#pragma once

#include "llama-batch.h"

#include "llama-graph.h"

#include "llama-kv-cells.h"

#include "llama-memory.h"

#include <unordered_map>

#include <vector>

struct llama_cparams;

struct llama_hparams;

struct llama_model;

struct llama_context;

//

// llama_kv_cache

//

class llama_kv_cache : public llama_memory_i {

public:

    struct stream_copy_info {

        bool empty() const {

            assert(ssrc.size() == sdst.size());

            return ssrc.empty();

        }

        std::vector<uint32_t> ssrc;

        std::vector<uint32_t> sdst;

    };

    // for each ubatch, create a slot_info that contains information about where the ubatch should be inserted in the

    //   KV cells. for example, cell indices for each token, such that: token[i] -> goes to cells[idxs[i]]

    struct slot_info {

        // data for ggml_set_rows

        using idx_vec_t = std::vector<uint32_t>;

        // number of streams: ns = s1 - s0 + 1

        uint32_t s0;

        uint32_t s1;

        std::vector<llama_seq_id> strm; // [ns]

        std::vector<idx_vec_t>    idxs; // [ns]

        uint32_t head() const {

            GGML_ASSERT(idxs.size() == 1);

            GGML_ASSERT(!idxs[0].empty());

            return idxs[0][0];

        }

        void resize(size_t n) {

            strm.resize(n);

            idxs.resize(n);

        }

        size_t size() const {

            GGML_ASSERT(idxs.size() == strm.size());

            GGML_ASSERT(!idxs.empty());

            return idxs[0].size();

        }

        size_t n_stream() const {

            return strm.size();

        }

        bool empty() const {

            return idxs.empty();

        }

        void clear() {

            idxs.clear();

        }

    };

    using slot_info_vec_t = std::vector<slot_info>;

    llama_kv_cache(

            const llama_model & model,

                    ggml_type   type_k,

                    ggml_type   type_v,

                         bool   v_trans,

                         bool   offload,

                         bool   unified,

                     uint32_t   kv_size,

                     uint32_t   n_seq_max,

                     uint32_t   n_pad,

                     uint32_t   n_swa,

               llama_swa_type   swa_type,

        const layer_filter_cb & filter,

        const  layer_reuse_cb & reuse);

    ~llama_kv_cache() = default;

    //

    // llama_memory_i

    //

    llama_memory_context_ptr init_batch(

            llama_batch_allocr & balloc,

            uint32_t n_ubatch,

            bool embd_all) override;

    llama_memory_context_ptr init_full() override;

    llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;

    bool get_can_shift() const override;

    void clear(bool data) override;

    bool seq_rm  (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1) override;

    void seq_cp  (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) override;

    void seq_keep(llama_seq_id seq_id)                                                          override;

    void seq_add (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, llama_pos shift) override;

    void seq_div (llama_seq_id seq_id,                              llama_pos p0, llama_pos p1, int d) override;

    llama_pos seq_pos_min(llama_seq_id seq_id) const override;

    llama_pos seq_pos_max(llama_seq_id seq_id) const override;

    std::map<ggml_backend_buffer_type_t, size_t> memory_breakdown() const override;

    // state write/load

    void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) const override;

    void state_read (llama_io_read_i  & io, llama_seq_id seq_id = -1, llama_state_seq_flags flags = 0) override;

    //

    // llama_kv_cache specific API

    //

    uint32_t get_size()     const;

    uint32_t get_n_stream() const;

    bool get_has_shift() const;

    //

    // graph_build API

    //

    uint32_t get_n_kv(const slot_info & sinfo) const;

    // get views of the current state of the cache

    ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;

    ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;

    // store k_cur and v_cur in the cache based on the provided head location

    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const;

    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const;

    //

    // preparation API

    //

    // find places for the provided ubatches in the cache, returns the slot infos

    // return empty vector on failure

    slot_info_vec_t prepare(const std::vector<llama_ubatch> & ubatches);

    bool update(llama_context * lctx, bool do_shift, const stream_copy_info & sc_info);

    // find a slot of kv cells that can hold the ubatch

    // if cont == true, then the slot must be continuous

    // return empty slot_info on failure

    slot_info find_slot(const llama_ubatch & ubatch, bool cont) const;

    // emplace the ubatch context into slot: [sinfo.idxs[0...ubatch.n_tokens - 1]]

    void apply_ubatch(const slot_info & sinfo, const llama_ubatch & ubatch);

    //

    // input API

    //

    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;

    ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;

    void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;

    void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const;

    void set_input_k_shift(ggml_tensor * dst) const;

    void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;

    void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;

private:

    const llama_model & model;

    const llama_hparams & hparams;

    struct kv_layer {

        // layer index in the model

        // note: can be different from the layer index in the KV cache

        uint32_t il;

        ggml_tensor * k;

        ggml_tensor * v;

        std::vector<ggml_tensor *> k_stream;

        std::vector<ggml_tensor *> v_stream;

    };

    bool v_trans = true;  // the value tensor is transposed

    const uint32_t n_seq_max = 1;

    const uint32_t n_stream  = 1;

    // required padding

    const uint32_t n_pad = 1;

    // SWA

    const uint32_t n_swa = 0;

    // env: LLAMA_KV_CACHE_DEBUG

    int debug = 0;

    // this is the SWA type of the cache - not to be confused with the model SWA type

    const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;

    // ggml contexts for the KV cache along with the allocated backend buffers:

    std::vector<std::pair<ggml_context_ptr, ggml_backend_buffer_ptr>> ctxs_bufs;

    // the current index from where we start searching for a free slot in the ring buffer of KV cells (see find_slot())

    // note: this is not part of the KV state and it's only used to speed-up the find_slot() method

    std::vector<uint32_t> v_heads;

    std::vector<llama_kv_cells> v_cells;

    // maps from a sequence id to a stream id

    std::vector<uint32_t> seq_to_stream;

    // pending stream copies that will be applied during the next update

    stream_copy_info sc_info;

    std::vector<kv_layer> layers;

    // model layer id -> KV cache layer id

    std::unordered_map<int32_t, int32_t> map_layer_ids;

    size_t total_size() const;

    size_t size_k_bytes() const;

    size_t size_v_bytes() const;

    bool is_masked_swa(llama_pos p0, llama_pos p1) const;

    ggml_tensor * build_rope_shift(

            const llama_cparams & cparams,

                   ggml_context * ctx,

                    ggml_tensor * cur,

                    ggml_tensor * shift,

                    ggml_tensor * factors,

                          float   freq_base,

                          float   freq_scale) const;

    ggml_cgraph * build_graph_shift(

               llm_graph_result * res,

                  llama_context * lctx) const;

    struct cell_ranges_t {

        uint32_t strm;

        std::vector<std::pair<uint32_t, uint32_t>> data; // ranges, from inclusive, to exclusive

    };

    void state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id = -1) const;

    void state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const;

    bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id = -1);

    bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count);

};

class llama_kv_cache_context : public llama_memory_context_i {

public:

    // some shorthands

    using slot_info_vec_t  = llama_kv_cache::slot_info_vec_t;

    using stream_copy_info = llama_kv_cache::stream_copy_info;

    // used for errors

    llama_kv_cache_context(llama_memory_status status);

    // used to create a full-cache context

    llama_kv_cache_context(

            llama_kv_cache * kv);

    // used to create an update context

    llama_kv_cache_context(

            llama_kv_cache * kv,

            llama_context * lctx,

            bool do_shift,

            stream_copy_info sc_info);

    // used to create a batch procesing context from a batch

    llama_kv_cache_context(

            llama_kv_cache * kv,

            slot_info_vec_t sinfos,

            std::vector<llama_ubatch> ubatches);

    virtual ~llama_kv_cache_context();

    //

    // llama_memory_context_i

    //

    bool next()  override;

    bool apply() override;

    llama_memory_status  get_status() const override;

    const llama_ubatch & get_ubatch() const override;

    //

    // llama_kv_cache_context specific API

    //

    uint32_t get_n_kv() const;

    // get views of the current state of the cache

    ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;

    ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;

    // store k_cur and v_cur in the cache based on the provided head location

    // note: the heads in k_cur and v_cur should be layed out contiguously in memory

    //   - k_cur  [n_embd_head_k, n_head_k, n_tokens]

    //   - k_idxs [n_tokens]

    //   - v_cur  [n_embd_head_v, n_head_v, n_tokens]

    //   - v_idxs [n_tokens] or [n_tokens*n_embd_v_gqa] depending if V cache is transposed

    ggml_tensor * cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il) const;

    ggml_tensor * cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il) const;

    // create destination indices for each head of the current batch for where it would be written in the KV cache

    // the indices address the global KV cache (not per stream) - this is not relevant for the user of this API, but

    //   helps understand the implementation logic of cpy_k and cpy_v

    ggml_tensor * build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;

    ggml_tensor * build_input_v_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const;

    void set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;

    void set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch) const;

    void set_input_k_shift   (ggml_tensor * dst) const;

    void set_input_kq_mask   (ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const;

    void set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const;

private:

    llama_memory_status status;

    llama_kv_cache * kv;

    llama_context * lctx;

    //

    // update context

    //

    bool do_shift = false;

    stream_copy_info sc_info;

    //

    // batch processing context

    //

    // the index of the cur ubatch to process

    size_t i_cur = 0;

    slot_info_vec_t sinfos;

    std::vector<llama_ubatch> ubatches;

    //

    // data needed for building the compute graph for the current ubatch:

    //

    // a heuristic, to avoid attending the full cache if it is not yet utilized

    // as the cache gets filled, the benefit from this heuristic disappears

    int32_t n_kv;

};