
"""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=32			,					_UpperCamelCase=10			,					_UpperCamelCase=100			,					_UpperCamelCase=1_026			,					_UpperCamelCase=True			,					_UpperCamelCase="data/tokenized_stories_train_wikitext103.jbl"			,					_UpperCamelCase="igf_context_pairs.jbl"			,					):






  """simple docstring"""



  set_seed(3    )
  # generate train_data and objective_set
  _lowercase			,					_lowercase:      Optional[int]   			=  generate_datasets(
      _UpperCamelCase			,					_UpperCamelCase			,					number=_UpperCamelCase			,					min_len=1_026			,					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?
  _lowercase:      List[Any]   			=  torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''    )

  # load pretrained model
  _lowercase:      int   			=  load_gpta('''gpt2'''    ).to(_UpperCamelCase    )
  print('''computing perplexity on objective set'''    )
  _lowercase:      List[Any]   			=  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			,					_UpperCamelCase=15			,					_UpperCamelCase=128			,					_UpperCamelCase=100			,					_UpperCamelCase="igf_model.pt"			,					):






  """simple docstring"""



  set_seed(42    )

  # Load pre-trained model
  _lowercase:      Optional[int]   			=  GPTaLMHeadModel.from_pretrained('''gpt2'''    )

  # Initialize secondary learner to use embedding weights of model
  _lowercase:      int   			=  SecondaryLearner(_UpperCamelCase    )

  # Train secondary learner
  _lowercase:      Union[str, Any]   			=  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			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase=32			,					_UpperCamelCase=1_000			,					_UpperCamelCase=16			,					_UpperCamelCase=1.0			,					_UpperCamelCase=recopy_gpta			,					_UpperCamelCase=None			,					_UpperCamelCase=10			,					_UpperCamelCase="gpt2_finetuned.pt"			,					):






  """simple docstring"""



  _lowercase:      Union[str, Any]   			=  torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''    )
  _lowercase:      Tuple   			=  RandomSampler(_UpperCamelCase    )
  _lowercase:      Optional[int]   			=  DataLoader(_UpperCamelCase			,					sampler=_UpperCamelCase    )

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

  model.train()
  if secondary_learner is not None:
    secondary_learner.to(_UpperCamelCase    )
    secondary_learner.eval()
  _lowercase:      Tuple   			=  []
  _lowercase:      List[str]   			=  0

  _lowercase:      Union[str, Any]   			=  []
  _lowercase:      str   			=  []

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

      if secondary_learner is not None:
        _lowercase:      int   			=  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:
          _lowercase:      Any   			=  -1
        if predicted_q < threshold:
          _lowercase:      Union[str, Any]   			=  False

            # If we passed the filter, add the context to the batch!
      if do_backprop:
        contexts.append(np.array(context.cpu()    )    )
        _lowercase:      Optional[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()
        _lowercase:      Dict   			=  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:      Tuple   			=  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"""



  _lowercase:      Union[str, Any]   			=  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=1_000			,					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=1_026			,					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=1_026			,					trim=_UpperCamelCase			,					data_file='''data/tokenized_stories_train_wikitext103.jbl'''			,					igf_data_file='''igf_context_pairs.jbl'''			,					)

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

  # Train secondary learner
  _lowercase:      str   			=  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
  _lowercase:      str   			=  GPTaLMHeadModel.from_pretrained('''gpt2'''    )
  set_seed(42    )

  # Generate train and test data to train and evaluate gpt2 model
  _lowercase			,					_lowercase:      Union[str, Any]   			=  generate_datasets(
      context_len=32			,					file='''data/tokenized_stories_train_wikitext103.jbl'''			,					number=100			,					min_len=1_026			,					trim=_UpperCamelCase    )

  # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
  finetune(
      _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					context_len=32			,					max_steps=1_000			,					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"""




import argparse
import os

# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A__ :       List[Any]				= 1_6
A__ :       Union[str, Any]				= 3_2




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase = 16    ):






  """simple docstring"""



  _lowercase:      Any   			=  AutoTokenizer.from_pretrained('''bert-base-cased'''    )
  _lowercase:      List[str]   			=  load_dataset('''glue'''			,					'''mrpc'''    )

  def tokenize_function(_UpperCamelCase    ):
    # max_length=None => use the model max length (it's actually the default)
    _lowercase:      Union[str, Any]   			=  tokenizer(examples['''sentence1''']			,					examples['''sentence2''']			,					truncation=_UpperCamelCase			,					max_length=_UpperCamelCase    )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    _lowercase:      Tuple   			=  datasets.map(
        _UpperCamelCase			,					batched=_UpperCamelCase			,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,					)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _lowercase:      Optional[Any]   			=  tokenized_datasets.rename_column('''label'''			,					'''labels'''    )

  def collate_fn(_UpperCamelCase    ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    _lowercase:      Union[str, Any]   			=  128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      _lowercase:      int   			=  16
    elif accelerator.mixed_precision != "no":
      _lowercase:      Tuple   			=  8
    else:
      _lowercase:      str   			=  None

    return tokenizer.pad(
        _UpperCamelCase			,					padding='''longest'''			,					max_length=_UpperCamelCase			,					pad_to_multiple_of=_UpperCamelCase			,					return_tensors='''pt'''			,					)

  # Instantiate dataloaders.
  _lowercase:      List[Any]   			=  DataLoader(
      tokenized_datasets['''train''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )
  _lowercase:      int   			=  DataLoader(
      tokenized_datasets['''validation''']			,					shuffle=_UpperCamelCase			,					collate_fn=_UpperCamelCase			,					batch_size=_UpperCamelCase    )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
  from accelerate.test_utils.training import mocked_dataloaders

  A__ :       List[Any]				= mocked_dataloaders  # noqa: F811




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''			,					_UpperCamelCase    ) == "1":
    _lowercase:      Tuple   			=  2
  # Initialize accelerator
  _lowercase:      Optional[Any]   			=  Accelerator(cpu=args.cpu			,					mixed_precision=args.mixed_precision    )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _lowercase:      Optional[Any]   			=  config['''lr''']
  _lowercase:      Tuple   			=  int(config['''num_epochs''']    )
  _lowercase:      Any   			=  int(config['''seed''']    )
  _lowercase:      List[Any]   			=  int(config['''batch_size''']    )

  _lowercase:      Optional[int]   			=  evaluate.load('''glue'''			,					'''mrpc'''    )

  # New Code #
  # We now can define an inner training loop function. It should take a batch size as the only parameter,
  # and build the dataloaders in there.
  # It also gets our decorator
  @find_executable_batch_size(starting_batch_size=_UpperCamelCase    )
  def inner_training_loop(_UpperCamelCase    ):
    # And now just move everything below under this function
    # We need to bring in the Accelerator object from earlier
    nonlocal accelerator
    # And reset all of its attributes that could hold onto any memory:
    accelerator.free_memory()

    # Then we can declare the model, optimizer, and everything else:
    set_seed(_UpperCamelCase    )

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _lowercase:      Optional[int]   			=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''			,					return_dict=_UpperCamelCase    )

    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
    _lowercase:      List[Any]   			=  model.to(accelerator.device    )

    # Instantiate optimizer
    _lowercase:      List[str]   			=  AdamW(params=model.parameters()			,					lr=_UpperCamelCase    )
    _lowercase			,					_lowercase:      Tuple   			=  get_dataloaders(_UpperCamelCase			,					_UpperCamelCase    )

    # Instantiate scheduler
    _lowercase:      str   			=  get_linear_schedule_with_warmup(
        optimizer=_UpperCamelCase			,					num_warmup_steps=100			,					num_training_steps=(len(_UpperCamelCase    ) * num_epochs)			,					)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    _lowercase			,					_lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Union[str, Any]   			=  accelerator.prepare(
        _UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    )

    # Now we train the model
    for epoch in range(_UpperCamelCase    ):
      model.train()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        _lowercase:      Tuple   			=  model(**_UpperCamelCase    )
        _lowercase:      Union[str, Any]   			=  outputs.loss
        accelerator.backward(_UpperCamelCase    )
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_UpperCamelCase    ):
        # We could avoid this line since we set the accelerator with `device_placement=True`.
        batch.to(accelerator.device    )
        with torch.no_grad():
          _lowercase:      str   			=  model(**_UpperCamelCase    )
        _lowercase:      int   			=  outputs.logits.argmax(dim=-1    )
        _lowercase			,					_lowercase:      List[str]   			=  accelerator.gather_for_metrics((predictions, batch['''labels'''])    )
        metric.add_batch(
            predictions=_UpperCamelCase			,					references=_UpperCamelCase			,					)

      _lowercase:      int   			=  metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f'''epoch {epoch}:'''			,					_UpperCamelCase    )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
  inner_training_loop()




def 			_lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:      Dict   			=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
  parser.add_argument(
      '''--mixed_precision'''			,					type=_UpperCamelCase			,					default=_UpperCamelCase			,					choices=['''no''', '''fp16''', '''bf16''', '''fp8''']			,					help='''Whether to use mixed precision. Choose'''
      '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
      '''and an Nvidia Ampere GPU.'''			,					)
  parser.add_argument('''--cpu'''			,					action='''store_true'''			,					help='''If passed, will train on the CPU.'''    )
  _lowercase:      Tuple   			=  parser.parse_args()
  _lowercase:      str   			=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(_UpperCamelCase			,					_UpperCamelCase    )


if __name__ == "__main__":
  main()
