#pragma once

#include "llama.h"

#include <cstdint>

#include <vector>

#define LLAMA_MAX_SEQ 256

struct llama_cparams {

    uint32_t n_ctx;           // context size used during inference

    uint32_t n_ctx_seq;       // context for a single sequence

    uint32_t n_batch;

    uint32_t n_ubatch;

    uint32_t n_seq_max;

    uint32_t n_rs_seq;        // number of recurrent-state snapshots per seq for rollback

    uint32_t n_outputs_max;   // max outputs supported by the context

    int32_t  n_threads;       // number of threads to use for generation

    int32_t  n_threads_batch; // number of threads to use for batch processing

    int32_t  nextn_layer_offset = 0;

    float rope_freq_base;

    float rope_freq_scale;

    uint32_t n_ctx_orig_yarn;

    // These hyperparameters are not exposed in GGUF, because all

    // existing YaRN models use the same values for them.

    float yarn_ext_factor;

    float yarn_attn_factor;

    float yarn_beta_fast;

    float yarn_beta_slow;

    bool embeddings;

    bool embeddings_nextn;        // also extract the hidden state before the final output norm

    bool embeddings_nextn_masked; // extract for only rows where batch.logits != 0

    bool causal_attn;

    bool offload_kqv;

    bool flash_attn;

    bool auto_fa;

    bool fused_gdn_ar;       // use fused gated delta net (autoregressive)

    bool fused_gdn_ch;       // use fused gated delta net (chunked)

    bool auto_fgdn;

    bool fused_lid;          // use fused lightning indexer

    bool auto_flid;

    bool no_perf;

    bool warmup;             // TODO: remove [TAG_LLAMA_GRAPH_NO_WARMUP]

    bool op_offload;

    bool kv_unified;

    bool pipeline_parallel;

    std::vector<bool> embeddings_layer_inp; // [n_layer()] extract input embeddings for layer

    enum llama_context_type ctx_type;

    enum llama_pooling_type pooling_type;

    ggml_backend_sched_eval_callback cb_eval;

    void * cb_eval_user_data;

    llama_context * ctx_other;

};