


'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a   :   Any												=   logging.get_logger(__name__)

_a   :   Optional[Any]												=   {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  List[Any]          =	"transfo-xl"
   _SCREAMING_SNAKE_CASE  :  Any          =	["mems"]
   _SCREAMING_SNAKE_CASE  :  int          =	{
       "n_token": "vocab_size",
       "hidden_size": "d_model",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }
   def __init__(						self       :      int       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=26_7735       ,   SCREAMING_SNAKE_CASE_       :      Any=[2_0000, 4_0000, 20_0000]       ,   SCREAMING_SNAKE_CASE_       :      Any=1024       ,   SCREAMING_SNAKE_CASE_       :      Any=1024       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=16       ,   SCREAMING_SNAKE_CASE_       :      List[str]=64       ,   SCREAMING_SNAKE_CASE_       :      Tuple=4096       ,   SCREAMING_SNAKE_CASE_       :      Any=4       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=False       ,   SCREAMING_SNAKE_CASE_       :      List[Any]=18       ,   SCREAMING_SNAKE_CASE_       :      int=1600       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=1000       ,   SCREAMING_SNAKE_CASE_       :      Tuple=True       ,   SCREAMING_SNAKE_CASE_       :      Any=True       ,   SCREAMING_SNAKE_CASE_       :      Any=0       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=-1       ,   SCREAMING_SNAKE_CASE_       :      List[str]=True       ,   SCREAMING_SNAKE_CASE_       :      str=0.1       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0.0       ,   SCREAMING_SNAKE_CASE_       :      Union[str, Any]=True       ,   SCREAMING_SNAKE_CASE_       :      List[str]="normal"       ,   SCREAMING_SNAKE_CASE_       :      Any=0.0_1       ,   SCREAMING_SNAKE_CASE_       :      Tuple=0.0_1       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=0.0_2       ,   SCREAMING_SNAKE_CASE_       :      int=1e-5       ,   SCREAMING_SNAKE_CASE_       :      Optional[int]=0       ,   **SCREAMING_SNAKE_CASE_       :      Union[str, Any]       ,   ) ->							Optional[int]:
        __snake_case           =						vocab_size
        __snake_case           =						[]
        self.cutoffs.extend(SCREAMING_SNAKE_CASE_      )
        if proj_share_all_but_first:
             __snake_case           =						[False] + [True] * len(self.cutoffs      )
        else:
             __snake_case           =						[False] + [False] * len(self.cutoffs      )
        __snake_case           =						d_model
        __snake_case           =						d_embed
        __snake_case           =						d_head
        __snake_case           =						d_inner
        __snake_case           =						div_val
        __snake_case           =						pre_lnorm
        __snake_case           =						n_layer
        __snake_case           =						n_head
        __snake_case           =						mem_len
        __snake_case           =						same_length
        __snake_case           =						attn_type
        __snake_case           =						clamp_len
        __snake_case           =						sample_softmax
        __snake_case           =						adaptive
        __snake_case           =						dropout
        __snake_case           =						dropatt
        __snake_case           =						untie_r
        __snake_case           =						init
        __snake_case           =						init_range
        __snake_case           =						proj_init_std
        __snake_case           =						init_std
        __snake_case           =						layer_norm_epsilon
        super().__init__(eos_token_id=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   @property
   def a  (						self       :      Optional[int]      ) ->							str:
        # Message copied from Transformer-XL documentation
        logger.info(f'The model {self.model_type} is one of the few models that has no sequence length limit.'      )
        return -1





   @max_position_embeddings.setter
   def a  (						self       :      Dict       ,   SCREAMING_SNAKE_CASE_       :      List[Any]      ) ->							List[str]:
        # Message copied from Transformer-XL documentation
        raise NotImplementedError(
            f'The model {self.model_type} is one of the few models that has no sequence length limit.'      )





'''simple docstring'''


import requests

__snake_case						  =		"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""

def   A_      (       SCREAMING_SNAKE_CASE_						)			->None:
  # fetching a list of articles in json format
  lowercase_     =       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>""")

