#pragma once

#include "llama.h"

#include <array>

#include <cassert>

// bump if necessary

#define LLAMA_MAX_LAYERS  512

#define LLAMA_MAX_EXPERTS 512 // Qwen3 Next

enum llama_expert_gating_func_type {

    LLAMA_EXPERT_GATING_FUNC_TYPE_NONE           = 0,

    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX        = 1,

    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID        = 2,

    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT = 3, // applied to the router weights instead of the logits

    LLAMA_EXPERT_GATING_FUNC_TYPE_SQRT_SOFTPLUS  = 4,

};

enum llama_swa_type {

    LLAMA_SWA_TYPE_NONE      = 0,

    LLAMA_SWA_TYPE_STANDARD  = 1,

    LLAMA_SWA_TYPE_CHUNKED   = 2,

    LLAMA_SWA_TYPE_SYMMETRIC = 3,

};

// forward declaration; full definition in llama-graph.h

enum llm_ffn_op_type : int;

struct llama_hparams_posnet {

    uint32_t n_embd;

    uint32_t n_layer;

};

struct llama_hparams_convnext {

    uint32_t n_embd;

    uint32_t n_layer;

};

struct llama_hparams {

    // note: use the `_impl` suffix to avoid name conflict between members and getters

    //       for example: n_embd_out() vs n_embd_out_impl

    bool vocab_only;

    bool no_alloc;

    bool rope_finetuned;

    bool use_par_res;

    bool swin_norm;

    bool norm_before_residual = false;

    uint32_t n_ctx_train; // context size the model was trained on

    uint32_t n_embd;

    uint32_t n_layer_all;

    uint32_t n_layer_nextn = 0;

    uint32_t n_expert = 0;

    uint32_t n_expert_used = 0;

    uint32_t n_rel_attn_bkts = 0;

    // TODO: this needs to be reworked

    int32_t  n_layer_kv_from_start = -1; // if non-negative, the first n_layer_kv_from_start layers have KV cache

    // different head size for full_attention and SWA layers

    uint32_t n_embd_head_k_full; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads

    uint32_t n_embd_head_v_full; // dimension of values (d_v) aka n_embd_head

    uint32_t n_embd_head_k_swa;

    uint32_t n_embd_head_v_swa;

    // different RoPE dimensions for full_attention and SWA layers

    uint32_t n_rot_full;

    uint32_t n_rot_swa;

    // note: deepseek2 using MLA converts into MQA with larger heads, then decompresses to MHA

    uint32_t n_embd_head_k_mla_impl = 0;

    uint32_t n_embd_head_v_mla_impl = 0;

    // for WavTokenizer

    struct llama_hparams_posnet   posnet;

    struct llama_hparams_convnext convnext;

    uint32_t n_shortconv_l_cache  = 0;

    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;

    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;

    std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;

    uint32_t n_layer_dense_lead = 0;

    uint32_t n_lora_q           = 0;

    uint32_t n_lora_kv          = 0;

    uint32_t n_ff_exp           = 0;

    uint32_t n_ff_shexp         = 0;

    uint32_t n_ff_chexp         = 0;

    uint32_t n_expert_shared    = 0;

    uint32_t n_norm_groups      = 0;

    uint32_t n_expert_groups    = 0;

    uint32_t n_group_used       = 0;

    uint32_t n_group_experts    = 0;

    float    expert_group_scale   = 0.05f;

    float    expert_weights_scale = 0.0f;

    bool     expert_weights_norm  = false;

    uint32_t expert_gating_func   = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;

    uint32_t moe_every_n_layers   = 0;

    uint32_t moe_latent_size      = 0;

    float f_norm_eps;

    float f_norm_rms_eps;

    float f_norm_group_eps;

    float f_attn_logit_softcapping   = 50.0f;

    float f_router_logit_softcapping = 30.0f;

    float f_final_logit_softcapping  = 30.0f;

    // for RWKV

    uint32_t rescale_every_n_layers = 0;

    uint32_t time_mix_extra_dim     = 0;

    uint32_t time_decay_extra_dim   = 0;

    uint32_t wkv_head_size          = 0;

    uint32_t token_shift_count      = 2;

    uint32_t n_lora_decay           = 0;

    uint32_t n_lora_iclr            = 0;

    uint32_t n_lora_value_res_mix   = 0;

    uint32_t n_lora_gate            = 0;

    float    rope_attn_factor = 1.0f;

    float    rope_freq_base_train;

    float    rope_freq_base_train_swa  = 10000.0f;

    float    rope_freq_scale_train;

    float    rope_freq_scale_train_swa = 1.0f;

    float    rope_scaling_alpha        = 0.0f;  // NTK-aware alpha for XDRoPE

    uint32_t n_ctx_orig_yarn;

    float    rope_yarn_log_mul = 0.0f;

    float    yarn_ext_factor  = -1.0f;

    float    yarn_attn_factor =  1.0f;

    float    yarn_beta_fast   = 32.0f;

    float    yarn_beta_slow   =  1.0f;

    std::array<int, 4> rope_sections;

    // Sliding Window Attention (SWA)

    llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;

    // the size of the sliding window (0 - no SWA)

    uint32_t n_swa = 0;

    // if is_swa_impl[il] == 1, then layer il is SWA

    // if is_swa_impl[il] == 0, then layer il is dense (i.e. non-SWA)

    // by default, all layers are dense

    // note: using uint32_t type for compatibility reason

    std::array<uint32_t, LLAMA_MAX_LAYERS> is_swa_impl;

    // for hybrid state space models

    std::array<uint32_t, LLAMA_MAX_LAYERS> is_recr_impl;

    // for State Space Models

    uint32_t ssm_d_conv  = 0;

    uint32_t ssm_d_inner = 0;

    uint32_t ssm_d_state = 0;

    uint32_t ssm_dt_rank = 0;

    uint32_t ssm_n_group = 0;

    // for Kimi Linear KDA

    uint32_t n_embd_head_kda = 0;

    bool ssm_dt_b_c_rms = false;

    float f_clamp_kqv      = 0.0f;

    float f_max_alibi_bias = 0.0f;

    float f_logit_scale    = 0.0f;

    // Additional scale factors (Granite/Granite MoE)

    float f_residual_scale  = 0.0f;

    float f_embedding_scale = 0.0f;

    float f_attention_scale = 0.0f;

    // grok-2

    float    f_attn_out_scale = 0.0f;

    uint32_t attn_temp_length = 0;

    float    f_attn_value_scale = 0.0f;

    bool causal_attn   = true;

    bool use_alibi     = false;

    bool attn_soft_cap = false;

    bool use_kq_norm   = false;

    // for Classifiers

    uint32_t n_cls_out = 1;

    // input embedding dimension (0 = use n_embd)

    uint32_t n_embd_inp_impl = 0;

    // encoder input embedding dimension (0 = use n_embd_inp())

    // e.g. the eagle3 encoder fuses target_layers * target_hidden features

    uint32_t n_embd_inp_enc_impl = 0;

    // output embedding dimension (0 = use n_embd)

    uint32_t n_embd_out_impl = 0;

    // llama4 smallthinker

    uint32_t n_moe_layer_step        = 0;

    uint32_t n_no_rope_layer_step    = 4;

    uint32_t n_attn_temp_floor_scale = 0;

    float    f_attn_temp_scale       = 0.0f;

    float    f_attn_temp_offset      = 0.0f; // offset position index

    // gemma3n altup

    uint32_t n_altup      = 4; // altup_num_inputs

    uint32_t i_altup_act  = 0; // altup_active_idx

    uint32_t laurel_rank  = 64;

    uint32_t n_embd_altup = 256;

    // needed for sentence-transformers dense layers

    uint32_t dense_2_feat_in  = 0;  // in_features of the 2_Dense

    uint32_t dense_2_feat_out = 0;  // out_features of the 2_Dense

    uint32_t dense_3_feat_in  = 0;  // in_features of the 3_Dense

    uint32_t dense_3_feat_out = 0;  // out_features of the 3_Dense

    // xIELU

    std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_n;

    std::array<float, LLAMA_MAX_LAYERS> xielu_alpha_p;

    std::array<float, LLAMA_MAX_LAYERS> xielu_beta;

    std::array<float, LLAMA_MAX_LAYERS> xielu_eps;

    // DSA (deepseek sparse attention)

    uint32_t indexer_n_head    = 0;

    uint32_t indexer_head_size = 0;

    uint32_t indexer_top_k     = 0;

    // DeepSeek-V4

    uint32_t dsv4_o_group_count        = 0;

    uint32_t dsv4_o_lora_rank          = 0;

    uint32_t dsv4_hc_mult              = 0;

    uint32_t dsv4_hc_sinkhorn_iters    = 0;

    uint32_t dsv4_hash_layer_count     = 0;

    float    dsv4_compress_rope_base   = 0.0f;

    float    dsv4_hc_eps               = 0.0f;

    std::array<uint32_t, LLAMA_MAX_LAYERS> dsv4_compress_ratios;

    // qwen3vl deepstack

    // When parsed from GGUF, this implies the first N layers consume the first

    // N deepstack embeddings. Use deepstack_mapping_arr if you need a more

    // complex mapping. If using deepstack_mapping_arr, also make sure to set

    // n_deepstack_layers to the number of unique deepstack layers so that

    // n_embd_imp is accurate (see granite.cpp).

    // TODO: can be expressed via the `new n_embd_inp_impl` and remove this param

    uint32_t n_deepstack_layers = 0;

    // deepstack layer array (Granite4 Vision)

    // -1  => no deepstack

    // >=0 => input embedding index for deepstack injection

    std::array<int32_t, LLAMA_MAX_LAYERS> deepstack_mapping_arr;

    // gemma4 per-layer embedding

    uint32_t n_embd_per_layer = 0;

    // needed by encoder-decoder models (e.g. T5, FLAN-T5)

    // ref: https://github.com/ggml-org/llama.cpp/pull/8141

    llama_token dec_start_token_id = LLAMA_TOKEN_NULL;

    uint32_t    dec_n_layer        = 0;

    enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;

    enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;

    enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;

    // Resolved FFN gated activation flavor for archs that read

    // `<arch>.hidden_activation` from the GGUF (e.g. ModernBert derivatives).

    // Defaults to LLM_FFN_NONE (sentinel = 0); the mapping from the GGUF

    // string to a real op is done at hparam-load time via

    // llm_ffn_op_type_from_string() in llama-model.cpp, mirroring how

    // rope_scaling_type_train is handled.

    enum llm_ffn_op_type llm_ffn_op;

    // Step35: optional per-layer clamps for (Swi)GLU

    std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_exp; // clamping for expert FFN

    std::array<float, LLAMA_MAX_LAYERS> swiglu_clamp_shexp; // shared expert

    // this value n_pattern means that every nth layer is dense (i.e. non-SWA)

    // dense_first means whether the pattern is start with a dense layer

    // note that if n_pattern == 0, all layers are SWA

    //           if n_pattern == 1, all layers are dense

    // example 1: n_pattern = 3, dense_first = false

    //   il == 0: swa

    //   il == 1: swa

    //   il == 2: dense

    //   il == 3: swa

    //   il == 4: swa

    //   il == 5: dense

    //   il == 6: swa

    //   etc ...

    // example 2: n_pattern = 2, dense_first = true

    //   il == 0: dense

    //   il == 1: swa

    //   il == 2: dense

    //   il == 3: swa

    //   etc ...

    void set_swa_pattern(uint32_t n_pattern, bool dense_first = false);

    // return true if one of the layers is SWA

    bool is_swa_any() const;

    bool is_swa(uint32_t il) const;

    void set_recr_pattern(uint32_t n_pattern, bool dense_first = false);

    // whether or not the given layer is recurrent (for hybrid models)

    bool is_recr(uint32_t il) const;

    uint32_t n_head(uint32_t il = 0) const;

    uint32_t n_head_kv(uint32_t il = 0) const;

    uint32_t n_ff(uint32_t il = 0) const;

    uint32_t n_gqa(uint32_t il = 0) const;

    uint32_t n_rot(uint32_t il = 0) const;

    // dimension of main + auxiliary input embeddings

    uint32_t n_embd_inp() const;

    // dimension of the encoder input embeddings

    uint32_t n_embd_inp_enc() const;

    // dimension of output embeddings

    uint32_t n_embd_out() const;

    // dimension of key/value embeddings for each head (per layer)

    uint32_t n_embd_head_k(uint32_t il = 0) const;

    uint32_t n_embd_head_v(uint32_t il = 0) const;

    // dimension of key embeddings across all k-v heads

    uint32_t n_embd_k_gqa(uint32_t il = 0) const;

    // dimension of value embeddings across all k-v heads

    uint32_t n_embd_v_gqa(uint32_t il = 0) const;

    // true if any layer has a different n_embd_k_gqa/n_embd_v_gqa

    bool is_n_embd_k_gqa_variable() const;

    bool is_n_embd_v_gqa_variable() const;

    // return the maximum n_embd_k_gqa/n_embd_v_gqa across all layers

    uint32_t n_embd_k_gqa_max() const;

    uint32_t n_embd_v_gqa_max() const;

    // dimension of the rolling state embeddings

    // corresponds to Mamba's conv_states size or RWKV's token_shift states size

    uint32_t n_embd_r() const;

    // dimension of the recurrent state embeddings

    uint32_t n_embd_s() const;

    uint32_t n_pos_per_embd() const;

    // note: currently only support if either all or none of the layers are MLA

    bool is_mla() const;

    uint32_t n_embd_head_k_mla() const;

    uint32_t n_embd_head_v_mla() const;

    bool has_kv(uint32_t il) const;

    // number of effective layers (excludes nextn layers)

    uint32_t n_layer() const;

    // note that this function uses different SWA parameters from those in the hparams

    // note: inlined on purpose for performance reasons

    // TODO: think of a better place for this function

    // TODO: pack the SWA params in a struct?

    static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1) {

        assert(p0 >= 0 && p1 >= 0);

        switch (swa_type) {

            case LLAMA_SWA_TYPE_NONE:

                {

                } break;

            case LLAMA_SWA_TYPE_STANDARD:

                {

                    if (p1 - p0 >= (int32_t) n_swa) {

                        return true;

                    }

                } break;

            case LLAMA_SWA_TYPE_CHUNKED:

                {

                    const llama_pos pos_chunk_start = (p1 / n_swa) * n_swa;

                    if (p0 < pos_chunk_start) {

                        return true;

                    }

                } break;

            case LLAMA_SWA_TYPE_SYMMETRIC:

                {

                    const int32_t half_n_swa = (int32_t) n_swa / 2;

                    const int32_t pos_diff = p1 - p0;

                    // Mask if outside the symmetric window

                    if (pos_diff < -half_n_swa || pos_diff > half_n_swa) {

                        return true;

                    }

                } break;

        }

        return false;

    }

    bool use_mrope() const;

};

static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");