using System;
using System.Collections.Generic;
using UnityEngine;
using UnityEditor;
#if UNITY_MATHEMATICS
using Unity.Mathematics;
using static Unity.Mathematics.math;
#endif

namespace NanoBrain {

    /// <summary>
    /// A neuron is a basic Nucleus
    /// </summary>
    [Serializable]
    public class Neuron : Nucleus {

        /// <summary>
        /// Create a new Neuron in a Cluster instance
        /// </summary>
        /// <param name="parent">The parent cluster in which the new Neuron should be created</param>
        /// <param name="name">The name of the new Neuron</param>
        public Neuron(Cluster parent, string name) {
            this.parent = parent;
            this.name = name;
            this.parent?.nuclei.Add(this);
        }
        /// <summary>
        /// Create a new Neuron in a Cluster Prefab
        /// </summary>
        /// <param name="prefab">The Cluster Preafb in which the new Neuron should be created</param>
        /// <param name="name">The name of the new Neuron</param>
        // public Neuron(ClusterPrefab prefab, string name) {
        //     this.clusterPrefab = prefab;
        //     this.name = name;
        //     if (this.clusterPrefab != null) {
        //         this.clusterPrefab.cluster.nuclei.Add(this);
        //         this.clusterPrefab.cluster.RefreshOutputs();
        //     }
        //     else
        //         Debug.LogError("No prefab when adding neuron to prefab");
        // }

        #region Serialization

        /// <summary>
        /// The bias
        /// </summary>
        /// The bias which a value which is always added to the combined value of the neuron
        /// It does not have a synapse and therefore no weight of source nucleus
        public Vector3 bias = Vector3.zero;

        #region Synapses

        [SerializeField]
        private List<Synapse> _synapses = new();
        /// <summary>
        /// The synapses of the nucleus
        /// </summary>
        public List<Synapse> synapses => _synapses;

        /// <summary>
        /// Add a new synapse to this nuclues
        /// </summary>
        /// <param name="sendingNucleus">The nucleus from which the signals may originate</param>
        /// <param name="weight">The weight applied to the input. Default value = 1</param>
        /// <returns>The created Synapse</returns>
        /// This will add a new input to this nucleus with the given weight.
        public Synapse AddSynapse(Neuron sendingNucleus, float weight = 1) {
            Synapse synapse = new(sendingNucleus, weight);
            this.synapses.Add(synapse);
            return synapse;
        }

        // public Synapse AddSynapse(ClusterPrefab clusterPrefab, string neuronName, float weight = 1) {

        //     }

        /// <summary>
        /// Find a synapse
        /// </summary>
        /// <param name="sender">The sender of the input to the Synapse</param>
        /// <returns>The found Synapse or null when the sender has no synapse to this nucleus.</returns>
        public Synapse GetSynapse(Nucleus sender) {
            foreach (Synapse synapse in this.synapses)
                if (synapse.neuron == sender)
                    return synapse;
            return null;
        }

        /// <summary>
        /// Remove a synapse from a Nucleus
        /// </summary>
        /// <param name="sendingNucleus">Remote the synapse connecting to this Nucleus</param>
        public void RemoveSynapse(Nucleus sendingNucleus) {
            this.synapses.RemoveAll(synapse => synapse.neuron == sendingNucleus);
        }

        #endregion Synapses

        /// <summary>
        /// Set the bias, recalculate the output and update all Nuclei receiving from this Nucleus
        /// </summary>
        /// <param name="inputValue"></param>
        public virtual void SetBias(Vector3 inputValue) {
            this.bias = inputValue;
            this.parent?.UpdateFromNucleus(this);
        }

        /// <summary>
        /// The type of combinators
        /// </summary>
        /// A combinator combines the weighted values of the synapses to a single value
        public enum CombinatorType {
            /// Add the weighted values together
            Sum,
            /// Multiply the weighted values
            Product,
            /// Take the maximum of all the weighted values
            Max,
        }
        /// <summary>
        /// The type of combinator used for this Neuron
        /// </summary>
        public CombinatorType combinator = CombinatorType.Sum;

        /// <summary>
        /// The type of 
        /// </summary>
        public enum ActivationType {
            Linear,
            Power,
            Sqrt,
            Reciprocal,
            Tanh,
            Binary,
            Normalized,
            Custom
        }
        [SerializeField]
        public ActivationType _curvePreset;
        public ActivationType curvePreset {
            get { return _curvePreset; }
            set {
                _curvePreset = value;
                this.curve = GenerateCurve();
            }
        }
        public AnimationCurve curve;
        public float curveMax = 1.0f;

        public AnimationCurve GenerateCurve() {
            switch (this.curvePreset) {
                case ActivationType.Linear:
                    this.curveMax = 1;
                    return Presets.Linear(1);
                case ActivationType.Power:
                    this.curveMax = 1;
                    return Presets.Power(2.0f, 1);
                case ActivationType.Sqrt:
                    this.curveMax = 1;
                    return Presets.Power(0.5f, 1);
                case ActivationType.Reciprocal:
                    this.curveMax = 1 / 0.01f * 1;
                    return Presets.Reciprocal(1);
                case ActivationType.Tanh:
                    this.curveMax = 1;
                    return Presets.Tanh(1);
                case ActivationType.Binary:
                    this.curveMax = 1;
                    return Presets.Binary();
                case ActivationType.Normalized:
                    this.curveMax = 1;
                    return Presets.Binary();
                default:
                    this.curveMax = 1;
                    return this.curve;
            }
        }

        public static class Presets {
            private const int samples = 32;
            public static AnimationCurve Linear(float weight) {
                return AnimationCurve.Linear(0f, 0f, 1000f, weight * 1000);
            }
            public static AnimationCurve Power(float exponent, float weight) {
                // build keyframes
                Keyframe[] keys = new Keyframe[samples];
                for (int i = 0; i < samples; i++) {
                    float t = i / (float)(samples - 1);
                    float v = Mathf.Pow(t, exponent) * weight;
                    keys[i] = new Keyframe(t, v);
                }

                AnimationCurve curve = new(keys);

                // set tangent modes for each key to Auto (smooth). Use Linear if you prefer straight segments.
                for (int i = 0; i < curve.length; i++) {
                    AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Auto);
                    AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Auto);
                }

                return curve;
            }
            public static AnimationCurve Reciprocal(float weight) {
                int samples = 128;
                float xMin = 0.001f;
                float xMax = 1;
                var keys = new Keyframe[samples];
                for (int i = 0; i < samples; i++) {
                    float t = i / (float)(samples - 1);
                    float x = Mathf.Lerp(xMin, xMax, t);
                    float y = 1f / x * weight;
                    keys[i] = new Keyframe(x, y);
                }
                var curve = new AnimationCurve(keys);
                for (int i = 0; i < curve.length; i++) {
                    AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
                    AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
                }
                return curve;
            }
            public static AnimationCurve Tanh(float weight) {
                //int samples = 128;
                float xMin = 0.001f;
                float xMax = 1;
                var keys = new Keyframe[samples];
                for (int i = 0; i < samples; i++) {
                    float t = i / (float)(samples - 1);
                    float x = Mathf.Lerp(xMin, xMax, t);
                    float y = MathF.Tanh(x * weight);
                    keys[i] = new Keyframe(x, y);
                }
                var curve = new AnimationCurve(keys);
                for (int i = 0; i < curve.length; i++) {
                    AnimationUtility.SetKeyLeftTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
                    AnimationUtility.SetKeyRightTangentMode(curve, i, AnimationUtility.TangentMode.Linear);
                }
                return curve;

            }
            public static AnimationCurve Binary() {
                return AnimationCurve.Linear(0, 0, 1, 1);
            }
        }

        #endregion Serialization

#if UNITY_MATHEMATICS

        protected float3 _outputValue;
        public virtual float3 outputValue {
            get { return _outputValue; }
            set {
                _outputValue = value;
                if (this.isFiring)
                    WhenFiring?.Invoke();
            }
        }
        public float outputMagnitude => length(_outputValue);
        public float outputSqrMagnitude => lengthsq(_outputValue);

#else

    protected Vector3 _outputValue;
    public virtual Vector3 outputValue {
        get { return _outputValue; }
        set {
            _outputValue = value;
            if (this.isFiring)
                WhenFiring?.Invoke();
        }
    }
    public float outputMagnitude => _outputValue.magnitude;    
    public float outputSqrMagnitude => _outputValue.sqrMagnitude;

#endif
        public bool isFiring {
            get {
                SleepCheck();
                return this.outputMagnitude > 0.5f;
            }
        }
        public Action WhenFiring;


        public virtual bool isSleeping => Time.time - this.lastUpdate > this.timeToSleep; //this.outputMagnitude == 0;
        public void SleepCheck() {
            if (this.isSleeping) {
#if UNITY_MATHEMATICS
                this._outputValue = new float3(0, 0, 0);
#else
                this._outputValue = new Vector3(0,0,0);
#endif
            }
        }

        /// <summary>
        /// Toggle for printing debugging trace data
        /// </summary>
        public bool trace = false;

        [NonSerialized]
        public float lastUpdate = 0;
        public readonly float timeToSleep = 1f;

        /// \copydoc NanoBrain::Nucleus::ShallowCloneTo
        public override Nucleus ShallowCloneTo(Cluster newParent) {
            Neuron clone = new(newParent, this.name) {
                // prefabNucleus = this
            };
            CloneFields(clone);
            return clone;
        }

        /// \copydoc NanoBrain::Nucleus::Clone
        public override Nucleus Clone(ClusterPrefab prefab) {
            Neuron clone = new(prefab.cluster, this.name);
            CloneFields(clone);
            foreach (Synapse synapse in this.synapses) {
                Synapse clonedSynapse = clone.AddSynapse(synapse.neuron);
                clonedSynapse.weight = synapse.weight;
            }
            foreach (Nucleus receiver in this.receivers) {
                clone.AddReceiver(receiver);
            }
            return clone;
        }

        protected virtual void CloneFields(Neuron clone) {
            clone.bias = this.bias;
            clone.combinator = this.combinator;
            clone.curve = this.curve;
            clone.curvePreset = this.curvePreset;
            clone.curveMax = this.curveMax;
        }

        public static void Delete(Nucleus nucleus) {
            if (nucleus == null)
                return;
            if (nucleus is Neuron neuron) {
                foreach (Synapse synapse in neuron.synapses) {
                    if (synapse.neuron is Neuron synapse_nucleus) {
                        if (synapse_nucleus.receivers.Count > 1) {
                            // there is another nucleus feeding into this input nucleus
                            synapse_nucleus.receivers.RemoveAll(r => r == nucleus);
                        }
                        else {
                            // No other links, delete it.
                            Neuron.Delete(synapse_nucleus);
                        }
                    }
                }
                foreach (Nucleus receiver in neuron.receivers) {
                    if (receiver is not Neuron receiverNeuron)
                        continue;
                    if (receiver != null && receiverNeuron.synapses != null)
                        receiverNeuron.synapses.RemoveAll(s => s.neuron == nucleus);
                }
            }
            else if (nucleus is Cluster cluster) {
                // remove all receivers for this cluster
                foreach (Nucleus clusterNucleus in cluster.nuclei) {
                    if (clusterNucleus is Neuron output) {
                        foreach (Nucleus receiver in output.receivers) {
                            if (receiver is not Neuron receiverNeuron)
                                continue;
                            receiverNeuron.synapses.RemoveAll(s => s.neuron == output);
                        }
                    }
                }
            }


            if (nucleus.parent.prefab != null) {
                nucleus.parent.prefab.cluster.nuclei.RemoveAll(n => n == nucleus);
                nucleus.parent.prefab.cluster.RefreshOutputs();
                nucleus.parent.prefab.GarbageCollection();
            }
        }

        public override void UpdateStateIsolated() {
            CheckSleepingSynapses();
            var result = Combinator();
            this.outputValue = Activator(result);
            this.lastUpdate = Time.time;
        }

        protected void CheckSleepingSynapses() {
            foreach (Synapse synapse in this.synapses) {
                if (synapse.isSleeping) {
                    synapse.neuron.outputValue = Vector3.zero;
                }
            }
        }

        #region Combinator

#if UNITY_MATHEMATICS

        protected Func<float3> Combinator => combinator switch {
            CombinatorType.Sum => CombinatorSum,
            CombinatorType.Product => CombinatorProduct,
            CombinatorType.Max => CombinatorMax,
            _ => CombinatorSum
        };

        public float3 CombinatorSum() {
            float3 sum = this.bias;
            foreach (Synapse synapse in this.synapses)
                sum += synapse.weight * synapse.neuron.outputValue;
            return sum;
        }

        public float3 CombinatorProduct() {
            float3 product = this.bias;
            foreach (Synapse synapse in this.synapses) {
                product *= synapse.weight * synapse.neuron.outputValue;
            }
            return product;
        }

        public float3 CombinatorMax() {
            float3 max = this.bias;
            float maxLength = length(max);

            //Applying the weight factors
            foreach (Synapse synapse in this.synapses) {
                float3 input = synapse.weight * synapse.neuron.outputValue;

                float inputLength = length(input);
                if (inputLength > maxLength) {
                    max = input;
                    maxLength = inputLength;
                }
            }
            return max;
        }

#else

    protected Func<Vector3> Combinator => combinator switch {
        CombinatorType.Sum => CombinatorSum,
        CombinatorType.Product => CombinatorProduct,
        CombinatorType.Max => CombinatorMax,
        _ => CombinatorSum
    };

    public Vector3 CombinatorSum() {
        Vector3 sum = this.bias;
        foreach (Synapse synapse in this.synapses)
            sum += synapse.weight * synapse.neuron.outputValue;
        return sum;
    }

    public Vector3 CombinatorProduct() {
        Vector3 product = this.bias;
        foreach (Synapse synapse in this.synapses) {
            //product *= synapse.weight * synapse.neuron.outputValue;
            product = Vector3.Scale(product, synapse.weight * synapse.neuron.outputValue);
        }
        return product;
    }

    public Vector3 CombinatorMax() {
        Vector3 max = this.bias;
        float maxLength = max.magnitude;

        //Applying the weight factors
        foreach (Synapse synapse in this.synapses) {
            Vector3 input = synapse.weight * synapse.neuron.outputValue;

            float inputLength = input.magnitude;
            if (inputLength > maxLength) {
                max = input;
                maxLength = inputLength;
            }
        }
        return max;
    }
#endif
        #endregion Combinator

        #region Activator

#if UNITY_MATHEMATICS

        public Func<float3, float3> Activator => this.curvePreset switch {
            ActivationType.Linear => ActivatorLinear,
            ActivationType.Sqrt => ActivatorSqrt,
            ActivationType.Power => ActivatorPower,
            ActivationType.Reciprocal => ActivatorReciprocal,
            ActivationType.Tanh => ActivatorTanh,
            ActivationType.Binary => ActivatorBinary,
            ActivationType.Normalized => ActivatorNormalized,
            _ => ActivatorCustom
        };

        protected float3 ActivatorLinear(float3 input) {
            return input;
        }

        protected float3 ActivatorSqrt(float3 input) {
            float3 result = normalize(input) * System.MathF.Sqrt(length(input));
            return result;
        }

        protected float3 ActivatorPower(float3 input) {
            float3 result = normalize(input) * System.MathF.Pow(length(input), 2);
            return result;
        }

        protected float3 ActivatorReciprocal(float3 input) {
            float magnitude = length(input);
            if (magnitude == 0)
                return new float3(0, 0, 0);

            float3 result = normalize(input) * (1 / magnitude);
            return result;
        }

        protected float3 ActivatorTanh(float3 input) {
            float magnitude = length(input);
            float3 result = normalize(input) * MathF.Tanh(magnitude);
            return result;
        }
        protected float3 ActivatorBinary(float3 input) {
            float magnitude = length(input);
            float value = Mathf.Clamp01(magnitude);
            return float3(value, value, value);
        }

        protected float3 ActivatorNormalized(float3 input) {
            if (lengthsq(input) == 0)
                return input;
            float3 result = normalize(input);
            return result;
        }

        protected float3 ActivatorCustom(float3 input) {
            float activatedValue = this.curve.Evaluate(length(input));
            float3 result = normalize(input) * activatedValue;
            return result;
        }

#else

    public Func<Vector3, Vector3> Activator => this.curvePreset switch {
        CurvePresets.Linear => ActivatorLinear,
        CurvePresets.Sqrt => ActivatorSqrt,
        CurvePresets.Power => ActivatorPower,
        CurvePresets.Reciprocal => ActivatorReciprocal,
        _ => ActivatorCustom
    };

    protected Vector3 ActivatorLinear(Vector3 input) {
        return input;
    }

    protected Vector3 ActivatorSqrt(Vector3 input) {
        Vector3 result = input.normalized * System.MathF.Sqrt(input.magnitude);
        return result;
    }

    protected Vector3 ActivatorPower(Vector3 input) {
        Vector3 result = input.normalized * System.MathF.Pow(input.magnitude, 2);
        return result;
    }

    protected Vector3 ActivatorReciprocal(Vector3 input) {
        float magnitude = input.magnitude;
        if (magnitude == 0)
            return new Vector3(0, 0, 0);

        Vector3 result = input.normalized * (1 / magnitude);
        return result;
    }

    protected Vector3 ActivatorCustom(Vector3 input) {
        float activatedValue = this.curve.Evaluate(input.magnitude);
        Vector3 result = input.normalized * activatedValue;
        return result;
    }

#endif

        #endregion Activator

        #region Receivers

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

        public virtual void AddReceiver(Nucleus receiverToAdd, float weight = 1) {
            if (receiverToAdd is not Neuron receiverNeuron)
                return;
            this._receivers.Add(receiverNeuron);
            receiverNeuron.AddSynapse(this, weight);
            //Debug.Log($"Add synapse {this.clusterPrefab.name}.{this.name} -> {receiverToAdd.name} --- [{this.receivers.Count}]");

        }

        public virtual void RemoveReceiver(Nucleus receiverToRemove) {
            if (receiverToRemove is not Neuron receiverNeuron)
                return;
            this._receivers.RemoveAll(receiver => receiver == receiverNeuron);
            receiverNeuron.synapses.RemoveAll(synapse => synapse.neuron == this);

            // Nucleus prefabReceiver = receiverToRemove.prefabNucleus;
            // if (this.prefabNucleus is Neuron prefabNeuron && prefabReceiver != null) {
            //     prefabNeuron.receivers.RemoveAll(receiver => receiver == prefabReceiver);
            //     prefabReceiver.synapses.RemoveAll(synapse => synapse.neuron == prefabNeuron);
            // }
        }


        #endregion Receivers

        public override void ProcessStimulus(Vector3 inputValue) {
            this.lastUpdate = Time.time;
            this.bias = inputValue;
            this.parent?.UpdateFromNucleus(this);
        }
    }

}