"""

Separated testing for OmiEmbed

"""

import time

from util import util

from params.test_params import TestParams

from datasets import create_single_dataloader

from models import create_model

from util.visualizer import Visualizer

if __name__ == '__main__':

    # Get testing parameter

    param = TestParams().parse()

    if param.deterministic:

        util.setup_seed(param.seed)

    # Dataset related

    dataloader, sample_list = create_single_dataloader(param, shuffle=False)  # No shuffle for testing

    print('The size of testing set is {}'.format(len(dataloader)))

    # Get sample list for the dataset

    param.sample_list = dataloader.get_sample_list()

    # Get the dimension of input omics data

    param.omics_dims = dataloader.get_omics_dims()

    if param.downstream_task == 'classification' or param.downstream_task == 'multitask':

        # Get the number of classes for the classification task

        if param.class_num == 0:

            param.class_num = dataloader.get_class_num()

        print('The number of classes: {}'.format(param.class_num))

    if param.downstream_task == 'regression' or param.downstream_task == 'multitask':

        # Get the range of the target values

        values_min = dataloader.get_values_min()

        values_max = dataloader.get_values_max()

        if param.regression_scale == 1:

            param.regression_scale = values_max

        print('The range of the target values is [{}, {}]'.format(values_min, values_max))

    if param.downstream_task == 'survival' or param.downstream_task == 'multitask':

        # Get the range of T

        survival_T_min = dataloader.get_survival_T_min()

        survival_T_max = dataloader.get_survival_T_max()

        if param.survival_T_max == -1:

            param.survival_T_max = survival_T_max

        print('The range of survival T is [{}, {}]'.format(survival_T_min, survival_T_max))

    # Model related

    model = create_model(param)     # Create a model given param.model and other parameters

    model.setup(param)              # Regular setup for the model: load and print networks, create schedulers

    visualizer = Visualizer(param)  # Create a visualizer to print results

    # TESTING

    model.set_eval()

    test_start_time = time.time()  # Start time of testing

    output_dict, losses_dict, metrics_dict = model.init_log_dict()  # Initialize the log dictionaries

    if param.save_latent_space:

        latent_dict = model.init_latent_dict()

    # Start testing loop

    for i, data in enumerate(dataloader):

        dataset_size = len(dataloader)

        actual_batch_size = len(data['index'])

        model.set_input(data)  # Unpack input data from the output dictionary of the dataloader

        model.test()  # Run forward to get the output tensors

        model.update_log_dict(output_dict, losses_dict, metrics_dict, actual_batch_size)  # Update the log dictionaries

        if param.save_latent_space:

            latent_dict = model.update_latent_dict(latent_dict)  # Update the latent space array

        if i % param.print_freq == 0:  # Print testing log

            visualizer.print_test_log(param.epoch_to_load, i, losses_dict, metrics_dict, param.batch_size, dataset_size)

    test_time = time.time() - test_start_time

    visualizer.print_test_summary(param.epoch_to_load, losses_dict, output_dict, test_time)

    visualizer.save_output_dict(output_dict)

    if param.save_latent_space:

        visualizer.save_latent_space(latent_dict, sample_list)