# Copyright 2026 Modilify
# SPDX-License-Identifier: LicenseRef-Modilify-Open-Model-1.0
"""Compile-friendly Mk1 decoder: static prefix KV + fixed-shape sliding gather."""

from __future__ import annotations

from typing import Any
import time

import mlx.core as mx
import mlx.nn as nn

from mlx_vlm.models.base import scaled_dot_product_attention
from mlx_vlm.models.diffusion_gemma.language import geglu


def make_static_cache(encoder, max_size: int):
    """Fixed-capacity prefix cache so decoder_state has a static key length."""

    return encoder.make_cache(max_size=max(int(max_size), 1))


def _cache_capacity(cache) -> int:
    first = cache[0]
    keys = getattr(first, "keys", None)
    if keys is None:
        return int(getattr(first, "max_size", 0) or 0)
    return int(keys.shape[2])


def _prefix_length(cache) -> int:
    offset = getattr(cache[0], "offset", 0)
    if isinstance(offset, mx.array):
        return int(mx.max(offset).item())
    return int(offset)


def build_decoder_masks(
    *,
    prefix_len: int,
    canvas_length: int,
    cache_capacity: int,
    sliding_window: int,
    batch_size: int = 1,
) -> tuple[mx.array, mx.array]:
    """Boolean SDPA masks with static shapes for full and sliding layers."""

    cache_valid = mx.arange(cache_capacity) < int(prefix_len)
    canvas_valid = mx.ones((canvas_length,), dtype=mx.bool_)
    full_row = mx.concatenate([cache_valid, canvas_valid], axis=0)
    full = mx.broadcast_to(
        full_row.reshape(1, 1, 1, -1),
        (batch_size, 1, canvas_length, cache_capacity + canvas_length),
    )

    window = max(int(sliding_window) - 1, 1)
    slide_k = min(window, cache_capacity)
    if slide_k < cache_capacity:
        raw_idx = mx.arange(slide_k) + (int(prefix_len) - slide_k)
        slide_valid = (raw_idx >= 0) & (raw_idx < int(prefix_len))
    else:
        slide_valid = mx.arange(cache_capacity) < int(prefix_len)
    slide_row = mx.concatenate([slide_valid, canvas_valid], axis=0)
    slide = mx.broadcast_to(
        slide_row.reshape(1, 1, 1, -1),
        (batch_size, 1, canvas_length, slide_k + canvas_length),
    )
    return full, slide


def _gather_window(tensor: mx.array, offset: mx.array, window: int) -> mx.array:
    max_size = tensor.shape[2]
    idx = mx.clip(mx.arange(window) + offset - window, 0, max_size - 1)
    gather = idx.reshape(1, 1, window, 1)
    return mx.take_along_axis(tensor, gather, axis=2)


def _decoder_attention(
    attn: nn.Module,
    x: mx.array,
    mask: mx.array | None,
    cache,
    offset: mx.array,
) -> mx.array:
    batch, length, _ = x.shape
    queries = attn.q_proj(x).reshape(batch, length, attn.n_heads, attn.head_dim)
    queries = attn.q_norm(queries).transpose(0, 2, 1, 3)
    queries = attn.rope(queries, offset=offset)

    keys = attn.k_proj(x).reshape(batch, length, attn.n_kv_heads, attn.head_dim)
    values = (
        attn.v_proj(x).reshape(batch, length, attn.n_kv_heads, attn.head_dim)
        if attn.v_proj is not None
        else keys
    )
    keys = attn.k_norm(keys).transpose(0, 2, 1, 3)
    keys = attn.rope(keys, offset=offset)
    values = attn.v_norm(values).transpose(0, 2, 1, 3)

    encoder_keys, encoder_values = cache.decoder_state
    if attn.is_sliding:
        window = max(int(attn.config.sliding_window) - 1, 1)
        slide_k = min(window, int(encoder_keys.shape[2]))
        if slide_k < int(encoder_keys.shape[2]):
            encoder_keys = _gather_window(encoder_keys, offset, slide_k)
            encoder_values = _gather_window(encoder_values, offset, slide_k)
    keys = mx.concatenate([encoder_keys, keys], axis=2)
    values = mx.concatenate([encoder_values, values], axis=2)
    output = scaled_dot_product_attention(
        queries, keys, values, cache=None, scale=attn.scale, mask=mask
    )
    output = output.transpose(0, 2, 1, 3).reshape(batch, length, -1)
    return attn.o_proj(output)


def _experts_unsorted(experts: nn.Module, x: mx.array, top_k_indices, top_k_weights):
    """Expert FFN without argsort gather, so the decoder graph can compile."""

    x = mx.expand_dims(x, (-2, -3))
    gate_up = experts.gate_up_proj(x, top_k_indices, sorted_indices=False)
    gate = gate_up[..., : experts.hidden_dims]
    up = gate_up[..., experts.hidden_dims :]
    y = experts.down_proj(geglu(gate, up), top_k_indices, sorted_indices=False)
    y = y.squeeze(-2)
    return (y * top_k_weights[..., None]).sum(axis=-2)


def _decoder_layer(
    layer: nn.Module,
    x: mx.array,
    mask: mx.array | None,
    cache,
    offset: mx.array,
) -> mx.array:
    residual = x
    hidden = layer.input_layernorm(x)
    hidden = _decoder_attention(layer.self_attn, hidden, mask, cache, offset)
    hidden = layer.post_attention_layernorm(hidden)
    hidden = residual + hidden

    residual = hidden
    shared = layer.pre_feedforward_layernorm(hidden)
    shared = layer.mlp(shared)
    shared = layer.post_feedforward_layernorm_1(shared)

    flat = residual.reshape(-1, residual.shape[-1])
    top_k_indices, top_k_weights = layer.router(flat)
    routed = layer.pre_feedforward_layernorm_2(flat)
    routed = layer.experts(routed, top_k_indices, top_k_weights)
    routed = routed.reshape(residual.shape)
    routed = layer.post_feedforward_layernorm_2(routed)

    hidden = layer.post_feedforward_layernorm(shared + routed)
    return residual + hidden


def _attn_residual(
    layer: nn.Module,
    x: mx.array,
    mask: mx.array | None,
    cache,
    offset: mx.array,
) -> mx.array:
    residual = x
    hidden = layer.input_layernorm(x)
    hidden = _decoder_attention(layer.self_attn, hidden, mask, cache, offset)
    hidden = layer.post_attention_layernorm(hidden)
    return residual + hidden


def _ffn_residual(layer: nn.Module, hidden: mx.array) -> mx.array:
    residual = hidden
    shared = layer.pre_feedforward_layernorm(hidden)
    shared = layer.mlp(shared)
    shared = layer.post_feedforward_layernorm_1(shared)

    flat = residual.reshape(-1, residual.shape[-1])
    top_k_indices, top_k_weights = layer.router(flat)
    routed = layer.pre_feedforward_layernorm_2(flat)
    routed = layer.experts(routed, top_k_indices, top_k_weights)
    routed = routed.reshape(residual.shape)
    routed = layer.post_feedforward_layernorm_2(routed)

    hidden = layer.post_feedforward_layernorm(shared + routed)
    return (residual + hidden) * layer.layer_scalar


def make_compiled_attn_layers(decoder: nn.Module, cache) -> list:
    """Compile attention residuals only. Expert FFNs stay eager."""

    compiled = []
    for layer, layer_cache in zip(decoder.layers, cache):
        def _fn(x, offset, mask, _layer=layer, _cache=layer_cache):
            return _attn_residual(_layer, x, mask, _cache, offset)

        compiled.append(mx.compile(_fn, shapeless=True))
    return compiled


def decoder_hidden_states(
    decoder: nn.Module,
    canvas_ids: mx.array,
    latent_context: mx.array,
    cache,
    offset: mx.array,
    full_mask: mx.array,
    slide_mask: mx.array,
    compiled_attn_layers=None,
    profiler=None,
) -> mx.array:
    hidden = decoder._embed_canvas(
        canvas_ids,
        self_conditioning_embeddings=latent_context,
    )
    if profiler is None:
        for layer, layer_cache in zip(decoder.layers, cache):
            mask = slide_mask if layer.layer_type == "sliding_attention" else full_mask
            hidden = _decoder_layer(layer, hidden, mask, layer_cache, offset)
            hidden = hidden * layer.layer_scalar
        return decoder.norm(hidden)

    attn_acc = 0.0
    moe_acc = 0.0
    for layer, layer_cache in zip(decoder.layers, cache):
        mask = slide_mask if layer.layer_type == "sliding_attention" else full_mask
        mx.eval(hidden)
        started = time.perf_counter()
        hidden = _attn_residual(layer, hidden, mask, layer_cache, offset)
        mx.eval(hidden)
        attn_acc += time.perf_counter() - started
        started = time.perf_counter()
        hidden = _ffn_residual(layer, hidden)
        mx.eval(hidden)
        moe_acc += time.perf_counter() - started
    profiler.add("attn", attn_acc)
    profiler.add("moe", moe_acc)
    return decoder.norm(hidden)


def make_compiled_decoder(
    decoder: nn.Module,
    cache,
    softcap: float,
):
    """Compile canvas + latent-context → (logits, hidden)."""

    def _step(
        canvas_ids: mx.array,
        latent_context: mx.array,
        offset: mx.array,
        full_mask: mx.array,
        slide_mask: mx.array,
    ):
        hidden = decoder_hidden_states(
            decoder,
            canvas_ids,
            latent_context,
            cache,
            offset,
            full_mask,
            slide_mask,
        )
        logits = decoder.embed_tokens.as_linear(hidden)
        logits = mx.tanh(logits.astype(mx.float32) / softcap) * softcap
        return logits, hidden

    return mx.compile(_step, shapeless=True)


def quantize_experts(model: nn.Module, *, bits: int = 8, group_size: int = 64) -> None:
    """Quantize MoE expert projections only. Attention / embeddings stay bf16."""

    if bits >= 16:
        return

    def predicate(path: str, module: nn.Module):
        if "experts" not in path:
            return False
        if not hasattr(module, "to_quantized"):
            return False
        if not (path.endswith("gate_up_proj") or path.endswith("down_proj")):
            return False
        return {"group_size": group_size, "bits": int(bits), "mode": "affine"}

    print(f"[mk1] quantizing experts to {bits}-bit", flush=True)
    before = sum(arr.nbytes for _, arr in model.parameters().items()) if False else None
    del before
    quantized = {"count": 0}

    def counting_predicate(path: str, module: nn.Module):
        result = predicate(path, module)
        if result:
            quantized["count"] += 1
        return result

    nn.quantize(model, class_predicate=counting_predicate)
    print(f"[mk1] quantized {quantized['count']} expert projections", flush=True)
    mx.eval(model.parameters())