





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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase=32		,      _UpperCAmelCase=10		,      _UpperCAmelCase=100		,      _UpperCAmelCase=1026		,      _UpperCAmelCase=True		,      _UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl"		,      _UpperCAmelCase="igf_context_pairs.jbl"		,      )     ->	Optional[int]:
    set_seed(3					)
    # generate train_data and objective_set
    lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						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__       : int   					=						torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'					)

    # load pretrained model
    lowerCamelCase__       : Dict   					=						load_gpta('gpt2'					).to(_UpperCAmelCase					)
    print('computing perplexity on objective set'					)
    lowerCamelCase__       : Union[str, 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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase=15		,      _UpperCAmelCase=128		,      _UpperCAmelCase=100		,      _UpperCAmelCase="igf_model.pt"		,      )     ->	Optional[int]:
    set_seed(42					)

    # Load pre-trained model
    lowerCamelCase__       : Union[str, Any]   					=						GPTaLMHeadModel.from_pretrained('gpt2'					)

    # Initialize secondary learner to use embedding weights of model
    lowerCamelCase__       : Any   					=						SecondaryLearner(_UpperCAmelCase					)

    # Train secondary learner
    lowerCamelCase__       : Optional[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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=32		,      _UpperCAmelCase=1000		,      _UpperCAmelCase=16		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=recopy_gpta		,      _UpperCAmelCase=None		,      _UpperCAmelCase=10		,      _UpperCAmelCase="gpt2_finetuned.pt"		,      )     ->	List[Any]:
    lowerCamelCase__       : Tuple   					=						torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'					)
    lowerCamelCase__       : Optional[Any]   					=						RandomSampler(_UpperCAmelCase					)
    lowerCamelCase__       : Optional[Any]   					=						DataLoader(_UpperCAmelCase		,      sampler=_UpperCAmelCase					)

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

    model.train()
    if secondary_learner is not None:
        secondary_learner.to(_UpperCAmelCase					)
        secondary_learner.eval()
    lowerCamelCase__       : str   					=						[]
    lowerCamelCase__       : int   					=						0

    lowerCamelCase__       : Optional[Any]   					=						[]
    lowerCamelCase__       : Dict   					=						[]

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

            if secondary_learner is not None:
                lowerCamelCase__       : Optional[Any]   					=						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__       : Any   					=						-1
                if predicted_q < threshold:
                    lowerCamelCase__       : Optional[Any]   					=						False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()					)					)
                lowerCamelCase__       : Optional[int]   					=						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__       : 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:
                    lowerCamelCase__       : int   					=						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 			SCREAMING_SNAKE_CASE (			)     ->	List[Any]:
    lowerCamelCase__       : List[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=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__       : List[str]   					=						joblib.load('data/IGF_values.jbl'					)

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

    # Generate train and test data to train and evaluate gpt2 model
    lowerCamelCase__					,				lowerCamelCase__       : Optional[Any]   					=						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()












def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase					)     ->	Union[str, Any]:
    lowerCamelCase__       : List[Any]   					=						[1]
    for i in range(2		,      _UpperCAmelCase					):
        factorials.append(factorials[-1] * i					)
    assert 0 <= k < factorials[-1] * n, "k out of bounds"

    lowerCamelCase__       : str   					=						[]
    lowerCamelCase__       : Optional[Any]   					=						list(range(_UpperCAmelCase					)					)

    # Find permutation
    while factorials:
        lowerCamelCase__       : List[str]   					=						factorials.pop()
        lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						divmod(_UpperCAmelCase		,      _UpperCAmelCase					)
        permutation.append(elements[number]					)
        elements.remove(elements[number]					)
    permutation.append(elements[0]					)

    return permutation


if __name__ == "__main__":
    import doctest

    doctest.testmod()





