#pragma once

#include "llama.h"

#include "llama-cparams.h"

#include <array>

#include <vector>

#include <set>

#include <bitset>

#include <memory>

#include <unordered_map>

// keep this struct lightweight

struct llama_ubatch {

    bool equal_seqs() const {

        return b_equal_seqs != 0;

    }

    // typical for M-RoPE cases:

    //   0 - sequential position of the tokens/embeddings in the sequence

    //   1 - y position in the image

    //   2 - x position in the image

    //   3 - other

    bool is_pos_2d() const {

        // TODO @ngxson : we may need to check for model arch when more models use >1 positions

        return n_pos >= 3;

    }

    uint32_t b_equal_seqs; // note: this is a boolean, but we use an int32_t for alignment

                           //       otherwise address sanitizer complains

    // TODO: whole_seqs for embeddings?

    uint32_t n_tokens;     // total tokens (n_seq_tokens * n_seqs)

    uint32_t n_seq_tokens; // tokens per sequence set

    uint32_t n_seqs;       // sequence sets in the ubatch

    uint32_t n_seqs_unq;   // unique sequence ids in the ubatch

    uint32_t n_pos;        // number of position inputs for each token/embedding

    // seq_id_unq: unique sequence ids in the ubatch

    // seq_idx:    indices of the unique sequence ids in the ubatch in [0, n_seqs_unq)

    //             used for extracting sequence pooled embeddings

    //                          // size               | idx | val

    llama_token  *  token;      // [n_tokens]         | i   | id, token

    float        *  embd;       // [n_embd, n_tokens] | i   | embd

    llama_pos    *  pos;        // [n_tokens*n_pos]   | i   | pos

    int32_t      *  n_seq_id;   // [n_tokens]         | i   | -

    llama_seq_id ** seq_id;     // [n_tokens]         | s   | s0, s1, seq_id

    llama_seq_id *  seq_id_unq; // [n_seqs_unq]       | s   | seq_id

    int32_t      *  seq_idx;    // [LLAMA_MAX_SEQ]    | -   | seq_idx

    int8_t       *  output;     // [n_tokens]         | i   | -

    struct data_t {

        std::vector<llama_token>    token;

        std::vector<float>          embd;

        std::vector<llama_pos>      pos;

        std::vector<int32_t>        n_seq_id;

        std::vector<llama_seq_id *> seq_id;      // these point into the seq_id_data below

        std::vector<llama_seq_id>   seq_id_unq;

        std::vector<int32_t>        seq_idx;

        std::vector<int8_t>         output;

        std::vector<llama_seq_id> seq_id_data;

    };

    // the llama_ubatch pointers above point to this data if set. otherwise - point to external non-owning data

    std::shared_ptr<data_t> data;

};

// a helper for sanitizing, fulfilling and splitting a batch

class llama_batch_allocr {

public:

    llama_batch_allocr(uint32_t n_pos_per_embd);

    // sanitize and auto-gen missing data in the input batch

    // memory is optional. if provided will be used to check for sequence continuity and to determine the positions

    bool init(

            const llama_batch & batch_inp,

            const llama_vocab & vocab,

            const llama_memory_i * memory,

            uint32_t n_embd,

            uint32_t n_seq_max,

            bool output_all);

    const llama_batch & get_batch() const;

    uint32_t get_n_tokens()  const;

    uint32_t get_n_outputs() const;

    uint32_t get_n_used()    const;

    // the array of output indices in the order they were encountered during the ubatch splitting

    std::vector<int32_t> & get_out_ids();

    // min/max positions of each sequence in the current ubatch

    llama_pos seq_pos_min(llama_seq_id seq_id) const;

    llama_pos seq_pos_max(llama_seq_id seq_id) const;

    // call once before splitting the batch to reset the internal state

    void split_reset();

    // simple split, unknown number of sequence sets of unequal lengths

    llama_ubatch split_simple(uint32_t n_ubatch);

    // make ubatches of equal-length sequences sets

    // if sequential == true, the tokens in the ubatch will have increasing sequential sequence ids

    // n_keep_tail = minimum trailing tokens of a seq that must land in the same ubatch

    llama_ubatch split_equal(uint32_t n_ubatch, bool sequential, uint32_t n_keep_tail);

    // sequence-set-wise split - each ubatch contains a single sequence-set

    llama_ubatch split_seq(uint32_t n_ubatch);

    // a helper method for creating a well-defined ubatch of tokens

    // TODO: support embeddings if needed in the future

    llama_ubatch ubatch_reserve(uint32_t n_seq_tokens, uint32_t n_seqs);

private:

    void clear();

    // create the next ubatch based on the provided batch indices (idxs) and the number of sequence sets (n_seqs)

    // return llama_ubatch.n_tokens == 0 if the entire batch was consumed

    llama_ubatch ubatch_add(const std::vector<int32_t> & idxs, uint32_t n_seqs, bool equal_seqs);

    // for debugging, start with LLAMA_BATCH_DEBUG=2

    void ubatch_print(const llama_ubatch & ubatch, int debug);

    llama_batch batch;

    // only for debugging purposes

    const llama_vocab * vocab;

    // TODO: this is more of a temporary solution until we have a better way to handle multiple positions per token/embd

    //       ref: https://github.com/ggml-org/llama.cpp/issues/13694#issuecomment-2983871762

    const uint32_t n_pos_per_embd;

    uint32_t n_embd;

    uint32_t n_seq_max;

    uint32_t n_outputs;

    std::array<llama_seq_id, 1> seq_id_0 = {{ 0 }}; // default sequence id

    std::vector<llama_pos>      pos;

    std::vector<int32_t>        n_seq_id;

    std::vector<llama_seq_id *> seq_id;

    std::vector<llama_seq_id>   seq_id_unq;

    std::vector<int32_t>        seq_idx;

    std::vector<int8_t>         output;

    using pos_set_t = std::set<llama_pos>;

    using seq_cpl_t = std::vector<bool>;

    // helper flag to quickly determine if there are any coupled sequences in the batch

    bool has_cpl = false;

    std::vector<pos_set_t> seq_pos; // seq_pos[s]: the set of positions in sequence s

    std::vector<seq_cpl_t> seq_cpl; // seq_cpl[s0][s1]: if sequence s0 is coupled to sequence s1

    using idx_vec_t = std::vector<int32_t>;

    using seq_set_t = std::bitset<LLAMA_MAX_SEQ>;

    std::vector<seq_set_t> seq_set; // seq_set[i]: the sequence set of token i

    std::unordered_map<seq_set_t, idx_vec_t> seq_set_map; // the indices at which the sequence set appears

    // batch indices of the output

    std::vector<int32_t> out_ids;

    uint32_t n_used;

    // used[i] indicates if token i has already been used in a previous ubatch

    std::vector<bool> used;

    int debug;

};