import random, sys, argparse, os, logging, torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from datasets import load_from_disk

if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    # hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument("--epochs", type=int, default=3)
    parser.add_argument("--train-batch-size", type=int, default=32)
    parser.add_argument("--eval-batch-size", type=int, default=64)
    parser.add_argument("--save-strategy", type=str, default='no')
    parser.add_argument("--save-steps", type=int, default=500)
    parser.add_argument("--model-name", type=str)
    parser.add_argument("--learning-rate", type=str, default=5e-5)

    # Data, model, and output directories
    parser.add_argument("--output-data-dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
    parser.add_argument("--model-dir", type=str, default=os.environ["SM_MODEL_DIR"])
    parser.add_argument("--n-gpus", type=str, default=os.environ["SM_NUM_GPUS"])
    parser.add_argument("--train-dir", type=str, default=os.environ["SM_CHANNEL_TRAIN"])
    parser.add_argument("--valid-dir", type=str, default=os.environ["SM_CHANNEL_VALID"])

    args, _ = parser.parse_known_args()

    # load datasets
    train_dataset = load_from_disk(args.train_dir)
    valid_dataset = load_from_disk(args.valid_dir)
    
    logger = logging.getLogger(__name__)
    logger.info(f" loaded train_dataset length is: {len(train_dataset)}")
    logger.info(f" loaded valid_dataset length is: {len(valid_dataset)}")

    # compute metrics function for binary classification
    def compute_metrics(pred):
        labels = pred.label_ids
        preds = pred.predictions.argmax(-1)
        precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average="binary")
        acc = accuracy_score(labels, preds)
        return {"accuracy": acc, "f1": f1, "precision": precision, "recall": recall}

    # download model from model hub
    model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
    
    # download the tokenizer too, which will be saved in the model artifact
    # and used at prediction time
    tokenizer = AutoTokenizer.from_pretrained(args.model_name)

    # define training args
    training_args = TrainingArguments(
        output_dir=args.model_dir,
        num_train_epochs=args.epochs,
        per_device_train_batch_size=args.train_batch_size,
        per_device_eval_batch_size=args.eval_batch_size,
        save_strategy=args.save_strategy,
        save_steps=args.save_steps,
        evaluation_strategy="epoch",
        logging_dir=f"{args.output_data_dir}/logs",
        learning_rate=float(args.learning_rate),
    )

    # create Trainer instance
    trainer = Trainer(
        model=model,
        args=training_args,
        tokenizer=tokenizer,
        compute_metrics=compute_metrics,
        train_dataset=train_dataset,
        eval_dataset=valid_dataset,
    )

    # train model
    trainer.train()

    # evaluate model
    eval_result = trainer.evaluate(eval_dataset=valid_dataset)

    # writes eval result to file which can be accessed later in s3 output
    with open(os.path.join(args.output_data_dir, "eval_results.txt"), "w") as writer:
        print(f"***** Eval results *****")
        for key, value in sorted(eval_result.items()):
            writer.write(f"{key} = {value}\n")

    # Saves the model to s3
    trainer.save_model(args.model_dir)