/* 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/c/builtin_op_data.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/integer_ops/pooling.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/pooling.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/padding.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/kernels/pooling.h"

namespace tflite {

const int kPoolingInputTensor = 0;
const int kPoolingOutputTensor = 0;

TfLiteStatus CalculateOpDataPooling(const TfLiteContext* context,
                                    const TfLitePoolParams* params,
                                    const TfLiteTensor* input,
                                    const TfLiteTensor* output,
                                    OpDataPooling* data) {
  // input: batch, height, width, channel
  int height = SizeOfDimension(input, 1);
  int width = SizeOfDimension(input, 2);

  int out_height, out_width;

  data->padding = ComputePaddingHeightWidth(
      params->stride_height, params->stride_width,
      /*dilation_rate_height=*/1,
      /*dilation_rate_width=*/1, height, width, params->filter_height,
      params->filter_width, params->padding, &out_height, &out_width);

  return kTfLiteOk;
}

TfLiteStatus PoolingPrepare(TfLiteContext* context, TfLiteNode* node) {
  TFLITE_DCHECK(node->builtin_data != nullptr);
  auto* params = reinterpret_cast<TfLitePoolParams*>(node->builtin_data);

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

  MicroContext* micro_context = GetMicroContext(context);

  TfLiteTensor* input =
      micro_context->AllocateTempInputTensor(node, kPoolingInputTensor);
  TF_LITE_ENSURE(context, input != nullptr);
  TfLiteTensor* output =
      micro_context->AllocateTempOutputTensor(node, kPoolingOutputTensor);
  TF_LITE_ENSURE(context, output != nullptr);

  TF_LITE_ENSURE_STATUS(
      CalculateOpDataPooling(context, params, input, output, data));

  if (input->type == kTfLiteFloat32) {
    CalculateActivationRange(params->activation, &data->activation_min_f32,
                             &data->activation_max_f32);
  } else if (input->type == kTfLiteInt8 || input->type == kTfLiteInt16) {
    CalculateActivationRangeQuantized(context, params->activation, output,
                                      &data->activation_min,
                                      &data->activation_max);
  } else {
    MicroPrintf("Type %s (%d) not supported.", TfLiteTypeGetName(input->type),
                input->type);
    return kTfLiteError;
  }

  micro_context->DeallocateTempTfLiteTensor(input);
  micro_context->DeallocateTempTfLiteTensor(output);

  return kTfLiteOk;
}

void AveragePoolingEvalFloat(const TfLiteContext* context,
                             const TfLiteNode* node,
                             const TfLitePoolParams* params,
                             const OpDataPooling* data,
                             const TfLiteEvalTensor* input,
                             TfLiteEvalTensor* output) {
  PoolParams op_params;
  op_params.stride_height = params->stride_height;
  op_params.stride_width = params->stride_width;
  op_params.filter_height = params->filter_height;
  op_params.filter_width = params->filter_width;
  op_params.padding_values.height = data->padding.height;
  op_params.padding_values.width = data->padding.width;
  op_params.float_activation_min = data->activation_min_f32;
  op_params.float_activation_max = data->activation_max_f32;
  reference_ops::AveragePool(op_params, tflite::micro::GetTensorShape(input),
                             tflite::micro::GetTensorData<float>(input),
                             tflite::micro::GetTensorShape(output),
                             tflite::micro::GetTensorData<float>(output));
}

void MaxPoolingEvalFloat(TfLiteContext* context, TfLiteNode* node,
                         TfLitePoolParams* params, const OpDataPooling* data,
                         const TfLiteEvalTensor* input,
                         TfLiteEvalTensor* output) {
  tflite::PoolParams op_params;
  op_params.stride_height = params->stride_height;
  op_params.stride_width = params->stride_width;
  op_params.filter_height = params->filter_height;
  op_params.filter_width = params->filter_width;
  op_params.padding_values.height = data->padding.height;
  op_params.padding_values.width = data->padding.width;
  op_params.float_activation_min = data->activation_min_f32;
  op_params.float_activation_max = data->activation_max_f32;
  reference_ops::MaxPool(op_params, tflite::micro::GetTensorShape(input),
                         tflite::micro::GetTensorData<float>(input),
                         tflite::micro::GetTensorShape(output),
                         tflite::micro::GetTensorData<float>(output));
}

}  // namespace tflite
