





'''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__						:      Dict	=    logging.getLogger(__name__)


lowercase__						:      Union[str, Any]	=    'Hello world! cécé herlolip'


lowercase__						:      List[str]	=    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__    (    lowercase :					Any, lowercase :					Optional[int]				)		->		Tuple:





     """simple docstring"""

     _UpperCamelCase       =	BertAbsConfig(
         temp_dir='''.''', finetune_bert=lowerCamelCase__, large=lowerCamelCase__, share_emb=lowerCamelCase__, use_bert_emb=lowerCamelCase__, 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, )
     _UpperCamelCase       =	torch.load(lowerCamelCase__, lambda lowercase, lowercase				: storage				)
     _UpperCamelCase       =	AbsSummarizer(lowerCamelCase__, torch.device('''cpu'''				), lowerCamelCase__				)
     original.eval()

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

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

     _UpperCamelCase       =	new_model(
         lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__				)[0]
     _UpperCamelCase       =	new_model.generator(lowerCamelCase__				)

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

     _UpperCamelCase       =	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__":
   lowercase__						:      List[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.',
   )
   lowercase__						:      List[Any]	=    parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )










'''simple docstring'''

def  a__    (    lowercase :					int				)		->		int:





     """simple docstring"""

     if not isinstance(lowercase, lowercase				):
          raise TypeError('''Input value must be an \'int\' type'''				)

     _UpperCamelCase       =	0
     while number:
          position += 1
          number >>= 1

     return position


if __name__ == "__main__":
   import doctest

   doctest.testmod()



