

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase		:       List[Any]						    =	logging.get_logger(__name__)

_UpperCAmelCase		:       Dict						    =	{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			Optional[int]	      =      '''big_bird'''




				def __init__( self				,       snake_case=5_0358				,       snake_case=768				,       snake_case=12				,       snake_case=12				,       snake_case=3072				,       snake_case="gelu_new"				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=4096				,       snake_case=2				,       snake_case=0.02				,       snake_case=1e-1_2				,       snake_case=True				,       snake_case=0				,       snake_case=1				,       snake_case=2				,       snake_case=66				,       snake_case="block_sparse"				,       snake_case=True				,       snake_case=False				,       snake_case=64				,       snake_case=3				,       snake_case=None				,       **snake_case				,       ):
											super().__init__(
											    pad_token_id=snake_case				,       bos_token_id=snake_case				,       eos_token_id=snake_case				,       sep_token_id=snake_case				,       **snake_case				,       )

											snake_case_          =   vocab_size
											snake_case_          =   max_position_embeddings
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   intermediate_size
											snake_case_          =   hidden_act
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   initializer_range
											snake_case_          =   type_vocab_size
											snake_case_          =   layer_norm_eps
											snake_case_          =   use_cache

											snake_case_          =   rescale_embeddings
											snake_case_          =   attention_type
											snake_case_          =   use_bias
											snake_case_          =   block_size
											snake_case_          =   num_random_blocks
											snake_case_          =   classifier_dropout







class 			lowercase    (	lowercase_      ):




				@property
				def    a							( self	):
											if self.task == "multiple-choice":
																		snake_case_          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		snake_case_          =   {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('input_ids', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]	)







import requests

_UpperCAmelCase		:       Union[str, Any]						    =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   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>""")




