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#ifndef TIM_LAYOUT_INFER_ACTIVATION_LAYOUT_INFERENCE_H_
#define TIM_LAYOUT_INFER_ACTIVATION_LAYOUT_INFERENCE_H_

#include "tim/vx/ops/activations.h"

#include "ops/op_layout_inference.h"
#include "permute_vector.h"
#include "direct_map_op_impl.h"

namespace tim {
namespace transform {
template <typename OpType>
class ActivationLayoutInfer : public OpLayoutInfer {
 public:
  ActivationLayoutInfer(
      const std::shared_ptr<vx::Operation> op,
      std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
      : OpLayoutInfer(op, context) {}

  void OnInputs(
      std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
    // Transmit input pv to out pv directly for activation ops
    assert(op_->impl()->InputsTensor().size() == 1);
    auto i_src = op_->impl()->InputsTensor()[0];
    auto input_pv = context_->GetPermuteVector(i_src);
    auto activation = context_->infer_graph_->CreateOperation<OpType>();
    auto out_infer = CreateOutputsTensor(input_pv);
    (*activation)
        .BindInput(context_->GetMapedTensor(i_src))
        .BindOutput(out_infer[0]);
    context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], input_pv);
    next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
  }
};

class LeakyReluLayoutInfer : public OpLayoutInfer {
 public:
  LeakyReluLayoutInfer(
      const std::shared_ptr<vx::Operation> op,
      std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
      : OpLayoutInfer(op, context) {}

  void OnInputs(
      std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
    assert(op_->impl()->InputsTensor().size() == 1);
    auto i_src = op_->impl()->InputsTensor()[0];
    auto input_pv = context_->GetPermuteVector(i_src);
    auto leaky_relu =
        context_->infer_graph_->CreateOperation<vx::ops::LeakyRelu>(
            op_->impl()->node()->nn_param.activation.leaky_ratio);
    auto out_infer = CreateOutputsTensor(input_pv);
    (*leaky_relu)
        .BindInput(context_->GetMapedTensor(i_src))
        .BindOutput(out_infer[0]);
    context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], input_pv);
    next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
  }
};

class PReluLayoutInfer : public OpLayoutInfer {
 public:
  PReluLayoutInfer(
      const std::shared_ptr<vx::Operation> op,
      std::shared_ptr<layout_inference_impl::LayoutInferContext>& context)
      : OpLayoutInfer(op, context) {}

  void OnInputs(
      std::vector<std::shared_ptr<vx::Tensor>>& next_tensors) override {
    ReverseInputsPermuteVector();
    auto src_input = op_->impl()->InputsTensor()[0];
    auto input_pv = context_->GetPermuteVector(src_input);
    auto prelu = context_->infer_graph_->CreateOperation<vx::ops::Prelu>(
        op_->impl()->node()->nn_param.prelu.axis);
    auto out_infer = CreateOutputsTensor(input_pv);
    for (const auto& i_src : op_->impl()->InputsTensor()) {
      (*prelu).BindInput(context_->GetMapedTensor(i_src));
    }
    (*prelu).BindOutput(out_infer[0]);
    context_->SetPermuteVector(op_->impl()->OutputsTensor()[0], input_pv);
    next_tensors.push_back(op_->impl()->OutputsTensor()[0]);
  }
};

using ReluLayoutInfer = ActivationLayoutInfer<vx::ops::Relu>;
using Relu1LayoutInfer = ActivationLayoutInfer<vx::ops::Relu1>;
using Relu6LayoutInfer = ActivationLayoutInfer<vx::ops::Relu6>;
using EluLayoutInfer = ActivationLayoutInfer<vx::ops::Elu>;
using SigmoidLayoutInfer = ActivationLayoutInfer<vx::ops::Sigmoid>;
using MishLayoutInfer = ActivationLayoutInfer<vx::ops::Mish>;
using HardSigmoidLayoutInfer = ActivationLayoutInfer<vx::ops::HardSigmoid>;
using SoftReluLayoutInfer = ActivationLayoutInfer<vx::ops::SoftRelu>;
using HardSwishLayoutInfer = ActivationLayoutInfer<vx::ops::HardSwish>;
using TanhLayoutInfer = ActivationLayoutInfer<vx::ops::Tanh>;

}  // namespace transform
}  // namespace tim

#endif