


"""simple docstring"""






import requests

__snake_case							:     List[str]   =					'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='


def     _lowercase       (						__snake_case			)		->     None:
			# fetching a list of articles in json format
			__lowerCAmelCase   :						Optional[int]				= 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>')




"""simple docstring"""






import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case			)		->     Tuple:
			# Initialise PyTorch model
			__lowerCAmelCase   :						Any				= AlbertConfig.from_json_file(__snake_case			)
			print(F"""Building PyTorch model from configuration: {config}"""			)
			__lowerCAmelCase   :						str				= AlbertForPreTraining(__snake_case			)

			# Load weights from tf checkpoint
			load_tf_weights_in_albert(__snake_case      ,__snake_case      ,__snake_case			)

			# Save pytorch-model
			print(F"""Save PyTorch model to {pytorch_dump_path}"""			)
			torch.save(model.state_dict()      ,__snake_case			)


if __name__ == "__main__":
	__snake_case							:     Union[str, Any]   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
	)
	parser.add_argument(
	    '--albert_config_file',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        'The config json file corresponding to the pre-trained ALBERT model. \n'
	        'This specifies the model architecture.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	__snake_case							:     str   =					parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)