import logging

import tensorflow as tf

import neptune
from interface import Interface
from neptune.incremental_learning.incremental_learning import IncrementalConfig

LOG = logging.getLogger(__name__)
MODEL_URL = IncrementalConfig().model_url


def main():
    tf.set_random_seed(22)

    class_names = neptune.context.get_parameters("class_names")

    # load dataset.
    train_data = neptune.load_train_dataset(data_format='txt', with_image=False)

    # read parameters from deployment config.
    obj_threshold = neptune.context.get_parameters("obj_threshold")
    nms_threshold = neptune.context.get_parameters("nms_threshold")
    input_shape = neptune.context.get_parameters("input_shape")
    epochs = neptune.context.get_parameters('epochs')
    batch_size = neptune.context.get_parameters('batch_size')

    tf.flags.DEFINE_string('train_url', default=MODEL_URL, help='train url for model')
    tf.flags.DEFINE_string('log_url', default=None, help='log url for model')
    tf.flags.DEFINE_string('checkpoint_url', default=None, help='checkpoint url for model')
    tf.flags.DEFINE_string('model_name', default=None, help='url for train annotation files')
    tf.flags.DEFINE_list('class_names', default=class_names.split(','),  # 'helmet,helmet-on,person,helmet-off'
                         help='label names for the training datasets')
    tf.flags.DEFINE_list('input_shape', default=[int(x) for x in input_shape.split(',')],
                         help='input_shape')  # [352, 640]
    tf.flags.DEFINE_integer('max_epochs', default=epochs, help='training number of epochs')
    tf.flags.DEFINE_integer('batch_size', default=batch_size, help='training batch size')
    tf.flags.DEFINE_boolean('load_imagenet_weights', default=False, help='if load imagenet weights or not')
    tf.flags.DEFINE_string('inference_device',
                           default='GPU',
                           help='which type of device is used to do inference, only CPU, GPU or 310D')
    tf.flags.DEFINE_boolean('copy_to_local', default=True, help='if load imagenet weights or not')
    tf.flags.DEFINE_integer('num_gpus', default=1, help='use number of gpus')
    tf.flags.DEFINE_boolean('finetuning', default=False, help='use number of gpus')
    tf.flags.DEFINE_boolean('label_changed', default=False, help='whether number of labels is changed or not')
    tf.flags.DEFINE_string('learning_rate', default='0.001', help='label names for the training datasets')
    tf.flags.DEFINE_string('obj_threshold', default=obj_threshold, help='label names for the training datasets')
    tf.flags.DEFINE_string('nms_threshold', default=nms_threshold, help='label names for the training datasets')
    tf.flags.DEFINE_string('net_type', default='resnet18', help='resnet18 or resnet18_nas')
    tf.flags.DEFINE_string('nas_sequence', default='64_1-2111-2-1112', help='resnet18 or resnet18_nas')
    tf.flags.DEFINE_string('deploy_model_format', default=None, help='the format for the converted model')
    tf.flags.DEFINE_string('result_url', default=None, help='result url for training')

    model = Interface()

    model = neptune.incremental_learning.train(model=model,
                                               train_data=train_data,
                                               epochs=epochs,
                                               batch_size=batch_size,
                                               class_names=class_names,
                                               input_shape=input_shape,
                                               obj_threshold=obj_threshold,
                                               nms_threshold=nms_threshold)

    # Save the model based on the config.
    # neptune.save_model(model)


if __name__ == '__main__':
    main()