'''simple docstring'''

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "EncodecFeatureExtractor"
       SCREAMING_SNAKE_CASE							=    ("T5Tokenizer", "T5TokenizerFast")

       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Union[str, Any]:
        super().__init__(__lowerCAmelCase	,   __lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					self.feature_extractor
        lowercase__				:    List[Any]  =					False

       def _lowerCAmelCase(						self	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase=True )  ->  int:
        return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase	,   language=__lowerCAmelCase	,   no_timestamps=__lowerCAmelCase )

       def __call__(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  Optional[Any]:
        # For backward compatibility
        if self._in_target_context_manager:
         return self.current_processor(*__lowerCAmelCase	,   **__lowerCAmelCase )

        lowercase__				:    int  =					kwargs.pop('''audio'''	,   __lowerCAmelCase )
        lowercase__				:    Optional[int]  =					kwargs.pop('''sampling_rate'''	,   __lowerCAmelCase )
        lowercase__				:    Tuple  =					kwargs.pop('''text'''	,   __lowerCAmelCase )
        if len(__lowerCAmelCase ) > 0:
         lowercase__				:    str  =					args[0]
         lowercase__				:    List[str]  =					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 text is not None:
         lowercase__				:    Tuple  =					self.tokenizer(__lowerCAmelCase	,   **__lowerCAmelCase )

        if audio is not None:
         lowercase__				:    int  =					self.feature_extractor(__lowerCAmelCase	,   *__lowerCAmelCase	,   sampling_rate=__lowerCAmelCase	,   **__lowerCAmelCase )

        if audio is None:
         return inputs

        elif text is None:
         return audio_inputs

        else:
         lowercase__				:    Optional[Any]  =					audio_inputs['''input_values''']
         if "padding_mask" in audio_inputs:
          lowercase__				:    Dict  =					audio_inputs['''padding_mask''']
         return inputs

       def _lowerCAmelCase(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  Dict:
        lowercase__				:    Tuple  =					kwargs.pop('''audio'''	,   __lowerCAmelCase )
        lowercase__				:    int  =					kwargs.pop('''padding_mask'''	,   __lowerCAmelCase )

        if len(__lowerCAmelCase ) > 0:
         lowercase__				:    List[Any]  =					args[0]
         lowercase__				:    Dict  =					args[1:]

        if audio_values is not None:
         return self._decode_audio(__lowerCAmelCase	,   padding_mask=__lowerCAmelCase )
        else:
         return self.tokenizer.batch_decode(*__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   *__lowerCAmelCase	,   **__lowerCAmelCase )  ->  Union[str, Any]:
        return self.tokenizer.decode(*__lowerCAmelCase	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None )  ->  List[np.ndarray]:
        lowercase__				:    Optional[int]  =					to_numpy(__lowerCAmelCase )
        lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[Any]  =					audio_values.shape

        if padding_mask is None:
         return list(__lowerCAmelCase )

        lowercase__				:    List[Any]  =					to_numpy(__lowerCAmelCase )

        # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
        # token (so that the generated audio values are **not** treated as padded tokens)
        lowercase__				:    Dict  =					seq_len - padding_mask.shape[-1]
        lowercase__				:    Dict  =					1 - self.feature_extractor.padding_value
        lowercase__				:    Optional[Any]  =					np.pad(__lowerCAmelCase	,   ((0, 0), (0, difference))	,   '''constant'''	,   constant_values=__lowerCAmelCase )

        lowercase__				:    Any  =					audio_values.tolist()
        for i in range(__lowerCAmelCase ):
         lowercase__				:    List[Any]  =					np.asarray(audio_values[i] )[
             padding_mask[i][None, :] != self.feature_extractor.padding_value
         ]
         lowercase__				:    Tuple  =					sliced_audio.reshape(__lowerCAmelCase	,   -1 )

        return audio_values


'''simple docstring'''

import requests

__a: str          =   """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 # fetching a list of articles in json format
 lowercase__				:    Optional[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>""")

