text: // Copyright (c) Microsoft Corporation. All rights reserved.
  // Licensed under the MIT License.
  
  #include "core/providers/shared/utils/utils.h"
  #include "core/providers/coreml/builders/helper.h"
  #include "core/providers/coreml/builders/model_builder.h"
  #include "core/providers/coreml/builders/op_builder_factory.h"
  
  #include "base_op_builder.h"
  
  namespace onnxruntime {
  namespace coreml {
  
  class CastOpBuilder : public BaseOpBuilder {
    // Add operator related
   private:
    Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
                                 const logging::Logger& logger) const override ORT_MUST_USE_RESULT;
  
    // Operator support related
   private:
    bool IsOpSupportedImpl(const Node& node, const OpBuilderInputParams& input_params,
                           const logging::Logger& logger) const override;
  };
  
  // Add operator related
  
  Status CastOpBuilder::AddToModelBuilderImpl(ModelBuilder& /* model_builder */,
                                              const Node& /* node */,
                                              const logging::Logger& /* logger */) const {
    // This is a special handling case for ArgMax Op, where argmax is followed by a cast to int32 type.
    // The ArgMax is fused with the Cast node and produces an int32 output.
    // Cast node is not provided in CoreML model, so we're skipping adding the Cast node here.
    return Status::OK();
  }
  
  // Operator support related
  
  bool CastOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputParams& input_params,
                                        const logging::Logger& logger) const {
    if (node.GetInputEdgesCount() == 0) {
      LOGS(logger, VERBOSE) << "Cast has no preceding nodes.";
      return false;
    }
  
    if (node.GetInputEdgesCount() > 1) {
      LOGS(logger, VERBOSE) << "Multiple nodes producing Cast's input.";
      return false;
    }
  
    const auto& prec_node = node.InputEdgesBegin()->GetNode();
  
    /*Cast node is only aimed for supporting argmax and we are only handling the case where an argmax 
      followed by a cast node. We need to check if the preceding node is an argmax and also if it's a
      supported argmax op type.*/
    if (prec_node.OpType() != "ArgMax") {
      LOGS(logger, VERBOSE) << "Cast's producing node is not ArgMax is not supported."
                            << "Current producing node: [" << prec_node.OpType()
                            << "]";
      return false;
    }
    if (!IsNodeSupported(prec_node, input_params.graph_viewer, logger)) {
      LOGS(logger, VERBOSE) << "Cast's producing node ["
                            << prec_node.OpType()
                            << "] is not a supported op.";
      return false;
    }
  
    // Check if the output type of cast node is int32
    NodeAttrHelper helper(node);
    const auto cast_to_type = helper.Get("to", ONNX_NAMESPACE::TensorProto::UNDEFINED);
    if (cast_to_type != ONNX_NAMESPACE::TensorProto::INT32) {
      LOGS(logger, VERBOSE) << "[" << node.OpType()
                            << "] Output type: [" << cast_to_type
                            << "] is not supported.";
      return false;
    }
  
    return true;
  }
  
  void CreateCastOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
    op_registrations.builders.push_back(std::make_unique<CastOpBuilder>());
    op_registrations.op_builder_map.emplace(op_type, op_registrations.builders.back().get());
  }
  
  }  // namespace coreml
  }  // namespace onnxruntime
  