





'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """Wav2Vec2FeatureExtractor"""
   _UpperCamelCase       		=   """AutoTokenizer"""


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       super().__init__(_UpperCAmelCase    ,   _UpperCAmelCase					)
       A       :       List[Any]							=						self.feature_extractor
       A       :       List[str]							=						False


   @classmethod
   def 					snake_case				( cls    ,   _UpperCAmelCase    ,   **_UpperCAmelCase					):
       try:
           return super().from_pretrained(_UpperCAmelCase    ,   **_UpperCAmelCase					)
       except OSError:
           warnings.warn(
               f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
               ''' include a `tokenizer_class` attribute is deprecated and will be '''
               '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
               ''' attribute to either your `config.json` or `tokenizer_config.json` '''
               '''file to suppress this warning: '''    ,   _UpperCAmelCase    ,   )

           A       :       Optional[Any]							=						WavaVecaFeatureExtractor.from_pretrained(_UpperCAmelCase    ,   **_UpperCAmelCase					)
           A       :       List[Any]							=						WavaVecaCTCTokenizer.from_pretrained(_UpperCAmelCase    ,   **_UpperCAmelCase					)

           return cls(feature_extractor=_UpperCAmelCase    ,   tokenizer=_UpperCAmelCase					)


   def __call__( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*_UpperCAmelCase    ,   **_UpperCAmelCase					)

       if "raw_speech" in kwargs:
           warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''					)
           A       :       Union[str, Any]							=						kwargs.pop('''raw_speech'''					)
       else:
           A       :       Any							=						kwargs.pop('''audio'''    ,   _UpperCAmelCase					)
       A       :       Any							=						kwargs.pop('''sampling_rate'''    ,   _UpperCAmelCase					)
       A       :       Dict							=						kwargs.pop('''text'''    ,   _UpperCAmelCase					)
       if len(_UpperCAmelCase					) > 0:
           A       :       Tuple							=						args[0]
           A       :       int							=						args[1:]

       if audio is None and text is None:
           raise ValueError('''You need to specify either an `audio` or `text` input to process.'''					)

       if audio is not None:
           A       :       List[Any]							=						self.feature_extractor(_UpperCAmelCase    ,   *_UpperCAmelCase    ,   sampling_rate=_UpperCAmelCase    ,   **_UpperCAmelCase					)
       if text is not None:
           A       :       Tuple							=						self.tokenizer(_UpperCAmelCase    ,   **_UpperCAmelCase					)

       if text is None:
           return inputs
       elif audio is None:
           return encodings
       else:
           A       :       List[str]							=						encodings['''input_ids''']
           return inputs


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor.pad(*_UpperCAmelCase    ,   **_UpperCAmelCase					)

       A       :       Optional[Any]							=						kwargs.pop('''input_features'''    ,   _UpperCAmelCase					)
       A       :       Tuple							=						kwargs.pop('''labels'''    ,   _UpperCAmelCase					)
       if len(_UpperCAmelCase					) > 0:
           A       :       Dict							=						args[0]
           A       :       Tuple							=						args[1:]

       if input_features is not None:
           A       :       Optional[int]							=						self.feature_extractor.pad(_UpperCAmelCase    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					)
       if labels is not None:
           A       :       Dict							=						self.tokenizer.pad(_UpperCAmelCase    ,   **_UpperCAmelCase					)

       if labels is None:
           return input_features
       elif input_features is None:
           return labels
       else:
           A       :       List[str]							=						labels['''input_ids''']
           return input_features


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.batch_decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       return self.tokenizer.decode(*_UpperCAmelCase    ,   **_UpperCAmelCase					)


   @contextmanager
   def 					snake_case				( self					):
       warnings.warn(
           '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
           '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
           '''your audio inputs, or in a separate call.'''					)
       A       :       Dict							=						True
       A       :       Optional[int]							=						self.tokenizer
       yield
       A       :       Any							=						self.feature_extractor
       A       :       Optional[int]							=						False







'''simple docstring'''






import requests

snake_case_     =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 							_lowerCamelCase(  UpperCamelCase__    : str		)		->  None:
    # fetching a list of articles in json format
    A       :       Any							=						requests.get(_NEWS_API + bbc_news_api_key		).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page['''articles''']    ,       1		):
        print(F'''{i}.) {article['title']}'''		)


if __name__ == "__main__":
     fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")
