


'''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       (   lowerCamelCase	:   Tuple=32				,    lowerCamelCase	:   List[str]=10				,    lowerCamelCase	:   List[str]=100				,    lowerCamelCase	:   List[str]=1026				,    lowerCamelCase	:   Dict=True				,    lowerCamelCase	:   Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"				,    lowerCamelCase	:   List[str]="igf_context_pairs.jbl"				,    ):
      set_seed(3)
      # generate train_data and objective_set
      A_    :	Any							 =					generate_datasets(
          SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    number=SCREAMING_SNAKE_CASE__				,    min_len=1026				,    trim=SCREAMING_SNAKE_CASE__)
      # 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?
      A_    :	Tuple							 =					torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""")

      # load pretrained model
      A_    :	int							 =					load_gpta("""gpt2""").to(SCREAMING_SNAKE_CASE__)
      print("""computing perplexity on objective set""")
      A_    :	List[Any]							 =					compute_perplexity(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__).item()
      print("""perplexity on objective set:"""				,    SCREAMING_SNAKE_CASE__)

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

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




def   lowerCamelCase       (   lowerCamelCase	:   Union[str, Any]				,    lowerCamelCase	:   str=15				,    lowerCamelCase	:   int=128				,    lowerCamelCase	:   int=100				,    lowerCamelCase	:   List[Any]="igf_model.pt"				,    ):
      set_seed(42)

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

      # Initialize secondary learner to use embedding weights of model
      A_    :	List[str]							 =					SecondaryLearner(SCREAMING_SNAKE_CASE__)

      # Train secondary learner
      A_    :	Any							 =					train_secondary_learner(
          SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    max_epochs=SCREAMING_SNAKE_CASE__				,    batch_size=SCREAMING_SNAKE_CASE__				,    eval_freq=100				,    igf_model_path=SCREAMING_SNAKE_CASE__				,    )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner




def   lowerCamelCase       (   lowerCamelCase	:   List[str]				,    lowerCamelCase	:   Dict				,    lowerCamelCase	:   List[Any]				,    lowerCamelCase	:   int=32				,    lowerCamelCase	:   List[str]=1000				,    lowerCamelCase	:   Optional[int]=16				,    lowerCamelCase	:   Tuple=1.0				,    lowerCamelCase	:   List[Any]=recopy_gpta				,    lowerCamelCase	:   Tuple=None				,    lowerCamelCase	:   int=10				,    lowerCamelCase	:   Optional[int]="gpt2_finetuned.pt"				,    ):
      A_    :	List[Any]							 =					torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""")
      A_    :	Any							 =					RandomSampler(SCREAMING_SNAKE_CASE__)
      A_    :	int							 =					DataLoader(SCREAMING_SNAKE_CASE__				,    sampler=SCREAMING_SNAKE_CASE__)

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

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(SCREAMING_SNAKE_CASE__)
            secondary_learner.eval()
      A_    :	Dict							 =					[]
      A_    :	str							 =					0

      A_    :	List[str]							 =					[]
      A_    :	int							 =					[]

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

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

                        # 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:
                              A_    :	Dict							 =					-1
                        if predicted_q < threshold:
                              A_    :	Optional[Any]							 =					False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()))
                        A_    :	List[str]							 =					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()
                        A_    :	Optional[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:
                              A_    :	Optional[int]							 =					compute_perplexity(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__)
                              test_perps.append(SCREAMING_SNAKE_CASE__)

                              print("""Test perplexity, step"""				,    SCREAMING_SNAKE_CASE__				,    """:"""				,    SCREAMING_SNAKE_CASE__)
            # 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()				,    SCREAMING_SNAKE_CASE__)
      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       (   ):
      A_    :	Tuple							 =					argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""")

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

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

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

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

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

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

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

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

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

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

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

      parser.add_argument(
          """--threshold"""				,    default=1.0				,    type=SCREAMING_SNAKE_CASE__				,    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=SCREAMING_SNAKE_CASE__				,    help="""finetuned_model_name""")

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

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

      # Train secondary learner
      A_    :	List[str]							 =					training_secondary_learner(
          SCREAMING_SNAKE_CASE__				,    secondary_learner_max_epochs=15				,    secondary_learner_batch_size=128				,    eval_freq=100				,    igf_model_path="""igf_model.pt"""				,    )

      # load pretrained gpt2 model
      A_    :	Optional[int]							 =					GPTaLMHeadModel.from_pretrained("""gpt2""")
      set_seed(42)

      # Generate train and test data to train and evaluate gpt2 model
      A_    :	Tuple							 =					generate_datasets(
          context_len=32				,    file="""data/tokenized_stories_train_wikitext103.jbl"""				,    number=100				,    min_len=1026				,    trim=SCREAMING_SNAKE_CASE__)

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


if __name__ == "__main__":
     main()


'''simple docstring'''
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,
)


