





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__					:							int													=     'Hello world! cécé herlolip'


snake_case__					:							List[Any]													=     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				(	lowerCamelCase:   int					,				lowerCamelCase:   str )	->						Dict:







      '''simple docstring'''






      __A          =				BertAbsConfig(
          temp_dir='''.'''					,				finetune_bert=lowerCamelCase					,				large=lowerCamelCase					,				share_emb=lowerCamelCase					,				use_bert_emb=lowerCamelCase					,				encoder='''bert'''					,				max_pos=5_12					,				enc_layers=6					,				enc_hidden_size=5_12					,				enc_heads=8					,				enc_ff_size=5_12					,				enc_dropout=0.2					,				dec_layers=6					,				dec_hidden_size=7_68					,				dec_heads=8					,				dec_ff_size=20_48					,				dec_dropout=0.2					,				)
      __A          =				torch.load(lowerCamelCase					,				lambda lowerCamelCase					,				lowerCamelCase : storage )
      __A          =				AbsSummarizer(lowerCamelCase					,				torch.device('''cpu''' )					,				lowerCamelCase )
      original.eval()

      __A          =				BertAbsSummarizer(lowerCamelCase					,				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          =				BertTokenizer.from_pretrained('''bert-base-uncased''' )

      # prepare the model inputs
      __A          =				tokenizer.encode('''This is sample éàalj\'-.''' )
      encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase )) )
      __A          =				torch.tensor(lowerCamelCase ).unsqueeze(0 )
      __A          =				tokenizer.encode('''This is sample 3 éàalj\'-.''' )
      decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase )) )
      __A          =				torch.tensor(lowerCamelCase ).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          =				encoder_input_ids
      __A          =				decoder_input_ids
      __A          =				__A          =				None
      __A          =				None
      __A          =				__A          =				None
      __A          =				__A          =				None
      __A          =				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          =				original(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )[0]
      __A          =				original.generator(lowerCamelCase )

      __A          =				new_model(
          lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )[0]
      __A          =				new_model.generator(lowerCamelCase )

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

      __A          =				torch.allclose(lowerCamelCase					,				lowerCamelCase					,				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__					:							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.',
       )
       snake_case__					:							Dict													=     parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )












from itertools import permutations



def _a				(	lowerCamelCase:   tuple )	->						bool:







      '''simple docstring'''






      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      __A          =				[7, 11, 13, 17]
      for i, test in enumerate(lowerCamelCase ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def _a				(	lowerCamelCase:   int = 10 )	->						int:







      '''simple docstring'''






      return sum(
          int(''''''.join(map(lowerCamelCase					,				lowerCamelCase ) ) )
          for num in permutations(range(lowerCamelCase ) )
          if is_substring_divisible(lowerCamelCase ) )


if __name__ == "__main__":
       print(f'{solution() = }')





