




"""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       UpperCAmelCase__ (snake_case__							:      int=32	,     snake_case__							:      List[str]=10	,     snake_case__							:      Optional[int]=1_00	,     snake_case__							:      List[Any]=10_26	,     snake_case__							:      List[str]=True	,     snake_case__							:      Optional[int]="data/tokenized_stories_train_wikitext103.jbl"	,     snake_case__							:      List[str]="igf_context_pairs.jbl"	,     ):
  """simple docstring"""

  set_seed(3					)
  # generate train_data and objective_set
  _snake_case	,      _snake_case							:	Tuple			=						generate_datasets(
      _UpperCamelCase	,     _UpperCamelCase	,     number=_UpperCamelCase	,     min_len=10_26	,     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?
  _snake_case							:	int			=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""					)

  # load pretrained model
  _snake_case							:	List[Any]			=						load_gpta("""gpt2"""					).to(_UpperCamelCase					)
  print("""computing perplexity on objective set"""					)
  _snake_case							:	Optional[int]			=						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       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      Optional[int]=15	,     snake_case__							:      List[Any]=1_28	,     snake_case__							:      Any=1_00	,     snake_case__							:      Union[str, Any]="igf_model.pt"	,     ):
  """simple docstring"""

  set_seed(42					)

  # Load pre-trained model
  _snake_case							:	List[str]			=						GPTaLMHeadModel.from_pretrained("""gpt2"""					)

  # Initialize secondary learner to use embedding weights of model
  _snake_case							:	Optional[Any]			=						SecondaryLearner(_UpperCamelCase					)

  # Train secondary learner
  _snake_case							:	Dict			=						train_secondary_learner(
      _UpperCamelCase	,     _UpperCamelCase	,     max_epochs=_UpperCamelCase	,     batch_size=_UpperCamelCase	,     eval_freq=1_00	,     igf_model_path=_UpperCamelCase	,     )

  del model, secondary_learner_train_data
  torch.cuda.empty_cache()

  return secondary_learner


def       UpperCAmelCase__ (snake_case__							:      Tuple	,     snake_case__							:      Dict	,     snake_case__							:      List[Any]	,     snake_case__							:      List[Any]=32	,     snake_case__							:      Any=10_00	,     snake_case__							:      List[Any]=16	,     snake_case__							:      List[str]=1.0	,     snake_case__							:      List[Any]=recopy_gpta	,     snake_case__							:      List[Any]=None	,     snake_case__							:      int=10	,     snake_case__							:      Any="gpt2_finetuned.pt"	,     ):
  """simple docstring"""

  _snake_case							:	List[Any]			=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""					)
  _snake_case							:	str			=						RandomSampler(_UpperCamelCase					)
  _snake_case							:	Optional[Any]			=						DataLoader(_UpperCamelCase	,     sampler=_UpperCamelCase					)

  _snake_case							:	List[str]			=						max_steps // (len(_UpperCamelCase					)) + 1
  _snake_case							:	int			=						0
  _snake_case							:	Optional[int]			=						torch.zeros((1, context_len)	,     dtype=torch.long	,     device=_UpperCamelCase					)
  _snake_case	,      _snake_case	,      _snake_case							:	Optional[Any]			=						recopy_model(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase					)

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

  _snake_case							:	Optional[int]			=						[]
  _snake_case							:	Tuple			=						[]

  # Compute the performance of the transformer model at the beginning
  _snake_case							:	Tuple			=						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()
      _snake_case							:	Optional[Any]			=						random.randint(0	,     example.size(2					) - context_len - 1					)
      _snake_case							:	Any			=						example[0, 0, start : start + context_len]
      lm_optimizer.zero_grad()
      _snake_case							:	List[Any]			=						model(_UpperCamelCase	,     labels=_UpperCamelCase					)
      _snake_case							:	Optional[int]			=						True

      if secondary_learner is not None:
        _snake_case							:	List[str]			=						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:
          _snake_case							:	Optional[Any]			=						-1
        if predicted_q < threshold:
          _snake_case							:	Optional[int]			=						False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()					)					)
        _snake_case							:	Union[str, Any]			=						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()
        _snake_case							:	int			=						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:
          _snake_case							:	Any			=						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       UpperCAmelCase__ ():
  """simple docstring"""

  _snake_case							:	str			=						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=1_00	,     type=_UpperCamelCase	,     help="""number of articles that are long enough to be used as our objective set"""	,     )
  parser.add_argument(
      """--eval_freq"""	,     default=1_00	,     type=_UpperCamelCase	,     help="""secondary model evaluation is triggered at eval_freq"""					)

  parser.add_argument("""--max_steps"""	,     default=10_00	,     type=_UpperCamelCase	,     help="""To calculate training epochs"""					)

  parser.add_argument(
      """--secondary_learner_batch_size"""	,     default=1_28	,     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=1_00	,     type=_UpperCamelCase	,     help="""The number of examples split to be used as objective_set/test_data"""					)

  parser.add_argument(
      """--min_len"""	,     default=10_26	,     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=1_00	,     min_len=10_26	,     trim=_UpperCamelCase	,     data_file="""data/tokenized_stories_train_wikitext103.jbl"""	,     igf_data_file="""igf_context_pairs.jbl"""	,     )

  # Load train data for secondary learner
  _snake_case							:	List[Any]			=						joblib.load("""data/IGF_values.jbl"""					)

  # Train secondary learner
  _snake_case							:	Optional[Any]			=						training_secondary_learner(
      _UpperCamelCase	,     secondary_learner_max_epochs=15	,     secondary_learner_batch_size=1_28	,     eval_freq=1_00	,     igf_model_path="""igf_model.pt"""	,     )

  # load pretrained gpt2 model
  _snake_case							:	Union[str, Any]			=						GPTaLMHeadModel.from_pretrained("""gpt2"""					)
  set_seed(42					)

  # Generate train and test data to train and evaluate gpt2 model
  _snake_case	,      _snake_case							:	Union[str, Any]			=						generate_datasets(
      context_len=32	,     file="""data/tokenized_stories_train_wikitext103.jbl"""	,     number=1_00	,     min_len=10_26	,     trim=_UpperCamelCase					)

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      _UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase	,     context_len=32	,     max_steps=10_00	,     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 abc import ABC, abstractmethod
from argparse import ArgumentParser






class       lowercase(  __a				):







 '''simple docstring'''





 @staticmethod
 @abstractmethod
 def 				UpperCamelCase_     (       a_:	ArgumentParser	):




   '''simple docstring'''



   raise NotImplementedError()


 @abstractmethod
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   raise NotImplementedError()


