import torch
import torchvision as tv

# Import mlpug for Pytorch backend
import mlpug.pytorch as mlp


# MLPug needs a TrainModel that outputs the loss
class TrainModel(torch.nn.Module):
    def __init__(self, classifier):
        super(TrainModel, self).__init__()

        self.classifier = classifier
        self.loss_func = torch.nn.CrossEntropyLoss()

    def forward(self, batch_data, evaluate_settings, inference_mode=None):
        images, true_labels = batch_data

        logits = self.classifier(images)
        return self.loss_func(logits, true_labels)


if __name__ == '__main__':
    # ################ SETTINGS ################
    batch_size = 32
    num_epochs = 10

    progress_log_period = 500

    torch.random.manual_seed(0)  # For reproducibility
    mlp.logging.use_fancy_colors()
    # ##########################################

    # ############## PROCESS DATA ##############
    # Load data
    # Note : the data is already scaled

    transform = tv.transforms.ToTensor()

    training_data = tv.datasets.FashionMNIST('./mlpug-datasets-temp/', train=True, download=True, transform=transform)
    test_data = tv.datasets.FashionMNIST('./mlpug-datasets-temp/', train=False, download=True, transform=transform)
    # ##########################################

    # ########## SETUP BATCH DATASETS ##########
    training_dataset = torch.utils.data.DataLoader(training_data,
                                                   batch_size=batch_size,
                                                   shuffle=False,
                                                   num_workers=3)

    # Using the test set as a validation set, just for demonstration purposes
    validation_dataset = torch.utils.data.DataLoader(test_data,
                                                     batch_size=batch_size,
                                                     shuffle=False,
                                                     num_workers=3)
    # ##########################################

    # ############ BUILD THE MODEL #############
    classifier = torch.nn.Sequential(
        torch.nn.Flatten(),
        torch.nn.Linear(784, 128),
        torch.nn.ReLU(),
        torch.nn.Linear(128, 10))
    # ############################################

    # ######### BUILD THE TRAINING MODEL #########
    train_model = TrainModel(classifier)
    # ############################################

    # ############ SETUP OPTIMIZER #############
    optimizer = torch.optim.Adam(classifier.parameters(), eps=1e-7)  # Same parameters as for TF
    # ##########################################

    # ############# SETUP TRAINING ##############
    trainer = mlp.trainers.DefaultTrainer(optimizers=optimizer, model_components=classifier)

    # At minimum you want to log the loss in the training progress
    # By default the batch loss and the moving average of the loss are calculated and logged
    loss_evaluator = mlp.evaluation.MetricEvaluator(trainer=trainer)
    callbacks = [
        mlp.callbacks.TrainingMetricsLogger(metric_evaluator=loss_evaluator),
        # Calculate validation loss only once per epoch over the whole dataset
        mlp.callbacks.TestMetricsLogger(validation_dataset,
                                        'validation',
                                        metric_evaluator=loss_evaluator,
                                        batch_level=False),
        mlp.callbacks.LogProgress(log_period=progress_log_period, set_names=['training', 'validation']),
    ]

    manager = mlp.trainers.TrainingManager(trainer,
                                           training_dataset,
                                           num_epochs=num_epochs,
                                           callbacks=callbacks)

    trainer.set_training_model(train_model)
    # ##########################################

    # ################# START! #################
    manager.start_training()
    # ##########################################

    # ######### USE THE TRAINED MODEL ##########
    print("\nUsing the classifier ...")
    first_sample = next(iter(test_data))
    image = first_sample[0]
    real_label = first_sample[1]

    logits = classifier(image)
    probabilities = torch.softmax(logits, dim=-1)

    predicted_label = torch.argmax(probabilities)

    print(f"real label = {real_label}, predicted label = {predicted_label}\n")
