

'''simple docstring'''



from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

_UpperCamelCase						:  Union[str, Any]							       =				TypeVar("T")


def       snake_case   (	snake_case      : int			) ->  int:


    """simple docstring"""


    return (position - 1) // 2


def       snake_case   (	snake_case      : int			) ->  int:


    """simple docstring"""


    return (2 * position) + 1


def       snake_case   (	snake_case      : int			) ->  int:


    """simple docstring"""


    return (2 * position) + 2



class 				_snake_case					(   Generic[T]			):

  def __init__(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							[]
      lowerCAmelCase 						=							{}
      lowerCAmelCase 						=							0

  def __len__(				self	):





      '''simple docstring'''

      return self.elements

  def __repr__(				self	):





      '''simple docstring'''

      return str(self.heap	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      return self.elements == 0

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      self.heap.append((elem, weight)	)
      lowerCAmelCase 						=							self.elements
      self.elements += 1
      self._bubble_up(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      if self.elements > 1:
          self._swap_nodes(0      , self.elements - 1	)
      lowerCAmelCase						,   lowerCAmelCase 						=							self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
          lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[0]
          self._bubble_down(_SCREAMING_SNAKE_CASE	)
      return elem

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.position_map[elem]
      lowerCAmelCase 						=							(elem, weight)
      if position > 0:
          lowerCAmelCase 						=							get_parent_position(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[parent_position]
          if parent_weight > weight:
              self._bubble_up(_SCREAMING_SNAKE_CASE	)
          else:
              self._bubble_down(_SCREAMING_SNAKE_CASE	)
      else:
          self._bubble_down(_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.position_map[elem]
      if curr_pos == 0:
          return None
      lowerCAmelCase 						=							get_parent_position(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[curr_pos]
      lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[parent_position]
      if parent_weight > weight:
          self._swap_nodes(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
          return self._bubble_up(_SCREAMING_SNAKE_CASE	)
      return None

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.position_map[elem]
      lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[curr_pos]
      lowerCAmelCase 						=							get_child_left_position(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							get_child_right_position(_SCREAMING_SNAKE_CASE	)
      if child_left_position < self.elements and child_right_position < self.elements:
          lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[child_left_position]
          lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[child_right_position]
          if child_right_weight < child_left_weight and child_right_weight < weight:
              self._swap_nodes(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
              return self._bubble_down(_SCREAMING_SNAKE_CASE	)
      if child_left_position < self.elements:
          lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[child_left_position]
          if child_left_weight < weight:
              self._swap_nodes(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
              return self._bubble_down(_SCREAMING_SNAKE_CASE	)
      else:
          return None
      if child_right_position < self.elements:
          lowerCAmelCase						,   lowerCAmelCase 						=							self.heap[child_right_position]
          if child_right_weight < weight:
              self._swap_nodes(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)
              return self._bubble_down(_SCREAMING_SNAKE_CASE	)
      return None


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.heap[nodea_pos][0]
      lowerCAmelCase 						=							self.heap[nodea_pos][0]
      lowerCAmelCase						,   lowerCAmelCase 						=							(
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      lowerCAmelCase 						=							nodea_pos
      lowerCAmelCase 						=							nodea_pos



class 				_snake_case					(   Generic[T]			):

  def __init__(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							{}
      lowerCAmelCase 						=							0

  def __repr__(				self	):





      '''simple docstring'''

      return str(self.connections	)

  def __len__(				self	):





      '''simple docstring'''

      return self.nodes

  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      if node not in self.connections:
          lowerCAmelCase 						=							{}
          self.nodes += 1


  def   _SCREAMING_SNAKE_CASE				(				self      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      self.add_node(_SCREAMING_SNAKE_CASE	)
      self.add_node(_SCREAMING_SNAKE_CASE	)
      lowerCAmelCase 						=							weight
      lowerCAmelCase 						=							weight



def       snake_case   (	snake_case      : GraphUndirectedWeighted[T]					,		) ->  tuple[dict[T, int], dict[T, T | None]]:


    """simple docstring"""


    lowerCAmelCase 						=							{node: maxsize for node in graph.connections}
    lowerCAmelCase 						=							{node: None for node in graph.connections}

    lowerCAmelCase 						=							MinPriorityQueue()
    for node, weight in dist.items():
        priority_queue.push(snake_case					,		snake_case			)

    if priority_queue.is_empty():
        return dist, parent

    # initialization
    lowerCAmelCase 						=							priority_queue.extract_min()
    lowerCAmelCase 						=							0
    for neighbour in graph.connections[node]:
        if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            lowerCAmelCase 						=							dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(snake_case					,		dist[neighbour]			)
            lowerCAmelCase 						=							node

    # running prim's algorithm
    while not priority_queue.is_empty():
        lowerCAmelCase 						=							priority_queue.extract_min()
        for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                lowerCAmelCase 						=							dist[node] + graph.connections[node][neighbour]
                priority_queue.update_key(snake_case					,		dist[neighbour]			)
                lowerCAmelCase 						=							node
    return dist, parent








'''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)
_UpperCamelCase						:  Optional[Any]							       =				logging.getLogger(__name__)


_UpperCamelCase						:  List[Any]							       =				"Hello world! cécé herlolip"


_UpperCamelCase						:  int							       =				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       snake_case   (	snake_case      : int					,		snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


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

    lowerCAmelCase 						=							BertAbsSummarizer(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] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(snake_case			).unsqueeze(0			)
    lowerCAmelCase 						=							tokenizer.encode('This is sample 3 éàalj\'-.'			)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(snake_case			))			)
    lowerCAmelCase 						=							torch.tensor(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(snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							original.generator(snake_case			)

    lowerCAmelCase 						=							new_model(
        snake_case					,		snake_case					,		snake_case					,		snake_case					,		snake_case			)[0]
    lowerCAmelCase 						=							new_model.generator(snake_case			)

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

    lowerCAmelCase 						=							torch.allclose(snake_case					,		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						:  List[str]							       =				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						:  Optional[int]							       =				parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )





