text: //
  // Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
  // SPDX-License-Identifier: MIT
  //
  
  #pragma once
  
  #include "DelegateUtils.hpp"
  #include <armnn/utility/IgnoreUnused.hpp>
  
  #include <armnn/Descriptors.hpp>
  
  #include <tensorflow/lite/builtin_ops.h>
  #include <tensorflow/lite/c/builtin_op_data.h>
  #include <tensorflow/lite/c/common.h>
  #include <tensorflow/lite/minimal_logging.h>
  #include <tensorflow/lite/kernels/internal/tensor_ctypes.h>
  
  namespace armnnDelegate
  {
  
  
  
  TfLiteStatus ValidateResizeOperator(DelegateData& delegateData,
                                      TfLiteContext* tfLiteContext,
                                      const armnn::TensorInfo& inputInfo,
                                      const armnn::TensorInfo& outputInfo,
                                      const armnn::ResizeDescriptor& descriptor)
  {
      bool isSupported = false;
      FORWARD_LAYER_SUPPORT_FUNC(__func__,
                                 tfLiteContext,
                                 IsResizeSupported,
                                 delegateData.m_Backends,
                                 isSupported,
                                 inputInfo,
                                 outputInfo,
                                 descriptor);
  
      return isSupported ? kTfLiteOk : kTfLiteError;
  }
  
  TfLiteStatus VisitResizeOperator(DelegateData& delegateData,
                                   TfLiteContext* tfLiteContext,
                                   TfLiteNode* tfLiteNode,
                                   int nodeIndex,
                                   int32_t resizeOperatorCode)
  {
      TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
      TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
  
      const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
  
      // The first input contains the data of the image that should be resized [batch, height, width, channels]
      const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
      if (IsDynamicTensor(tfLiteInputTensor))
      {
          TF_LITE_MAYBE_KERNEL_LOG(
              tfLiteContext,
              "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
              resizeOperatorCode, nodeIndex);
          return kTfLiteError;
      }
  
      // The second input contains a size tensor. The size tensor contains two integer values
      // that describe the new height and width of the image [new_height, new_width]
      const TfLiteTensor& tfLiteSizeTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
      if (IsDynamicTensor(tfLiteSizeTensor))
      {
          TF_LITE_MAYBE_KERNEL_LOG(
              tfLiteContext,
              "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
              resizeOperatorCode, nodeIndex);
          return kTfLiteError;
      }
  
      // The output tensor should have the shape [batch, new_height, new_width, channels]
      const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
      if (IsDynamicTensor(tfLiteOutputTensor))
      {
          TF_LITE_MAYBE_KERNEL_LOG(
              tfLiteContext,
              "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
              resizeOperatorCode, nodeIndex);
          return kTfLiteError;
      }
  
      const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
      const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
  
      std::string layerName("Resize");
  
      // Fill descriptor
      armnn::ResizeDescriptor desc;
      switch (resizeOperatorCode)
      {
          case kTfLiteBuiltinResizeBilinear:
          {
              desc.m_Method = armnn::ResizeMethod::Bilinear;
  
              layerName += "Bilinear:" + std::to_string(nodeIndex);
  
              TfLiteResizeBilinearParams* biliniarOptions =
                      reinterpret_cast<TfLiteResizeBilinearParams*>(tfLiteNode->builtin_data);
  
              desc.m_AlignCorners = biliniarOptions->align_corners;
              desc.m_HalfPixelCenters = biliniarOptions->half_pixel_centers;
              break;
          }
          case kTfLiteBuiltinResizeNearestNeighbor:
          {
              desc.m_Method =  armnn::ResizeMethod::NearestNeighbor;
              layerName += "NearestNeighbor:" + std::to_string(nodeIndex);
  
              TfLiteResizeNearestNeighborParams* nearestNeighborOptions =
                      reinterpret_cast<TfLiteResizeNearestNeighborParams*>(tfLiteNode->builtin_data);
  
              desc.m_AlignCorners = nearestNeighborOptions->align_corners;
              desc.m_HalfPixelCenters = nearestNeighborOptions->half_pixel_centers;
              break;
          }
          default:
          {
              TF_LITE_MAYBE_KERNEL_LOG(
                      tfLiteContext,
                      "TfLiteArmnnDelegate: Unknown TfLite built in operation for Resize. Given operator: #%d node #%d: ",
                      resizeOperatorCode, nodeIndex);
              return kTfLiteError;
          }
      }
  
      // In armnn the values of the size input tensor [new_hight, new_width] is saved in the operator
      // descriptor. We have to read it from the input tensor and write it to the descriptor.
  
      auto* sizeTensorDataPtr = tflite::GetTensorData<int32_t>(&tfLiteSizeTensor);
      auto sizeTensorNumDimensions = tfLiteSizeTensor.dims->size;
      // The size tensor is only a 1D tensor -> [new_hight, new width]
      if (sizeTensorNumDimensions != 1)
      {
          TF_LITE_MAYBE_KERNEL_LOG(
                  tfLiteContext,
                  "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
                  "dynamic tensor. Operator: #%d node #%d: ",
                  resizeOperatorCode, nodeIndex);
          return kTfLiteError;
      }
  
      // Get number of values in the size tensor
      auto sizeTensorNumValues = tfLiteSizeTensor.dims->data[0];
      if (sizeTensorNumValues == 0)
      {
          TF_LITE_MAYBE_KERNEL_LOG(
                  tfLiteContext,
                  "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation is not allowed to be a "
                  "dynamic tensor. Operator: #%d node #%d: ",
                  resizeOperatorCode, nodeIndex);
          return kTfLiteError;
      }
      else if (sizeTensorNumValues != 2)
      {
          TF_LITE_MAYBE_KERNEL_LOG(
                  tfLiteContext,
                  "TfLiteArmnnDelegate: The Size-Input-Tensor of the Resize operation requires to "
                  "have a dimension of 2 [new_hight, new width] but a tensor with a dimension of #%d was given. "
                  "Operator: #%d node #%d: ",
                  sizeTensorNumValues, resizeOperatorCode, nodeIndex);
          return kTfLiteError;
      }
      // get size tensor data
      std::vector<int32_t> sizeTensorData(sizeTensorDataPtr, sizeTensorDataPtr+sizeTensorNumValues);
  
      desc.m_TargetHeight = static_cast<uint32_t> (sizeTensorData[0]);
      desc.m_TargetWidth  = static_cast<uint32_t> (sizeTensorData[1]);
      desc.m_DataLayout   = armnn::DataLayout::NHWC;
  
      // No network pointer indicates that only support for this operator should be checked
      if (!delegateData.m_Network)
      {
          return ValidateResizeOperator(delegateData,
                                        tfLiteContext,
                                        inputTensorInfo,
                                        outputTensorInfo,
                                        desc);
      }
  
  
      armnn::IConnectableLayer* resizeLayer = nullptr;
      resizeLayer = delegateData.m_Network->AddResizeLayer(desc, layerName.c_str());
  
      armnn::IOutputSlot& outputSlot = resizeLayer->GetOutputSlot(0);
      outputSlot.SetTensorInfo(outputTensorInfo);
  
      ARMNN_ASSERT(resizeLayer != nullptr);
  
      return Connect(resizeLayer, tfLiteNode, delegateData);
  }
  
  } // namespace armnnDelegate
  