/* Copyright 2020 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.
==============================================================================*/
#ifndef TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_
#define TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_

#include <stdint.h>

#include <algorithm>

#include "ruy/profiler/instrumentation.h"  // from @ruy
#include "tensorflow/lite/kernels/internal/common.h"
#include "tensorflow/lite/kernels/internal/compatibility.h"
#include "tensorflow/lite/kernels/internal/reference/broadcast_loop.h"
#include "tensorflow/lite/kernels/internal/types.h"

namespace tflite {

namespace reference_ops {

template <typename T, typename F>
inline void BroadcastSubCommon(const ArithmeticParams& params,
                               const RuntimeShape& input1_shape,
                               const T* input1_data,
                               const RuntimeShape& input2_shape,
                               const T* input2_data,
                               const RuntimeShape& output_shape, T* output_data,
                               F binary_func) {
  auto op = [&params, binary_func](T a, T b) {
    return binary_func(a, b, params);
  };
  BroadcastBinaryOpSimple(input1_shape, input1_data, input2_shape, input2_data,
                          output_shape, output_data, op);
}

template <typename T>
void BroadcastSubSlow(const ArithmeticParams& params,
                      const RuntimeShape& input1_shape, const T* input1_data,
                      const RuntimeShape& input2_shape, const T* input2_data,
                      const RuntimeShape& output_shape, T* output_data) {
  ruy::profiler::ScopeLabel label("BroadcastSubSlow/T");
  BroadcastSubCommon<T>(
      params, input1_shape, input1_data, input2_shape, input2_data,
      output_shape, output_data,
      [](T input1_val, T input2_val, const ArithmeticParams& params) {
        T activation_min, activation_max;
        GetActivationParams(params, &activation_min, &activation_max);
        return ActivationFunctionWithMinMax(input1_val - input2_val,
                                            activation_min, activation_max);
      });
}

inline void BroadcastSub16POTSlow(const ArithmeticParams& params,
                                  const RuntimeShape& input1_shape,
                                  const int16_t* input1_data,
                                  const RuntimeShape& input2_shape,
                                  const int16_t* input2_data,
                                  const RuntimeShape& output_shape,
                                  int16_t* output_data) {
  ruy::profiler::ScopeLabel label("BroadcastSub16POTSlow/int16_t");
  BroadcastSubCommon<int16_t>(
      params, input1_shape, input1_data, input2_shape, input2_data,
      output_shape, output_data,
      [](int16_t input1_val, int16_t input2_val,
         const ArithmeticParams& params) {
        const int32_t scaled_input1_val =
            gemmlowp::RoundingDivideByPOT(input1_val, -params.input1_shift);
        const int32_t scaled_input2_val =
            gemmlowp::RoundingDivideByPOT(input2_val, -params.input2_shift);
        const int32_t raw_output = scaled_input1_val - scaled_input2_val;
        const int32_t clamped_output =
            std::min(params.quantized_activation_max,
                     std::max(params.quantized_activation_min, raw_output));
        return static_cast<int16_t>(clamped_output);
      });
}

template <typename T>
void BroadcastQuantSubSlow(const ArithmeticParams& params,
                           const RuntimeShape& input1_shape,
                           const T* input1_data,
                           const RuntimeShape& input2_shape,
                           const T* input2_data,
                           const RuntimeShape& output_shape, T* output_data) {
  ruy::profiler::ScopeLabel label("BroadcastQuantSubSlow/T");
  BroadcastSubCommon<T>(
      params, input1_shape, input1_data, input2_shape, input2_data,
      output_shape, output_data,
      [](T input1_val, T input2_val, const ArithmeticParams& params) {
        const int32_t shifted_input1_val =
            (params.input1_offset + input1_val) * (1 << params.left_shift);
        const int32_t shifted_input2_val =
            (params.input2_offset + input2_val) * (1 << params.left_shift);
        const int32_t scaled_input1_val =
            MultiplyByQuantizedMultiplierSmallerThanOneExp(
                shifted_input1_val, params.input1_multiplier,
                params.input1_shift);
        const int32_t scaled_input2_val =
            MultiplyByQuantizedMultiplierSmallerThanOneExp(
                shifted_input2_val, params.input2_multiplier,
                params.input2_shift);
        const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
        const int32_t raw_output =
            MultiplyByQuantizedMultiplierSmallerThanOneExp(
                raw_sub, params.output_multiplier, params.output_shift) +
            params.output_offset;
        const int32_t clamped_output =
            std::min(params.quantized_activation_max,
                     std::max(params.quantized_activation_min, raw_output));
        return static_cast<T>(clamped_output);
      });
}

// Element-wise add that can often be used for inner loop of broadcast add as
// well as the non-broadcast add.
template <typename T>
inline void SubElementwise(int size, const ArithmeticParams& params,
                           const T* input1_data, const T* input2_data,
                           T* output_data) {
  for (int i = 0; i < size; ++i) {
    const int32_t input1_val = params.input1_offset + input1_data[i];
    const int32_t input2_val = params.input2_offset + input2_data[i];
    const int32_t shifted_input1_val = input1_val * (1 << params.left_shift);
    const int32_t shifted_input2_val = input2_val * (1 << params.left_shift);
    const int32_t scaled_input1_val =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            shifted_input1_val, params.input1_multiplier, params.input1_shift);
    const int32_t scaled_input2_val =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            shifted_input2_val, params.input2_multiplier, params.input2_shift);
    const int32_t raw_sub = scaled_input1_val - scaled_input2_val;
    const int32_t raw_output =
        MultiplyByQuantizedMultiplierSmallerThanOneExp(
            raw_sub, params.output_multiplier, params.output_shift) +
        params.output_offset;
    const int32_t clamped_output =
        std::min(params.quantized_activation_max,
                 std::max(params.quantized_activation_min, raw_output));
    output_data[i] = static_cast<T>(clamped_output);
  }
}

inline void Sub(const ArithmeticParams& params,
                const RuntimeShape& input1_shape, const uint8_t* input1_data,
                const RuntimeShape& input2_shape, const uint8_t* input2_data,
                const RuntimeShape& output_shape, uint8_t* output_data) {
  TFLITE_DCHECK_LE(params.quantized_activation_min,
                   params.quantized_activation_max);
  const int flat_size =
      MatchingElementsSize(input1_shape, input2_shape, output_shape);

  TFLITE_DCHECK_GT(params.input1_offset, -256);
  TFLITE_DCHECK_GT(params.input2_offset, -256);
  TFLITE_DCHECK_LT(params.input1_offset, 256);
  TFLITE_DCHECK_LT(params.input2_offset, 256);
  SubElementwise(flat_size, params, input1_data, input2_data, output_data);
}

inline void Sub(const ArithmeticParams& params,
                const RuntimeShape& input1_shape, const int8_t* input1_data,
                const RuntimeShape& input2_shape, const int8_t* input2_data,
                const RuntimeShape& output_shape, int8_t* output_data) {
  TFLITE_DCHECK_LE(params.quantized_activation_min,
                   params.quantized_activation_max);

  const int flat_size =
      MatchingElementsSize(input1_shape, input2_shape, output_shape);

  TFLITE_DCHECK_GE(params.input1_offset, -128);
  TFLITE_DCHECK_GE(params.input2_offset, -128);
  // offset = -quantization_params.zero_point in PrepareGeneralSubOp().
  // So it's maximum can be 128 not 127.
  TFLITE_DCHECK_LE(params.input1_offset, 128);
  TFLITE_DCHECK_LE(params.input2_offset, 128);
  SubElementwise(flat_size, params, input1_data, input2_data, output_data);
}

inline void Sub(const ArithmeticParams& params,
                const RuntimeShape& input1_shape, const int16_t* input1_data,
                const RuntimeShape& input2_shape, const int16_t* input2_data,
                const RuntimeShape& output_shape, int16_t* output_data) {
  TFLITE_DCHECK_LE(params.quantized_activation_min,
                   params.quantized_activation_max);

  const int flat_size =
      MatchingElementsSize(input1_shape, input2_shape, output_shape);

  TFLITE_DCHECK_EQ(params.input1_offset, 0);
  TFLITE_DCHECK_EQ(params.input2_offset, 0);
  SubElementwise(flat_size, params, input1_data, input2_data, output_data);
}

template <typename T>
void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape,
         const T* input1_data, const RuntimeShape& input2_shape,
         const T* input2_data, const RuntimeShape& output_shape,
         T* output_data) {
  BroadcastSubCommon<T>(
      params, input1_shape, input1_data, input2_shape, input2_data,
      output_shape, output_data,
      [](T input1_val, T input2_val, const ArithmeticParams& params) {
        return input1_val - input2_val;
      });
}

inline void SetActivationMinMax(const ArithmeticParams& params,
                                int32_t* activation_min,
                                int32_t* activation_max) {
  *activation_min = params.quantized_activation_min;
  *activation_max = params.quantized_activation_max;
}

inline void SetActivationMinMax(const ArithmeticParams& params,
                                float* activation_min, float* activation_max) {
  *activation_min = params.float_activation_min;
  *activation_max = params.float_activation_max;
}

inline void SetActivationMinMax(const ArithmeticParams& params,
                                int64_t* activation_min,
                                int64_t* activation_max) {
  *activation_min = params.int64_activation_min;
  *activation_max = params.int64_activation_max;
}

template <typename T>
inline void SubWithActivation(
    const ArithmeticParams& params, const RuntimeShape& input1_shape,
    const T* input1_data, const RuntimeShape& input2_shape,
    const T* input2_data, const RuntimeShape& output_shape, T* output_data) {
  ruy::profiler::ScopeLabel label("SubWithActivation");
  const int flat_size =
      MatchingElementsSize(input1_shape, input2_shape, output_shape);
  T activation_min, activation_max;
  SetActivationMinMax(params, &activation_min, &activation_max);

  for (int i = 0; i < flat_size; ++i) {
    output_data[i] = ActivationFunctionWithMinMax(
        input1_data[i] - input2_data[i], activation_min, activation_max);
  }
}

}  // namespace reference_ops
}  // namespace tflite

#endif  // TENSORFLOW_LITE_KERNELS_INTERNAL_REFERENCE_SUB_H_