import os
import pandas as pd
import sys
import tensorflow as tf
import yaml

from lib.dcrnn_utils import load_graph_data
from model.dcrnn_supervisor import DCRNNSupervisor

flags = tf.app.flags
FLAGS = flags.FLAGS

flags.DEFINE_bool('use_cpu_only', False, 'Whether to run tensorflow on cpu.')


def run_dcrnn(traffic_reading_df):
    # run_id = 'dcrnn_DR_2_h_12_64-64_lr_0.01_bs_64_d_0.00_sl_12_MAE_1207002222'
    run_id = 'dcrnn_DR_2_h_12_64-64_lr_0.01_bs_64_d_0.00_sl_12_MAE_0606021843'

    log_dir = os.path.join('data/model', run_id)

    config_filename = 'config_75.yaml'
    graph_pkl_filename = 'data/sensor_graph/adj_mx.pkl'
    with open(os.path.join(log_dir, config_filename)) as f:
        config = yaml.load(f)
    tf_config = tf.ConfigProto()
    if FLAGS.use_cpu_only:
        tf_config = tf.ConfigProto(device_count={'GPU': 0})
    tf_config.gpu_options.allow_growth = True
    _, _, adj_mx = load_graph_data(graph_pkl_filename)
    with tf.Session(config=tf_config) as sess:
        supervisor = DCRNNSupervisor(traffic_reading_df, config=config, adj_mx=adj_mx)
        supervisor.restore(sess, config=config)
        df_preds = supervisor.test_and_write_result(sess, config['global_step'])
        for horizon_i in df_preds:
            df_pred = df_preds[horizon_i]
            filename = os.path.join('data/results/', 'dcrnn_prediction_%d.h5' % (horizon_i + 1))
            df_pred.to_hdf(filename, 'results')
        print('Predictions saved as data/results/dcrnn_seq2seq_prediction_[1-12].h5...')


if __name__ == '__main__':
    sys.path.append(os.getcwd())
    traffic_df_filename = 'data/df_highway_2012_4mon_sample.h5'
    traffic_reading_df = pd.read_hdf(traffic_df_filename)
    run_dcrnn(traffic_reading_df)
    # run_fc_lstm(traffic_reading_df)