



'''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)
lowerCAmelCase	:		Optional[Any]				=   logging.getLogger(__name__)


lowerCAmelCase	:		List[Any]				=   '''Hello world! cécé herlolip'''


lowerCAmelCase	:		Tuple				=   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					(							lowerCamelCase   :						Dict				,		lowerCamelCase   :						int					):






 '''simple docstring'''




 __lowerCAmelCase												=  BertAbsConfig(
     temp_dir="."				,		finetune_bert=lowerCamelCase				,		large=lowerCamelCase				,		share_emb=lowerCamelCase				,		use_bert_emb=lowerCamelCase				,		encoder="bert"				,		max_pos=5_12				,		enc_layers=6				,		enc_hidden_size=5_12				,		enc_heads=8				,		enc_ff_size=5_12				,		enc_dropout=0.2				,		dec_layers=6				,		dec_hidden_size=7_68				,		dec_heads=8				,		dec_ff_size=20_48				,		dec_dropout=0.2				,		)
 __lowerCAmelCase												=  torch.load(lowerCamelCase				,		lambda lowerCamelCase				,		lowerCamelCase					: storage					)
 __lowerCAmelCase												=  AbsSummarizer(lowerCamelCase				,		torch.device("cpu"					)				,		lowerCamelCase					)
 original.eval()

 __lowerCAmelCase												=  BertAbsSummarizer(lowerCamelCase				,		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"					)
 __lowerCAmelCase												=  BertTokenizer.from_pretrained("bert-base-uncased"					)

 # prepare the model inputs
 __lowerCAmelCase												=  tokenizer.encode("This is sample éàalj'-."					)
 encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase					))					)
 __lowerCAmelCase												=  torch.tensor(lowerCamelCase					).unsqueeze(0					)
 __lowerCAmelCase												=  tokenizer.encode("This is sample 3 éàalj'-."					)
 decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase					))					)
 __lowerCAmelCase												=  torch.tensor(lowerCamelCase					).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
 __lowerCAmelCase												=  encoder_input_ids
 __lowerCAmelCase												=  decoder_input_ids
 __lowerCAmelCase												=  __lowerCAmelCase												=  None
 __lowerCAmelCase												=  None
 __lowerCAmelCase												=  __lowerCAmelCase												=  None
 __lowerCAmelCase												=  __lowerCAmelCase												=  None
 __lowerCAmelCase												=  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
 __lowerCAmelCase												=  original(lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)[0]
 __lowerCAmelCase												=  original.generator(lowerCamelCase					)

 __lowerCAmelCase												=  new_model(
     lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase				,		lowerCamelCase					)[0]
 __lowerCAmelCase												=  new_model.generator(lowerCamelCase					)

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

 __lowerCAmelCase												=  torch.allclose(lowerCamelCase				,		lowerCamelCase				,		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__":
 lowerCAmelCase	:		Union[str, 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.''',
 )
 lowerCAmelCase	:		Optional[int]				=   parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )




'''simple docstring'''



import requests
from bsa import BeautifulSoup



def   __lowerCAmelCase					(							lowerCamelCase   :						str = "https://www.worldometers.info/coronavirus"					):






	'''simple docstring'''




	__lowerCAmelCase												=  BeautifulSoup(requests.get(lowerCamelCase					).text				,		"html.parser"					)
	__lowerCAmelCase												=  soup.findAll("h1"					)
	__lowerCAmelCase												=  soup.findAll("div"				,		{"class": "maincounter-number"}					)
	keys += soup.findAll("span"				,		{"class": "panel-title"}					)
	values += soup.findAll("div"				,		{"class": "number-table-main"}					)
	return {key.text.strip(): value.text.strip() for key, value in zip(lowerCamelCase				,		lowerCamelCase					)}


if __name__ == "__main__":
	print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
	for key, value in world_covidaa_stats().items():
		print(f'{key}\n{value}\n')