/* Copyright 2021 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 "tensorflow/lite/kernels/internal/reference/add.h"

#include "tensorflow/lite/c/builtin_op_data.h"
#include "tensorflow/lite/c/common.h"
#include "tensorflow/lite/kernels/internal/quantization_util.h"
#include "tensorflow/lite/kernels/internal/reference/integer_ops/add.h"
#include "tensorflow/lite/kernels/internal/reference/process_broadcast_shapes.h"
#include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "tensorflow/lite/kernels/kernel_util.h"
#include "tensorflow/lite/kernels/op_macros.h"
#include "tensorflow/lite/micro/kernels/add.h"
#include "tensorflow/lite/micro/kernels/kernel_util.h"
#include "tensorflow/lite/micro/memory_helpers.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"

namespace tflite {

void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params,
             const OpDataAdd* data, const TfLiteEvalTensor* input1,
             const TfLiteEvalTensor* input2, TfLiteEvalTensor* output) {
  tflite::ArithmeticParams op_params;
  SetActivationParams(data->output_activation_min_f32,
                      data->output_activation_max_f32, &op_params);
  if (data->requires_broadcast) {
    reference_ops::BroadcastAdd4DSlow(
        op_params, tflite::micro::GetTensorShape(input1),
        tflite::micro::GetTensorData<float>(input1),
        tflite::micro::GetTensorShape(input2),
        tflite::micro::GetTensorData<float>(input2),
        tflite::micro::GetTensorShape(output),
        tflite::micro::GetTensorData<float>(output));
  } else {
    reference_ops::Add(op_params, tflite::micro::GetTensorShape(input1),
                       tflite::micro::GetTensorData<float>(input1),
                       tflite::micro::GetTensorShape(input2),
                       tflite::micro::GetTensorData<float>(input2),
                       tflite::micro::GetTensorShape(output),
                       tflite::micro::GetTensorData<float>(output));
  }
}

TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node,
                              TfLiteAddParams* params, const OpDataAdd* data,
                              const TfLiteEvalTensor* input1,
                              const TfLiteEvalTensor* input2,
                              TfLiteEvalTensor* output) {
  tflite::ArithmeticParams op_params;
  op_params.left_shift = data->left_shift;
  op_params.input1_offset = data->input1_offset;
  op_params.input1_multiplier = data->input1_multiplier;
  op_params.input1_shift = data->input1_shift;
  op_params.input2_offset = data->input2_offset;
  op_params.input2_multiplier = data->input2_multiplier;
  op_params.input2_shift = data->input2_shift;
  op_params.output_offset = data->output_offset;
  op_params.output_multiplier = data->output_multiplier;
  op_params.output_shift = data->output_shift;
  SetActivationParams(data->output_activation_min, data->output_activation_max,
                      &op_params);
  bool need_broadcast = reference_ops::ProcessBroadcastShapes(
      tflite::micro::GetTensorShape(input1),
      tflite::micro::GetTensorShape(input2), &op_params);

  switch (output->type) {
    case kTfLiteInt8: {
      if (need_broadcast) {
        reference_integer_ops::BroadcastAdd4DSlow(
            op_params, tflite::micro::GetTensorShape(input1),
            tflite::micro::GetTensorData<int8_t>(input1),
            tflite::micro::GetTensorShape(input2),
            tflite::micro::GetTensorData<int8_t>(input2),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<int8_t>(output));
      } else {
        reference_integer_ops::Add(
            op_params, tflite::micro::GetTensorShape(input1),
            tflite::micro::GetTensorData<int8_t>(input1),
            tflite::micro::GetTensorShape(input2),
            tflite::micro::GetTensorData<int8_t>(input2),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<int8_t>(output));
      }
      break;
    }
    case kTfLiteInt16: {
      if (need_broadcast) {
        reference_ops::BroadcastAdd4DSlow(
            op_params, tflite::micro::GetTensorShape(input1),
            tflite::micro::GetTensorData<int16_t>(input1),
            tflite::micro::GetTensorShape(input2),
            tflite::micro::GetTensorData<int16_t>(input2),
            tflite::micro::GetTensorShape(output),
            tflite::micro::GetTensorData<int16_t>(output));
      } else {
        reference_ops::Add(op_params, tflite::micro::GetTensorShape(input1),
                           tflite::micro::GetTensorData<int16_t>(input1),
                           tflite::micro::GetTensorShape(input2),
                           tflite::micro::GetTensorData<int16_t>(input2),
                           tflite::micro::GetTensorShape(output),
                           tflite::micro::GetTensorData<int16_t>(output),
                           false);
      }
      break;
    }
    default:
      MicroPrintf("Type %s (%d) not supported.",
                  TfLiteTypeGetName(output->type), output->type);
      return kTfLiteError;
  }

  return kTfLiteOk;
}

void* AddInit(TfLiteContext* context, const char* buffer, size_t length) {
  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
  return context->AllocatePersistentBuffer(context, sizeof(OpDataAdd));
}

TfLiteStatus AddEval(TfLiteContext* context, TfLiteNode* node) {
  auto* params = reinterpret_cast<TfLiteAddParams*>(node->builtin_data);

  TFLITE_DCHECK(node->user_data != nullptr);
  const OpDataAdd* data = static_cast<const OpDataAdd*>(node->user_data);

  const TfLiteEvalTensor* input1 =
      tflite::micro::GetEvalInput(context, node, kAddInputTensor1);
  const TfLiteEvalTensor* input2 =
      tflite::micro::GetEvalInput(context, node, kAddInputTensor2);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kAddOutputTensor);

  if (output->type == kTfLiteFloat32) {
    EvalAdd(context, node, params, data, input1, input2, output);
  } else if (output->type == kTfLiteInt8 || output->type == kTfLiteInt16) {
    TF_LITE_ENSURE_OK(context, EvalAddQuantized(context, node, params, data,
                                                input1, input2, output));
  } else {
    MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(output->type),
                output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}

TfLiteRegistration Register_ADD() {
  return {/*init=*/AddInit,
          /*free=*/nullptr,
          /*prepare=*/AddPrepare,
          /*invoke=*/AddEval,
          /*profiling_string=*/nullptr,
          /*builtin_code=*/0,
          /*custom_name=*/nullptr,
          /*version=*/0};
}

}  // namespace tflite
