/* 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 "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/resize_bilinear.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/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"
#include "edge-impulse-sdk/tensorflow/lite/micro/micro_utils.h"

namespace tflite {
namespace {

constexpr int kInputTensor = 0;
constexpr int kSizeTensor = 1;
constexpr int kOutputTensor = 0;

TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
  MicroContext* micro_context = GetMicroContext(context);

  TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
  TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);

  TfLiteTensor* input =
      micro_context->AllocateTempInputTensor(node, kInputTensor);
  TfLiteTensor* size =
      micro_context->AllocateTempInputTensor(node, kSizeTensor);
  TfLiteTensor* output =
      micro_context->AllocateTempOutputTensor(node, kOutputTensor);

  TF_LITE_ENSURE_EQ(context, NumDimensions(input), 4);
  TF_LITE_ENSURE_EQ(context, NumDimensions(size), 1);

  TF_LITE_ENSURE_EQ(context, size->type, kTfLiteInt32);
  output->type = input->type;

  TF_LITE_ENSURE_MSG(context, IsConstantTensor(size),
                     "Non constant size tensor not supported");

  // Ensure params are valid.
  auto* params =
      reinterpret_cast<TfLiteResizeBilinearParams*>(node->builtin_data);
  if (params->half_pixel_centers && params->align_corners) {
    MicroPrintf("If half_pixel_centers is True, align_corners must be False.");
    return kTfLiteError;
  }

  micro_context->DeallocateTempTfLiteTensor(input);
  micro_context->DeallocateTempTfLiteTensor(size);
  micro_context->DeallocateTempTfLiteTensor(output);
  return kTfLiteOk;
}

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

  const TfLiteEvalTensor* input =
      tflite::micro::GetEvalInput(context, node, kInputTensor);
  const TfLiteEvalTensor* size =
      tflite::micro::GetEvalInput(context, node, kSizeTensor);
  TfLiteEvalTensor* output =
      tflite::micro::GetEvalOutput(context, node, kOutputTensor);

  if (output->type == kTfLiteFloat32) {
    tflite::ResizeBilinearParams op_params;
    op_params.align_corners = params->align_corners;
    op_params.half_pixel_centers = params->half_pixel_centers;
    reference_ops::ResizeBilinear(op_params,
                                  tflite::micro::GetTensorShape(input),
                                  tflite::micro::GetTensorData<float>(input),
                                  tflite::micro::GetTensorShape(size),
                                  tflite::micro::GetTensorData<int32_t>(size),
                                  tflite::micro::GetTensorShape(output),
                                  tflite::micro::GetTensorData<float>(output));
  } else if (output->type == kTfLiteInt8) {
    tflite::ResizeBilinearParams op_params;
    op_params.align_corners = params->align_corners;
    op_params.half_pixel_centers = params->half_pixel_centers;
    reference_ops::ResizeBilinearInteger(
        op_params, tflite::micro::GetTensorShape(input),
        tflite::micro::GetTensorData<int8_t>(input),
        tflite::micro::GetTensorShape(size),
        tflite::micro::GetTensorData<int32_t>(size),
        tflite::micro::GetTensorShape(output),
        tflite::micro::GetTensorData<int8_t>(output));
  } else {
    MicroPrintf("Output type is %d, requires float or int8.", output->type);
    return kTfLiteError;
  }

  return kTfLiteOk;
}

}  // namespace

TfLiteRegistration Register_RESIZE_BILINEAR() {
  return tflite::micro::RegisterOp(nullptr, Prepare, Eval);
}

}  // namespace tflite
