text: /* Copyright 2017 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/contrib/lite/builtin_op_data.h"
  #include "tensorflow/contrib/lite/context.h"
  #include "tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h"
  #include "tensorflow/contrib/lite/kernels/internal/quantization_util.h"
  #include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h"
  #include "tensorflow/contrib/lite/kernels/internal/tensor.h"
  #include "tensorflow/contrib/lite/kernels/kernel_util.h"
  #include "tensorflow/contrib/lite/kernels/op_macros.h"
  
  namespace tflite {
  namespace ops {
  namespace builtin {
  namespace div {
  
  // This file has three implementation of Div.
  enum KernelType {
    kReference,
    kGenericOptimized,  // Neon-free
    kNeonOptimized,
  };
  
  constexpr int kInputTensor1 = 0;
  constexpr int kInputTensor2 = 1;
  constexpr int kOutputTensor = 0;
  
  struct OpData {
    bool requires_broadcast;
  };
  
  void* Init(TfLiteContext* context, const char* buffer, size_t length) {
    auto* data = new OpData;
    data->requires_broadcast = false;
    return data;
  }
  
  void Free(TfLiteContext* context, void* buffer) {
    delete reinterpret_cast<OpData*>(buffer);
  }
  
  TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
    OpData* data = reinterpret_cast<OpData*>(node->user_data);
  
    TF_LITE_ENSURE_EQ(context, NumInputs(node), 2);
    TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1);
  
    const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
    const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
    TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  
    TF_LITE_ENSURE_EQ(context, input1->type, input2->type);
    output->type = input2->type;
  
    data->requires_broadcast = !HaveSameShapes(input1, input2);
  
    TfLiteIntArray* output_size = nullptr;
    if (data->requires_broadcast) {
      TF_LITE_ENSURE_OK(context, CalculateShapeForBroadcast(
                                     context, input1, input2, &output_size));
    } else {
      output_size = TfLiteIntArrayCopy(input1->dims);
    }
  
    return context->ResizeTensor(context, output, output_size);
  }
  
  template <KernelType kernel_type>
  void EvalFloat(TfLiteContext* context, TfLiteNode* node,
                 TfLiteDivParams* params, const OpData* data,
                 const TfLiteTensor* input1, const TfLiteTensor* input2,
                 TfLiteTensor* output) {
    float output_activation_min, output_activation_max;
    CalculateActivationRangeFloat(params->activation, &output_activation_min,
                                  &output_activation_max);
  #define TF_LITE_DIV(type, opname)                                   \
    type::opname(GetTensorData<float>(input1), GetTensorDims(input1), \
                 GetTensorData<float>(input2), GetTensorDims(input2), \
                 output_activation_min, output_activation_max,        \
                 GetTensorData<float>(output), GetTensorDims(output))
    if (kernel_type == kReference) {
      if (data->requires_broadcast) {
        TF_LITE_DIV(reference_ops, BroadcastDiv);
      } else {
        TF_LITE_DIV(reference_ops, Div);
      }
    } else {
      if (data->requires_broadcast) {
        TF_LITE_DIV(optimized_ops, BroadcastDiv);
      } else {
        TF_LITE_DIV(optimized_ops, Div);
      }
    }
  #undef TF_LITE_DIV
  }
  
  template <KernelType kernel_type>
  TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
    auto* params = reinterpret_cast<TfLiteDivParams*>(node->builtin_data);
    OpData* data = reinterpret_cast<OpData*>(node->user_data);
  
    const TfLiteTensor* input1 = GetInput(context, node, kInputTensor1);
    const TfLiteTensor* input2 = GetInput(context, node, kInputTensor2);
    TfLiteTensor* output = GetOutput(context, node, kOutputTensor);
  
    if (output->type == kTfLiteFloat32) {
      EvalFloat<kernel_type>(context, node, params, data, input1, input2, output);
    } else {
      context->ReportError(
          context, "Div only supports FLOAT32 and quantized UINT8 now, got %d.",
          output->type);
      return kTfLiteError;
    }
  
    return kTfLiteOk;
  }
  
  }  // namespace div
  
  TfLiteRegistration* Register_DIV_REF() {
    static TfLiteRegistration r = {div::Init, div::Free, div::Prepare,
                                   div::Eval<div::kReference>};
    return &r;
  }
  
  TfLiteRegistration* Register_DIV_GENERIC_OPT() {
    static TfLiteRegistration r = {div::Init, div::Free, div::Prepare,
                                   div::Eval<div::kGenericOptimized>};
    return &r;
  }
  
  TfLiteRegistration* Register_DIV_NEON_OPT() {
    static TfLiteRegistration r = {div::Init, div::Free, div::Prepare,
                                   div::Eval<div::kNeonOptimized>};
    return &r;
  }
  
  TfLiteRegistration* Register_DIV() {
  #ifdef USE_NEON
    return Register_DIV_NEON_OPT();
  #else
    return Register_DIV_GENERIC_OPT();
  #endif
  }
  
  }  // namespace builtin
  }  // namespace ops
  }  // namespace tflite
  