

'''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__      		=			logging.getLogger(__name__)


lowerCAmelCase__      		=			'''Hello world! cécé herlolip'''


lowerCAmelCase__      		=			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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				BertAbsConfig(
     temp_dir='''.'''  ,       finetune_bert=A__  ,       large=A__  ,       share_emb=A__  ,       use_bert_emb=A__  ,       encoder='''bert'''  ,       max_pos=512  ,       enc_layers=6  ,       enc_hidden_size=512  ,       enc_heads=8  ,       enc_ff_size=512  ,       enc_dropout=0.2  ,       dec_layers=6  ,       dec_hidden_size=768  ,       dec_heads=8  ,       dec_ff_size=2048  ,       dec_dropout=0.2  ,       )
 __lowercase													=				torch.load(A__  ,       lambda A__  ,       A__   : storage   )
 __lowercase													=				AbsSummarizer(A__  ,       torch.device('''cpu'''   )  ,       A__   )
 original.eval()

 __lowercase													=				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'''   )
 __lowercase													=				BertTokenizer.from_pretrained('''bert-base-uncased'''   )

 # prepare the model inputs
 __lowercase													=				tokenizer.encode('''This is sample éàalj\'-.'''   )
 encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).unsqueeze(0   )
 __lowercase													=				tokenizer.encode('''This is sample 3 éàalj\'-.'''   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				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
 __lowercase													=				encoder_input_ids
 __lowercase													=				decoder_input_ids
 __lowercase													=				__lowercase													=				None
 __lowercase													=				None
 __lowercase													=				__lowercase													=				None
 __lowercase													=				__lowercase													=				None
 __lowercase													=				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
 __lowercase													=				original(A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				original.generator(A__   )

 __lowercase													=				new_model(
     A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				new_model.generator(A__   )

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

 __lowercase													=				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__":
 lowerCAmelCase__      		=			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__      		=			parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )




'''simple docstring'''






def 	_A  (					):






 """simple docstring"""



 for n in range(1  ,       1000000   ):
  yield n * (n + 1) // 2


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				1
 __lowercase													=				2
 while i * i <= n:
  __lowercase													=				0
  while n % i == 0:
   n //= i
   multiplicity += 1
  divisors_count *= multiplicity + 1
  i += 1
 if n > 1:
  divisors_count *= 2
 return divisors_count


def 	_A  (					):






 """simple docstring"""



 return next(i for i in triangle_number_generator() if count_divisors(A__   ) > 500   )


if __name__ == "__main__":
 print(solution())

