



'''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)
lowercase							       =			logging.getLogger(__name__)


lowercase							       =			'''Hello world! cécé herlolip'''


lowercase							       =			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  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=BertAbsConfig(
         temp_dir="."       ,	finetune_bert=lowercase__       ,	large=lowercase__       ,	share_emb=lowercase__       ,	use_bert_emb=lowercase__       ,	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(lowercase__       ,	lambda lowercase__       ,	lowercase__       : storage       )
     a_       		=AbsSummarizer(lowercase__       ,	torch.device("cpu"       )       ,	lowercase__       )
     original.eval()

     a_       		=BertAbsSummarizer(lowercase__       ,	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(lowercase__       ))       )
     a_       		=torch.tensor(lowercase__       ).unsqueeze(0       )
     a_       		=tokenizer.encode("This is sample 3 éàalj'-."       )
     decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(lowercase__       ))       )
     a_       		=torch.tensor(lowercase__       ).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(lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       )[0]
     a_       		=original.generator(lowercase__       )

     a_       		=new_model(
         lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__       )[0]
     a_       		=new_model.generator(lowercase__       )

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

     a_       		=torch.allclose(lowercase__       ,	lowercase__       ,	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__":
  lowercase							       =			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.''',
  )
  lowercase							       =			parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )







'''simple docstring'''


from collections.abc import Generator






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_		,						a_       		=0, 1
     while True:
          a_		,						a_       		=b, a + b
          yield b






def  UpperCAmelCase_       (		lowercase__ = 1_0_0_0       ):


     '''simple docstring'''







     a_       		=1
     a_       		=fibonacci_generator()
     while len(str(next(lowercase__       )       )       ) < n:
          answer += 1
     return answer + 1


if __name__ == "__main__":
  print(solution(int(str(input()).strip())))


