/*
 * Copyright (c) 2020-2025, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#pragma once
#include "./moe_gemm_kernels.h"
#include "cutlass/gemm/gemm.h"
#include "tensorrt_llm/common/assert.h"
#include "tensorrt_llm/common/cudaUtils.h"
#include "tensorrt_llm/common/quantization.h"
#include "tensorrt_llm/kernels/cutlass_kernels/fp8_blockscale_gemm/fp8_blockscale_gemm.h"
#ifdef ENABLE_FP4
#include <cuda_fp4.h>
#endif
#include <NvInferRuntime.h>
#include <array>
#include <cuda_runtime_api.h>
#include <map>
#include <optional>
#include <random>
#include <utility>

namespace tensorrt_llm::kernels
{

namespace cutlass_kernels
{

// These kernels are used in moeUtilOp.cpp
int64_t computeNumTokensPerBlock(int64_t const num_tokens, int64_t const num_experts_per_node);

bool fusedBuildExpertMapsSortFirstToken(int const* token_selected_experts, int* unpermuted_token_selected_experts,
    int* permuted_source_token_ids, int64_t* expert_first_token_offset, int64_t const num_tokens,
    int const num_experts_per_node, int const experts_per_token, int const start_expert, int const end_expert,
    cudaStream_t stream);

void threeStepBuildExpertMapsSortFirstToken(int const* token_selected_experts, int* permuted_token_selected_experts,
    int* permuted_row_to_unpermuted_row, int* unpermuted_row_to_permuted_row, int64_t* expert_first_token_offset,
    int* blocked_expert_counts, int* blocked_expert_counts_cumsum, int* blocked_row_to_unpermuted_row,
    int64_t const num_tokens, int64_t const num_experts_per_node, int64_t const num_experts_per_token,
    int const start_expert_id, cudaStream_t stream);

template <class InputActivationsType, class ExpandedActivationsType>
void expandInputRowsKernelLauncher(InputActivationsType const* unpermuted_input,
    ExpandedActivationsType* permuted_output, float const* unpermuted_scales, float* permuted_scales,
    int const* permuted_row_to_unpermuted_row, int64_t const num_rows, int64_t const hidden_size, int const k,
    int const num_experts_per_node, QuantParams const& quant_params, bool use_per_expert_act_scale,
    int64_t* expert_first_token_offset, TmaWarpSpecializedGroupedGemmInput::ElementSF* fc1_act_sf_flat,
    TmaWarpSpecializedGroupedGemmInput::ElementSF const* input_sf, bool const swizzled_input_sf,
    void const* prequant_scales, cudaStream_t stream);

template <class OutputType, class GemmOutputType, class ScaleBiasType>
void finalizeMoeRoutingKernelLauncher(GemmOutputType const* expanded_permuted_rows,
    OutputType* reduced_unpermuted_output, ScaleBiasType const* bias, float const* final_scales,
    int const* unpermuted_row_to_permuted_row, int const* permuted_row_to_unpermuted_row,
    int const* token_selected_experts, int64_t const* expert_first_token_offset, int64_t const num_rows,
    int64_t const cols, int64_t const experts_per_token, int64_t const num_experts_per_node,
    MOEParallelismConfig parallelism_config, bool const enable_alltoall, cudaStream_t stream);

} // namespace cutlass_kernels
} // namespace tensorrt_llm::kernels
