using System.Collections;
using System.Collections.Generic;
using UnityEngine;
#if UNITY_EDITOR
using UnityEditor;
#endif
using System.Linq;
#if ENABLE_TENSORFLOW
using TensorFlow;

#endif

namespace MLAgents
{
    /// CoreBrain which decides actions using internally embedded TensorFlow model.
    public class CoreBrainInternal : ScriptableObject, CoreBrain
    {
        [SerializeField] [Tooltip("If checked, the brain will broadcast states and actions to Python.")]
#pragma warning disable
        private bool broadcast = true;
#pragma warning restore

        [System.Serializable]
        private struct TensorFlowAgentPlaceholder
        {
            public enum TensorType
            {
                Integer,
                FloatingPoint
            };

            public string name;
            public TensorType valueType;
            public float minValue;
            public float maxValue;
        }

        Batcher brainBatcher;

        [Tooltip("This must be the bytes file corresponding to the pretrained TensorFlow graph.")]
        /// Modify only in inspector : Reference to the Graph asset
        public TextAsset graphModel;

        /// Modify only in inspector : If a scope was used when training the model, specify it here
        public string graphScope;

        [SerializeField]
        [Tooltip(
            "If your graph takes additional inputs that are fixed (example: noise level) you can specify them here.")]
        ///  Modify only in inspector : If your graph takes additional inputs that are fixed you can specify them here.
        private TensorFlowAgentPlaceholder[] graphPlaceholders;

        ///  Modify only in inspector : Name of the placholder of the batch size
        public string BatchSizePlaceholderName = "batch_size";

        ///  Modify only in inspector : Name of the state placeholder
        public string VectorObservationPlacholderName = "vector_observation";

        ///  Modify only in inspector : Name of the recurrent input
        public string RecurrentInPlaceholderName = "recurrent_in";

        ///  Modify only in inspector : Name of the recurrent output
        public string RecurrentOutPlaceholderName = "recurrent_out";

        /// Modify only in inspector : Names of the observations placeholders
        public string[] VisualObservationPlaceholderName;

        /// Modify only in inspector : Name of the action node
        public string ActionPlaceholderName = "action";

        /// Modify only in inspector : Name of the previous action node
        public string PreviousActionPlaceholderName = "prev_action";
#if ENABLE_TENSORFLOW
        TFGraph graph;
        TFSession session;
        bool hasRecurrent;
        bool hasState;
        bool hasBatchSize;
        bool hasPrevAction;
        float[,] inputState;
        int[] inputPrevAction;
        List<float[,,,]> observationMatrixList;
        float[,] inputOldMemories;
        List<Texture2D> texturesHolder;
        int memorySize;
#endif

        /// Reference to the brain that uses this CoreBrainInternal
        public Brain brain;

        /// Create the reference to the brain
        public void SetBrain(Brain b)
        {
            brain = b;
        }

        /// Loads the tensorflow graph model to generate a TFGraph object
        public void InitializeCoreBrain(MLAgents.Batcher brainBatcher)
        {
#if ENABLE_TENSORFLOW
#if UNITY_ANDROID
// This needs to ba called only once and will raise an exception if 
// there are multiple internal brains
        try{
            TensorFlowSharp.Android.NativeBinding.Init();
        }
        catch{
            
        }
#endif
            if ((brainBatcher == null)
                || (!broadcast))
            {
                this.brainBatcher = null;
            }
            else
            {
                this.brainBatcher = brainBatcher;
                this.brainBatcher.SubscribeBrain(brain.gameObject.name);
            }

            if (graphModel != null)
            {
                graph = new TFGraph();

                graph.Import(graphModel.bytes);

                session = new TFSession(graph);

                // TODO: Make this a loop over a dynamic set of graph inputs

                if ((graphScope.Length > 1) && (graphScope[graphScope.Length - 1] != '/'))
                {
                    graphScope = graphScope + '/';
                }

                if (graph[graphScope + BatchSizePlaceholderName] != null)
                {
                    hasBatchSize = true;
                }

                if ((graph[graphScope + RecurrentInPlaceholderName] != null) &&
                    (graph[graphScope + RecurrentOutPlaceholderName] != null))
                {
                    hasRecurrent = true;
                    var runner = session.GetRunner();
                    runner.Fetch(graph[graphScope + "memory_size"][0]);
                    var networkOutput = runner.Run()[0].GetValue();
                    memorySize = (int) networkOutput;
                }

                if (graph[graphScope + VectorObservationPlacholderName] != null)
                {
                    hasState = true;
                }

                if (graph[graphScope + PreviousActionPlaceholderName] != null)
                {
                    hasPrevAction = true;
                }
            }

            observationMatrixList = new List<float[,,,]>();
            texturesHolder = new List<Texture2D>();
#endif
        }


        /// Uses the stored information to run the tensorflow graph and generate 
        /// the actions.
        public void DecideAction(Dictionary<Agent, AgentInfo> agentInfo)
        {
#if ENABLE_TENSORFLOW
            if (brainBatcher != null)
            {
                brainBatcher.SendBrainInfo(brain.gameObject.name, agentInfo);
            }

            int currentBatchSize = agentInfo.Count();
            List<Agent> agentList = agentInfo.Keys.ToList();
            if (currentBatchSize == 0)
            {
                return;
            }


            // Create the state tensor
            if (hasState)
            {
                int stateLength = 1;
                stateLength = brain.brainParameters.vectorObservationSize;
                inputState =
                    new float[currentBatchSize, stateLength * brain.brainParameters.numStackedVectorObservations];

                var i = 0;
                foreach (Agent agent in agentList)
                {
                    List<float> stateList = agentInfo[agent].stackedVectorObservation;
                    for (int j =
                            0;
                        j < stateLength * brain.brainParameters.numStackedVectorObservations;
                        j++)
                    {
                        inputState[i, j] = stateList[j];
                    }

                    i++;
                }
            }

            // Create the state tensor
            if (hasPrevAction)
            {
                inputPrevAction = new int[currentBatchSize];
                var i = 0;
                foreach (Agent agent in agentList)
                {
                    float[] actionList = agentInfo[agent].storedVectorActions;
                    inputPrevAction[i] = Mathf.FloorToInt(actionList[0]);
                    i++;
                }
            }


            observationMatrixList.Clear();
            for (int observationIndex =
                    0;
                observationIndex < brain.brainParameters.cameraResolutions.Length;
                observationIndex++)
            {
                texturesHolder.Clear();
                foreach (Agent agent in agentList)
                {
                    texturesHolder.Add(agentInfo[agent].visualObservations[observationIndex]);
                }

                observationMatrixList.Add(
                    BatchVisualObservations(texturesHolder,
                        brain.brainParameters.cameraResolutions[observationIndex].blackAndWhite));
            }

            // Create the recurrent tensor
            if (hasRecurrent)
            {
                // Need to have variable memory size
                inputOldMemories = new float[currentBatchSize, memorySize];
                var i = 0;
                foreach (Agent agent in agentList)
                {
                    float[] m = agentInfo[agent].memories.ToArray();
                    for (int j = 0; j < m.Length; j++)
                    {
                        inputOldMemories[i, j] = m[j];
                    }

                    i++;
                }
            }


            var runner = session.GetRunner();
            try
            {
                runner.Fetch(graph[graphScope + ActionPlaceholderName][0]);
            }
            catch
            {
                throw new UnityAgentsException(string.Format(
                    @"The node {0} could not be found. Please make sure the graphScope {1} is correct",
                    graphScope + ActionPlaceholderName, graphScope));
            }

            if (hasBatchSize)
            {
                runner.AddInput(graph[graphScope + BatchSizePlaceholderName][0], new int[] {currentBatchSize});
            }

            foreach (TensorFlowAgentPlaceholder placeholder in graphPlaceholders)
            {
                try
                {
                    if (placeholder.valueType == TensorFlowAgentPlaceholder.TensorType.FloatingPoint)
                    {
                        runner.AddInput(graph[graphScope + placeholder.name][0],
                            new float[] {Random.Range(placeholder.minValue, placeholder.maxValue)});
                    }
                    else if (placeholder.valueType == TensorFlowAgentPlaceholder.TensorType.Integer)
                    {
                        runner.AddInput(graph[graphScope + placeholder.name][0],
                            new int[] {Random.Range((int) placeholder.minValue, (int) placeholder.maxValue + 1)});
                    }
                }
                catch
                {
                    throw new UnityAgentsException(string.Format(
                        @"One of the Tensorflow placeholder cound nout be found.
                In brain {0}, there are no {1} placeholder named {2}.",
                        brain.gameObject.name, placeholder.valueType.ToString(), graphScope + placeholder.name));
                }
            }

            // Create the state tensor
            if (hasState)
            {
                runner.AddInput(graph[graphScope + VectorObservationPlacholderName][0], inputState);
            }

            // Create the previous action tensor
            if (hasPrevAction)
            {
                runner.AddInput(graph[graphScope + PreviousActionPlaceholderName][0], inputPrevAction);
            }

            // Create the observation tensors
            for (int obsNumber =
                    0;
                obsNumber < brain.brainParameters.cameraResolutions.Length;
                obsNumber++)
            {
                runner.AddInput(graph[graphScope + VisualObservationPlaceholderName[obsNumber]][0],
                    observationMatrixList[obsNumber]);
            }

            if (hasRecurrent)
            {
                runner.AddInput(graph[graphScope + "sequence_length"][0], 1);
                runner.AddInput(graph[graphScope + RecurrentInPlaceholderName][0], inputOldMemories);
                runner.Fetch(graph[graphScope + RecurrentOutPlaceholderName][0]);
            }

            TFTensor[] networkOutput;
            try
            {
                networkOutput = runner.Run();
            }
            catch (TFException e)
            {
                string errorMessage = e.Message;
                try
                {
                    errorMessage =
                        $@"The tensorflow graph needs an input for {e.Message.Split(new string[] {"Node: "}, 0)[1].Split('=')[0]} of type {e.Message.Split(new string[] {"dtype="}, 0)[1].Split(',')[0]}";
                }
                finally
                {
                    throw new UnityAgentsException(errorMessage);
                }
            }

            // Create the recurrent tensor
            if (hasRecurrent)
            {
                float[,] recurrentTensor = networkOutput[1].GetValue() as float[,];

                var i = 0;
                foreach (Agent agent in agentList)
                {
                    var m = new float[memorySize];
                    for (int j = 0; j < memorySize; j++)
                    {
                        m[j] = recurrentTensor[i, j];
                    }

                    agent.UpdateMemoriesAction(m.ToList());
                    i++;
                }
            }

            if (brain.brainParameters.vectorActionSpaceType == SpaceType.continuous)
            {
                var output = networkOutput[0].GetValue() as float[,];
                var i = 0;
                foreach (Agent agent in agentList)
                {
                    var a = new float[brain.brainParameters.vectorActionSize];
                    for (int j = 0; j < brain.brainParameters.vectorActionSize; j++)
                    {
                        a[j] = output[i, j];
                    }

                    agent.UpdateVectorAction(a);
                    i++;
                }
            }
            else if (brain.brainParameters.vectorActionSpaceType == SpaceType.discrete)
            {
                long[,] output = networkOutput[0].GetValue() as long[,];
                var i = 0;
                foreach (Agent agent in agentList)
                {
                    var a = new float[1] {(float) (output[i, 0])};
                    agent.UpdateVectorAction(a);
                    i++;
                }
            }


#else
            if (agentInfo.Count > 0)
            {
                throw new UnityAgentsException(string.Format(
                    @"The brain {0} was set to Internal but the Tensorflow 
                        library is not present in the Unity project.",
                    brain.gameObject.name));
            }
#endif
        }

        /// Displays the parameters of the CoreBrainInternal in the Inspector 
        public void OnInspector()
        {
#if ENABLE_TENSORFLOW && UNITY_EDITOR
            EditorGUILayout.LabelField("", GUI.skin.horizontalSlider);
            broadcast = EditorGUILayout.Toggle(new GUIContent("Broadcast",
                "If checked, the brain will broadcast states and actions to Python."), broadcast);

            var serializedBrain = new SerializedObject(this);
            GUILayout.Label("Edit the Tensorflow graph parameters here");
            var tfGraphModel = serializedBrain.FindProperty("graphModel");
            serializedBrain.Update();
            EditorGUILayout.ObjectField(tfGraphModel);
            serializedBrain.ApplyModifiedProperties();

            if (graphModel == null)
            {
                EditorGUILayout.HelpBox("Please provide a tensorflow graph as a bytes file.", MessageType.Error);
            }


            graphScope =
                EditorGUILayout.TextField(new GUIContent("Graph Scope",
                    "If you set a scope while training your tensorflow model, " +
                    "all your placeholder name will have a prefix. You must specify that prefix here."), graphScope);

            if (BatchSizePlaceholderName == "")
            {
                BatchSizePlaceholderName = "batch_size";
            }

            BatchSizePlaceholderName =
                EditorGUILayout.TextField(new GUIContent("Batch Size Node Name", "If the batch size is one of " +
                                                                                 "the inputs of your graph, you must specify the name if the placeholder here."),
                    BatchSizePlaceholderName);
            if (VectorObservationPlacholderName == "")
            {
                VectorObservationPlacholderName = "state";
            }

            VectorObservationPlacholderName =
                EditorGUILayout.TextField(new GUIContent("Vector Observation Node Name",
                    "If your graph uses the state as an input, " +
                    "you must specify the name if the placeholder here."), VectorObservationPlacholderName);
            if (RecurrentInPlaceholderName == "")
            {
                RecurrentInPlaceholderName = "recurrent_in";
            }

            RecurrentInPlaceholderName =
                EditorGUILayout.TextField(new GUIContent("Recurrent Input Node Name", "If your graph uses a " +
                                                                                      "recurrent input / memory as input and outputs new recurrent input / memory, " +
                                                                                      "you must specify the name if the input placeholder here."),
                    RecurrentInPlaceholderName);
            if (RecurrentOutPlaceholderName == "")
            {
                RecurrentOutPlaceholderName = "recurrent_out";
            }

            RecurrentOutPlaceholderName =
                EditorGUILayout.TextField(new GUIContent("Recurrent Output Node Name", " If your graph uses a " +
                                                                                       "recurrent input / memory as input and outputs new recurrent input / memory, you must specify the name if " +
                                                                                       "the output placeholder here."),
                    RecurrentOutPlaceholderName);

            if (brain.brainParameters.cameraResolutions != null)
            {
                if (brain.brainParameters.cameraResolutions.Count() > 0)
                {
                    if (VisualObservationPlaceholderName == null)
                    {
                        VisualObservationPlaceholderName =
                            new string[brain.brainParameters.cameraResolutions.Count()];
                    }

                    if (VisualObservationPlaceholderName.Count() != brain.brainParameters.cameraResolutions.Count())
                    {
                        VisualObservationPlaceholderName =
                            new string[brain.brainParameters.cameraResolutions.Count()];
                    }

                    for (int obs_number =
                            0;
                        obs_number < brain.brainParameters.cameraResolutions.Count();
                        obs_number++)
                    {
                        if ((VisualObservationPlaceholderName[obs_number] == "") ||
                            (VisualObservationPlaceholderName[obs_number] == null))
                        {
                            VisualObservationPlaceholderName[obs_number] =
                                "visual_observation_" + obs_number;
                        }
                    }

                    var opn = serializedBrain.FindProperty("VisualObservationPlaceholderName");
                    serializedBrain.Update();
                    EditorGUILayout.PropertyField(opn, true);
                    serializedBrain.ApplyModifiedProperties();
                }
            }

            if (ActionPlaceholderName == "")
            {
                ActionPlaceholderName = "action";
            }

            ActionPlaceholderName =
                EditorGUILayout.TextField(new GUIContent("Action Node Name", "Specify the name of the " +
                                                                             "placeholder corresponding to the actions of the brain in your graph. If the action space type is " +
                                                                             "continuous, the output must be a one dimensional tensor of float of length Action Space Size, " +
                                                                             "if the action space type is discrete, the output must be a one dimensional tensor of int " +
                                                                             "of length 1."), ActionPlaceholderName);


            var tfPlaceholders = serializedBrain.FindProperty("graphPlaceholders");
            serializedBrain.Update();
            EditorGUILayout.PropertyField(tfPlaceholders, true);
            serializedBrain.ApplyModifiedProperties();
#endif
#if !ENABLE_TENSORFLOW && UNITY_EDITOR
            EditorGUILayout.HelpBox(
                "You need to install and enable the TensorflowSharp plugin in " +
                "order to use the internal brain.", MessageType.Error);
            if (GUILayout.Button("Show me how"))
            {
                Application.OpenURL(
                    "https://github.com/Unity-Technologies/ml-agents/blob/master/docs/Getting-Started-with-" +
                    "Balance-Ball.md#embedding-the-trained-brain-into-the-unity-environment-experimental");
            }
#endif
        }

        /// <summary>
        /// Converts a list of Texture2D into a Tensor.
        /// </summary>
        /// <returns>
        /// A 4 dimensional float Tensor of dimension
        /// [batch_size, height, width, channel].
        /// Where batch_size is the number of input textures,
        /// height corresponds to the height of the texture,
        /// width corresponds to the width of the texture,
        /// channel corresponds to the number of channels extracted from the
        /// input textures (based on the input blackAndWhite flag
        /// (3 if the flag is false, 1 otherwise).
        /// The values of the Tensor are between 0 and 1.
        /// </returns>
        /// <param name="textures">
        /// The list of textures to be put into the tensor.
        /// Note that the textures must have same width and height.
        /// </param>
        /// <param name="blackAndWhite">
        /// If set to <c>true</c> the textures
        /// will be converted to grayscale before being stored in the tensor.
        /// </param>
        public static float[,,,] BatchVisualObservations(
            List<Texture2D> textures, bool blackAndWhite)
        {
            int batchSize = textures.Count();
            int width = textures[0].width;
            int height = textures[0].height;
            int pixels = 0;
            if (blackAndWhite)
                pixels = 1;
            else
                pixels = 3;
            float[,,,] result = new float[batchSize, height, width, pixels];

            for (int b = 0; b < batchSize; b++)
            {
                Color32[] cc = textures[b].GetPixels32();
                for (int w = 0; w < width; w++)
                {
                    for (int h = 0; h < height; h++)
                    {
                        Color32 currentPixel = cc[h * width + w];
                        if (!blackAndWhite)
                        {
                            // For Color32, the r, g and b values are between
                            // 0 and 255.
                            result[b, textures[b].height - h - 1, w, 0] =
                                currentPixel.r / 255.0f;
                            result[b, textures[b].height - h - 1, w, 1] =
                                currentPixel.g / 255.0f;
                            result[b, textures[b].height - h - 1, w, 2] =
                                currentPixel.b / 255.0f;
                        }
                        else
                        {
                            result[b, textures[b].height - h - 1, w, 0] =
                                (currentPixel.r + currentPixel.g + currentPixel.b)
                                / 3;
                        }
                    }
                }
            }

            return result;
        }
    }
}