


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


__UpperCAmelCase : Tuple 			=				logging.get_logger(__name__)

__UpperCAmelCase : Any 			=				{
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class    UpperCAmelCase_	(							__UpperCAmelCase):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					"xglm"
       __UpperCamelCase      :     Union[str, Any]							=					["past_key_values"]

       __UpperCamelCase      :     str							=					{
           "num_attention_heads": "attention_heads",
           "hidden_size": "d_model",
           "num_hidden_layers": "num_layers",
       }






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=256_008 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=1_024 ,					__SCREAMING_SNAKE_CASE=4_096 ,					__SCREAMING_SNAKE_CASE=24 ,					__SCREAMING_SNAKE_CASE=16 ,					__SCREAMING_SNAKE_CASE="gelu" ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=0 ,					__SCREAMING_SNAKE_CASE=2 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		vocab_size
              UpperCamelCase    :	Any   	=		max_position_embeddings
              UpperCamelCase    :	Tuple   	=		d_model
              UpperCamelCase    :	List[str]   	=		ffn_dim
              UpperCamelCase    :	List[str]   	=		num_layers
              UpperCamelCase    :	Optional[Any]   	=		attention_heads
              UpperCamelCase    :	Dict   	=		activation_function
              UpperCamelCase    :	int   	=		dropout
              UpperCamelCase    :	List[Any]   	=		attention_dropout
              UpperCamelCase    :	List[str]   	=		activation_dropout
              UpperCamelCase    :	List[Any]   	=		layerdrop
              UpperCamelCase    :	Dict   	=		init_std
              UpperCamelCase    :	List[str]   	=		scale_embedding  # scale factor will be sqrt(d_model) if True
              UpperCamelCase    :	Optional[Any]   	=		use_cache

              super().__init__(
                  pad_token_id=_lowerCamelCase ,					bos_token_id=_lowerCamelCase ,					eos_token_id=_lowerCamelCase ,					decoder_start_token_id=_lowerCamelCase ,					**_lowerCamelCase ,					)





import requests
from bsa import BeautifulSoup


def      a	(							SCREAMING_SNAKE_CASE_      :		str = "AAPL"       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		F"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
       UpperCamelCase    :	Any   	=		BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_       ).text							,			'''html.parser'''       )
       UpperCamelCase    :	Dict   	=		'''My(6px) Pos(r) smartphone_Mt(6px)'''
       return soup.find('''div'''							,			class_=class_       ).find('''span'''       ).text


if __name__ == "__main__":
  for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
    print(f'''Current {symbol:<4} stock price is {stock_price(symbol):>8}''')

