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       __magic_name__(  __UpperCAmelCase=32 ,						__UpperCAmelCase=10 ,						__UpperCAmelCase=100 ,						__UpperCAmelCase=1026 ,						__UpperCAmelCase=True ,						__UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl" ,						__UpperCAmelCase="igf_context_pairs.jbl" ,						)						->    Dict:



      '''simple docstring'''

      set_seed(3		)
      # generate train_data and objective_set
      _lowerCamelCase    ,					_lowerCamelCase			    =				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?
      _lowerCamelCase			    =				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)

      # load pretrained model
      _lowerCamelCase			    =				load_gpta('''gpt2'''		).to(__UpperCAmelCase		)
      print('''computing perplexity on objective set'''		)
      _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=15 ,						__UpperCAmelCase=128 ,						__UpperCAmelCase=100 ,						__UpperCAmelCase="igf_model.pt" ,						)						->    Optional[Any]:



      '''simple docstring'''

      set_seed(42		)

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

      # Initialize secondary learner to use embedding weights of model
      _lowerCamelCase			    =				SecondaryLearner(__UpperCAmelCase		)

      # Train secondary learner
      _lowerCamelCase			    =				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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=32 ,						__UpperCAmelCase=1000 ,						__UpperCAmelCase=16 ,						__UpperCAmelCase=1.0 ,						__UpperCAmelCase=recopy_gpta ,						__UpperCAmelCase=None ,						__UpperCAmelCase=10 ,						__UpperCAmelCase="gpt2_finetuned.pt" ,						)						->    str:



      '''simple docstring'''

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

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

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

      _lowerCamelCase			    =				[]
      _lowerCamelCase			    =				[]

      # Compute the performance of the transformer model at the beginning
      _lowerCamelCase			    =				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()
                  _lowerCamelCase			    =				random.randint(0 ,						example.size(2		) - context_len - 1		)
                  _lowerCamelCase			    =				example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  _lowerCamelCase			    =				model(__UpperCAmelCase ,						labels=__UpperCAmelCase		)
                  _lowerCamelCase			    =				True

                  if secondary_learner is not None:
                        _lowerCamelCase			    =				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:
                              _lowerCamelCase			    =				-1
                        if predicted_q < threshold:
                              _lowerCamelCase			    =				False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()		)		)
                        _lowerCamelCase			    =				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()
                        _lowerCamelCase			    =				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:
                              _lowerCamelCase			    =				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       __magic_name__(  )						->    Tuple:



      '''simple docstring'''

      _lowerCamelCase			    =				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
      _lowerCamelCase			    =				joblib.load('''data/IGF_values.jbl'''		)

      # Train secondary learner
      _lowerCamelCase			    =				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
      _lowerCamelCase			    =				GPTaLMHeadModel.from_pretrained('''gpt2'''		)
      set_seed(42		)

      # Generate train and test data to train and evaluate gpt2 model
      _lowerCamelCase    ,					_lowerCamelCase			    =				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()
from .data_collator import (
    DataCollatorForLanguageModeling,
    DataCollatorForPermutationLanguageModeling,
    DataCollatorForSeqaSeq,
    DataCollatorForSOP,
    DataCollatorForTokenClassification,
    DataCollatorForWholeWordMask,
    DataCollatorWithPadding,
    DefaultDataCollator,
    default_data_collator,
)
from .metrics import glue_compute_metrics, xnli_compute_metrics
from .processors import (
    DataProcessor,
    InputExample,
    InputFeatures,
    SingleSentenceClassificationProcessor,
    SquadExample,
    SquadFeatures,
    SquadVaProcessor,
    SquadVaProcessor,
    glue_convert_examples_to_features,
    glue_output_modes,
    glue_processors,
    glue_tasks_num_labels,
    squad_convert_examples_to_features,
    xnli_output_modes,
    xnli_processors,
    xnli_tasks_num_labels,
)