// Various helper functions and utilities

#pragma once

#include "llama.h"

#include "sampling.h"

#define LOG_NO_FILE_LINE_FUNCTION
#include "log.h"

#include <string>
#include <vector>
#include <random>
#include <thread>
#include <unordered_map>
#include <tuple>

#ifdef _WIN32
#define DIRECTORY_SEPARATOR '\\'
#else
#define DIRECTORY_SEPARATOR '/'
#endif // _WIN32

#define BUILD_NUMBER 1383
#define BUILD_COMMIT "Embedded in WasmEdge"
#define BUILD_COMPILER "Embedded in WasmEdge"
#define BUILD_TARGET "Embedded in WasmEdge"

#define die(msg)          do { fputs("error: " msg "\n", stderr);                exit(1); } while (0)
#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)

#define print_build_info() do {                                                             \
    fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);         \
    fprintf(stderr, "%s: built with %s for %s\n", __func__, BUILD_COMPILER, BUILD_TARGET);  \
} while(0)

//
// CLI argument parsing
//
int32_t get_num_physical_cores();

struct gpt_params {
    uint32_t seed                           = -1;   // RNG seed
    int32_t n_threads                       = get_num_physical_cores();
    int32_t n_threads_batch                 = -1;   // number of threads to use for batch processing (-1 = use n_threads)
    int32_t n_predict                       = -1;   // new tokens to predict
    int32_t n_ctx                           = 512;  // context size
    int32_t n_batch                         = 512;  // batch size for prompt processing (must be >=32 to use BLAS)
    int32_t n_keep                          = 0;    // number of tokens to keep from initial prompt
    int32_t n_draft                         = 16;   // number of tokens to draft during speculative decoding
    int32_t n_chunks                        = -1;   // max number of chunks to process (-1 = unlimited)
    int32_t n_parallel                      = 1;    // number of parallel sequences to decode
    int32_t n_sequences                     = 1;    // number of sequences to decode
    int32_t n_gpu_layers                    = -1;   // number of layers to store in VRAM (-1 - use default)
    int32_t n_gpu_layers_draft              = -1;   // number of layers to store in VRAM for the draft model (-1 - use default)
    int32_t main_gpu                        = 0;    // the GPU that is used for scratch and small tensors
    float   tensor_split[LLAMA_MAX_DEVICES] = {0};  // how split tensors should be distributed across GPUs
    int32_t n_beams                         = 0;    // if non-zero then use beam search of given width.
    float   rope_freq_base                  = 0.0f; // RoPE base frequency
    float   rope_freq_scale                 = 0.0f; // RoPE frequency scaling factor

    // sampling parameters
    struct llama_sampling_params sampling_params;

    std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
    std::string model_draft       = "";                              // draft model for speculative decoding
    std::string model_alias       = "unknown"; // model alias
    std::string prompt            = "";
    std::string prompt_file       = "";  // store the external prompt file name
    std::string path_prompt_cache = "";  // path to file for saving/loading prompt eval state
    std::string input_prefix      = "";  // string to prefix user inputs with
    std::string input_suffix      = "";  // string to suffix user inputs with
    std::string grammar           = "";  // optional BNF-like grammar to constrain sampling
    std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
    std::string logdir            = "";  // directory in which to save YAML log files

    std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
    std::string lora_base  = "";                              // base model path for the lora adapter

    int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
    int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
                                    //                                       (which is more convenient to use for plotting)
                                    //
    bool hellaswag         = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
    size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score

    bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
    bool memory_f16        = true;  // use f16 instead of f32 for memory kv
    bool random_prompt     = false; // do not randomize prompt if none provided
    bool use_color         = false; // use color to distinguish generations and inputs
    bool interactive       = false; // interactive mode
    bool prompt_cache_all  = false; // save user input and generations to prompt cache
    bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it

    bool embedding         = false; // get only sentence embedding
    bool escape            = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
    bool interactive_first = false; // wait for user input immediately
    bool multiline_input   = false; // reverse the usage of `\`
    bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
    bool cont_batching     = false; // insert new sequences for decoding on-the-fly

    bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
    bool ignore_eos        = false; // ignore generated EOS tokens
    bool instruct          = false; // instruction mode (used for Alpaca models)
    bool logits_all        = false; // return logits for all tokens in the batch
    bool use_mmap          = true;  // use mmap for faster loads
    bool use_mlock         = false; // use mlock to keep model in memory
    bool numa              = false; // attempt optimizations that help on some NUMA systems
    bool verbose_prompt    = false; // print prompt tokens before generation
    bool infill            = false; // use infill mode

    // multimodal models (see examples/llava)
    std::string mmproj = ""; // path to multimodal projector
    std::string image = ""; // path to an image file
};

bool gpt_params_parse(int argc, char ** argv, gpt_params & params);

void gpt_print_usage(int argc, char ** argv, const gpt_params & params);

std::string get_system_info(const gpt_params & params);

std::string gpt_random_prompt(std::mt19937 & rng);

void process_escapes(std::string& input);

//
// Model utils
//

std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
struct llama_model_params   llama_model_params_from_gpt_params(const gpt_params & params);
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);

//
// Vocab utils
//

// tokenizes a string into a vector of tokens
// should work similar to Python's `tokenizer.encode`
std::vector<llama_token> llama_tokenize(
  const struct llama_context * ctx,
           const std::string & text,
                        bool   add_bos);

std::vector<llama_token> llama_tokenize(
    const struct llama_model * model,
           const std::string & text,
                        bool   add_bos);

// tokenizes a token into a piece
// should work similar to Python's `tokenizer.id_to_piece`
std::string llama_token_to_piece(
        const struct llama_context * ctx,
                       llama_token   token);

// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function
//       that takes into account the tokenizer type and decides how to handle the leading space
//
// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
// removes the leading space from the first non-BOS token
std::string llama_detokenize_spm(
                         llama_context * ctx,
        const std::vector<llama_token> & tokens);

// detokenizes a vector of tokens into a string
// should work similar to Python's `tokenizer.decode`
std::string llama_detokenize_bpe(
                         llama_context * ctx,
        const std::vector<llama_token> & tokens);

//
// YAML utils
//

bool create_directory_with_parents(const std::string & path);
void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
std::string get_sortable_timestamp();

void dump_non_result_info_yaml(
    FILE * stream, const gpt_params & params, const llama_context * lctx,
    const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
