// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.

#include "core/providers/common.h"
#include "core/providers/shared/utils/utils.h"
#include "core/providers/coreml/builders/helper.h"
#ifdef __APPLE__
#include "core/providers/coreml/builders/model_builder.h"
#endif
#include "core/providers/coreml/builders/op_builder_factory.h"

#include "base_op_builder.h"
#include "builder_utils.h"

namespace onnxruntime {
namespace coreml {

class BatchNormalizationOpBuilder : public BaseOpBuilder {
  // Add operator related
#ifdef __APPLE__
 public:
  void AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const override;

 private:
  [[nodiscard]] Status AddToModelBuilderImpl(ModelBuilder& model_builder, const Node& node,
                                             const logging::Logger& logger) const override;
#endif

  // Operator support related
 private:
  bool IsOpSupportedImpl(const Node& node, const OpBuilderInputParams& input_params,
                         const logging::Logger& logger) const override;

  // BatchNormalization opset 6- has unsupported attributes
  int GetMinSupportedOpSet(const Node& /* node */) const override { return 7; }
};

// Add operator related

#ifdef __APPLE__
void BatchNormalizationOpBuilder::AddInitializersToSkip(ModelBuilder& model_builder, const Node& node) const {
  // skip everything except input0 for BatchNormalization
  const auto& input_defs = node.InputDefs();
  model_builder.AddInitializerToSkip(input_defs[1]->Name());  // scale
  model_builder.AddInitializerToSkip(input_defs[2]->Name());  // B
  model_builder.AddInitializerToSkip(input_defs[3]->Name());  // mean
  model_builder.AddInitializerToSkip(input_defs[4]->Name());  // var
}

Status BatchNormalizationOpBuilder::AddToModelBuilderImpl(ModelBuilder& model_builder,
                                                          const Node& node,
                                                          const logging::Logger& /* logger */) const {
  std::unique_ptr<COREML_SPEC::NeuralNetworkLayer> layer = CreateNNLayer(model_builder, node);

  const auto& input_defs = node.InputDefs();
  const auto& initializers(model_builder.GetInitializerTensors());
  NodeAttrHelper helper(node);

  const auto& scale_tensor = *initializers.at(input_defs[1]->Name());
  const auto& bias_tensor = *initializers.at(input_defs[2]->Name());
  const auto& mean_tensor = *initializers.at(input_defs[3]->Name());
  const auto& var_tensor = *initializers.at(input_defs[4]->Name());
  const auto eps = helper.Get("epsilon", 1e-5f);
  const auto channels = scale_tensor.dims()[0];

  auto* coreml_batch_norm = layer->mutable_batchnorm();
  coreml_batch_norm->set_channels(channels);
  coreml_batch_norm->set_epsilon(eps);
  coreml_batch_norm->set_computemeanvar(false);
  coreml_batch_norm->set_instancenormalization(false);

  ORT_RETURN_IF_ERROR(CreateCoreMLWeight(*coreml_batch_norm->mutable_gamma(), scale_tensor));   // scale
  ORT_RETURN_IF_ERROR(CreateCoreMLWeight(*coreml_batch_norm->mutable_beta(), bias_tensor));     // B
  ORT_RETURN_IF_ERROR(CreateCoreMLWeight(*coreml_batch_norm->mutable_mean(), mean_tensor));     // mean
  ORT_RETURN_IF_ERROR(CreateCoreMLWeight(*coreml_batch_norm->mutable_variance(), var_tensor));  // var

  *layer->mutable_input()->Add() = node.InputDefs()[0]->Name();
  *layer->mutable_output()->Add() = node.OutputDefs()[0]->Name();

  model_builder.AddLayer(std::move(layer));
  return Status::OK();
}
#endif

// Operator support related

bool BatchNormalizationOpBuilder::IsOpSupportedImpl(const Node& node, const OpBuilderInputParams& input_params,
                                                    const logging::Logger& logger) const {
  if (node.OutputDefs().size() != 1) {
    LOGS(logger, VERBOSE) << "Your onnx model may be in training mode, please export "
                             "it in test mode.";
    return false;
  }

  const auto& input_defs = node.InputDefs();
  std::vector<int64_t> input_shape;
  if (!GetShape(*input_defs[0], input_shape, logger))
    return false;

  const auto input_size = input_shape.size();
  // TODO, support 1d batch normalization (input is 3d)
  // To map 1d input {N,C,H} to 2d {N,C,H,1} first and then squeeze back after
  if (input_size != 4) {
    LOGS(logger, VERBOSE) << "BN only support 4d shape for now, input is "
                          << input_size << "d shape";
    return false;
  }

  NodeAttrHelper helper(node);
  const auto spatial = helper.Get("spatial", 1);
  if (spatial != 1) {
    LOGS(logger, VERBOSE) << "Non-spatial BN is not supported";
    return false;
  }

  const auto& scale_name = input_defs[1]->Name();
  const auto& b_name = input_defs[2]->Name();
  const auto& mean_name = input_defs[3]->Name();
  const auto& var_name = input_defs[4]->Name();
  const auto& initializers = input_params.graph_viewer.GetAllInitializedTensors();
  if (!Contains(initializers, scale_name)) {
    LOGS(logger, VERBOSE) << "Scale of BN must be a constant initializer";
    return false;
  }
  if (!Contains(initializers, b_name)) {
    LOGS(logger, VERBOSE) << "B of BN must be a constant initializer";
    return false;
  }
  if (!Contains(initializers, mean_name)) {
    LOGS(logger, VERBOSE) << "Mean of BN must be a constant initializer";
    return false;
  }
  if (!Contains(initializers, var_name)) {
    LOGS(logger, VERBOSE) << "Var of BN must be a constant initializer";
    return false;
  }

  return true;
}

void CreateBatchNormalizationOpBuilder(const std::string& op_type, OpBuilderRegistrations& op_registrations) {
  op_registrations.builders.push_back(std::make_unique<BatchNormalizationOpBuilder>());
  op_registrations.op_builder_map.emplace(op_type, op_registrations.builders.back().get());
}

}  // namespace coreml
}  // namespace onnxruntime
