

import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel
def   a   (    A__			:     Optional[int]=32   ,     A__			:     str=10   ,     A__			:     Tuple=100   ,     A__			:     Union[str, Any]=1026   ,     A__			:     Tuple=True   ,     A__			:     Dict="data/tokenized_stories_train_wikitext103.jbl"   ,     A__			:     Dict="igf_context_pairs.jbl"   ,     )					->   Optional[int]:





    """simple docstring"""



    set_seed(3  )
    # generate train_data and objective_set
    _lowercase , _lowercase			 =generate_datasets(
        A__   ,     A__   ,     number=A__   ,     min_len=1026   ,     trim=A__  )
    # keeps model same across runs
    set_seed(4  )
    # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
    # can we train on GPU?
    _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

    # load pretrained model
    _lowercase			 =load_gpta('gpt2'  ).to(A__  )
    print('computing perplexity on objective set'  )
    _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  ).item()
    print('perplexity on objective set:'   ,     A__  )

    # collect igf pairs and save to file demo.jbl
    collect_objective_set(A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__  )

    # clean up, delete model and data we don't need anymore
    del model, train_data, objective_set
    torch.cuda.empty_cache()
def   a   (    A__			:     str   ,     A__			:     str=15   ,     A__			:     Optional[int]=128   ,     A__			:     Dict=100   ,     A__			:     Optional[Any]="igf_model.pt"   ,     )					->   int:





    """simple docstring"""



    set_seed(42  )

    # Load pre-trained model
    _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )

    # Initialize secondary learner to use embedding weights of model
    _lowercase			 =SecondaryLearner(A__  )

    # Train secondary learner
    _lowercase			 =train_secondary_learner(
        A__   ,     A__   ,     max_epochs=A__   ,     batch_size=A__   ,     eval_freq=100   ,     igf_model_path=A__   ,     )

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner
def   a   (    A__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     str   ,     A__			:     Union[str, Any]=32   ,     A__			:     List[str]=1000   ,     A__			:     int=16   ,     A__			:     List[str]=1.0   ,     A__			:     Tuple=recopy_gpta   ,     A__			:     Any=None   ,     A__			:     Tuple=10   ,     A__			:     Tuple="gpt2_finetuned.pt"   ,     )					->   str:





    """simple docstring"""



    _lowercase			 =torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
    _lowercase			 =RandomSampler(A__  )
    _lowercase			 =DataLoader(A__   ,     sampler=A__  )

    _lowercase			 =max_steps // (len(A__  )) + 1
    _lowercase			 =0
    _lowercase			 =torch.zeros((1, context_len)   ,     dtype=torch.long   ,     device=A__  )
    _lowercase , _lowercase , _lowercase			 =recopy_model(A__   ,     A__   ,     A__  )

    model.train()
    if secondary_learner is not None:
        secondary_learner.to(A__  )
        secondary_learner.eval()
    _lowercase			 =[]
    _lowercase			 =0

    _lowercase			 =[]
    _lowercase			 =[]

    # Compute the performance of the transformer model at the beginning
    _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  )
    test_perps.append(A__  )
    print('Test perplexity, step'   ,     A__   ,     ':'   ,     A__  )
    for epoch in range(int(A__  )  ):
        for step, example in enumerate(A__  ):
            torch.cuda.empty_cache()
            _lowercase			 =random.randint(0   ,     example.size(2  ) - context_len - 1  )
            _lowercase			 =example[0, 0, start : start + context_len]
            lm_optimizer.zero_grad()
            _lowercase			 =model(A__   ,     labels=A__  )
            _lowercase			 =True

            if secondary_learner is not None:
                _lowercase			 =secondary_learner.forward(
                    torch.tensor(A__   ,     dtype=torch.long   ,     device=A__  ).unsqueeze(0  )  )[0].item()
                observed_qs.append(float(A__  )  )

                # Here we implement the simple non-constant threshold for the predicted IG(X) value
                # We will decay the selectivity of our secondary learner filter from
                # 1 standard deviation above average to 1 below average after 10 batches.

                if global_step == 10:
                    _lowercase			 =-1
                if predicted_q < threshold:
                    _lowercase			 =False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()  )  )
                _lowercase			 =outputs[0]
                lm_loss.backward()
                examples += 1

            del outputs

            # Once the batch is filled with enough contexts, backprop on the batch.
            if examples == batch_size:
                torch.cuda.empty_cache()
                _lowercase			 =0
                # Do LM backprop
                torch.nn.utils.clip_grad_norm_(model.parameters()   ,     3.0  )
                lm_optimizer.step()
                lm_scheduler.step()  # Update learning rate schedule
                global_step += 1
                # Compute the performance of the transformer model at this batch
                if global_step % eval_interval == 0:
                    _lowercase			 =compute_perplexity(A__   ,     A__   ,     A__  )
                    test_perps.append(A__  )

                    print('Test perplexity, step'   ,     A__   ,     ':'   ,     A__  )
            # Break out of the loop after 60 batches
            if max_steps > 0 and global_step > 60:
                break
        if max_steps > 0 and global_step > 60:
            break

    # save finetuned transformer model
    torch.save(model.state_dict()   ,     A__  )
    torch.cuda.empty_cache()
    # Do some cleaning up so we can reinitialize for the next run of this function
    del lm_optimizer
    del lm_scheduler
    return model
def   a   (    )					->   Optional[int]:





    """simple docstring"""



    _lowercase			 =argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

    # Required parameters
    parser.add_argument(
        '--data_dir'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='The input data dir. Should contain data files for WikiText.'   ,     )
    parser.add_argument(
        '--model_name_or_path'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='Path to pretrained model or model identifier from huggingface.co/models'   ,     )
    parser.add_argument(
        '--data_file'   ,     type=A__   ,     default=A__   ,     help=(
            'A jbl file containing tokenized data which can be split as objective dataset, '
            'train_dataset and test_dataset.'
        )   ,     )

    parser.add_argument(
        '--igf_data_file'   ,     type=A__   ,     default=A__   ,     help='A jbl file containing the context and information gain pairs to train secondary learner.'   ,     )

    parser.add_argument(
        '--output_dir'   ,     default=A__   ,     type=A__   ,     required=A__   ,     help='The output directory where the final fine-tuned model is stored.'   ,     )

    parser.add_argument(
        '--tokenizer_name'   ,     default=A__   ,     type=A__   ,     help='Pretrained tokenizer name or path if not the same as model_name'   ,     )
    parser.add_argument('--seed'   ,     type=A__   ,     default=A__   ,     help='A seed for reproducible training.'  )

    parser.add_argument(
        '--context_len'   ,     default=32   ,     type=A__   ,     help=(
            'The maximum total input sequence length after tokenization. Sequences longer '
            'than this will be truncated, sequences shorter will be padded.'
        )   ,     )

    parser.add_argument(
        '--size_objective_set'   ,     default=100   ,     type=A__   ,     help='number of articles that are long enough to be used as our objective set'   ,     )
    parser.add_argument(
        '--eval_freq'   ,     default=100   ,     type=A__   ,     help='secondary model evaluation is triggered at eval_freq'  )

    parser.add_argument('--max_steps'   ,     default=1000   ,     type=A__   ,     help='To calculate training epochs'  )

    parser.add_argument(
        '--secondary_learner_batch_size'   ,     default=128   ,     type=A__   ,     help='batch size of training data for secondary learner'   ,     )

    parser.add_argument(
        '--batch_size'   ,     default=16   ,     type=A__   ,     help='batch size of training data of language model(gpt2) '  )

    parser.add_argument(
        '--eval_interval'   ,     default=10   ,     type=A__   ,     help=(
            'decay the selectivity of our secondary learner filter from'
            '1 standard deviation above average to 1 below average after 10 batches'
        )   ,     )

    parser.add_argument(
        '--number'   ,     default=100   ,     type=A__   ,     help='The number of examples split to be used as objective_set/test_data'  )

    parser.add_argument(
        '--min_len'   ,     default=1026   ,     type=A__   ,     help='The minimum length of the article to be used as objective set'  )

    parser.add_argument(
        '--secondary_learner_max_epochs'   ,     default=15   ,     type=A__   ,     help='number of epochs to train secondary learner'  )

    parser.add_argument('--trim'   ,     default=A__   ,     type=A__   ,     help='truncate the example if it exceeds context length'  )

    parser.add_argument(
        '--threshold'   ,     default=1.0   ,     type=A__   ,     help=(
            'The threshold value used by secondary learner to filter the train_data and allow only'
            ' informative data as input to the model'
        )   ,     )

    parser.add_argument('--finetuned_model_name'   ,     default='gpt2_finetuned.pt'   ,     type=A__   ,     help='finetuned_model_name'  )

    parser.add_argument(
        '--recopy_model'   ,     default=A__   ,     type=A__   ,     help='Reset the model to the original pretrained GPT-2 weights after each iteration'   ,     )

    # function calls
    # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
    generate_n_pairs(
        context_len=32   ,     max_steps=10   ,     size_objective_set=100   ,     min_len=1026   ,     trim=A__   ,     data_file='data/tokenized_stories_train_wikitext103.jbl'   ,     igf_data_file='igf_context_pairs.jbl'   ,     )

    # Load train data for secondary learner
    _lowercase			 =joblib.load('data/IGF_values.jbl'  )

    # Train secondary learner
    _lowercase			 =training_secondary_learner(
        A__   ,     secondary_learner_max_epochs=15   ,     secondary_learner_batch_size=128   ,     eval_freq=100   ,     igf_model_path='igf_model.pt'   ,     )

    # load pretrained gpt2 model
    _lowercase			 =GPTaLMHeadModel.from_pretrained('gpt2'  )
    set_seed(42  )

    # Generate train and test data to train and evaluate gpt2 model
    _lowercase , _lowercase			 =generate_datasets(
        context_len=32   ,     file='data/tokenized_stories_train_wikitext103.jbl'   ,     number=100   ,     min_len=1026   ,     trim=A__  )

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        A__   ,     A__   ,     A__   ,     context_len=32   ,     max_steps=1000   ,     batch_size=16   ,     threshold=1.0   ,     recopy_model=A__   ,     secondary_learner=A__   ,     eval_interval=10   ,     finetuned_model_name='gpt2_finetuned.pt'   ,     )


if __name__ == "__main__":
  main()





import unittest

from knapsack import knapsack as k





class 				__lowerCAmelCase  (      unittest.TestCase							):




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        _lowercase			 =0
        _lowercase			 =[0]
        _lowercase			 =[0]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     0      )

        _lowercase			 =[60]
        _lowercase			 =[10]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     0      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =3
        _lowercase			 =[1, 2, 3]
        _lowercase			 =[3, 2, 1]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     5      )




  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =50
        _lowercase			 =[60, 100, 120]
        _lowercase			 =[10, 20, 30]
        _lowercase			 =len(lowerCAmelCase      )
        self.assertEqual(k.knapsack(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )							,     220      )


if __name__ == "__main__":
  unittest.main()


