






import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase        =				logging.get_logger(__name__)




def 				A		(					lowercase__  :  str ,		lowercase__  :  str  )		-> int:
	UpperCamelCase__ :List[Any]						=      RobertaPreLayerNormConfig.from_pretrained(
	    lowercase__ ,		architectures=["""RobertaPreLayerNormForMaskedLM"""]  )

	# convert state_dict
	UpperCamelCase__ :Union[str, Any]						=      torch.load(hf_hub_download(repo_id=lowercase__ ,		filename="""pytorch_model.bin"""  )  )
	UpperCamelCase__ :List[str]						=      {}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""  ):
			UpperCamelCase__ :Tuple						=      """roberta_prelayernorm.""" + tensor_key[len("""roberta."""  ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""  ) or tensor_key.endswith(""".self.LayerNorm.bias"""  ):
			continue

		UpperCamelCase__ :Dict						=      tensor_value

	UpperCamelCase__ :Dict						=      RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowercase__ ,		config=lowercase__ ,		state_dict=lowercase__  )
	model.save_pretrained(lowercase__  )

	# convert tokenizer
	UpperCamelCase__ :Tuple						=      AutoTokenizer.from_pretrained(lowercase__  )
	tokenizer.save_pretrained(lowercase__  )


if __name__ == "__main__":
							UpperCamelCase        =				argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--checkpoint-repo",
							    default=None,
							    type=str,
							    required=True,
							    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCamelCase        =				parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






"""simple docstring"""



import requests

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







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		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>""")




