



"""simple docstring"""




from typing import List, Optional, Union

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


_lowerCAmelCase							:   Tuple				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Optional[int]				=				{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  str						=					'time_series_transformer'
      __SCREAMING_SNAKE_CASE		:  Dict						=					{
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
          'num_hidden_layers': 'encoder_layers',
      }



      def __init__(	self	:      List[str]	,      A	:      Optional[int] = None	,      A	:      Optional[int] = None	,      A	:      str = "student_t"	,      A	:      str = "nll"	,      A	:      int = 1	,      A	:      List[int] = [1, 2, 3, 4, 5, 6, 7]	,      A	:      Optional[Union[str, bool]] = "mean"	,      A	:      int = 0	,      A	:      int = 0	,      A	:      int = 0	,      A	:      int = 0	,      A	:      Optional[List[int]] = None	,      A	:      Optional[List[int]] = None	,      A	:      int = 3_2	,      A	:      int = 3_2	,      A	:      int = 2	,      A	:      int = 2	,      A	:      int = 2	,      A	:      int = 2	,      A	:      bool = True	,      A	:      str = "gelu"	,      A	:      int = 6_4	,      A	:      float = 0.1	,      A	:      float = 0.1	,      A	:      float = 0.1	,      A	:      float = 0.1	,      A	:      float = 0.1	,      A	:      int = 1_0_0	,      A	:      float = 0.02	,      A	:      List[str]=True	,      **A	:      Dict	,      ):
           # time series specific configuration
           _UpperCAmelCase     :      int														=       prediction_length
           _UpperCAmelCase     :      str														=       context_length or prediction_length
           _UpperCAmelCase     :      Tuple														=       distribution_output
           _UpperCAmelCase     :      Union[str, Any]														=       loss
           _UpperCAmelCase     :      List[str]														=       input_size
           _UpperCAmelCase     :      Union[str, Any]														=       num_time_features
           _UpperCAmelCase     :      Tuple														=       lags_sequence
           _UpperCAmelCase     :      int														=       scaling
           _UpperCAmelCase     :      List[Any]														=       num_dynamic_real_features
           _UpperCAmelCase     :      str														=       num_static_real_features
           _UpperCAmelCase     :      Union[str, Any]														=       num_static_categorical_features
           if cardinality and num_static_categorical_features > 0:
                if len(A		) != num_static_categorical_features:
                     raise ValueError(
                         "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
                _UpperCAmelCase     :      List[str]														=       cardinality
           else:
                _UpperCAmelCase     :      Any														=       [0]
           if embedding_dimension and num_static_categorical_features > 0:
                if len(A		) != num_static_categorical_features:
                     raise ValueError(
                         "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
                _UpperCAmelCase     :      Tuple														=       embedding_dimension
           else:
                _UpperCAmelCase     :      List[str]														=       [min(5_0	,      (cat + 1) // 2		) for cat in self.cardinality]
           _UpperCAmelCase     :      Dict														=       num_parallel_samples

           # Transformer architecture configuration
           _UpperCAmelCase     :      Tuple														=       input_size * len(A		) + self._number_of_features
           _UpperCAmelCase     :      Optional[int]														=       d_model
           _UpperCAmelCase     :      List[str]														=       encoder_attention_heads
           _UpperCAmelCase     :      Dict														=       decoder_attention_heads
           _UpperCAmelCase     :      Optional[int]														=       encoder_ffn_dim
           _UpperCAmelCase     :      str														=       decoder_ffn_dim
           _UpperCAmelCase     :      str														=       encoder_layers
           _UpperCAmelCase     :      Optional[int]														=       decoder_layers

           _UpperCAmelCase     :      Optional[int]														=       dropout
           _UpperCAmelCase     :      str														=       attention_dropout
           _UpperCAmelCase     :      Optional[Any]														=       activation_dropout
           _UpperCAmelCase     :      int														=       encoder_layerdrop
           _UpperCAmelCase     :      Dict														=       decoder_layerdrop

           _UpperCAmelCase     :      Dict														=       activation_function
           _UpperCAmelCase     :      Any														=       init_std

           _UpperCAmelCase     :      int														=       use_cache

           super().__init__(is_encoder_decoder=A	,      **A		)



      @property
      def 		snake_case_   (	self	:      List[Any]		):
           return (
               sum(self.embedding_dimension		)
               + self.num_dynamic_real_features
               + self.num_time_features
               + self.num_static_real_features
               + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
           )





"""simple docstring"""




import requests

_lowerCAmelCase							:   List[Any]				=				"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  None:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       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>")
