"""simple docstring"""



import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
SCREAMING_SNAKE_CASE__:Union[str, Any]		       =       logging.getLogger(__name__)


SCREAMING_SNAKE_CASE__:int		       =       'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE__:List[Any]		       =       namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)



def      _lowerCamelCase(     a					,  a							):
       __a	      =  BertAbsConfig(
           temp_dir="."					,  finetune_bert=a					,  large=a					,  share_emb=a					,  use_bert_emb=a					,  encoder="bert"					,  max_pos=5_1_2					,  enc_layers=6					,  enc_hidden_size=5_1_2					,  enc_heads=8					,  enc_ff_size=5_1_2					,  enc_dropout=0.2					,  dec_layers=6					,  dec_hidden_size=7_6_8					,  dec_heads=8					,  dec_ff_size=2_0_4_8					,  dec_dropout=0.2					,  )
       __a	      =  torch.load(a					,  lambda a					,  a							: storage							)
       __a	      =  AbsSummarizer(a					,  torch.device("cpu"							)					,  a							)
       original.eval()

       __a	      =  BertAbsSummarizer(a					,  torch.device("cpu"							)							)
       new_model.eval()

       # -------------------
       # Convert the weights
       # -------------------

       logging.info("convert the model"							)
       new_model.bert.load_state_dict(original.bert.state_dict()							)
       new_model.decoder.load_state_dict(original.decoder.state_dict()							)
       new_model.generator.load_state_dict(original.generator.state_dict()							)

       # ----------------------------------
       # Make sure the outpus are identical
       # ----------------------------------

       logging.info("Make sure that the models\' outputs are identical"							)
       __a	      =  BertTokenizer.from_pretrained("bert-base-uncased"							)

       # prepare the model inputs
       __a	      =  tokenizer.encode("This is sample éàalj\'-."							)
       encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a							))							)
       __a	      =  torch.tensor(a							).unsqueeze(0							)
       __a	      =  tokenizer.encode("This is sample 3 éàalj\'-."							)
       decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(a							))							)
       __a	      =  torch.tensor(a							).unsqueeze(0							)

       # failsafe to make sure the weights reset does not affect the
       # loaded weights.
       assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight							)							) == 0

       # forward pass
       __a	      =  encoder_input_ids
       __a	      =  decoder_input_ids
       __a	      =  __a	      =  None
       __a	      =  None
       __a	      =  __a	      =  None
       __a	      =  __a	      =  None
       __a	      =  None

       # The original model does not apply the geneator layer immediatly but rather in
       # the beam search (where it combines softmax + linear layer). Since we already
       # apply the softmax in our generation process we only apply the linear layer here.
       # We make sure that the outputs of the full stack are identical
       __a	      =  original(a					,  a					,  a					,  a					,  a					,  a					,  a							)[0]
       __a	      =  original.generator(a							)

       __a	      =  new_model(
           a					,  a					,  a					,  a					,  a							)[0]
       __a	      =  new_model.generator(a							)

       __a	      =  torch.max(torch.abs(output_converted_model - output_original_model							)							).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(a							)							)
       __a	      =  torch.max(torch.abs(output_converted_generator - output_original_generator							)							).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(a							)							)

       __a	      =  torch.allclose(a					,  a					,  atol=1E-3							)
       if are_identical:
              logging.info("all weights are equal up to 1e-3"							)
       else:
              raise ValueError("the weights are different. The new model is likely different from the original one."							)

       # The model has been saved with torch.save(model) and this is bound to the exact
       # directory structure. We save the state_dict instead.
       logging.info("saving the model\'s state dictionary"							)
       torch.save(
           new_model.state_dict()					,  "./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin"							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Any		       =       argparse.ArgumentParser()
   parser.add_argument(
       """--bertabs_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path the official PyTorch dump.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the output PyTorch model.""",
   )
   SCREAMING_SNAKE_CASE__:Dict		       =       parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )







import requests

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



def _a				(	lowerCamelCase:   str )	->						None:







      '''simple docstring'''






      __A          =				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>')





