/* Copyright 2019 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/reduce.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/quantization_util.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/reference/integer_ops/mean.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/tensor_ctypes.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/internal/types.h"
#include "edge-impulse-sdk/tensorflow/lite/kernels/kernel_util.h"
#include "edge-impulse-sdk/tensorflow/lite/micro/micro_utils.h"

namespace tflite {
namespace ops {
namespace micro {
namespace reduce {

constexpr int kMaxNumberOfAxis = 4;
constexpr int kMaxNumberOfReducedAxis = 2;

struct OpData {
  int32_t multiplier;
  int shift;
  int temp_buffer_idx;
  int resolved_axis_idx;
  int input_zp;
  float input_scale;
  int output_zp;
  float output_scale;
  int num_output_elements;
};

void* InitReduce(TfLiteContext* context, const char* buffer, size_t length) {
  TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
  void* data = nullptr;
  if (context->AllocatePersistentBuffer(context, sizeof(OpData), &data) ==
      kTfLiteError) {
    return nullptr;
  }
  return data;
}

TfLiteStatus PrepareSimple(TfLiteContext* context, TfLiteNode* node) {
  // Inputs Tensor (dtype depends on quantization):
  // [0] = Input
  // [1] = Axis
  const TfLiteTensor* input = GetInput(context, node, 0);

  // Outputs Tensor (dtype depends on quantization):
  // [0] = Output

  // Validate number of inputs and outputs
  TF_LITE_ENSURE_EQ(context, node->inputs->size, 2);
  TF_LITE_ENSURE_EQ(context, node->outputs->size, 1);

  // Validate axis type
  const TfLiteTensor* axis = GetInput(context, node, 1);
  TF_LITE_ENSURE(context, axis != nullptr);
  TF_LITE_ENSURE_TYPES_EQ(context, axis->type, kTfLiteInt32);

  if (input->type == kTfLiteInt8) {
    OpData* data = static_cast<OpData*>(node->user_data);
    const TfLiteTensor* output = GetOutput(context, node, 0);
    const double real_multiplier = static_cast<double>(input->params.scale) /
                                   static_cast<double>(output->params.scale);
    QuantizeMultiplier(real_multiplier, &data->multiplier, &data->shift);
  }

  return kTfLiteOk;
}

TfLiteStatus PrepareMax(TfLiteContext* context, TfLiteNode* node) {
  TF_LITE_ENSURE_OK(context, PrepareSimple(context, node));

  OpData* op_data = static_cast<OpData*>(node->user_data);
  const TfLiteTensor* input = GetInput(context, node, 0);
  const TfLiteTensor* output = GetOutput(context, node, 0);
  const TfLiteTensor* axis = GetInput(context, node, 1);

  op_data->input_scale = input->params.scale;
  op_data->output_scale = output->params.scale;
  op_data->num_output_elements = NumElements(output);

  context->RequestScratchBufferInArena(context, sizeof(int) * input->dims->size,
                                       &op_data->temp_buffer_idx);
  context->RequestScratchBufferInArena(
      context, sizeof(int) * static_cast<int>(ElementCount(*axis->dims)),
      &op_data->resolved_axis_idx);

  return kTfLiteOk;
}

TfLiteStatus PrepareMeanOrSum(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, 0);
  OpData* op_data = reinterpret_cast<OpData*>(node->user_data);
  const TfLiteTensor* output = GetOutput(context, node, 0);
  if (input->type == kTfLiteInt8) {
    const double real_multiplier = static_cast<double>(input->params.scale) /
                                   static_cast<double>(output->params.scale);
    QuantizeMultiplier(real_multiplier, &op_data->multiplier, &op_data->shift);
  }

  int output_size = NumElements(output);
  if (input->type == kTfLiteInt8 || input->type == kTfLiteUInt8) {
    context->RequestScratchBufferInArena(context, output_size * sizeof(int32_t),
                                         &op_data->temp_buffer_idx);
    op_data->input_zp = input->params.zero_point;
    op_data->input_scale = input->params.scale;
    op_data->output_zp = output->params.zero_point;
    op_data->output_scale = output->params.scale;
  }

  TF_LITE_ENSURE_OK(context, PrepareSimple(context, node));
  // TODO(b/144955155): Support uint8_t(b/144955155) and int8_t(b/144955018)
  return kTfLiteOk;
}

void ResolveAxis(const int* axis_data, int axis_count,
                 tflite::MeanParams* op_params) {
  int i = 0;
  for (; i < axis_count; ++i) {
    op_params->axis[i] = static_cast<int16_t>(axis_data[i]);
  }
  for (; i < 4; ++i) {
    op_params->axis[i] = 1;
  }
  op_params->axis_count = axis_count;
}

TfLiteStatus EvalMean(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, 0);
  const TfLiteTensor* axis = GetInput(context, node, 1);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TfLiteReducerParams* params =
      reinterpret_cast<TfLiteReducerParams*>(node->builtin_data);
  OpData* op_data = reinterpret_cast<OpData*>(node->user_data);

  int num_axis = static_cast<int>(ElementCount(*axis->dims));
  int temp_index[kMaxNumberOfAxis];
  int resolved_axis[kMaxNumberOfReducedAxis];

  tflite::MeanParams op_params;
  ResolveAxis(GetTensorData<int>(axis), num_axis, &op_params);

  // Special case mean implementation exists for 4D mean across axes 1 and 2.
  bool special_case_4d_axes_1_and_2 =
      input->dims->size == 4 && op_params.axis_count == 2 &&
      ((op_params.axis[0] == 1 && op_params.axis[1] == 2) ||
       (op_params.axis[0] == 2 && op_params.axis[1] == 1));

  switch (input->type) {
    case kTfLiteFloat32: {
      // Defer to specialized implementation for 4D Mean across axes 1 & 2.
      if (params->keep_dims && special_case_4d_axes_1_and_2) {
        reference_ops::Mean(op_params, GetTensorShape(input),
                            GetTensorData<float>(input),
                            GetTensorShape(output),
                            GetTensorData<float>(output));
      } else {
        TF_LITE_ENSURE(
            context,
            reference_ops::Mean(
                GetTensorData<float>(input), input->dims->data,
                input->dims->size, GetTensorData<float>(output),
                output->dims->data, output->dims->size,
                GetTensorData<int>(axis), num_axis,
                params->keep_dims, temp_index, resolved_axis,
                GetTensorData<float>(output)));
      }
    } break;
    case kTfLiteInt8: {
      // Defer to specialized implementation for 4D Mean across axes 1 & 2.
      if (params->keep_dims && special_case_4d_axes_1_and_2) {
        reference_integer_ops::Mean(
            op_params, op_data->multiplier, op_data->shift,
            GetTensorShape(input),
            GetTensorData<int8_t>(input), op_data->input_zp,
            GetTensorShape(output),
            GetTensorData<int8_t>(output), op_data->output_zp);
      } else if (op_data->input_zp == op_data->output_zp &&
                 op_data->input_scale == op_data->output_scale) {
        int32_t* temp_buffer = static_cast<int32_t*>(
            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
        TF_LITE_ENSURE(
            context,
            reference_ops::Mean(
                GetTensorData<int8_t>(input), input->dims->data,
                input->dims->size, GetTensorData<int8_t>(output),
                output->dims->data, output->dims->size,
                GetTensorData<int>(axis), num_axis,
                params->keep_dims, temp_index, resolved_axis, temp_buffer));
      } else {
        int32_t* temp_buffer = static_cast<int32_t*>(
            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
        TF_LITE_ENSURE(
            context,
            reference_ops::QuantizedMeanOrSum(
                GetTensorData<int8_t>(input), op_data->input_zp,
                op_data->input_scale, input->dims->data, input->dims->size,
                GetTensorData<int8_t>(output),
                op_data->output_zp, op_data->output_scale, output->dims->data,
                output->dims->size, GetTensorData<int>(axis),
                num_axis, params->keep_dims, temp_index, resolved_axis,
                temp_buffer, false));
      }
    } break;
    case kTfLiteUInt8: {
      // Defer to specialized implementation for 4D Mean across axes 1 & 2.
      if (params->keep_dims && special_case_4d_axes_1_and_2) {
        reference_ops::Mean(op_params, GetTensorShape(input),
                            GetTensorData<uint8_t>(input),
                            op_data->input_zp, op_data->input_scale,
                            GetTensorShape(output),
                            GetTensorData<uint8_t>(output),
                            op_data->output_zp, op_data->output_scale);
      } else if (op_data->input_zp == op_data->output_zp &&
                 op_data->input_scale == op_data->output_scale) {
        uint32_t* temp_buffer = static_cast<uint32_t*>(
            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
        TF_LITE_ENSURE(
            context,
            reference_ops::Mean(GetTensorData<uint8_t>(input),
                                input->dims->data, input->dims->size,
                                GetTensorData<uint8_t>(output),
                                output->dims->data, output->dims->size,
                                GetTensorData<int>(axis),
                                num_axis, params->keep_dims, temp_index,
                                resolved_axis, temp_buffer));
      } else {
        uint32_t* temp_buffer = static_cast<uint32_t*>(
            context->GetScratchBuffer(context, op_data->temp_buffer_idx));
        TF_LITE_ENSURE(
            context,
            reference_ops::QuantizedMeanOrSum(
                GetTensorData<uint8_t>(input), op_data->input_zp,
                op_data->input_scale, input->dims->data, input->dims->size,
                GetTensorData<uint8_t>(output),
                op_data->output_zp, op_data->output_scale, output->dims->data,
                output->dims->size, GetTensorData<int>(axis),
                num_axis, params->keep_dims, temp_index, resolved_axis,
                temp_buffer, false));
      }
    } break;
    default:
      TF_LITE_ENSURE_MSG(context, false,
                         "Currently, only float32, int8 or uint8 input type "
                         "is supported.");
  }
  return kTfLiteOk;
}

TfLiteStatus EvalMax(TfLiteContext* context, TfLiteNode* node) {
  const TfLiteTensor* input = GetInput(context, node, 0);
  const TfLiteTensor* axis = GetInput(context, node, 1);
  TfLiteTensor* output = GetOutput(context, node, 0);
  TF_LITE_ENSURE_TYPES_EQ(context, input->type, output->type);
  TfLiteReducerParams* params =
      static_cast<TfLiteReducerParams*>(node->builtin_data);
  OpData* op_data = static_cast<OpData*>(node->user_data);

  // Interpret an axis tensor with null dimensions as a scalar
  int num_axis = static_cast<int>(ElementCount(*axis->dims));
  int* temp_buffer = static_cast<int*>(
      context->GetScratchBuffer(context, op_data->temp_buffer_idx));
  int* resolved_axis = static_cast<int*>(
      context->GetScratchBuffer(context, op_data->resolved_axis_idx));
  switch (input->type) {
    case kTfLiteFloat32:
      TF_LITE_ENSURE(
          context,
          reference_ops::ReduceGeneric<float>(
              GetTensorData<float>(input), input->dims->data,
              input->dims->size, GetTensorData<float>(output),
              output->dims->data, output->dims->size,
              GetTensorData<int>(axis), num_axis,
              params->keep_dims, temp_buffer, resolved_axis,
              std::numeric_limits<float>::lowest(),
              [](const float current, const float in) -> float {
                return (in > current) ? in : current;
              }));
      break;
    case kTfLiteInt8:
      TF_LITE_ENSURE_EQ(context, static_cast<double>(op_data->input_scale),
                        static_cast<double>(op_data->output_scale));
      TF_LITE_ENSURE_EQ(context, op_data->input_zp, op_data->output_zp);
      TF_LITE_ENSURE(
          context,
          reference_ops::ReduceGeneric<int8_t>(
              GetTensorData<int8_t>(input), input->dims->data,
              input->dims->size, GetTensorData<int8_t>(output),
              output->dims->data, output->dims->size,
              GetTensorData<int>(axis), num_axis,
              params->keep_dims, temp_buffer, resolved_axis,
              std::numeric_limits<int8_t>::lowest(),
              [](const int8_t current, const int8_t in) -> int8_t {
                return (in > current) ? in : current;
              }));
      break;
    default:
      TF_LITE_KERNEL_LOG(context,
                         "Only float32 and int8 types are supported.\n");
      return kTfLiteError;
  }
  return kTfLiteOk;
}

}  // namespace reduce

TfLiteRegistration* Register_MEAN() {
  static TfLiteRegistration r = {/*init=*/reduce::InitReduce,
          /*free=*/nullptr,
          /*prepare=*/reduce::PrepareMeanOrSum,
          /*invoke=*/reduce::EvalMean,
          /*profiling_string=*/nullptr,
          /*builtin_code=*/0,
          /*custom_name=*/nullptr,
          /*version=*/0};
  return &r;
}

TfLiteRegistration* Register_REDUCE_MAX() {
  static TfLiteRegistration r = {/*init=*/reduce::InitReduce,
          /*free=*/nullptr,
          /*prepare=*/reduce::PrepareMax,
          /*invoke=*/reduce::EvalMax,
          /*profiling_string=*/nullptr,
          /*builtin_code=*/0,
          /*custom_name=*/nullptr,
          /*version=*/0};
  return &r;
}

}  // namespace micro
}  // namespace ops
}  // namespace tflite
