'''simple docstring'''
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  __lowerCamelCase       (      _UpperCamelCase  :  int=32							,    _UpperCamelCase  :  List[str]=10							,    _UpperCamelCase  :  Optional[int]=100							,    _UpperCamelCase  :  List[Any]=1026							,    _UpperCamelCase  :  List[str]=True							,    _UpperCamelCase  :  Optional[int]="data/tokenized_stories_train_wikitext103.jbl"							,    _UpperCamelCase  :  List[str]="igf_context_pairs.jbl"							,    ):


      '''simple docstring'''


      set_seed(3					)
      # generate train_data and objective_set
      UpperCAmelCase_      ,		UpperCAmelCase_            =  generate_datasets(
          _UpperCamelCase							,    _UpperCamelCase							,    number=_UpperCamelCase							,    min_len=1026							,    trim=_UpperCamelCase					)
      # 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(_UpperCamelCase					)
      print('''computing perplexity on objective set'''					)
      UpperCAmelCase_            =  compute_perplexity(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					).item()
      print('''perplexity on objective set:'''							,    _UpperCamelCase					)

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

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



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]=15							,    _UpperCamelCase  :  List[Any]=128							,    _UpperCamelCase  :  Any=100							,    _UpperCamelCase  :  Union[str, Any]="igf_model.pt"							,    ):


      '''simple docstring'''


      set_seed(42					)

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

      # Initialize secondary learner to use embedding weights of model
      UpperCAmelCase_            =  SecondaryLearner(_UpperCamelCase					)

      # Train secondary learner
      UpperCAmelCase_            =  train_secondary_learner(
          _UpperCamelCase							,    _UpperCamelCase							,    max_epochs=_UpperCamelCase							,    batch_size=_UpperCamelCase							,    eval_freq=100							,    igf_model_path=_UpperCamelCase							,    )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner



def  __lowerCamelCase       (      _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  List[Any]=32							,    _UpperCamelCase  :  Any=1000							,    _UpperCamelCase  :  List[Any]=16							,    _UpperCamelCase  :  List[str]=1.0							,    _UpperCamelCase  :  List[Any]=recopy_gpta							,    _UpperCamelCase  :  List[Any]=None							,    _UpperCamelCase  :  int=10							,    _UpperCamelCase  :  Any="gpt2_finetuned.pt"							,    ):


      '''simple docstring'''


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

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

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

      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  []

      # Compute the performance of the transformer model at the beginning
      UpperCAmelCase_            =  compute_perplexity(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
      test_perps.append(_UpperCamelCase					)
      print('''Test perplexity, step'''							,    _UpperCamelCase							,    ''':'''							,    _UpperCamelCase					)
      for epoch in range(int(_UpperCamelCase					)					):
            for step, example in enumerate(_UpperCamelCase					):
                  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(_UpperCamelCase							,    labels=_UpperCamelCase					)
                  UpperCAmelCase_            =  True

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

                        # 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(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)
                              test_perps.append(_UpperCamelCase					)

                              print('''Test perplexity, step'''							,    _UpperCamelCase							,    ''':'''							,    _UpperCamelCase					)
            # 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()							,    _UpperCamelCase					)
      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  __lowerCamelCase       (      ):


      '''simple docstring'''


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

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

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

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

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

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

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

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

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

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

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

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

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

      parser.add_argument(
          '''--recopy_model'''							,    default=_UpperCamelCase							,    type=_UpperCamelCase							,    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=_UpperCamelCase							,    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(
          _UpperCamelCase							,    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=_UpperCamelCase					)

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


if __name__ == "__main__":
      main()






'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      for param in module.parameters():
            UpperCAmelCase_            =  False



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCAmelCase_            =  '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''					)
      return device



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]					):


      '''simple docstring'''


      UpperCAmelCase_            =  plt.imshow(_UpperCamelCase					)
      fig.axes.get_xaxis().set_visible(_UpperCamelCase					)
      fig.axes.get_yaxis().set_visible(_UpperCamelCase					)
      plt.show()





def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  datetime.now()
      UpperCAmelCase_            =  current_time.strftime('''%H:%M:%S'''					)
      return timestamp





