#include <Python.h> // NOLINT(build/include_alpha)

// Produce deprecation warnings (needs to come before arrayobject.h inclusion).
#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION

#include <boost/make_shared.hpp>
#include <boost/python.hpp>
#include <boost/python/raw_function.hpp>
#include <boost/python/suite/indexing/vector_indexing_suite.hpp>
#include <boost/python/enum.hpp>
#include <numpy/arrayobject.h>

// these need to be included after boost on OS X
#include <string> // NOLINT(build/include_order)
#include <vector> // NOLINT(build/include_order)
#include <fstream> // NOLINT

#include "caffe/caffe.hpp"
#include "caffe/layers/memory_data_layer.hpp"
#include "caffe/layers/python_layer.hpp"
#include "caffe/sgd_solvers.hpp"

// Temporary solution for numpy < 1.7 versions: old macro, no promises.
// You're strongly advised to upgrade to >= 1.7.
#ifndef NPY_ARRAY_C_CONTIGUOUS
#define NPY_ARRAY_C_CONTIGUOUS NPY_C_CONTIGUOUS
#define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
#endif

/* Fix to avoid registration warnings in pycaffe (#3960) */
#define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \
 const boost::python::type_info info = \
 boost::python::type_id<shared_ptr<PTR > >(); \
 const boost::python::converter::registration* reg = \
 boost::python::converter::registry::query(info); \
 if (reg == NULL) { \
 bp::register_ptr_to_python<shared_ptr<PTR > >(); \
 } else if ((*reg).m_to_python == NULL) { \
 bp::register_ptr_to_python<shared_ptr<PTR > >(); \
 } \
} while (0)

namespace bp = boost::python;

namespace caffe {

// For Python, for now, we'll just always use float as the type.
typedef float Dtype;
const int NPY_DTYPE = NPY_FLOAT32;

// Selecting mode.
void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }

void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }

// For convenience, check that input files can be opened, and raise an
// exception that boost will send to Python if not (caffe could still crash
// later if the input files are disturbed before they are actually used, but
// this saves frustration in most cases).
static void CheckFile(const string& filename) {
 std::ifstream f(filename.c_str());
 if (!f.good()) {
 f.close();
 throw std::runtime_error("Could not open file " + filename);
 }
 f.close();
}

void CheckContiguousArray(PyArrayObject* arr, string name,
 int channels, int height, int width) {
 if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
 throw std::runtime_error(name + " must be C contiguous");
 }
 if (PyArray_NDIM(arr) != 4) {
 throw std::runtime_error(name + " must be 4-d");
 }
 if (PyArray_TYPE(arr) != NPY_FLOAT32) {
 throw std::runtime_error(name + " must be float32");
 }
 if (PyArray_DIMS(arr)[1] != channels) {
 throw std::runtime_error(name + " has wrong number of channels");
 }
 if (PyArray_DIMS(arr)[2] != height) {
 throw std::runtime_error(name + " has wrong height");
 }
 if (PyArray_DIMS(arr)[3] != width) {
 throw std::runtime_error(name + " has wrong width");
 }
}

// Net constructor
shared_ptr<Net<Dtype> > Net_Init(string network_file, int phase,
 const int level, const bp::object& stages,
 const bp::object& weights) {
 CheckFile(network_file);

 // Convert stages from list to vector
 vector<string> stages_vector;
 if (!stages.is_none()) {
 for (int i = 0; i < len(stages); i++) {
 stages_vector.push_back(bp::extract<string>(stages[i]));
 }
 }

 // Initialize net
 shared_ptr<Net<Dtype> > net(new Net<Dtype>(network_file,
 static_cast<Phase>(phase), level, &stages_vector));

 // Load weights
 if (!weights.is_none()) {
 std::string weights_file_str = bp::extract<std::string>(weights);
 CheckFile(weights_file_str);
 net->CopyTrainedLayersFrom(weights_file_str);
 }

 return net;
}

// Legacy Net construct-and-load convenience constructor
shared_ptr<Net<Dtype> > Net_Init_Load(
 string param_file, string pretrained_param_file, int phase) {
 LOG(WARNING) << "DEPRECATION WARNING - deprecated use of Python interface";
 LOG(WARNING) << "Use this instead (with the named \"weights\""
 << " parameter):";
 LOG(WARNING) << "Net('" << param_file << "', " << phase
 << ", weights='" << pretrained_param_file << "')";
 CheckFile(param_file);
 CheckFile(pretrained_param_file);

 shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
 static_cast<Phase>(phase)));
 net->CopyTrainedLayersFrom(pretrained_param_file);
 return net;
}

void Net_Save(const Net<Dtype>& net, string filename) {
 NetParameter net_param;
 net.ToProto(&net_param, false);
 WriteProtoToBinaryFile(net_param, filename.c_str());
}

void Net_SaveHDF5(const Net<Dtype>& net, string filename) {
 net.ToHDF5(filename);
}

void Net_LoadHDF5(Net<Dtype>* net, string filename) {
 net->CopyTrainedLayersFromHDF5(filename.c_str());
}

void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
 bp::object labels_obj) {
 // check that this network has an input MemoryDataLayer
 shared_ptr<MemoryDataLayer<Dtype> > md_layer =
 boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
 if (!md_layer) {
 throw std::runtime_error("set_input_arrays may only be called if the"
 " first layer is a MemoryDataLayer");
 }

 // check that we were passed appropriately-sized contiguous memory
 PyArrayObject* data_arr =
 reinterpret_cast<PyArrayObject*>(data_obj.ptr());
 PyArrayObject* labels_arr =
 reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
 CheckContiguousArray(data_arr, "data array", md_layer->channels(),
 md_layer->height(), md_layer->width());
 CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
 if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
 throw std::runtime_error("data and labels must have the same first"
 " dimension");
 }
 if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
 throw std::runtime_error("first dimensions of input arrays must be a"
 " multiple of batch size");
 }

 md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
 static_cast<Dtype*>(PyArray_DATA(labels_arr)),
 PyArray_DIMS(data_arr)[0]);
}

Solver<Dtype>* GetSolverFromFile(const string& filename) {
 SolverParameter param;
 ReadSolverParamsFromTextFileOrDie(filename, &param);
 return SolverRegistry<Dtype>::CreateSolver(param);
}

struct NdarrayConverterGenerator {
 template <typename T> struct apply;
};

template <>
struct NdarrayConverterGenerator::apply<Dtype*> {
 struct type {
 PyObject* operator() (Dtype* data) const {
 // Just store the data pointer, and add the shape information in postcall.
 return PyArray_SimpleNewFromData(0, NULL, NPY_DTYPE, data);
 }
 const PyTypeObject* get_pytype() {
 return &PyArray_Type;
 }
 };
};

struct NdarrayCallPolicies : public bp::default_call_policies {
 typedef NdarrayConverterGenerator result_converter;
 PyObject* postcall(PyObject* pyargs, PyObject* result) {
 bp::object pyblob = bp::extract<bp::tuple>(pyargs)()[0];
 shared_ptr<Blob<Dtype> > blob =
 bp::extract<shared_ptr<Blob<Dtype> > >(pyblob);
 // Free the temporary pointer-holding array, and construct a new one with
 // the shape information from the blob.
 void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
 Py_DECREF(result);
 const int num_axes = blob->num_axes();
 vector<npy_intp> dims(blob->shape().begin(), blob->shape().end());
 PyObject *arr_obj = PyArray_SimpleNewFromData(num_axes, dims.data(),
 NPY_FLOAT32, data);
 // SetBaseObject steals a ref, so we need to INCREF.
 Py_INCREF(pyblob.ptr());
 PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
 pyblob.ptr());
 return arr_obj;
 }
};

bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
 if (bp::len(kwargs) > 0) {
 throw std::runtime_error("Blob.reshape takes no kwargs");
 }
 Blob<Dtype>* self = bp::extract<Blob<Dtype>*>(args[0]);
 vector<int> shape(bp::len(args) - 1);
 for (int i = 1; i < bp::len(args); ++i) {
 shape[i - 1] = bp::extract<int>(args[i]);
 }
 self->Reshape(shape);
 // We need to explicitly return None to use bp::raw_function.
 return bp::object();
}

bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
 if (bp::len(kwargs) > 0) {
 throw std::runtime_error("BlobVec.add_blob takes no kwargs");
 }
 typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
 BlobVec* self = bp::extract<BlobVec*>(args[0]);
 vector<int> shape(bp::len(args) - 1);
 for (int i = 1; i < bp::len(args); ++i) {
 shape[i - 1] = bp::extract<int>(args[i]);
 }
 self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
 // We need to explicitly return None to use bp::raw_function.
 return bp::object();
}

template<typename Dtype>
class PythonCallback: public Solver<Dtype>::Callback {
 protected:
 bp::object on_start_, on_gradients_ready_;

 public:
 PythonCallback(bp::object on_start, bp::object on_gradients_ready)
 : on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }
 virtual void on_gradients_ready() {
 on_gradients_ready_();
 }
 virtual void on_start() {
 on_start_();
 }
};
template<typename Dtype>
void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
 bp::object on_gradients_ready) {
 solver->add_callback(new PythonCallback<Dtype>(on_start, on_gradients_ready));
}

BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);

BOOST_PYTHON_MODULE(_caffe) {
 // below, we prepend an underscore to methods that will be replaced
 // in Python

 bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);

 // Caffe utility functions
 bp::def("set_mode_cpu", &set_mode_cpu);
 bp::def("set_mode_gpu", &set_mode_gpu);
 bp::def("set_random_seed", &set_random_seed);
 bp::def("set_device", &Caffe::SetDevice);

 bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);

 bp::enum_<Phase>("Phase")
 .value("TRAIN", caffe::TRAIN)
 .value("TEST", caffe::TEST)
 .export_values();

 bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
 bp::no_init)
 // Constructor
 .def("__init__", bp::make_constructor(&Net_Init,
 bp::default_call_policies(), (bp::arg("network_file"), "phase",
 bp::arg("level")=0, bp::arg("stages")=bp::object(),
 bp::arg("weights")=bp::object())))
 // Legacy constructor
 .def("__init__", bp::make_constructor(&Net_Init_Load))
 .def("_forward", &Net<Dtype>::ForwardFromTo)
 .def("_backward", &Net<Dtype>::BackwardFromTo)
 .def("reshape", &Net<Dtype>::Reshape)
 .add_property("phase", bp::make_function(&Layer<Dtype>::phase))
 .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
 // The cast is to select a particular overload.
 .def("copy_from", static_cast<void (Net<Dtype>::*)(const string)>(
 &Net<Dtype>::CopyTrainedLayersFrom))
 .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
 .add_property("_blob_loss_weights", bp::make_function(
 &Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
 .def("_bottom_ids", bp::make_function(&Net<Dtype>::bottom_ids,
 bp::return_value_policy<bp::copy_const_reference>()))
 .def("_top_ids", bp::make_function(&Net<Dtype>::top_ids,
 bp::return_value_policy<bp::copy_const_reference>()))
 .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
 bp::return_internal_reference<>()))
 .add_property("layers", bp::make_function(&Net<Dtype>::layers,
 bp::return_internal_reference<>()))
 .add_property("_blob_names", bp::make_function(&Net<Dtype>::blob_names,
 bp::return_value_policy<bp::copy_const_reference>()))
 .add_property("_layer_names", bp::make_function(&Net<Dtype>::layer_names,
 bp::return_value_policy<bp::copy_const_reference>()))
 .add_property("_inputs", bp::make_function(&Net<Dtype>::input_blob_indices,
 bp::return_value_policy<bp::copy_const_reference>()))
 .add_property("_outputs",
 bp::make_function(&Net<Dtype>::output_blob_indices,
 bp::return_value_policy<bp::copy_const_reference>()))
 .def("_set_input_arrays", &Net_SetInputArrays,
 bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
 .def("save", &Net_Save)
 .def("save_hdf5", &Net_SaveHDF5)
 .def("load_hdf5", &Net_LoadHDF5);
 BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);

 bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
 "Blob", bp::no_init)
 .add_property("shape",
 bp::make_function(
 static_cast<const vector<int>& (Blob<Dtype>::*)() const>(
 &Blob<Dtype>::shape),
 bp::return_value_policy<bp::copy_const_reference>()))
 .add_property("num", &Blob<Dtype>::num)
 .add_property("channels", &Blob<Dtype>::channels)
 .add_property("height", &Blob<Dtype>::height)
 .add_property("width", &Blob<Dtype>::width)
 .add_property("count", static_cast<int (Blob<Dtype>::*)() const>(
 &Blob<Dtype>::count))
 .def("reshape", bp::raw_function(&Blob_Reshape))
 .add_property("data", bp::make_function(&Blob<Dtype>::mutable_cpu_data,
 NdarrayCallPolicies()))
 .add_property("diff", bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
 NdarrayCallPolicies()));
 BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);

 bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
 boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
 .add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,
 bp::return_internal_reference<>()))
 .def("setup", &Layer<Dtype>::LayerSetUp)
 .def("reshape", &Layer<Dtype>::Reshape)
 .add_property("type", bp::make_function(&Layer<Dtype>::type));
 BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);

 bp::class_<LayerParameter>("LayerParameter", bp::no_init);

 bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
 "Solver", bp::no_init)
 .add_property("net", &Solver<Dtype>::net)
 .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
 bp::return_internal_reference<>()))
 .add_property("iter", &Solver<Dtype>::iter)
 .def("add_callback", &Solver_add_callback<Dtype>)
 .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
 &Solver<Dtype>::Solve), SolveOverloads())
 .def("step", &Solver<Dtype>::Step)
 .def("restore", &Solver<Dtype>::Restore)
 .def("snapshot", &Solver<Dtype>::Snapshot);
 BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);

 bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
 shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
 "SGDSolver", bp::init<string>());
 bp::class_<NesterovSolver<Dtype>, bp::bases<Solver<Dtype> >,
 shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
 "NesterovSolver", bp::init<string>());
 bp::class_<AdaGradSolver<Dtype>, bp::bases<Solver<Dtype> >,
 shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
 "AdaGradSolver", bp::init<string>());
 bp::class_<RMSPropSolver<Dtype>, bp::bases<Solver<Dtype> >,
 shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
 "RMSPropSolver", bp::init<string>());
 bp::class_<AdaDeltaSolver<Dtype>, bp::bases<Solver<Dtype> >,
 shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
 "AdaDeltaSolver", bp::init<string>());
 bp::class_<AdamSolver<Dtype>, bp::bases<Solver<Dtype> >,
 shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>(
 "AdamSolver", bp::init<string>());

 bp::def("get_solver", &GetSolverFromFile,
 bp::return_value_policy<bp::manage_new_object>());

 // vector wrappers for all the vector types we use
 bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
 .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>())
 .def("add_blob", bp::raw_function(&BlobVec_add_blob));
 bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
 .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
 bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
 .def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
 bp::class_<vector<string> >("StringVec")
 .def(bp::vector_indexing_suite<vector<string> >());
 bp::class_<vector<int> >("IntVec")
 .def(bp::vector_indexing_suite<vector<int> >());
 bp::class_<vector<Dtype> >("DtypeVec")
 .def(bp::vector_indexing_suite<vector<Dtype> >());
 bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
 .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
 bp::class_<vector<bool> >("BoolVec")
 .def(bp::vector_indexing_suite<vector<bool> >());

 // boost python expects a void (missing) return value, while import_array
 // returns NULL for python3. import_array1() forces a void return value.
 import_array1();
}

} // namespace caffe