#include <iostream>
#include <iomanip>
#include <vector>
#include <string>
#include <iomanip>
#include <sstream>
#include <fstream>
#include <algorithm>
#include "Xenith/core.hpp"
#include "Xenith/token/token.hpp"
#include <chrono>



std::string currentSystemPrompt = ""; 

LayerStructure_t layers[] = {
    {MAX_CONTEXT * EMBED_DIM, SIGMOID},
    {256, SIGMOID},
    {MAX_VOCAB, SIGMOID}
};

std::vector<double> buildNetInput(const std::vector<int>& tokens, Embedder& emb) {
    std::vector<double> netInput;
    netInput.reserve(MAX_CONTEXT * EMBED_DIM);
    int start = (int)tokens.size() - MAX_CONTEXT;
    if (start < 0) start = 0;
    int count = 0;
    for (int i = start; i < (int)tokens.size(); i++) {
        std::vector<double> v = emb.get(tokens[i]);
        netInput.insert(netInput.end(), v.begin(), v.end());
        count++;
    }
    while (count < MAX_CONTEXT) {
        for (int d = 0; d < EMBED_DIM; d++) netInput.push_back(0.0);
        count++;
    }
    return netInput;
}

void trainOnSequence(NeuralNetwork& nn, Tokenizer& tok, Embedder& emb, const std::string& dataset, int epochs, double lr) {
    std::vector<int> allTokens = tok.textToTokens(dataset);
    if (allTokens.size() < 2) {
        std::cout << "Error: Sequence too short for training." << std::endl;
        return;
    }
    int numLayers = sizeof(layers) / sizeof(layers[0]);
    long long totalParams = 0;
    for (int i = 0; i < numLayers - 1; i++) {
        totalParams += (long long)layers[i].size * layers[i + 1].size + layers[i + 1].size;
    }

    std::string modelSizeStr;
    {
        std::stringstream ss;
        if (totalParams >= 1e12) ss << std::fixed << std::setprecision(1) << totalParams / 1e12 << "t";
        else if (totalParams >= 1e9) ss << std::fixed << std::setprecision(1) << totalParams / 1e9 << "b";
        else if (totalParams >= 1e6) ss << std::fixed << std::setprecision(1) << totalParams / 1e6 << "m";
        else if (totalParams >= 1e3) ss << std::fixed << std::setprecision(1) << totalParams / 1e3 << "k";
        else ss << totalParams;
        modelSizeStr = ss.str();
    }

    std::string sequenceStr = "";
    for (int tId : allTokens) {
        sequenceStr += "{" + tok.getWord(tId) + " (" + std::to_string(tId) + ")} ";
    }

    auto startTime = std::chrono::high_resolution_clock::now();
    int trainSteps = 0;
    double stepsPerSec = 0, maxLoss = 0;

    std::cout << "Training logic: Next Token Prediction..." << std::endl;

    std::cout << "\033[s\n\n"; 

    for (int e = 1; e <= epochs; e++) {
        double totalLoss = 0;
        for (size_t i = 1; i < allTokens.size(); i++) {
            std::vector<int> context(allTokens.begin(), allTokens.begin() + i);
            std::vector<double> target(MAX_VOCAB, 0.0);
            target[allTokens[i]] = 1.0;
            totalLoss += nn.train(buildNetInput(context, emb), target, lr);

            trainSteps++;
            auto currentTime = std::chrono::high_resolution_clock::now();
            if (std::chrono::duration<double>(currentTime - startTime).count() >= 0.1) {
                stepsPerSec = trainSteps / std::chrono::duration<double>(currentTime - startTime).count();
                trainSteps = 0;
                startTime = currentTime;
            }
            std::cout << "\033[u"; 

            std::cout << "Epoch " << std::setw(4) << e << "/" << epochs 
                    << " | Token: " << std::setw(4) << i << "/" << allTokens.size() 
                    << " | Loss: " << std::fixed << std::setprecision(6) << totalLoss 
                    << " | Max Loss: " << maxLoss << "\033[K\n"; 

            std::cout << "SPEED: " << std::setw(6) << std::fixed << std::setprecision(1) << stepsPerSec 
                    << " st/s | MODEL: " << std::setw(7) << modelSizeStr 
                    << " | CURRENT: [" << std::left << std::setw(15) << tok.getWord(allTokens[i]) << "]" 
                    << "\033[K" << std::flush; 

        }
        maxLoss = totalLoss;
    }
    std::cout << "\nDone!" << std::endl;
}



int main() {
    Tokenizer tok;
    Embedder emb(MAX_VOCAB, EMBED_DIM);
    
    int numLayers = sizeof(layers) / sizeof(layers[0]);

    NeuralNetwork nn(layers, numLayers, true);

    while (true) {
        std::cout << "xentith~$ ";

        std::string cmdIn;
        std::getline(std::cin, cmdIn);

        if (cmdIn == "/exit") break;

        if (cmdIn == "/train") {
            int epochs;
            double lr;
            std::cout << "--- Training Setup ---\n";
            std::cout << "Enter number of epochs: ";
            std::string epStr; std::getline(std::cin, epStr);
            epochs = std::stoi(epStr);
            
            std::cout << "Enter learning rate (e.g. 0.1): ";
            std::string lrStr; std::getline(std::cin, lrStr);
            lr = std::stod(lrStr);

            std::cout << "\n--- Example Content ---\n";
            std::cout << "User: ";
            std::string userPart;
            std::getline(std::cin, userPart);

            std::cout << "AI: ";
            std::string aiPart;
            std::getline(std::cin, aiPart);

            std::string finalData = "[SYS]" + currentSystemPrompt + 
                                    "[USER]" + userPart + 
                                    "[AI]" + aiPart + "<EOS>";

            std::cout << "\nTraining logic: Pattern Recognition..." << std::endl;
            trainOnSequence(nn, tok, emb, finalData, epochs, lr);
        }

        else if (cmdIn == "/trainFile") {
            std::string content;
            std::cout << "Enter filename: ";
            std::string filename;
            std::getline(std::cin, filename);
            std::ifstream file(filename);
            if (file.is_open()) {
                std::stringstream buffer;
                buffer << file.rdbuf();
                content = buffer.str();
                std::cout << "Loaded " << content.length() << " characters from file." << std::endl;
            } else {
                std::cout << "Could not open file!" << std::endl;
                continue;
            }

            int epochs;
            double lr;
            std::cout << "Enter number of epochs: ";
            std::string epStr; std::getline(std::cin, epStr);
            epochs = std::stoi(epStr);
            
            std::cout << "Enter learning rate (e.g. 0.1): ";
            std::string lrStr; std::getline(std::cin, lrStr);
            lr = std::stod(lrStr);

            std::string finalData = "[SYS]" + currentSystemPrompt + content + "<EOS>";
            trainOnSequence(nn, tok, emb, finalData, epochs, lr);

        } else if (cmdIn == "/sysPrompt") {
            std::cout << "Current System Prompt: " << currentSystemPrompt << std::endl;
            std::cout << "Enter new System Prompt: ";
            std::getline(std::cin, currentSystemPrompt);
            std::cout << "System Prompt updated!" << std::endl;

        } else if (cmdIn == "/trainVulkan") {
            std::cout << nn.trainVulkan() << "\n";

        } else if (cmdIn == "/help") {
            std::cout << "\n--- MENU ---" << std::endl;
            std::cout << "/train\n/trainFile\n/sysPrompt\n/help\n/exit\n";

        } else if (cmdIn == "/clr") {

            std::cout << "\033[2J\033[1;1H";

        } else {
            std::string prompt = "[SYS]" + currentSystemPrompt + "[USER]" + cmdIn + "[AI]";
            std::vector<int> currentTokens = tok.textToTokens(prompt);

            std::cout << "AI: ";
            long long totalParams = 0;
            for (int i = 0; i < numLayers - 1; i++) {
                long long weights = (long long)layers[i].size * layers[i + 1].size;
                long long biases = (long long)layers[i + 1].size;
                totalParams += (weights + biases);
            }
            std::string modelSizeStr;
            {
                std::stringstream ss;
                if (totalParams >= 1000000000000LL) ss << std::fixed << std::setprecision(1) << (double)totalParams / 1000000000000.0 << "t";
                else if (totalParams >= 1000000000LL) ss << std::fixed << std::setprecision(1) << (double)totalParams / 1000000000.0 << "b";
                else if (totalParams >= 1000000LL) ss << std::fixed << std::setprecision(1) << (double)totalParams / 1000000.0 << "m";
                else if (totalParams >= 1000LL) ss << std::fixed << std::setprecision(1) << (double)totalParams / 1000.0 << "k";
                else ss << totalParams;
                modelSizeStr = ss.str();
            }

            // Переменные для замера скорости
            auto startTime = std::chrono::high_resolution_clock::now();
            int tokensInSecond = 0;
            double tokensPerSec = 0;

            for (int g = 0; g < 1024; g++) {
                std::vector<double> out = nn.feedForward(buildNetInput(currentTokens, emb));

                int bestId = 0;
                for (int i = 0; i < MAX_VOCAB; i++) {
                    if (out[i] > out[bestId]) bestId = i;
                }

                if (bestId == 0) break;

                tokensInSecond++;
                auto currentTime = std::chrono::high_resolution_clock::now();
                std::chrono::duration<double> elapsed = currentTime - startTime;

                if (elapsed.count() >= 0.1) {
                    tokensPerSec = tokensInSecond / elapsed.count();
                    tokensInSecond = 0;
                    startTime = currentTime;
                }

                std::string word = tok.getWord(bestId);
                std::cout << word << std::flush;

                std::cout << "\033[s" << "\033[999;1H" << "\033[2K"
                    << "--- [ID: " << bestId << "] | "
                    << "Speed: " << std::fixed << std::setprecision(1) << tokensPerSec*10 << " t/s | "
                    << "Model: " << modelSizeStr << " params ---"
                    << "\033[u" << std::flush;

                currentTokens.push_back(bestId);
            }
            // Чтобы курсор не остался внизу после генерации
            std::cout << std::endl;
        }
    }

    return 0;
}