


"""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)
__snake_case             =   logging.getLogger(__name__)


__snake_case             =   """Hello world! cécé herlolip"""


__snake_case             =   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						(						lowercase  :  int						,							lowercase  :  Union[str, Any]			)     ->						List[Any]:


    """simple docstring"""





    snake_case   :				int				 =   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=2048						,							dec_dropout=0.2						,							)
    snake_case   :				Dict				 =   torch.load(lowercase						,							lambda lowercase						,							lowercase			: storage			)
    snake_case   :				str				 =   AbsSummarizer(lowercase						,							torch.device("cpu"			)						,							lowercase			)
    original.eval()

    snake_case   :				Dict				 =   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"			)
    snake_case   :				Dict				 =   BertTokenizer.from_pretrained("bert-base-uncased"			)

    # prepare the model inputs
    snake_case   :				int				 =   tokenizer.encode("This is sample éàalj'-."			)
    encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowercase			))			)
    snake_case   :				Any				 =   torch.tensor(lowercase			).unsqueeze(0			)
    snake_case   :				Union[str, Any]				 =   tokenizer.encode("This is sample 3 éàalj'-."			)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowercase			))			)
    snake_case   :				Dict				 =   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
    snake_case   :				Optional[Any]				 =   encoder_input_ids
    snake_case   :				Union[str, Any]				 =   decoder_input_ids
    snake_case   :				Optional[Any]				 =   None
    snake_case   :				Any				 =   None
    snake_case   :				List[Any]				 =   None
    snake_case   :				Dict				 =   None
    snake_case   :				Dict				 =   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   :				Optional[int]				 =   original(lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase			)[0]
    snake_case   :				List[Any]				 =   original.generator(lowercase			)

    snake_case   :				List[Any]				 =   new_model(
        lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase			)[0]
    snake_case   :				List[Any]				 =   new_model.generator(lowercase			)

    snake_case   :				List[Any]				 =   torch.max(torch.abs(output_converted_model - output_original_model			)			).item()
    print("Maximum absolute difference beween weights: {:.2f}".format(lowercase			)			)
    snake_case   :				int				 =   torch.max(torch.abs(output_converted_generator - output_original_generator			)			).item()
    print("Maximum absolute difference beween weights: {:.2f}".format(lowercase			)			)

    snake_case   :				List[Any]				 =   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__":
  __snake_case             =   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.""",
  )
  __snake_case             =   parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )









"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





    def update_area_of_max_square(lowercase  :  int						,							lowercase  :  int			) -> int:
        # BASE CASE
        if row >= rows or col >= cols:
            return 0

        snake_case   :				Dict				 =   update_area_of_max_square(lowercase						,							col + 1			)
        snake_case   :				Tuple				 =   update_area_of_max_square(row + 1						,							col + 1			)
        snake_case   :				Any				 =   update_area_of_max_square(row + 1						,							lowercase			)

        if mat[row][col]:
            snake_case   :				List[Any]				 =   1 + min([right, diagonal, down]			)
            snake_case   :				Any				 =   max(largest_square_area[0]						,							lowercase			)
            return sub_problem_sol
        else:
            return 0

    snake_case   :				int				 =   [0]
    update_area_of_max_square(0						,							0			)
    return largest_square_area[0]







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





    def update_area_of_max_square_using_dp_array(
        lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			) -> int:
        if row >= rows or col >= cols:
            return 0
        if dp_array[row][col] != -1:
            return dp_array[row][col]

        snake_case   :				List[str]				 =   update_area_of_max_square_using_dp_array(lowercase						,							col + 1						,							lowercase			)
        snake_case   :				Optional[int]				 =   update_area_of_max_square_using_dp_array(row + 1						,							col + 1						,							lowercase			)
        snake_case   :				Optional[int]				 =   update_area_of_max_square_using_dp_array(row + 1						,							lowercase						,							lowercase			)

        if mat[row][col]:
            snake_case   :				Dict				 =   1 + min([right, diagonal, down]			)
            snake_case   :				Union[str, Any]				 =   max(largest_square_area[0]						,							lowercase			)
            snake_case   :				str				 =   sub_problem_sol
            return sub_problem_sol
        else:
            return 0

    snake_case   :				Union[str, Any]				 =   [0]
    snake_case   :				int				 =   [[-1] * cols for _ in range(lowercase			)]
    update_area_of_max_square_using_dp_array(0						,							0						,							lowercase			)

    return largest_square_area[0]







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





    snake_case   :				int				 =   [[0] * (cols + 1) for _ in range(rows + 1			)]
    snake_case   :				List[Any]				 =   0
    for row in range(rows - 1						,							-1						,							-1			):
        for col in range(cols - 1						,							-1						,							-1			):
            snake_case   :				Tuple				 =   dp_array[row][col + 1]
            snake_case   :				Any				 =   dp_array[row + 1][col + 1]
            snake_case   :				List[str]				 =   dp_array[row + 1][col]

            if mat[row][col] == 1:
                snake_case   :				Optional[int]				 =   1 + min(lowercase						,							lowercase						,							lowercase			)
                snake_case   :				Tuple				 =   max(dp_array[row][col]						,							lowercase			)
            else:
                snake_case   :				List[Any]				 =   0

    return largest_square_area







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  int						,							lowercase  :  list[list[int]]			)     ->						int:


    """simple docstring"""





    snake_case   :				Any				 =   [0] * (cols + 1)
    snake_case   :				Any				 =   [0] * (cols + 1)
    snake_case   :				Any				 =   0
    for row in range(rows - 1						,							-1						,							-1			):
        for col in range(cols - 1						,							-1						,							-1			):
            snake_case   :				Dict				 =   current_row[col + 1]
            snake_case   :				List[Any]				 =   next_row[col + 1]
            snake_case   :				Dict				 =   next_row[col]

            if mat[row][col] == 1:
                snake_case   :				Union[str, Any]				 =   1 + min(lowercase						,							lowercase						,							lowercase			)
                snake_case   :				Optional[int]				 =   max(current_row[col]						,							lowercase			)
            else:
                snake_case   :				Optional[Any]				 =   0
        snake_case   :				str				 =   current_row

    return largest_square_area


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))





