using System;
using System.Collections.Generic;
using System.IO;
using System.Text;
using Tensorflow;
using static Tensorflow.Binding;

namespace TensorFlowNET.Examples.ImageProcessing.YOLO
{
    /// <summary>
    /// Implementation of YOLO v3 object detector in Tensorflow
    /// https://github.com/YunYang1994/tensorflow-yolov3
    /// </summary>
    public class Main : IExample
    {
        public bool Enabled { get; set; } = false;
        public bool IsImportingGraph { get; set; } = false;
        public string Name => "YOLOv3";

        #region args
        Dictionary<int, string> classes;
        int num_classes;
        float learn_rate_init;
        float learn_rate_end;
        int first_stage_epochs;
        int second_stage_epochs;
        int warmup_periods;
        string time;
        float moving_ave_decay;
        int max_bbox_per_scale;
        int steps_per_period;

        Dataset trainset, testset;

        Config cfg;

        Tensor input_data;
        Tensor label_sbbox;
        Tensor label_mbbox;
        Tensor label_lbbox;
        Tensor true_sbboxes;
        Tensor true_mbboxes;
        Tensor true_lbboxes;
        Tensor trainable; 
        #endregion

        public bool Run()
        {
            PrepareData();

            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();

            using (var sess = tf.Session(graph))
            {
                Train(sess);
            }

            return true;
        }

        public void Train(Session sess)
        {

        }

        public void Test(Session sess)
        {
            throw new NotImplementedException();
        }

        public Graph BuildGraph()
        {
            var graph = new Graph().as_default();

            tf_with(tf.name_scope("define_input"), scope =>
            {
                input_data = tf.placeholder(dtype: tf.float32, name: "input_data");
                label_sbbox = tf.placeholder(dtype: tf.float32, name: "label_sbbox");
                label_mbbox = tf.placeholder(dtype: tf.float32, name: "label_mbbox");
                label_lbbox = tf.placeholder(dtype: tf.float32, name: "label_lbbox");
                true_sbboxes = tf.placeholder(dtype: tf.float32, name: "sbboxes");
                true_mbboxes = tf.placeholder(dtype: tf.float32, name: "mbboxes");
                true_lbboxes = tf.placeholder(dtype: tf.float32, name: "lbboxes");
                trainable = tf.placeholder(dtype: tf.@bool, name: "training");
            });

            tf_with(tf.name_scope("define_loss"), scope =>
            {
                //model = new YOLOv3(input_data, trainable);
            });

            return graph;
        }

        public Graph ImportGraph()
        {
            throw new NotImplementedException();
        }

        public void Predict(Session sess)
        {
            throw new NotImplementedException();
        }

        public void PrepareData()
        {
            cfg = new Config(Name);

            string dataDir = Path.Combine(Name, "data");
            Directory.CreateDirectory(dataDir);

            classes = new Dictionary<int, string>();
            foreach (var line in File.ReadAllLines(cfg.YOLO.CLASSES))
                classes[classes.Count] = line;
            num_classes = classes.Count;

            learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT;
            learn_rate_end = cfg.TRAIN.LEARN_RATE_END;
            first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS;
            second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS;
            warmup_periods = cfg.TRAIN.WARMUP_EPOCHS;
            DateTime now = DateTime.Now;
            time = $"{now.Year}-{now.Month}-{now.Day}-{now.Hour}-{now.Minute}-{now.Minute}";
            moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY;
            max_bbox_per_scale = 150;
            trainset = new Dataset("train", cfg);
            testset = new Dataset("test", cfg);
            steps_per_period = trainset.Length;
        }
    }
}