





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


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


_A :   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 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







			"""simple docstring"""
			lowerCamelCase__       :						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      ,  )
			lowerCamelCase__       :						List[Any]        =							torch.load(lowercase_      ,  lambda UpperCAmelCase      ,  UpperCAmelCase					: storage					)
			lowerCamelCase__       :						Union[str, Any]        =							AbsSummarizer(lowercase_      ,  torch.device('''cpu'''					)      ,  lowercase_					)
			original.eval()

			lowerCamelCase__       :						Optional[int]        =							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'''					)
			lowerCamelCase__       :						List[Any]        =							BertTokenizer.from_pretrained('''bert-base-uncased'''					)

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

			lowerCamelCase__       :						Optional[Any]        =							new_model(
			    lowercase_      ,  lowercase_      ,  lowercase_      ,  lowercase_      ,  lowercase_					)[0]
			lowerCamelCase__       :						Union[str, Any]        =							new_model.generator(lowercase_					)

			lowerCamelCase__       :						str        =							torch.max(torch.abs(output_converted_model - output_original_model					)					).item()
			print('''Maximum absolute difference beween weights: {:.2f}'''.format(lowercase_					)					)
			lowerCamelCase__       :						Optional[int]        =							torch.max(torch.abs(output_converted_generator - output_original_generator					)					).item()
			print('''Maximum absolute difference beween weights: {:.2f}'''.format(lowercase_					)					)

			lowerCamelCase__       :						int        =							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__":
							_A :   Union[str, Any]  			=  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.',
							)
							_A :   Tuple  			=  parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)










def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""

   if not isinstance(UpperCAmelCase      ,  UpperCAmelCase					):
      raise TypeError('''only integers accepted as input'''					)
   else:
      lowerCamelCase__       :						Any        =							str(abs(UpperCAmelCase					)					)
      lowerCamelCase__       :						Union[str, Any]        =							[list(UpperCAmelCase					) for char in range(len(UpperCAmelCase					)					)]
      for index in range(len(UpperCAmelCase					)					):
         num_transpositions[index].pop(UpperCAmelCase					)
      return max(
          int(''''''.join(list(UpperCAmelCase					)					)					) for transposition in num_transpositions					)


if __name__ == "__main__":
       __import__('doctest').testmod()



