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

[Serializable]
public class Cluster : Nucleus {

    public string baseName {
        get {
            int colonPositon = this.name.IndexOf(':');
            if (colonPositon < 0)
                return this.name;
            return this.name[..colonPositon];
        }
    }

    #region Init

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

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

        ClonePrefab();
        _ = this.inputs;
        this.sortedNuclei = TopologicalSort(this.clusterNuclei);
    }

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

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

        ClonePrefab();
        _ = this.inputs;
        this.sortedNuclei = TopologicalSort(this.clusterNuclei);
    }

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

        // Now clone the connections
        for (int nucleusIx = 0; nucleusIx < prefabNuclei.Length; nucleusIx++) {
            Nucleus prefabNucleus = prefabNuclei[nucleusIx];
            if (prefabNucleus is not Neuron prefabNeuron)
                continue;

            Nucleus clonedNucleus = clonedNuclei[nucleusIx];
            if (clonedNucleus == null || clonedNucleus is not Neuron clonedNeuron)
                continue;

            // Copy the receivers, which will also create the synapses
            // Clusters do not have receivers...
            foreach (Nucleus receiver in prefabNeuron.receivers.ToArray()) {
                int ix = GetNucleusIndex(prefabNuclei, receiver);
                if (ix < 0)
                    continue;

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

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

                clonedNeuron.AddReceiver(clonedReceiver, weight);
            }
        }

        // Copy nucleus arrays for receptors
        for (int nucleusIx = 0; nucleusIx < prefabNuclei.Length; nucleusIx++) {
            Nucleus prefabNucleus = prefabNuclei[nucleusIx];
            if (prefabNucleus is not IReceptor prefabReceptor)
                continue;

            if (prefabReceptor.nucleiArray == null || prefabReceptor.nucleiArray.Length == 0)
                continue;

            IReceptor clonedNucleus = clonedNuclei[nucleusIx] as IReceptor;
            if (prefabReceptor == prefabReceptor.nucleiArray[0]) {
                // We clone the array only for the first entry
                NucleusArray clonedArray = new(prefabReceptor.nucleiArray.Length, "array");
                int arrayIx = 0;
                foreach (Nucleus prefabArrayNucleus in prefabReceptor.nucleiArray) {
                    int arrayNucleusIx = GetNucleusIndex(prefabNuclei, prefabArrayNucleus);
                    if (arrayNucleusIx >= 0) {
                        Nucleus clonedArrayNucleus = clonedNuclei[arrayNucleusIx];
                        clonedArray.nuclei[arrayIx] = clonedArrayNucleus;
                    }
                    else {
                        Debug.LogError($" Could not find prefab nucleus {prefabNucleus.name} in the clones");
                    }
                    arrayIx++;
                }
                //clonedNucleus.array = clonedArray;
                clonedNucleus.nucleiArray = clonedArray.nuclei;
            }
            else {
                // The others will refer to the array created for the first nucleus in the array
                int firstNucleusIx = GetNucleusIndex(prefabNuclei, prefabReceptor.nucleiArray[0]);
                IReceptor clonedFirstNucleus = clonedNuclei[firstNucleusIx] as IReceptor;
                clonedNucleus.nucleiArray = clonedFirstNucleus.nucleiArray;
            }
        }

        foreach (Nucleus nucleus in this.clusterNuclei) {
            if (nucleus is Cluster clonedSubCluster)
                RestoreAllExternalReceivers(clonedSubCluster, this.prefab, this);
        }
    }

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

        // Calculate in-degrees
        foreach (Nucleus node in nodes) {
            if (node is Cluster cluster) {
                foreach (Nucleus receiver in cluster.CollectReceivers())
                    inDegree[receiver]++;
            }
            else if (node is Neuron neuron) {
                foreach (Nucleus receiver in neuron.receivers)
                    inDegree[receiver]++;
            }
        }

        Queue<Nucleus> queue = new();
        foreach (Nucleus node in nodes) {
            if (inDegree[node] == 0) // Nodes with no dependencies            
                queue.Enqueue(node);
        }
        // The queue basically stores all input nuclei?

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

            if (current is Neuron neuron) {
                foreach (Nucleus receiver in neuron.receivers) {
                    inDegree[receiver]--;
                    if (inDegree[receiver] == 0) // If all dependencies resolved
                        queue.Enqueue(receiver);
                }
            }
            else if (current is Cluster cluster) {
                foreach (Nucleus receiver in cluster.CollectReceivers()) {
                    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 override Nucleus Clone(ClusterPrefab parent) {
        Cluster clone = new(this.prefab, parent);

        foreach (Synapse synapse in this.synapses) {
            Synapse clonedSynapse = clone.AddSynapse(synapse.neuron);
            clonedSynapse.weight = synapse.weight;
        }

        foreach (Neuron output in this.outputs) {
            foreach (Nucleus receiver in output.receivers) {
                int ix = GetNucleusIndex(this.clusterNuclei.ToArray(), output);
                if (ix < 0)
                    continue;

                if (clone.clusterNuclei[ix] is not Neuron clonedOutput)
                    continue;

                clonedOutput.AddReceiver(receiver);
            }
        }

        return clone;
    }

    public override Nucleus ShallowCloneTo(Cluster parent) {
        Cluster clone = new(this.prefab, parent) {
            name = this.name,
            clusterPrefab = this.clusterPrefab,
        };

        return clone;
    }

    private static void RestoreAllExternalReceivers(Cluster clonedCluster, ClusterPrefab prefabParent, Cluster clonedParent) {
        int clonedClusterIx = GetNucleusIndex(clonedParent.clusterNuclei, clonedCluster);
        if (prefabParent.nuclei[clonedClusterIx] is not Cluster sourceCluster)
            return;

        for (int nucleusIx = 0; nucleusIx < sourceCluster.clusterNuclei.Count; nucleusIx++) {
            Nucleus sourceNucleus = sourceCluster.clusterNuclei[nucleusIx];
            if (sourceNucleus is not Neuron sourceNeuron)
                continue;

            if (clonedCluster.clusterNuclei[nucleusIx] is not Neuron clonedNeuron)
                continue;

            // copy the receivers (and thus synapses) from the source to the clone
            foreach (Nucleus receiver in sourceNeuron.receivers) {
                int ix = GetNucleusIndex(prefabParent.nuclei, receiver);
                if (ix < 0 || ix >= clonedParent.clusterNuclei.Count)
                    continue;

                Nucleus clonedReceiver = clonedParent.clusterNuclei[ix];

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

                clonedNeuron.AddReceiver(clonedReceiver, weight);
                // Debug.Log($"external: {clonedReceiver.name} receives from {clonedNeuron.name} {clonedNeuron.GetHashCode()}");
            }
        }
    }

    protected int GetNucleusIndex(Nucleus[] nuclei, Nucleus nucleus) {
        for (int i = 0; i < nuclei.Length; i++) {
            if (nucleus == nuclei[i])
                return i;
        }
        return -1;
    }

    public static int GetNucleusIndex(List<Nucleus> nuclei, Nucleus nucleus) {
        int i = 0;
        foreach (Nucleus nucleiElement in nuclei) {
            //for (int i = 0; i < nuclei.Length; i++) {
            if (nucleus == nucleiElement)
                return i;
            i++;
        }
        return -1;
    }

    #endregion Init

    public ClusterPrefab prefab;


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

    public List<Nucleus> _inputs = null;
    public virtual List<Nucleus> inputs {
        get {
            if (this._inputs == null) {
                this._inputs = new();
                foreach (Nucleus nucleus in this.clusterNuclei) {
                    // inputs have no synapses
                    if (nucleus.synapses.Count == 0)
                        this._inputs.Add(nucleus);
                }
                ComputeOrders();
            }
            return this._inputs;
        }
    }

    public Dictionary<Nucleus, List<Nucleus>> computeOrders = new();
    private void ComputeOrders() {
        foreach (Nucleus input in this._inputs)
            computeOrders[input] = TopologicalSort2(input);
    }

    private List<Nucleus> TopologicalSort2(Nucleus startNode) {
        Dictionary<Nucleus, int> inDegree = new();
        HashSet<Nucleus> visited = new();

        // Initialize in-degrees and mark all nodes as unvisited
        foreach (Nucleus node in this.clusterNuclei)
            inDegree[node] = 0;

        // Calculate in-degrees for all nodes reachable from the start node
        Queue<Nucleus> queue = new Queue<Nucleus>();
        queue.Enqueue(startNode);
        visited.Add(startNode);

        while (queue.Count > 0) {
            Nucleus current = queue.Dequeue();
            List<Nucleus> receivers = null;
            if (current is Neuron neuron)
                receivers = neuron.receivers;
            else if (current is Cluster cluster)
                receivers = cluster.CollectReceivers();

            // if (current is Neuron neuron) {
            foreach (Nucleus receiver in receivers) {
                if (!visited.Contains(receiver)) {
                    visited.Add(receiver);
                    queue.Enqueue(receiver);
                }
                inDegree[receiver]++;
            }
            // }
        }

        // Perform topological sort on all reachable nodes
        queue.Clear();
        foreach (Nucleus node in visited) {
            if (inDegree[node] == 0)
                queue.Enqueue(node);
        }

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

            List<Nucleus> receivers = null;
            if (current is Neuron neuron)
                receivers = neuron.receivers;
            else if (current is Cluster cluster)
                receivers = cluster.CollectReceivers();

            //if (current is Neuron neuron) {

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

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

        return sortedOrder;
    }

    public virtual Neuron defaultOutput {//=> this.nuclei[0] as Nucleus;
        get {
            if (this.clusterNuclei.Count > 0)
                return this.clusterNuclei[0] as Neuron;
            return null;
        }
    }
    protected List<Neuron> _outputs = null;
    public List<Neuron> outputs {
        get {
            if (this._outputs == null) {
                this._outputs = new();
                foreach (Nucleus nucleus in this.clusterNuclei) {
                    if (nucleus is Neuron neuron) // && neuron.receivers.Count == 0)
                        this._outputs.Add(neuron);
                }
            }
            return this._outputs;
        }
    }

    public bool TryGetNucleus(string nucleusName, out Nucleus foundNucleus) {
        foreach (Nucleus receptor in this.clusterNuclei) {
            if (receptor is Nucleus nucleus)
                if (nucleus.name == nucleusName) {
                    foundNucleus = nucleus;
                    return true;
                }
        }
        foundNucleus = null;
        return false;
    }

    public Nucleus GetNucleus(string nucleusName) {
        int dotPosition = nucleusName.IndexOf('.');
        if (dotPosition >= 0) {
            string clusterName = nucleusName[..dotPosition];
            string clusterName0 = clusterName + ": 0";
            foreach (Nucleus nucleus in this.clusterNuclei) {
                if (nucleus is Cluster cluster) {
                    if (cluster.name == clusterName || cluster.name == clusterName0) {
                        string subNucleusName = nucleusName[(dotPosition + 1)..];
                        return cluster.GetNucleus(subNucleusName);
                    }
                }
            }
            return null;
        }
        else {
            string nucleusName0 = nucleusName + ": 0";
            foreach (Nucleus nucleus in this.clusterNuclei) {
                if (nucleus is IReceptor receptor) {
                    if (nucleus.name == nucleusName | nucleus.name == nucleusName0)
                        return nucleus;
                }
                else if (nucleus.name == nucleusName)
                    return nucleus;
            }
            return null;
        }
    }

    // [Obsolete("Use GetNucleus instead")]
    // public IReceptor GetReceptor(string receptorName) {
    //     return GetNucleus(receptorName) as IReceptor;
    // }

    #region Receivers

    public virtual List<Nucleus> CollectReceivers() {
        List<Nucleus> receivers = new();
        foreach (Neuron output in this.outputs) {
            foreach (Nucleus receiver in output.receivers) {
                // Only add receivers outside this cluster
                if (receiver.clusterPrefab != this.prefab)
                    receivers.Add(receiver);
                //receivers.AddRange(output.receivers);
            }
        }
        return receivers;
    }

    #endregion Receivers

    #region Update

    public void UpdateFromNucleus(Nucleus startNucleus) {
        // no bias+synapse input state calculation for now...

        if (this.computeOrders.ContainsKey(startNucleus) == false) {
            //Debug.LogError($"{this.name} compute orders does not contain an order for {startNucleus.name}");
            return;
        }

        List<Nucleus> computeOrder = this.computeOrders[startNucleus];
        if (startNucleus.trace)
            Debug.Log($"Update from {startNucleus.name}");
        foreach (Nucleus nucleus in computeOrder) {
            nucleus.UpdateStateIsolated();
            if (startNucleus.trace && nucleus is Neuron neuron)
                Debug.Log($"   {nucleus.name}[{nucleus.GetHashCode()}] = {neuron.outputValue}");
        }

        // continue in parent
        this.parent?.UpdateFromNucleus(this);

        UpdateNuclei();
    }

    public override void UpdateStateIsolated() {
        throw new Exception("Cluster should not be updated!");
        // float3 sum = this.bias;

        // //Applying the weight factors
        // foreach (Synapse synapse in this.synapses) {
        //     if (lengthsq(synapse.neuron.outputValue) > 0) {
        //         sum += synapse.weight * synapse.neuron.outputValue;
        //     }
        // }

        // foreach (Nucleus nucleus in this.sortedNuclei)
        //     nucleus.UpdateStateIsolated();

        // UpdateNuclei();
    }

    public override void UpdateNuclei() {
        foreach (Nucleus nucleus in this.clusterNuclei)
            nucleus.UpdateNuclei();
    }

    #endregion Update

}