





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


snake_case_     =	logging.get_logger(__name__)

snake_case_     =	{
    """s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}


class   _lowercase						(							a__				):
   _UpperCamelCase       		=   """open-llama"""


   def __init__( self    ,   _UpperCAmelCase=100_000    ,   _UpperCAmelCase=4_096    ,   _UpperCAmelCase=11_008    ,   _UpperCAmelCase=32    ,   _UpperCAmelCase=32    ,   _UpperCAmelCase="silu"    ,   _UpperCAmelCase=2_048    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1E-6    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=0    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   **_UpperCAmelCase    ,   ):
       A       :       int							=						vocab_size
       A       :       Dict							=						max_position_embeddings
       A       :       Optional[Any]							=						hidden_size
       A       :       List[str]							=						intermediate_size
       A       :       Tuple							=						num_hidden_layers
       A       :       Dict							=						num_attention_heads
       A       :       str							=						hidden_act
       A       :       Optional[int]							=						initializer_range
       A       :       Optional[int]							=						rms_norm_eps
       A       :       Tuple							=						use_cache
       A       :       int							=						kwargs.pop(
           '''use_memorry_efficient_attention'''    ,   _A					)
       A       :       List[Any]							=						hidden_dropout_prob
       A       :       Dict							=						attention_dropout_prob
       A       :       int							=						use_stable_embedding
       A       :       int							=						shared_input_output_embedding
       A       :       Optional[int]							=						rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=_A    ,   bos_token_id=_A    ,   eos_token_id=_A    ,   tie_word_embeddings=_A    ,   **_A    ,   )


   def 					snake_case				( self					):

       if self.rope_scaling is None:
           return

       if not isinstance(self.rope_scaling    ,   _A					) or len(self.rope_scaling					) != 2:
           raise ValueError(
               '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
               f'''got {self.rope_scaling}'''					)
       A       :       Optional[Any]							=						self.rope_scaling.get('''type'''    ,   _A					)
       A       :       Tuple							=						self.rope_scaling.get('''factor'''    ,   _A					)
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
           raise ValueError(
               f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''					)
       if rope_scaling_factor is None or not isinstance(_A    ,   _A					) or rope_scaling_factor <= 1.0:
           raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''					)







'''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>""")
