)
         return self.image_processor










'''simple docstring'''



from collections import namedtuple

import requests
from lxml import html  # type: ignore

A__						:   Tuple				=    namedtuple("""covid_data""", """cases deaths recovered""")



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str = "https://www.worldometers.info/coronavirus/"				)	->						covid_data:
     __lowerCamelCase  :      Union[str, Any]														=						'//div[@class = "maincounter-number"]/span/text()'
     return covid_data(*html.fromstring(requests.get(UpperCAmelCase_				).content				).xpath(UpperCAmelCase_				)				)


A__						:   str				=    """Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""

print(fmt.format(*covid_stats()))






