
'''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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE=3_2,		__SCREAMING_SNAKE_CASE=1_0,		__SCREAMING_SNAKE_CASE=1_0_0,		__SCREAMING_SNAKE_CASE=1_0_2_6,		__SCREAMING_SNAKE_CASE=True,		__SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl",		__SCREAMING_SNAKE_CASE="igf_context_pairs.jbl",		):
    """simple docstring"""






    set_seed(3       )
    # generate train_data and objective_set
    snake_case_ :    int     			=						generate_datasets(
        _lowerCamelCase,		_lowerCamelCase,		number=_lowerCamelCase,		min_len=1_0_2_6,		trim=_lowerCamelCase       )
    # 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?
    snake_case_ :    int     			=						torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )

    # load pretrained model
    snake_case_ :    Dict     			=						load_gpta("gpt2"       ).to(_lowerCamelCase       )
    print("computing perplexity on objective set"       )
    snake_case_ :    str     			=						compute_perplexity(_lowerCamelCase,		_lowerCamelCase,		_lowerCamelCase       ).item()
    print("perplexity on objective set:",		_lowerCamelCase       )

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

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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=1_5,		__SCREAMING_SNAKE_CASE=1_2_8,		__SCREAMING_SNAKE_CASE=1_0_0,		__SCREAMING_SNAKE_CASE="igf_model.pt",		):
    """simple docstring"""






    set_seed(4_2       )

    # Load pre-trained model
    snake_case_ :    int     			=						GPTaLMHeadModel.from_pretrained("gpt2"       )

    # Initialize secondary learner to use embedding weights of model
    snake_case_ :    Optional[int]     			=						SecondaryLearner(_lowerCamelCase       )

    # Train secondary learner
    snake_case_ :    int     			=						train_secondary_learner(
        _lowerCamelCase,		_lowerCamelCase,		max_epochs=_lowerCamelCase,		batch_size=_lowerCamelCase,		eval_freq=1_0_0,		igf_model_path=_lowerCamelCase,		)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=3_2,		__SCREAMING_SNAKE_CASE=1_0_0_0,		__SCREAMING_SNAKE_CASE=1_6,		__SCREAMING_SNAKE_CASE=1.0,		__SCREAMING_SNAKE_CASE=recopy_gpta,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=1_0,		__SCREAMING_SNAKE_CASE="gpt2_finetuned.pt",		):
    """simple docstring"""






    snake_case_ :    str     			=						torch.device("cuda:0" if torch.cuda.is_available() else "cpu"       )
    snake_case_ :    Tuple     			=						RandomSampler(_lowerCamelCase       )
    snake_case_ :    Tuple     			=						DataLoader(_lowerCamelCase,		sampler=_lowerCamelCase       )

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

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

    snake_case_ :    Optional[int]     			=						[]
    snake_case_ :    str     			=						[]

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

            if secondary_learner is not None:
                snake_case_ :    Dict     			=						secondary_learner.forward(
                    torch.tensor(_lowerCamelCase,		dtype=torch.long,		device=_lowerCamelCase       ).unsqueeze(0       )       )[0].item()
                observed_qs.append(float(_lowerCamelCase       )       )

                # 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 == 1_0:
                    snake_case_ :    Union[str, Any]     			=						-1
                if predicted_q < threshold:
                    snake_case_ :    List[Any]     			=						False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()       )       )
                snake_case_ :    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()
                snake_case_ :    str     			=						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:
                    snake_case_ :    Tuple     			=						compute_perplexity(_lowerCamelCase,		_lowerCamelCase,		_lowerCamelCase       )
                    test_perps.append(_lowerCamelCase       )

                    print("Test perplexity, step",		_lowerCamelCase,		":",		_lowerCamelCase       )
            # Break out of the loop after 60 batches
            if max_steps > 0 and global_step > 6_0:
                break
        if max_steps > 0 and global_step > 6_0:
            break

    # save finetuned transformer model
    torch.save(model.state_dict(),		_lowerCamelCase       )
    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 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Tuple     			=						argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task"       )

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

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

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

    parser.add_argument(
        "--context_len",		default=3_2,		type=_lowerCamelCase,		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=1_0_0,		type=_lowerCamelCase,		help="number of articles that are long enough to be used as our objective set",		)
    parser.add_argument(
        "--eval_freq",		default=1_0_0,		type=_lowerCamelCase,		help="secondary model evaluation is triggered at eval_freq"       )

    parser.add_argument("--max_steps",		default=1_0_0_0,		type=_lowerCamelCase,		help="To calculate training epochs"       )

    parser.add_argument(
        "--secondary_learner_batch_size",		default=1_2_8,		type=_lowerCamelCase,		help="batch size of training data for secondary learner",		)

    parser.add_argument(
        "--batch_size",		default=1_6,		type=_lowerCamelCase,		help="batch size of training data of language model(gpt2) "       )

    parser.add_argument(
        "--eval_interval",		default=1_0,		type=_lowerCamelCase,		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=1_0_0,		type=_lowerCamelCase,		help="The number of examples split to be used as objective_set/test_data"       )

    parser.add_argument(
        "--min_len",		default=1_0_2_6,		type=_lowerCamelCase,		help="The minimum length of the article to be used as objective set"       )

    parser.add_argument(
        "--secondary_learner_max_epochs",		default=1_5,		type=_lowerCamelCase,		help="number of epochs to train secondary learner"       )

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

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

    parser.add_argument(
        "--recopy_model",		default=_lowerCamelCase,		type=_lowerCamelCase,		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=3_2,		max_steps=1_0,		size_objective_set=1_0_0,		min_len=1_0_2_6,		trim=_lowerCamelCase,		data_file="data/tokenized_stories_train_wikitext103.jbl",		igf_data_file="igf_context_pairs.jbl",		)

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

    # Train secondary learner
    snake_case_ :    str     			=						training_secondary_learner(
        _lowerCamelCase,		secondary_learner_max_epochs=1_5,		secondary_learner_batch_size=1_2_8,		eval_freq=1_0_0,		igf_model_path="igf_model.pt",		)

    # load pretrained gpt2 model
    snake_case_ :    Any     			=						GPTaLMHeadModel.from_pretrained("gpt2"       )
    set_seed(4_2       )

    # Generate train and test data to train and evaluate gpt2 model
    snake_case_ :    Any     			=						generate_datasets(
        context_len=3_2,		file="data/tokenized_stories_train_wikitext103.jbl",		number=1_0_0,		min_len=1_0_2_6,		trim=_lowerCamelCase       )

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        _lowerCamelCase,		_lowerCamelCase,		_lowerCamelCase,		context_len=3_2,		max_steps=1_0_0_0,		batch_size=1_6,		threshold=1.0,		recopy_model=_lowerCamelCase,		secondary_learner=_lowerCamelCase,		eval_interval=1_0,		finetuned_model_name="gpt2_finetuned.pt",		)


if __name__ == "__main__":
  main()






'''simple docstring'''
from __future__ import annotations

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						0.00
    snake_case_ :    int     			=						0
    for resistor in resistors:
        if resistor <= 0:
            snake_case_ :    Dict     			=						f'Resistor at index {index} has a negative or zero value!'
            raise ValueError(__SCREAMING_SNAKE_CASE       )
        first_sum += 1 / float(__SCREAMING_SNAKE_CASE       )
        index += 1
    return 1 / first_sum

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						0.00
    snake_case_ :    List[Any]     			=						0
    for resistor in resistors:
        sum_r += resistor
        if resistor < 0:
            snake_case_ :    str     			=						f'Resistor at index {index} has a negative value!'
            raise ValueError(__SCREAMING_SNAKE_CASE       )
        index += 1
    return sum_r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




