# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from typing import Optional

import torch

import vllm.model_executor.layers.fused_moe.modular_kernel as mk
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
    TopKWeightAndReduceContiguous, TopKWeightAndReduceDelegate)
from vllm.model_executor.layers.fused_moe.utils import (
    moe_kernel_quantize_input)


class MoEPrepareAndFinalizeNoEP(mk.FusedMoEPrepareAndFinalize):

    @property
    def activation_format(self) -> mk.FusedMoEActivationFormat:
        return mk.FusedMoEActivationFormat.Standard

    def max_num_tokens_per_rank(self) -> Optional[int]:
        return None

    def topk_indices_dtype(self) -> Optional[torch.dtype]:
        return None

    def num_dispatchers(self) -> int:
        return 1

    def prepare(
        self,
        a1: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        num_experts: int,
        expert_map: Optional[torch.Tensor],
        apply_router_weight_on_input: bool,
        quant_config: FusedMoEQuantConfig,
    ) -> mk.PrepareResultType:

        if apply_router_weight_on_input:
            topk = topk_ids.size(1)
            # TODO: this only works for topK=1, will need to update for topK>1
            assert topk == 1, \
                "apply_router_weight_on_input is only implemented for topk=1"
            a1.mul_(topk_weights.to(a1.dtype))

        a1q, a1q_scale = moe_kernel_quantize_input(
            a1, quant_config.a1_scale, quant_config.quant_dtype,
            quant_config.per_act_token_quant, quant_config.block_shape)

        return a1q, a1q_scale, None, None, None

    def finalize(
        self,
        output: torch.Tensor,
        fused_expert_output: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        apply_router_weight_on_input: bool,
        weight_and_reduce_impl: mk.TopKWeightAndReduce,
    ) -> None:
        if isinstance(weight_and_reduce_impl, TopKWeightAndReduceDelegate):
            weight_and_reduce_impl = TopKWeightAndReduceContiguous()
        weight_and_reduce_impl.apply(
            output=output,
            fused_expert_output=fused_expert_output,
            topk_weights=topk_weights,
            topk_ids=topk_ids,
            apply_router_weight_on_input=apply_router_weight_on_input)
