#define LLAMAMODEL_H_I_KNOW_WHAT_I_AM_DOING_WHEN_INCLUDING_THIS_FILE
#include "llamamodel_impl.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <iostream>
#if defined(_WIN32) && defined(_MSC_VER)
    #define WIN32_LEAN_AND_MEAN
    #ifndef NOMINMAX
        #define NOMINMAX
    #endif
    #include <windows.h>
    #include <io.h>
    #include <stdio.h>
#else
    #include <unistd.h>
#endif
#include <random>
#include <thread>
#include <unordered_set>

#include <llama.h>
#include <ggml.h>


namespace {
const char *modelType_ = "LLaMA";
}

struct gpt_params {
    int32_t seed          = -1;   // RNG seed
    int32_t n_keep        = 0;    // number of tokens to keep from initial prompt
#if LLAMA_DATE <= 230511
    int32_t n_parts       = -1;   // amount of model parts (-1 = determine from model dimensions)
#endif

#if LLAMA_DATE >= 230519
    // sampling parameters
    float   tfs_z         = 1.0f; // 1.0 = disabled
    float   typical_p     = 1.0f; // 1.0 = disabled
#endif

    std::string prompt = "";

    bool memory_f16        = true;  // use f16 instead of f32 for memory kv

    bool use_mmap          = true;  // use mmap for faster loads
    bool use_mlock         = false; // use mlock to keep model in memory
};

#if LLAMA_DATE >= 230519
static int llama_sample_top_p_top_k(
        llama_context *ctx,
        const llama_token *last_n_tokens_data,
        int last_n_tokens_size,
        int top_k,
        float top_p,
        float temp,
        float repeat_penalty) {
    auto logits = llama_get_logits(ctx);
    auto n_vocab = llama_n_vocab(ctx);
    // Populate initial list of all candidates
    std::vector<llama_token_data> candidates;
    candidates.reserve(n_vocab);
    for (int token_id = 0; token_id < n_vocab; token_id++) {
        candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
    }
    llama_token_data_array candidates_p = {candidates.data(), candidates.size(), false};
    // Sample repeat penalty
    llama_sample_repetition_penalty(nullptr, &candidates_p, last_n_tokens_data, last_n_tokens_size, repeat_penalty);
    // Temperature sampling
    llama_sample_top_k(ctx, &candidates_p, top_k, 1);
    llama_sample_tail_free(ctx, &candidates_p, 1.0f, 1);
    llama_sample_typical(ctx, &candidates_p, 1.0f, 1);
    llama_sample_top_p(ctx, &candidates_p, top_p, 1);
    llama_sample_temperature(ctx, &candidates_p, temp);
    return llama_sample_token(ctx, &candidates_p);
}
#endif

struct LLamaPrivate {
    const std::string modelPath;
    bool modelLoaded;
    llama_context *ctx = nullptr;
    llama_context_params params;
    int64_t n_threads = 0;
    bool empty = true;
};

LLamaModel::LLamaModel()
    : d_ptr(new LLamaPrivate) {
    d_ptr->modelLoaded = false;
}

bool LLamaModel::loadModel(const std::string &modelPath)
{
    // load the model
    d_ptr->params = llama_context_default_params();

    gpt_params params;
    d_ptr->params.n_ctx      = 2048;
    d_ptr->params.seed       = params.seed;
    d_ptr->params.f16_kv     = params.memory_f16;
    d_ptr->params.use_mmap   = params.use_mmap;
    d_ptr->params.use_mlock  = params.use_mlock;
#if LLAMA_DATE <= 230511
    d_ptr->params.n_parts  = params.n_parts;
#endif

    d_ptr->ctx = llama_init_from_file(modelPath.c_str(), d_ptr->params);
    if (!d_ptr->ctx) {
        std::cerr << "LLAMA ERROR: failed to load model from " <<  modelPath << std::endl;
        return false;
    }

    d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
    d_ptr->modelLoaded = true;
    fflush(stderr);
    return true;
}

void LLamaModel::setThreadCount(int32_t n_threads) {
    d_ptr->n_threads = n_threads;
}

int32_t LLamaModel::threadCount() const {
    return d_ptr->n_threads;
}

LLamaModel::~LLamaModel()
{
    llama_free(d_ptr->ctx);
}

bool LLamaModel::isModelLoaded() const
{
    return d_ptr->modelLoaded;
}

size_t LLamaModel::stateSize() const
{
    return llama_get_state_size(d_ptr->ctx);
}

size_t LLamaModel::saveState(uint8_t *dest) const
{
    return llama_copy_state_data(d_ptr->ctx, dest);
}

size_t LLamaModel::restoreState(const uint8_t *src)
{
    // const_cast is required, see: https://github.com/ggerganov/llama.cpp/pull/1540
    return llama_set_state_data(d_ptr->ctx, const_cast<uint8_t*>(src));
}

void LLamaModel::prompt(const std::string &prompt,
        std::function<bool(int32_t)> promptCallback,
        std::function<bool(int32_t, const std::string&)> responseCallback,
        std::function<bool(bool)> recalculateCallback,
        PromptContext &promptCtx) {

    if (!isModelLoaded()) {
        std::cerr << "LLAMA ERROR: prompt won't work with an unloaded model!\n";
        return;
    }

    gpt_params params;
    params.prompt = prompt;

    // Add a space in front of the first character to match OG llama tokenizer behavior
    params.prompt.insert(0, 1, ' ');

    // tokenize the prompt
    std::vector<llama_token> embd_inp(params.prompt.size() + 4);
    int n = llama_tokenize(d_ptr->ctx, params.prompt.c_str(), embd_inp.data(), embd_inp.size(), d_ptr->empty);
    assert(n >= 0);
    embd_inp.resize(n);
    d_ptr->empty = false;

    // save the context size
    promptCtx.n_ctx = llama_n_ctx(d_ptr->ctx);

    if ((int) embd_inp.size() > promptCtx.n_ctx - 4) {
        responseCallback(-1, "The prompt size exceeds the context window size and cannot be processed.");
        std::cerr << "LLAMA ERROR: The prompt is" << embd_inp.size() <<
            "tokens and the context window is" << promptCtx.n_ctx << "!\n";
        return;
    }

    promptCtx.n_predict = std::min(promptCtx.n_predict, promptCtx.n_ctx - (int) embd_inp.size());
    promptCtx.n_past = std::min(promptCtx.n_past, promptCtx.n_ctx);

    // number of tokens to keep when resetting context
    params.n_keep = (int)embd_inp.size();

    // process the prompt in batches
    size_t i = 0;
    while (i < embd_inp.size()) {
        size_t batch_end = std::min(i + promptCtx.n_batch, embd_inp.size());
        std::vector<llama_token> batch(embd_inp.begin() + i, embd_inp.begin() + batch_end);

        // Check if the context has run out...
        if (promptCtx.n_past + int32_t(batch.size()) > promptCtx.n_ctx) {
            const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
            // Erase the first percentage of context from the tokens...
            std::cerr << "LLAMA: reached the end of the context window so resizing\n";
            promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
            promptCtx.n_past = promptCtx.tokens.size();
            recalculateContext(promptCtx, recalculateCallback);
            assert(promptCtx.n_past + int32_t(batch.size()) <= promptCtx.n_ctx);
        }

        if (!evalTokens(promptCtx, batch)) {
            std::cerr << "LLAMA ERROR: Failed to process prompt\n";
            return;
        }

        size_t tokens = batch_end - i;
        for (size_t t = 0; t < tokens; ++t) {
            if (int32_t(promptCtx.tokens.size()) == promptCtx.n_ctx)
                promptCtx.tokens.erase(promptCtx.tokens.begin());
            promptCtx.tokens.push_back(batch.at(t));
            if (!promptCallback(batch.at(t)))
                return;
        }
        promptCtx.n_past += batch.size();
        i = batch_end;
    }

    std::string cachedResponse;
    std::vector<llama_token> cachedTokens;
    std::unordered_set<std::string> reversePrompts
        = { "### Instruction", "### Prompt", "### Response", "### Human", "### Assistant" };

    // predict next tokens
    for (int i = 0; i < promptCtx.n_predict; i++) {
        // sample next token
        const size_t n_prev_toks = std::min((size_t) promptCtx.repeat_last_n, promptCtx.tokens.size());
        llama_token id = llama_sample_top_p_top_k(d_ptr->ctx,
            promptCtx.tokens.data() + promptCtx.tokens.size() - n_prev_toks,
            n_prev_toks, promptCtx.top_k, promptCtx.top_p, promptCtx.temp,
            promptCtx.repeat_penalty);

        // Check if the context has run out...
        if (promptCtx.n_past + 1 > promptCtx.n_ctx) {
            const int32_t erasePoint = promptCtx.n_ctx * promptCtx.contextErase;
            // Erase the first percentage of context from the tokens...
            std::cerr << "LLAMA: reached the end of the context window so resizing\n";
            promptCtx.tokens.erase(promptCtx.tokens.begin(), promptCtx.tokens.begin() + erasePoint);
            promptCtx.n_past = promptCtx.tokens.size();
            recalculateContext(promptCtx, recalculateCallback);
            assert(promptCtx.n_past + 1 <= promptCtx.n_ctx);
        }

        if (!evalTokens(promptCtx, { id })) {
            std::cerr << "LLAMA ERROR: Failed to predict next token\n";
            return;
        }

        promptCtx.n_past += 1;
        // display text
        if (id == llama_token_eos())
            return;

        const std::string str = llama_token_to_str(d_ptr->ctx, id);

        // Check if the provided str is part of our reverse prompts
        bool foundPartialReversePrompt = false;
        const std::string completed = cachedResponse + str;
        if (reversePrompts.find(completed) != reversePrompts.end()) {
            return;
        }

        // Check if it partially matches our reverse prompts and if so, cache
        for (const auto &s : reversePrompts) {
            if (s.compare(0, completed.size(), completed) == 0) {
                foundPartialReversePrompt = true;
                cachedResponse = completed;
                break;
            }
        }

        // Regardless the token gets added to our cache
        cachedTokens.push_back(id);

        // Continue if we have found a partial match
        if (foundPartialReversePrompt)
            continue;

        // Empty the cache
        for (auto t : cachedTokens) {
            if (int32_t(promptCtx.tokens.size()) == promptCtx.n_ctx)
                promptCtx.tokens.erase(promptCtx.tokens.begin());
            promptCtx.tokens.push_back(t);
            if (!responseCallback(t, llama_token_to_str(d_ptr->ctx, t)))
                return;
        }
        cachedTokens.clear();
    }
}

bool LLamaModel::evalTokens(PromptContext &ctx, const std::vector<int32_t> &tokens)
{
    return llama_eval(d_ptr->ctx, tokens.data(), tokens.size(), ctx.n_past, d_ptr->n_threads) == 0;
}

#if defined(_WIN32)
#define DLL_EXPORT __declspec(dllexport)
#else
#define DLL_EXPORT __attribute__ ((visibility ("default")))
#endif

extern "C" {
DLL_EXPORT bool is_g4a_backend_model_implementation() {
    return true;
}

DLL_EXPORT const char *get_model_type() {
    return modelType_;
}

DLL_EXPORT const char *get_build_variant() {
    return GGML_BUILD_VARIANT;
}

DLL_EXPORT bool magic_match(std::istream& f) {
    // Check magic
    uint32_t magic = 0;
    f.read(reinterpret_cast<char*>(&magic), sizeof(magic));
    if (magic != 0x67676a74) return false;
    // Check version
    uint32_t version = 0;
    f.read(reinterpret_cast<char*>(&version), sizeof(version));
    return version LLAMA_VERSIONS;
}

DLL_EXPORT LLModel *construct() {
    return new LLamaModel;
}
}