



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


_lowerCamelCase    :		str		      =    logging.get_logger(__name__)

_lowerCamelCase    :		int		      =    {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		'switch_transformers'
						UpperCAmelCase			:		Tuple		=		['past_key_values']
						UpperCAmelCase			:		List[Any]		=		{'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







						def __init__(  self    :				Any       ,	snake_case    :				Union[str, Any]=32128       ,	snake_case    :				Optional[int]=768       ,	snake_case    :				List[str]=64       ,	snake_case    :				List[str]=2048       ,	snake_case    :				List[str]=64       ,	snake_case    :				Tuple=12       ,	snake_case    :				int=3       ,	snake_case    :				Union[str, Any]=12       ,	snake_case    :				Tuple=3       ,	snake_case    :				Dict=12       ,	snake_case    :				Any=8       ,	snake_case    :				str=False       ,	snake_case    :				List[str]=0.01       ,	snake_case    :				Union[str, Any]="float32"       ,	snake_case    :				Optional[int]=False       ,	snake_case    :				int=32       ,	snake_case    :				str=128       ,	snake_case    :				Union[str, Any]=0.1       ,	snake_case    :				List[str]=1E-6       ,	snake_case    :				str=0.001       ,	snake_case    :				Any=0.001       ,	snake_case    :				int=1.0       ,	snake_case    :				str="relu"       ,	snake_case    :				Tuple=True       ,	snake_case    :				str=False       ,	snake_case    :				Optional[Any]=True       ,	snake_case    :				Tuple=0       ,	snake_case    :				int=1       ,	**snake_case    :				Any       ,	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[int]						=				vocab_size
											SCREAMING_SNAKE_CASE							:     int						=				d_model
											SCREAMING_SNAKE_CASE							:     Any						=				d_kv
											SCREAMING_SNAKE_CASE							:     str						=				d_ff

											SCREAMING_SNAKE_CASE							:     List[Any]						=				num_sparse_encoder_layers

											SCREAMING_SNAKE_CASE							:     List[str]						=				num_layers
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				num_sparse_decoder_layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_encoder_layers > 0:
																SCREAMING_SNAKE_CASE							:     Any						=				self.num_layers // self.num_sparse_encoder_layers
											else:
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.num_layers  # HACK: this will create 0 sparse layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_decoder_layers > 0:
																SCREAMING_SNAKE_CASE							:     List[str]						=				self.num_decoder_layers // self.num_sparse_decoder_layers
											else:
																SCREAMING_SNAKE_CASE							:     List[Any]						=				self.num_decoder_layers  # HACK: this will create 0 sparse layers

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				num_heads
											SCREAMING_SNAKE_CASE							:     List[Any]						=				num_experts
											SCREAMING_SNAKE_CASE							:     List[Any]						=				expert_capacity
											SCREAMING_SNAKE_CASE							:     str						=				router_bias
											SCREAMING_SNAKE_CASE							:     Any						=				router_jitter_noise
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																raise ValueError(f'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				router_dtype

											SCREAMING_SNAKE_CASE							:     List[Any]						=				router_ignore_padding_tokens
											SCREAMING_SNAKE_CASE							:     List[Any]						=				relative_attention_num_buckets
											SCREAMING_SNAKE_CASE							:     List[Any]						=				relative_attention_max_distance

											SCREAMING_SNAKE_CASE							:     Tuple						=				dropout_rate
											SCREAMING_SNAKE_CASE							:     int						=				layer_norm_epsilon
											SCREAMING_SNAKE_CASE							:     List[str]						=				initializer_factor
											SCREAMING_SNAKE_CASE							:     Tuple						=				feed_forward_proj
											SCREAMING_SNAKE_CASE							:     Dict						=				use_cache
											SCREAMING_SNAKE_CASE							:     List[str]						=				add_router_probs

											SCREAMING_SNAKE_CASE							:     List[Any]						=				router_z_loss_coef
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				router_aux_loss_coef

											SCREAMING_SNAKE_CASE							:     Tuple						=				self.feed_forward_proj.split('-'	)
											SCREAMING_SNAKE_CASE							:     Dict						=				act_info[-1]
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				act_info[0] == 'gated'

											if len(snake_case	) > 1 and act_info[0] != "gated" or len(snake_case	) > 2:
																raise ValueError(
																    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
																    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
																    '\'gated-gelu\' or \'relu\''	)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				'gelu_new'

											super().__init__(
											    pad_token_id=snake_case       ,	eos_token_id=snake_case       ,	is_encoder_decoder=snake_case       ,	**snake_case       ,	)




import requests

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

def        __a			(   __lowerCAmelCase   )     ->     None:
					# fetching a list of articles in json format
					SCREAMING_SNAKE_CASE							:     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>""")