




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[int]								=       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 SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	List[Any]:



       """simple docstring"""

       A__							=   BertAbsConfig(
           temp_dir='''.'''			,    finetune_bert=lowercase_			,    large=lowercase_			,    share_emb=lowercase_			,    use_bert_emb=lowercase_			,    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=2_048			,    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] * (512 - len(lowercase_						))						)
       A__							=   torch.tensor(lowercase_						).unsqueeze(0						)
       A__							=   tokenizer.encode('''This is sample 3 éàalj\'-.'''						)
       decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - 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__":
  _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     :		Any								=       parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )







def SCREAMING_SNAKE_CASE    (	lowercase_ = 50						)       ->	int:



       """simple docstring"""

       A__							=   [1] * (length + 1)

       for row_length in range(3			,    length + 1						):
              for block_length in range(3			,    row_length + 1						):
                     for block_start in range(row_length - block_length						):
                            ways_number[row_length] += ways_number[
                                row_length - block_start - block_length - 1
                            ]

                     ways_number[row_length] += 1

       return ways_number[length]


if __name__ == "__main__":
  print(F'''{solution() = }''')


