Revert code duplication changes

Author: dyashuni

python_bindings/bindings.cpp

@@ -79,40 +79,6 @@ inline void assert_true(bool expr, const std::string & msg) {
 }
 
 
-inline void set_input_array_shapes(const py::buffer_info& buffer, size_t* rows, size_t* features) {
-    if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
-    if (buffer.ndim == 2) {
-        *rows = buffer.shape[0];
-        *features = buffer.shape[1];
-    } else {
-        *rows = 1;
-        *features = buffer.shape[0];
-    }
-}
-
-
-inline std::vector<size_t> get_input_ids(const py::object& ids_, size_t rows) {
-    std::vector<size_t> ids;
-    if (!ids_.is_none()) {
-        py::array_t < size_t, py::array::c_style | py::array::forcecast > items(ids_);
-        auto ids_numpy = items.request();
-        if (ids_numpy.ndim == 1 && ids_numpy.shape[0] == rows) {
-            std::vector<size_t> ids1(ids_numpy.shape[0]);
-            for (size_t i = 0; i < ids1.size(); i++) {
-                ids1[i] = items.data()[i];
-            }
-            ids.swap(ids1);
-        } else if (ids_numpy.ndim == 0 && rows == 1) {
-            ids.push_back(*items.data());
-        } else {
-            throw std::runtime_error("wrong dimensionality of the labels");
-        }
-    }
-
-    return ids;
-}
-
-
 template<typename dist_t, typename data_t = float>
 class Index {
  public:
@@ -222,7 +188,15 @@ class Index {
             num_threads = num_threads_default;
 
         size_t rows, features;
-        set_input_array_shapes(buffer, &rows, &features);
+
+        if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
+        if (buffer.ndim == 2) {
+            rows = buffer.shape[0];
+            features = buffer.shape[1];
+        } else {
+            rows = 1;
+            features = buffer.shape[0];
+        }
 
         if (features != dim)
             throw std::runtime_error("wrong dimensionality of the vectors");
@@ -232,7 +206,23 @@ class Index {
             num_threads = 1;
         }
 
-        std::vector<size_t> ids = get_input_ids(ids_, rows);
+        std::vector<size_t> ids;
+
+        if (!ids_.is_none()) {
+            py::array_t < size_t, py::array::c_style | py::array::forcecast > items(ids_);
+            auto ids_numpy = items.request();
+            if (ids_numpy.ndim == 1 && ids_numpy.shape[0] == rows) {
+                std::vector<size_t> ids1(ids_numpy.shape[0]);
+                for (size_t i = 0; i < ids1.size(); i++) {
+                    ids1[i] = items.data()[i];
+                }
+                ids.swap(ids1);
+            } else if (ids_numpy.ndim == 0 && rows == 1) {
+                ids.push_back(*items.data());
+            } else {
+                throw std::runtime_error("wrong dimensionality of the labels");
+            }
+        }
 
         {
             int start = 0;
@@ -571,7 +561,15 @@ class Index {
 
         {
             py::gil_scoped_release l;
-            set_input_array_shapes(buffer, &rows, &features);
+
+            if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
+            if (buffer.ndim == 2) {
+                rows = buffer.shape[0];
+                features = buffer.shape[1];
+            } else {
+                rows = 1;
+                features = buffer.shape[0];
+            }
 
             // avoid using threads when the number of searches is small:
             if (rows <= num_threads * 4) {
@@ -727,12 +725,36 @@ class BFIndex {
         py::array_t < dist_t, py::array::c_style | py::array::forcecast > items(input);
         auto buffer = items.request();
         size_t rows, features;
-        set_input_array_shapes(buffer, &rows, &features);
+
+        if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
+        if (buffer.ndim == 2) {
+            rows = buffer.shape[0];
+            features = buffer.shape[1];
+        } else {
+            rows = 1;
+            features = buffer.shape[0];
+        }
 
         if (features != dim)
             throw std::runtime_error("wrong dimensionality of the vectors");
 
-        std::vector<size_t> ids = get_input_ids(ids_, rows);
+        std::vector<size_t> ids;
+
+        if (!ids_.is_none()) {
+            py::array_t < size_t, py::array::c_style | py::array::forcecast > items(ids_);
+            auto ids_numpy = items.request();
+            if (ids_numpy.ndim == 1 && ids_numpy.shape[0] == rows) {
+                std::vector<size_t> ids1(ids_numpy.shape[0]);
+                for (size_t i = 0; i < ids1.size(); i++) {
+                    ids1[i] = items.data()[i];
+                }
+                ids.swap(ids1);
+            } else if (ids_numpy.ndim == 0 && rows == 1) {
+                ids.push_back(*items.data());
+            } else {
+                throw std::runtime_error("wrong dimensionality of the labels");
+            }
+        }
 
         {
             for (size_t row = 0; row < rows; row++) {
@@ -780,7 +802,14 @@ class BFIndex {
         {
             py::gil_scoped_release l;
 
-            set_input_array_shapes(buffer, &rows, &features);
+            if (buffer.ndim != 2 && buffer.ndim != 1) throw std::runtime_error("data must be a 1d/2d array");
+            if (buffer.ndim == 2) {
+                rows = buffer.shape[0];
+                features = buffer.shape[1];
+            } else {
+                rows = 1;
+                features = buffer.shape[0];
+            }
 
             data_numpy_l = new hnswlib::labeltype[rows * k];
             data_numpy_d = new dist_t[rows * k];