using System;
using System.Collections.Generic;
using UnityEngine;
using Unity.Mathematics;
using static Unity.Mathematics.math;

[Serializable]
public class Cluster : INucleus {
    // The ScriptableObject asset from which the runtime object has been created

    [SerializeField]
    protected string _name;
    public virtual string name {
        get => _name;
        set => _name = value;
    }

    #region Init

    public Cluster(ClusterPrefab prefab, Cluster parent) {
        this.prefab = prefab;
        this.name = prefab.name;

        this.parent = parent;
        this.parent?.nuclei.Add(this);

        ClonePrefab();
        this.sortedNuclei = TopologicalSort(this.nuclei);
    }

    public Cluster(ClusterPrefab prefab, ClusterPrefab parent = null) {
        this.prefab = prefab;
        this.name = prefab.name;
        this.cluster = parent;

        if (this.cluster != null)
            this.cluster.nuclei.Add(this);

        ClonePrefab();
        this.sortedNuclei = TopologicalSort(this.nuclei);
    }

    // public Cluster(ClusterPrefab parent, ClusterPrefab realPrefab) {
    //     this.prefab = realPrefab;
    //     this.name = realPrefab.name;
    //     this.cluster = parent;
    //     if (this.cluster != null)
    //         this.cluster.nuclei.Add(this);

    //     ClonePrefab();
    // }

    private void ClonePrefab() {
        IReceptor[] nucleiArray = this.prefab.nuclei.ToArray();
        // first clone the nuclei without their connections
        foreach (IReceptor nucleus in this.prefab.nuclei)
            nucleus.ShallowCloneTo(this);
        IReceptor[] clonedNuclei = this.nuclei.ToArray();

        // Now clone the connections
        for (int nucleusIx = 0; nucleusIx < nucleiArray.Length; nucleusIx++) {
            IReceptor receptor = nucleiArray[nucleusIx];
            IReceptor clonedSender = clonedNuclei[nucleusIx];
            if (clonedSender == null)
                continue;

            // Copy the receivers, which will also create the synapses
            foreach (INucleus receiver in receptor.receivers) {
                int ix = GetNucleusIndex(nucleiArray, receiver);
                if (ix < 0)
                    continue;

                if (clonedNuclei[ix] is not INucleus clonedReceiver)
                    continue;

                // Find the synapse for the weight
                float weight = 1;
                foreach (Synapse synapse in receiver.synapses) {
                    if (synapse.nucleus == receptor) {
                        weight = synapse.weight;
                        break;
                    }
                }

                clonedSender.AddReceiver(clonedReceiver, weight);
            }
        }
    }

    // Sort the nuclei in a correct evaluation order
    private List<IReceptor> TopologicalSort(List<IReceptor> nodes) {
        Dictionary<IReceptor, int> inDegree = new();
        foreach (IReceptor node in nodes)
            inDegree[node] = 0; // Initialize in-degree to zero

        // Calculate in-degrees
        foreach (IReceptor node in nodes) {
            foreach (INucleus receiver in node.receivers)
                inDegree[receiver]++;            
        }

        Queue<IReceptor> queue = new();
        foreach (IReceptor node in nodes) {
            if (inDegree[node] == 0) // Nodes with no dependencies            
                queue.Enqueue(node);            
        }

        List<IReceptor> sortedOrder = new();
        while (queue.Count > 0) {
            IReceptor current = queue.Dequeue();
            sortedOrder.Add(current); // Process the node

            foreach (INucleus receiver in current.receivers) {
                inDegree[receiver]--;
                if (inDegree[receiver] == 0) // If all dependencies resolved
                    queue.Enqueue(receiver);
            }
        }

        // Check for cycles in the graph
        if (sortedOrder.Count != nodes.Count)
            throw new InvalidOperationException("Graph is not a DAG; a cycle exists.");        

        return sortedOrder;
    }

    public virtual IReceptor Clone() {
        Neuron clone = new(this.cluster, this.name) {
            array = this.array,
        };

        foreach (Synapse synapse in this.synapses) {
            Synapse clonedSynapse = clone.AddSynapse(synapse.nucleus);
            clonedSynapse.weight = synapse.weight;
        }
        foreach (INucleus receiver in this.receivers) {
            clone.AddReceiver(receiver);
        }
        return clone;
    }

    public IReceptor ShallowCloneTo(Cluster parent) {
        Cluster clone = new(this.prefab, parent) {
            name = this.name,
        };
        return clone;
    }

    private int GetNucleusIndex(IReceptor[] nucleiArray, IReceptor nucleus) {
        for (int i = 0; i < nucleiArray.Length; i++) {
            if (nucleus == nucleiArray[i])
                return i;
        }
        return -1;
    }

    #endregion Init

    public ClusterPrefab prefab;

    public ClusterPrefab cluster { get; set; }
    public Cluster parent { get; set; }

    [SerializeReference]
    public List<IReceptor> nuclei = new();
    // the nuclei sorted using topological sorting
    // to ensure that the cluster is computer in the right order
    public List<IReceptor> sortedNuclei;

    public List<INucleus> _inputs = null;
    public virtual List<INucleus> inputs {
        get {
            if (this._inputs == null) {
                this._inputs = new();
                foreach (IReceptor receptor in this.nuclei) {
                    if (receptor is INucleus nucleus) {
                        // inputs have no incoming synapses yet.
                        if (nucleus.synapses.Count == 0)
                            this._inputs.Add(nucleus);
                    }
                }
            }
            return this._inputs;
        }
    }

    public virtual INucleus output {//=> this.nuclei[0] as INucleus;
        get {
            if (this.nuclei.Count > 0)
                return this.nuclei[0] as INucleus;
            return null;
        }
    }

    // Not sure if this belongs here...
    [SerializeReference]
    private NucleusArray _array;
    public NucleusArray array {
        get { return _array; }
        set { _array = value; }
    }

    #region Synapses

    [SerializeField]
    private List<Synapse> _synapses = new();
    public List<Synapse> synapses => _synapses;

    public Synapse AddSynapse(IReceptor sendingNucleus, float weight = 1.0f) {
        Synapse synapse = new(sendingNucleus, weight);
        this._synapses.Add(synapse);
        return synapse;
    }

    // Does this even exist already?
    public void RemoveSynapse() {

    }

    #endregion Synapses

    #region Receivers

    [SerializeReference]
    private List<INucleus> _receivers = new();
    public List<INucleus> receivers {
        get { return _receivers; }
        set { _receivers = value; }
    }

    public virtual void AddReceiver(INucleus receivingNucleus, float weight = 1) {
        this._receivers.Add(receivingNucleus);
        receivingNucleus.AddSynapse(this, weight);
    }

    public void RemoveReceiver(INucleus receiverNucleus) {
        this._receivers.RemoveAll(receiver => receiver == receiverNucleus);
        receiverNucleus.synapses.RemoveAll(synapse => synapse.nucleus == this);
    }

    #endregion Receivers

    #region Runtime

    [NonSerialized]
    private int stale = 1000;
    public bool isSleeping => lengthsq(this.outputValue) == 0;

    [NonSerialized]
    protected float3 _outputValue;
    public virtual float3 outputValue {
        get { return _outputValue; }
        set {
            this.stale = 0;
            _outputValue = value;
        }
    }

    #region Update

    // public virtual void UpdateState() {
    //     UpdateState(new float3(0, 0, 0));
    // }

    // public void UpdateState(float3 bias) {
    //     float3 sum = bias; // new(0, 0, 0);

    //     //Applying the weight factors
    //     foreach (Synapse synapse in this.synapses) {
    //         sum += synapse.weight * synapse.nucleus.outputValue;
    //     }

    //     //this.inputs[0].UpdateState(sum);
    //     this.inputs[0].UpdateStateIsolated(sum);
    //     foreach (IReceptor receptor in this.sortedNuclei) {
    //         if (receptor is INucleus nucleus && nucleus != this.inputs[0])
    //             nucleus.UpdateStateIsolated();
    //     }

    //     UpdateResult(this.output.outputValue);
    // }

    public void UpdateStateIsolated() {
        float3 bias = new(0,0,0);
        UpdateStateIsolated(bias);
    }
    public void UpdateStateIsolated(float3 bias) {
        float3 sum = bias; // new(0, 0, 0);

        //Applying the weight factors
        foreach (Synapse synapse in this.synapses) {
            sum += synapse.weight * synapse.nucleus.outputValue;
        }

        //this.inputs[0].UpdateState(sum);
        this.inputs[0].UpdateStateIsolated(sum);
        foreach (IReceptor receptor in this.sortedNuclei) {
            if (receptor is INucleus nucleus && nucleus != this.inputs[0])
                nucleus.UpdateStateIsolated();
        }
        this.outputValue = this.output.outputValue;
    }

    // public virtual void UpdateResult(Vector3 result) {
    //     // float d = Vector3.Distance(result, this.outputValue);
    //     // if (d < 0.5f) {
    //     //     //Debug.Log($"insignificant update: {d}");
    //     //     return;
    //     // }

    //     this.outputValue = result;
    //     foreach (INucleus receiver in this.receivers)
    //         receiver.UpdateState();
    // }

    public void UpdateNuclei() {
        this.stale++;
        if (this.stale > 2)
            _outputValue = Vector3.zero;

        //foreach (IReceptor nucleus in this.prefab.nuclei)
        foreach (IReceptor nucleus in this.nuclei)
            nucleus.UpdateNuclei();
    }

    #endregion Update

    #endregion Runtime    
}