






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)
SCREAMING_SNAKE_CASE 							=						logging.getLogger(__name__)


SCREAMING_SNAKE_CASE 							=						'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE 							=						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	(						__A    :							Any		,		__A    :							Tuple )      ->					Any:
    _UpperCAmelCase				:      Union[str, Any]			  =				BertAbsConfig(
        temp_dir='''.'''		,		finetune_bert=__A		,		large=__A		,		share_emb=__A		,		use_bert_emb=__A		,		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=2_048		,		dec_dropout=0.2		,		)
    _UpperCAmelCase				:      Optional[int]			  =				torch.load(__A		,		lambda __A		,		__A : storage )
    _UpperCAmelCase				:      List[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				:      Union[str, Any]			  =				BertTokenizer.from_pretrained('''bert-base-uncased''' )

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

    _UpperCAmelCase				:      Union[str, Any]			  =				new_model(
        __A		,		__A		,		__A		,		__A		,		__A )[0]
    _UpperCAmelCase				:      int			  =				new_model.generator(__A )

    _UpperCAmelCase				:      Dict			  =				torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(__A ) )
    _UpperCAmelCase				:      Optional[Any]			  =				torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
    print('''Maximum absolute difference beween weights: {:.2f}'''.format(__A ) )

    _UpperCAmelCase				:      Optional[Any]			  =				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__":
 SCREAMING_SNAKE_CASE 							=						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.',
 )
 SCREAMING_SNAKE_CASE 							=						parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )












import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
  # Create universe of discourse in Python using linspace ()
  SCREAMING_SNAKE_CASE 							=						np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

  # Create two fuzzy sets by defining any membership function
  # (trapmf(), gbellmf(), gaussmf(), etc).
  SCREAMING_SNAKE_CASE 							=						[0, 25, 50]
  SCREAMING_SNAKE_CASE 							=						[25, 50, 75]
  SCREAMING_SNAKE_CASE 							=						fuzz.membership.trimf(X, abca)
  SCREAMING_SNAKE_CASE 							=						fuzz.membership.trimf(X, abca)

  # Compute the different operations using inbuilt functions.
  SCREAMING_SNAKE_CASE 							=						np.ones(75)
  SCREAMING_SNAKE_CASE 							=						np.zeros((75,))
  # 1. Union = max(µA(x), µB(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_or(X, young, X, middle_aged)[1]
  # 2. Intersection = min(µA(x), µB(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, young, X, middle_aged)[1]
  # 3. Complement (A) = (1- min(µA(x))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_not(young)
  # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
  # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
  SCREAMING_SNAKE_CASE 							=						young + middle_aged - (young * middle_aged)
  # 6. Algebraic Product = (µA(x) * µB(x))
  SCREAMING_SNAKE_CASE 							=						young * middle_aged
  # 7. Bounded Sum = min[1,(µA(x), µB(x))]
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
  # 8. Bounded difference = min[0,(µA(x), µB(x))]
  SCREAMING_SNAKE_CASE 							=						fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

  # max-min composition
  # max-product composition

  # Plot each set A, set B and each operation result using plot() and subplot().
  from matplotlib import pyplot as plt

  plt.figure()

  plt.subplot(4, 3, 1)
  plt.plot(X, young)
  plt.title('Young')
  plt.grid(True)

  plt.subplot(4, 3, 2)
  plt.plot(X, middle_aged)
  plt.title('Middle aged')
  plt.grid(True)

  plt.subplot(4, 3, 3)
  plt.plot(X, union)
  plt.title('union')
  plt.grid(True)

  plt.subplot(4, 3, 4)
  plt.plot(X, intersection)
  plt.title('intersection')
  plt.grid(True)

  plt.subplot(4, 3, 5)
  plt.plot(X, complement_a)
  plt.title('complement_a')
  plt.grid(True)

  plt.subplot(4, 3, 6)
  plt.plot(X, difference)
  plt.title('difference a/b')
  plt.grid(True)

  plt.subplot(4, 3, 7)
  plt.plot(X, alg_sum)
  plt.title('alg_sum')
  plt.grid(True)

  plt.subplot(4, 3, 8)
  plt.plot(X, alg_product)
  plt.title('alg_product')
  plt.grid(True)

  plt.subplot(4, 3, 9)
  plt.plot(X, bdd_sum)
  plt.title('bdd_sum')
  plt.grid(True)

  plt.subplot(4, 3, 10)
  plt.plot(X, bdd_difference)
  plt.title('bdd_difference')
  plt.grid(True)

  plt.subplots_adjust(hspace=0.5)
  plt.show()




