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from __future__ import annotations

import torch
from torch import nn

from tokenspeed.runtime.layers.moe.types import MoELayerSpec
from tokenspeed.runtime.layers.moe.weights.loaders import (
    make_weight_loader,
    round_up,
)
from tokenspeed.runtime.utils import set_weight_attrs


def create_fp8_block_scale_inverses(
    spec: MoELayerSpec,
    layer: nn.Module,
    *,
    intermediate_size_per_partition: int,
    block_shape: tuple[int, int],
) -> None:
    block_n, block_k = block_shape
    w13_weight_scale = torch.nn.Parameter(
        torch.ones(
            spec.num_local_experts,
            2 * round_up(intermediate_size_per_partition, block_n) // block_n,
            round_up(spec.hidden_size, block_k) // block_k,
            dtype=torch.float32,
        ),
        requires_grad=False,
    )
    w2_weight_scale = torch.nn.Parameter(
        torch.ones(
            spec.num_local_experts,
            round_up(spec.hidden_size, block_n) // block_n,
            round_up(intermediate_size_per_partition, block_k) // block_k,
            dtype=torch.float32,
        ),
        requires_grad=False,
    )
    layer.register_parameter("w13_weight_scale_inv", w13_weight_scale)
    layer.register_parameter("w2_weight_scale_inv", w2_weight_scale)

    weight_loader = make_weight_loader(spec)
    set_weight_attrs(w13_weight_scale, {"weight_loader": weight_loader})
    set_weight_attrs(w2_weight_scale, {"weight_loader": weight_loader})


__all__ = ["create_fp8_block_scale_inverses"]