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

[Serializable]
public class Cluster : Nucleus {

    #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) {
            // Debug.Log($"prefab clone {nucleus.name}");
            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.nucleus == 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;
            }
        }
    }

    // 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 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);
                }
            }
        }

        // 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.nucleus);
            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,
        };
        // This cloned the prefab with the clusternuclei,
        // but did not clone the receivers outside the cluster
        RestoreExternalReceivers(clone, this.clusterPrefab, parent);

        return clone;
    }

    protected void RestoreExternalReceivers(Cluster clone, ClusterPrefab prefabParent, Cluster clonedParent) {
        for (int nucleusIx = 0; nucleusIx < this.clusterNuclei.Count; nucleusIx++) {
            Nucleus prefabNucleus = this.clusterNuclei[nucleusIx];
            if (prefabNucleus is not Neuron prefabNeuron)
                continue;

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

            // Copy the receivers, which will also create the synapses
            foreach (Nucleus receiver in prefabNeuron.receivers) {
                int ix = GetNucleusIndex(prefabParent.nuclei, receiver);
                if (ix < 0)
                    continue;

                //if (clone.clusterNuclei[ix] is not Nucleus clonedReceiver)
                if (clonedParent.clusterNuclei[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.nucleus == prefabNucleus) {
                        weight = synapse.weight;
                        break;
                    }
                }

                clonedNeuron.AddReceiver(clonedReceiver, weight);
            }
        }


    }

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

    public 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 Dictionary<Nucleus, int>();
        HashSet<Nucleus> visited = new HashSet<Nucleus>();

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

        // Perform topological sort on all reachable nodes
        queue.Clear();
        foreach (var 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

            if (current is Neuron neuron) {
                foreach (Nucleus receiver in neuron.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;
    }

    private List<Nucleus> TopologicalSort3(Nucleus startNode) {
        Dictionary<Nucleus, int> inDegree = new();
        foreach (Nucleus node in this.clusterNuclei)
            inDegree[node] = 0; // Initialize in-degree to zero

        // Calculate in-degrees
        foreach (Nucleus node in this.clusterNuclei) {
            if (node is Neuron neuron) {
                foreach (Nucleus receiver in neuron.receivers)
                    inDegree[receiver]++;
            }
        }

        Queue<Nucleus> queue = new();
        queue.Enqueue(startNode);

        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);
                }
            }
        }

        Debug.Log($"Compute order for {startNode.name} length = {sortedOrder.Count}");
        // Check for cycles in the graph
        // if (sortedOrder.Count != this.nuclei.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) {
        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...

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

        this.outputValue = this.defaultOutput.outputValue;
        this.stale = 0;

        UpdateNuclei();
    }

    public override void UpdateStateIsolated() {
        float3 sum = this.bias;

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

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

        this.outputValue = this.defaultOutput.outputValue;
        this.stale = 0;

        UpdateNuclei();
    }

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

    #endregion Update

}