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

    # load pretrained model
    UpperCAmelCase__	:      Tuple     							=					load_gpta('gpt2' ).to(_snake_case )
    print('computing perplexity on objective set' )
    UpperCAmelCase__	:      Any     							=					compute_perplexity(_snake_case    ,_snake_case    ,_snake_case ).item()
    print('perplexity on objective set:'    ,_snake_case )

    # collect igf pairs and save to file demo.jbl
    collect_objective_set(_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_snake_case    ,_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   (     _snake_case    ,_snake_case=15    ,_snake_case=128    ,_snake_case=100    ,_snake_case="igf_model.pt"    ,):
    set_seed(42 )

    # Load pre-trained model
    UpperCAmelCase__	:      str     							=					GPTaLMHeadModel.from_pretrained('gpt2' )

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

    # Train secondary learner
    UpperCAmelCase__	:      Tuple     							=					train_secondary_learner(
        _snake_case    ,_snake_case    ,max_epochs=_snake_case    ,batch_size=_snake_case    ,eval_freq=100    ,igf_model_path=_snake_case    ,)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case    ,_snake_case=32    ,_snake_case=1000    ,_snake_case=16    ,_snake_case=1.0    ,_snake_case=recopy_gpta    ,_snake_case=None    ,_snake_case=10    ,_snake_case="gpt2_finetuned.pt"    ,):
    UpperCAmelCase__	:      Tuple     							=					torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
    UpperCAmelCase__	:      int     							=					RandomSampler(_snake_case )
    UpperCAmelCase__	:      List[Any]     							=					DataLoader(_snake_case    ,sampler=_snake_case )

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

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

    UpperCAmelCase__	:      List[Any]     							=					[]
    UpperCAmelCase__	:      Union[str, Any]     							=					[]

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

            if secondary_learner is not None:
                UpperCAmelCase__	:      List[str]     							=					secondary_learner.forward(
                    torch.tensor(_snake_case    ,dtype=torch.long    ,device=_snake_case ).unsqueeze(0 ) )[0].item()
                observed_qs.append(float(_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:
                    UpperCAmelCase__	:      List[Any]     							=					-1
                if predicted_q < threshold:
                    UpperCAmelCase__	:      Optional[int]     							=					False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu() ) )
                UpperCAmelCase__	:      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()
                UpperCAmelCase__	:      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:
                    UpperCAmelCase__	:      List[Any]     							=					compute_perplexity(_snake_case    ,_snake_case    ,_snake_case )
                    test_perps.append(_snake_case )

                    print('Test perplexity, step'    ,_snake_case    ,':'    ,_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()    ,_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   (     ):
    UpperCAmelCase__	:      Tuple     							=					argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task' )

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

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

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

    parser.add_argument(
        '--context_len'    ,default=32    ,type=_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=_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=_snake_case    ,help='secondary model evaluation is triggered at eval_freq' )

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

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

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

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

    parser.add_argument(
        '--min_len'    ,default=1026    ,type=_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=_snake_case    ,help='number of epochs to train secondary learner' )

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

    parser.add_argument(
        '--threshold'    ,default=1.0    ,type=_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=_snake_case    ,help='finetuned_model_name' )

    parser.add_argument(
        '--recopy_model'    ,default=_snake_case    ,type=_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=_snake_case    ,data_file='data/tokenized_stories_train_wikitext103.jbl'    ,igf_data_file='igf_context_pairs.jbl'    ,)

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

    # Train secondary learner
    UpperCAmelCase__	:      str     							=					training_secondary_learner(
        _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
    UpperCAmelCase__	:      Optional[int]     							=					GPTaLMHeadModel.from_pretrained('gpt2' )
    set_seed(42 )

    # Generate train and test data to train and evaluate gpt2 model
    UpperCAmelCase__							,							UpperCAmelCase__	:      str     							=					generate_datasets(
        context_len=32    ,file='data/tokenized_stories_train_wikitext103.jbl'    ,number=100    ,min_len=1026    ,trim=_snake_case )

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


if __name__ == "__main__":
    main()



"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

