/* Copyright 2022 The TensorFlow Authors. 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.
==============================================================================*/

#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/maximum_minimum.h"

#include "edge-impulse-sdk/tensorflow/lite/c/builtin_op_data.h"
#include "edge-impulse-sdk/tensorflow/lite/c/common.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/common.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/quantization_util.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/op_macros.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/micro_log.h"

namespace tflite {

namespace {

// This file has a reference implementation of TFMaximum/TFMinimum.
enum KernelType {
  kReference,
};

constexpr int kInputTensor1 = 0;
constexpr int kInputTensor2 = 1;
constexpr int kOutputTensor = 0;

struct OpContext {
  OpContext(TfLiteContext* context, TfLiteNode* node) {
    input1 = tflite::micro::GetEvalInput(context, node, kInputTensor1);
    input2 = tflite::micro::GetEvalInput(context, node, kInputTensor2);
    output = tflite::micro::GetEvalOutput(context, node, kOutputTensor);
  }
  const TfLiteEvalTensor* input1;
  const TfLiteEvalTensor* input2;
  TfLiteEvalTensor* output;
};

struct MaximumOp {
  template <typename data_type>
  static data_type op(data_type el1, data_type el2) {
    return el1 > el2 ? el1 : el2;
  }
};

struct MinimumOp {
  template <typename data_type>
  static data_type op(data_type el1, data_type el2) {
    return el1 < el2 ? el1 : el2;
  }
};

template <typename data_type, typename op_type>
void TFLiteOperation(TfLiteContext* context, TfLiteNode* node,
                     const OpContext& op_context) {
  reference_ops::MaximumMinimumBroadcastSlow(
      tflite::micro::GetTensorShape(op_context.input1),
      tflite::micro::GetTensorData<data_type>(op_context.input1),
      tflite::micro::GetTensorShape(op_context.input2),
      tflite::micro::GetTensorData<data_type>(op_context.input2),
      tflite::micro::GetTensorShape(op_context.output),
      tflite::micro::GetTensorData<data_type>(op_context.output),
      op_type::template op<data_type>);
}

template <KernelType kernel_type, typename OpType>
TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
  OpContext op_context(context, node);

  if (kernel_type == kReference) {
    switch (op_context.output->type) {
      case kTfLiteFloat32:
        TFLiteOperation<float, OpType>(context, node, op_context);
        break;
      case kTfLiteInt8:
        TFLiteOperation<int8_t, OpType>(context, node, op_context);
        break;
      case kTfLiteInt32:
        TFLiteOperation<int32_t, OpType>(context, node, op_context);
        break;
      case kTfLiteInt64:
        TFLiteOperation<int64_t, OpType>(context, node, op_context);
        break;
      default:
        MicroPrintf("Type %s (%d) is not supported by Maximum/Minimum.",
                    TfLiteTypeGetName(op_context.output->type),
                    op_context.output->type);
        return kTfLiteError;
    }
  } else {
    MicroPrintf("Kernel type not supported by Maximum/Minimum.");
    return kTfLiteError;
  }
  return kTfLiteOk;
}

}  // namespace

TfLiteRegistration Register_MAXIMUM() {
  return tflite::micro::RegisterOp(nullptr, nullptr,
                                   Eval<kReference, MaximumOp>);
}

TfLiteRegistration Register_MINIMUM() {
  return tflite::micro::RegisterOp(nullptr, nullptr,
                                   Eval<kReference, MinimumOp>);
}

}  // namespace tflite
