import torch
import torch.nn.functional as F


def fused_moe(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    gating_output: torch.Tensor,
    topk: int,
    renormalize: bool,
) -> torch.Tensor:
    """
    Args:
        hidden_states: [*, hidden_size]
        w1: [num_experts, intermediate_size * 2, hidden_size]
        w2: [num_experts, hidden_size, intermediate_size]
        gating_output: [*, num_experts]
    """
    orig_shape = hidden_states.shape
    hidden_size = hidden_states.shape[-1]
    num_tokens = hidden_states.shape[:-1].numel()
    num_experts = w1.shape[0]
    intermediate_size = w2.shape[-1]
    dtype = hidden_states.dtype

    hidden_states = hidden_states.view(num_tokens, hidden_size)
    gating_output = gating_output.view(num_tokens, num_experts)
    topk_weights = gating_output.softmax(dim=-1, dtype=torch.float)
    topk_weights, selected_experts = topk_weights.topk(topk, dim=-1)
    if renormalize:
        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
    topk_weights = topk_weights.to(dtype)

    final_hidden_states = None
    for expert_idx in range(num_experts):
        expert_w1 = w1[expert_idx]
        expert_w2 = w2[expert_idx]
        expert_mask = (selected_experts == expert_idx)
        expert_weights = (topk_weights * expert_mask).sum(dim=-1, keepdim=True)
        x = F.linear(hidden_states, expert_w1)
        gate = F.silu(x[:, :intermediate_size])
        x = x[:, intermediate_size:] * gate
        x = F.linear(x, expert_w2)
        current_hidden_states = x * expert_weights
        if final_hidden_states is None:
            final_hidden_states = current_hidden_states
        else:
            final_hidden_states = final_hidden_states + current_hidden_states

    return final_hidden_states.view(orig_shape)  # type: ignore
