






from bisect import bisect
from itertools import accumulate






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  sorted(zip(__lowercase     ,   __lowercase)     ,   key=lambda __lowercase: x[0] / x[1]     ,   reverse=__lowercase)
   UpperCamelCase_		,   UpperCamelCase_						=  [i[0] for i in r], [i[1] for i in r]
   UpperCamelCase_						=  list(accumulate(__lowercase))
   UpperCamelCase_						=  bisect(__lowercase     ,   __lowercase)
   return (
       0
       if k == 0
       else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
       if k != n
       else sum(vl[:k])
   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()













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      _snake_case     (__lowercase=32     ,   __lowercase=10     ,   __lowercase=100     ,   __lowercase=1026     ,   __lowercase=True     ,   __lowercase="data/tokenized_stories_train_wikitext103.jbl"     ,   __lowercase="igf_context_pairs.jbl"     ,   ):
   set_seed(3)
   # generate train_data and objective_set
   UpperCamelCase_		,   UpperCamelCase_						=  generate_datasets(
       __lowercase     ,   __lowercase     ,   number=__lowercase     ,   min_len=1026     ,   trim=__lowercase)
   # 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?
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

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

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

   # clean up, delete model and data we don't need anymore
   del model, train_data, objective_set
   torch.cuda.empty_cache()






def      _snake_case     (__lowercase     ,   __lowercase=15     ,   __lowercase=128     ,   __lowercase=100     ,   __lowercase="igf_model.pt"     ,   ):
   set_seed(42)

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

   # Initialize secondary learner to use embedding weights of model
   UpperCamelCase_						=  SecondaryLearner(__lowercase)

   # Train secondary learner
   UpperCamelCase_						=  train_secondary_learner(
       __lowercase     ,   __lowercase     ,   max_epochs=__lowercase     ,   batch_size=__lowercase     ,   eval_freq=100     ,   igf_model_path=__lowercase     ,   )

   del model, secondary_learner_train_data
   torch.cuda.empty_cache()

   return secondary_learner






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=32     ,   __lowercase=1000     ,   __lowercase=16     ,   __lowercase=1.0     ,   __lowercase=recopy_gpta     ,   __lowercase=None     ,   __lowercase=10     ,   __lowercase="gpt2_finetuned.pt"     ,   ):
   UpperCamelCase_						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
   UpperCamelCase_						=  RandomSampler(__lowercase)
   UpperCamelCase_						=  DataLoader(__lowercase     ,   sampler=__lowercase)

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

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

   UpperCamelCase_						=  []
   UpperCamelCase_						=  []

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

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

            # 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:
               UpperCamelCase_						=  -1
            if predicted_q < threshold:
               UpperCamelCase_						=  False

            # If we passed the filter, add the context to the batch!
         if do_backprop:
            contexts.append(np.array(context.cpu()))
            UpperCamelCase_						=  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()
            UpperCamelCase_						=  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:
               UpperCamelCase_						=  compute_perplexity(__lowercase     ,   __lowercase     ,   __lowercase)
               test_perps.append(__lowercase)

               print('Test perplexity, step'     ,   __lowercase     ,   ':'     ,   __lowercase)
            # 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()     ,   __lowercase)
   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      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

   # Required parameters
   parser.add_argument(
       '--data_dir'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='The input data dir. Should contain data files for WikiText.'     ,   )
   parser.add_argument(
       '--model_name_or_path'     ,   default=__lowercase     ,   type=__lowercase     ,   required=__lowercase     ,   help='Path to pretrained model or model identifier from huggingface.co/models'     ,   )
   parser.add_argument(
       '--data_file'     ,   type=__lowercase     ,   default=__lowercase     ,   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=__lowercase     ,   default=__lowercase     ,   help='A jbl file containing the context and information gain pairs to train secondary learner.'     ,   )

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

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

   parser.add_argument(
       '--context_len'     ,   default=32     ,   type=__lowercase     ,   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=__lowercase     ,   help='number of articles that are long enough to be used as our objective set'     ,   )
   parser.add_argument(
       '--eval_freq'     ,   default=100     ,   type=__lowercase     ,   help='secondary model evaluation is triggered at eval_freq')

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

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

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

   parser.add_argument(
       '--eval_interval'     ,   default=10     ,   type=__lowercase     ,   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=__lowercase     ,   help='The number of examples split to be used as objective_set/test_data')

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

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

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

   parser.add_argument(
       '--threshold'     ,   default=1.0     ,   type=__lowercase     ,   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=__lowercase     ,   help='finetuned_model_name')

   parser.add_argument(
       '--recopy_model'     ,   default=__lowercase     ,   type=__lowercase     ,   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=__lowercase     ,   data_file='data/tokenized_stories_train_wikitext103.jbl'     ,   igf_data_file='igf_context_pairs.jbl'     ,   )

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

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

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

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

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


if __name__ == "__main__":
 main()





