


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    :   Union[str, Any]	    =					logging.getLogger(__name__)


snake_case    :   Optional[int]	    =					'''Hello world! cécé herlolip'''


snake_case    :   List[str]	    =					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						(			UpperCAmelCase_						:		List[Any]			,  UpperCAmelCase_						:		Dict						):




      """simple docstring"""





      a						:Optional[int]										=	BertAbsConfig(
          temp_dir='''.'''			,  finetune_bert=UpperCAmelCase_			,  large=UpperCAmelCase_			,  share_emb=UpperCAmelCase_			,  use_bert_emb=UpperCAmelCase_			,  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			,  )
      a						:Optional[int]										=	torch.load(UpperCAmelCase_			,  lambda UpperCAmelCase_			,  UpperCAmelCase_						: storage						)
      a						:Tuple										=	AbsSummarizer(UpperCAmelCase_			,  torch.device('''cpu'''						)			,  UpperCAmelCase_						)
      original.eval()

      a						:Optional[Any]										=	BertAbsSummarizer(UpperCAmelCase_			,  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						:Dict										=	BertTokenizer.from_pretrained('''bert-base-uncased'''						)

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

      a						:List[Any]										=	new_model(
          UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_			,  UpperCAmelCase_						)[0]
      a						:Dict										=	new_model.generator(UpperCAmelCase_						)

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

      a						:Dict										=	torch.allclose(UpperCAmelCase_			,  UpperCAmelCase_			,  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    :   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.''',
     )
     snake_case    :   Any	    =					parser.parse_args()

     convert_bertabs_checkpoints(
         args.bertabs_checkpoint_path,
         args.pytorch_dump_folder_path,
     )









from ...configuration_utils import PretrainedConfig







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	'bert-generation'




 def __init__(		self ,  _lowerCamelCase=5_0358 ,  _lowerCamelCase=1024 ,  _lowerCamelCase=24 ,  _lowerCamelCase=16 ,  _lowerCamelCase=4096 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=1e-12 ,  _lowerCamelCase=0 ,  _lowerCamelCase=2 ,  _lowerCamelCase=1 ,  _lowerCamelCase="absolute" ,  _lowerCamelCase=True ,  **_lowerCamelCase ,  ):
       super().__init__(pad_token_id=_lowerCamelCase ,  bos_token_id=_lowerCamelCase ,  eos_token_id=_lowerCamelCase ,  **_lowerCamelCase   )

       a						:Optional[int]										=	vocab_size
       a						:Tuple										=	hidden_size
       a						:Any										=	num_hidden_layers
       a						:Any										=	num_attention_heads
       a						:List[Any]										=	hidden_act
       a						:Tuple										=	intermediate_size
       a						:Any										=	hidden_dropout_prob
       a						:int										=	attention_probs_dropout_prob
       a						:Dict										=	max_position_embeddings
       a						:int										=	initializer_range
       a						:Union[str, Any]										=	layer_norm_eps
       a						:str										=	position_embedding_type
       a						:int										=	use_cache





