



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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Any				)->Optional[int]:
      _lowerCAmelCase 			=  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=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     ,				)
      _lowerCAmelCase 			=  torch.load(_SCREAMING_SNAKE_CASE     ,				lambda _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				: storage				)
      _lowerCAmelCase 			=  AbsSummarizer(_SCREAMING_SNAKE_CASE     ,				torch.device('''cpu'''				)     ,				_SCREAMING_SNAKE_CASE				)
      original.eval()

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

      # prepare the model inputs
      _lowerCAmelCase 			=  tokenizer.encode('''This is sample éàalj\'-.'''				)
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_SCREAMING_SNAKE_CASE				))				)
      _lowerCAmelCase 			=  torch.tensor(_SCREAMING_SNAKE_CASE				).unsqueeze(0				)
      _lowerCAmelCase 			=  tokenizer.encode('''This is sample 3 éàalj\'-.'''				)
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_SCREAMING_SNAKE_CASE				))				)
      _lowerCAmelCase 			=  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
      _lowerCAmelCase 			=  encoder_input_ids
      _lowerCAmelCase 			=  decoder_input_ids
      _lowerCAmelCase 			=  _lowerCAmelCase 			=  None
      _lowerCAmelCase 			=  None
      _lowerCAmelCase 			=  _lowerCAmelCase 			=  None
      _lowerCAmelCase 			=  _lowerCAmelCase 			=  None
      _lowerCAmelCase 			=  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
      _lowerCAmelCase 			=  original(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)[0]
      _lowerCAmelCase 			=  original.generator(_SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase 			=  new_model(
          _SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)[0]
      _lowerCAmelCase 			=  new_model.generator(_SCREAMING_SNAKE_CASE				)

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

      _lowerCAmelCase 			=  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 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	Optional[Any]				)->Any:  # noqa: E741
      _lowerCAmelCase 			=  len(_SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase 			=  0
      _lowerCAmelCase 			=  [0] * n
      _lowerCAmelCase 			=  [False] * n
      _lowerCAmelCase 			=  [False] * n

      def dfs(_SCREAMING_SNAKE_CASE   :	List[str]     ,				_SCREAMING_SNAKE_CASE   :	List[Any]     ,				_SCREAMING_SNAKE_CASE   :	Optional[int]     ,				_SCREAMING_SNAKE_CASE   :	int				):
            if parent == root:
                  out_edge_count += 1
            _lowerCAmelCase 			=  True
            _lowerCAmelCase 			=  at

            for to in l[at]:
                  if to == parent:
                        pass
                  elif not visited[to]:
                        _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE				)
                        _lowerCAmelCase 			=  min(low[at]     ,				low[to]				)

                        # AP found via bridge
                        if at < low[to]:
                              _lowerCAmelCase 			=  True
                        # AP found via cycle
                        if at == low[to]:
                              _lowerCAmelCase 			=  True
                  else:
                        _lowerCAmelCase 			=  min(low[at]     ,				_SCREAMING_SNAKE_CASE				)
            return out_edge_count

      for i in range(_SCREAMING_SNAKE_CASE				):
            if not visited[i]:
                  _lowerCAmelCase 			=  0
                  _lowerCAmelCase 			=  dfs(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				-1     ,				_SCREAMING_SNAKE_CASE				)
                  _lowerCAmelCase 			=  out_edge_count > 1

      for x in range(len(_SCREAMING_SNAKE_CASE				)				):
            if is_art[x] is True:
                  print(_SCREAMING_SNAKE_CASE				)


# Adjacency list of graph
UpperCAmelCase_									=							{
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}


compute_ap(data)