


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)
UpperCAmelCase    =					logging.getLogger(__name__)


UpperCAmelCase    =					"""Hello world! cécé herlolip"""


UpperCAmelCase    =					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					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						str:




      """simple docstring"""

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

      snake_case_ =   BertAbsSummarizer(SCREAMING_SNAKE_CASE      ,      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'''      )
      snake_case_ =   BertTokenizer.from_pretrained('''bert-base-uncased'''      )

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

      snake_case_ =   new_model(
          SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )[0]
      snake_case_ =   new_model.generator(SCREAMING_SNAKE_CASE      )

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

      snake_case_ =   torch.allclose(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      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__":
   UpperCAmelCase    =					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.""",
   )
   UpperCAmelCase    =					parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )





def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						int:




      """simple docstring"""

      assert column_title.isupper()
      snake_case_ =   0
      snake_case_ =   len(SCREAMING_SNAKE_CASE      ) - 1
      snake_case_ =   0

      while index >= 0:
            snake_case_ =   (ord(column_title[index]      ) - 64) * pow(26      ,      SCREAMING_SNAKE_CASE      )
            answer += value
            power += 1
            index -= 1

      return answer


if __name__ == "__main__":
   from doctest import testmod

   testmod()