'''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)
a=  logging.getLogger(__name__)


a=  '''Hello world! cécé herlolip'''


a=  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  _UpperCamelCase (     _a	:	List[Any]				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  BertAbsConfig(
        temp_dir='.'				,     finetune_bert=_a				,     large=_a				,     share_emb=_a				,     use_bert_emb=_a				,     encoder='bert'				,     max_pos=5_1_2				,     enc_layers=6				,     enc_hidden_size=5_1_2				,     enc_heads=8				,     enc_ff_size=5_1_2				,     enc_dropout=0.2				,     dec_layers=6				,     dec_hidden_size=7_6_8				,     dec_heads=8				,     dec_ff_size=2_0_4_8				,     dec_dropout=0.2				,     )
    __UpperCamelCase	:	Any		    =  torch.load(_a				,     lambda _a				,     _a			: storage			)
    __UpperCamelCase	:	Any		    =  AbsSummarizer(_a				,     torch.device('cpu'			)				,     _a			)
    original.eval()

    __UpperCamelCase	:	List[str]		    =  BertAbsSummarizer(_a				,     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'			)
    __UpperCamelCase	:	Dict		    =  BertTokenizer.from_pretrained('bert-base-uncased'			)

    # prepare the model inputs
    __UpperCamelCase	:	Dict		    =  tokenizer.encode('This is sample éàalj\'-.'			)
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a			))			)
    __UpperCamelCase	:	List[Any]		    =  torch.tensor(_a			).unsqueeze(0			)
    __UpperCamelCase	:	Union[str, Any]		    =  tokenizer.encode('This is sample 3 éàalj\'-.'			)
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a			))			)
    __UpperCamelCase	:	str		    =  torch.tensor(_a			).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
    __UpperCamelCase	:	List[Any]		    =  encoder_input_ids
    __UpperCamelCase	:	int		    =  decoder_input_ids
    __UpperCamelCase	:	Tuple		    =  None
    __UpperCamelCase	:	Dict		    =  None
    __UpperCamelCase	:	Optional[Any]		    =  None
    __UpperCamelCase	:	Dict		    =  None
    __UpperCamelCase	:	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
    __UpperCamelCase	:	Dict		    =  original(_a				,     _a				,     _a				,     _a				,     _a				,     _a				,     _a			)[0]
    __UpperCamelCase	:	Any		    =  original.generator(_a			)

    __UpperCamelCase	:	Dict		    =  new_model(
        _a				,     _a				,     _a				,     _a				,     _a			)[0]
    __UpperCamelCase	:	Optional[int]		    =  new_model.generator(_a			)

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

    __UpperCamelCase	:	Tuple		    =  torch.allclose(_a				,     _a				,     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=  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=  parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )




'''simple docstring'''
from timeit import timeit

a=  {
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	str		    =  len(_a			) - 1
    while start_i < end_i:
        if s[start_i] == s[end_i]:
            start_i += 1
            end_i -= 1
        else:
            return False
    return True







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  len(_a			) // 2
    __UpperCamelCase	:	Tuple		    =  len(_a			)

    # We need to traverse till half of the length of string
    # as we can get access of the i'th last element from
    # i'th index.
    # eg: [0,1,2,3,4,5] => 4th index can be accessed
    # with the help of 1st index (i==n-i-1)
    # where n is length of string
    return all(s[i] == s[n - i - 1] for i in range(_a			)			)







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    if len(_a			) <= 2:
        return True
    if s[0] == s[len(_a			) - 1]:
        return is_palindrome_recursive(s[1:-1]			)
    else:
        return False







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    return s == s[::-1]







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  f"""all({name}(key) is value for key, value in test_data.items())"""
    __UpperCamelCase	:	Union[str, Any]		    =  f"""from __main__ import test_data, {name}"""
    __UpperCamelCase	:	Any		    =  5_0_0_0_0_0
    __UpperCamelCase	:	Tuple		    =  timeit(stmt=_a				,     setup=_a				,     number=_a			)
    print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""			)


if __name__ == "__main__":
  for key, value in test_data.items():
    assert is_palindrome(key) is is_palindrome_recursive(key)
    assert is_palindrome(key) is is_palindrome_slice(key)
    print(F"""{key:21} {value}""")
  print('''a man a plan a canal panama''')

  # finished 500,000 runs in 0.46793 seconds
  benchmark_function('''is_palindrome_slice''')
  # finished 500,000 runs in 0.85234 seconds
  benchmark_function('''is_palindrome''')
  # finished 500,000 runs in 1.32028 seconds
  benchmark_function('''is_palindrome_recursive''')
  # finished 500,000 runs in 2.08679 seconds
  benchmark_function('''is_palindrome_traversal''')



