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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
from tokenspeed.runtime.utils import set_weight_attrs


def create_dense_weight_pair(
    spec: MoELayerSpec,
    layer: nn.Module,
    *,
    params_dtype: torch.dtype,
    with_bias: bool = False,
) -> int:
    ispp = spec.intermediate_size // spec.tp_size
    w13_weight = torch.nn.Parameter(
        torch.empty(
            spec.num_local_experts,
            2 * ispp,
            spec.hidden_size,
            dtype=params_dtype,
        ),
        requires_grad=False,
    )
    w2_weight = torch.nn.Parameter(
        torch.empty(
            spec.num_local_experts,
            spec.hidden_size,
            ispp,
            dtype=params_dtype,
        ),
        requires_grad=False,
    )
    layer.register_parameter("w13_weight", w13_weight)
    layer.register_parameter("w2_weight", w2_weight)

    weight_loader = make_weight_loader(spec)
    set_weight_attrs(w13_weight, {"weight_loader": weight_loader})
    set_weight_attrs(w2_weight, {"weight_loader": weight_loader})

    if with_bias:
        w13_weight_bias = torch.nn.Parameter(
            torch.zeros(spec.num_local_experts, 2 * ispp, dtype=params_dtype),
            requires_grad=False,
        )
        w2_weight_bias = torch.nn.Parameter(
            torch.zeros(spec.num_local_experts, spec.hidden_size, dtype=params_dtype),
            requires_grad=False,
        )
        layer.register_parameter("w13_weight_bias", w13_weight_bias)
        layer.register_parameter("w2_weight_bias", w2_weight_bias)
        bias_loader = make_weight_loader(spec, is_bias=True)
        set_weight_attrs(w13_weight_bias, {"weight_loader": bias_loader})
        set_weight_attrs(w2_weight_bias, {"weight_loader": bias_loader})

    return ispp


__all__ = ["create_dense_weight_pair"]