Unskip tests2 (#686)

* unskip 194 tests

Author: Rubtsowa

dpnp/backend/kernels/dpnp_krnl_indexing.cpp

@@ -254,14 +254,23 @@ void dpnp_put_c(
     size_t* ind = reinterpret_cast<size_t*>(ind_in);
     _DataType* v = reinterpret_cast<_DataType*>(v_in);
 
+    if ((array_1 == nullptr) || (ind == nullptr) || (v == nullptr))
+    {
+        return;
+    }
+
+    if (size_v == 0)
+    {
+        return;
+    }
+
     for (size_t i = 0; i < size; ++i)
     {
         for (size_t j = 0; j < size_ind; ++j)
         {
-            if (i == ind[j])
+            if (i == ind[j] || (i == (size + ind[j])))
             {
                 array_1[i] = v[j % size_v];
-                break;
             }
         }
     }

dpnp/dparray.pyx

@@ -833,6 +833,12 @@ cdef class dparray:
     -------------------------------------------------------------------------
     """
 
+    def cumprod(x1, **kwargs):
+        return cumprod(x1, **kwargs)
+
+    def cumsum(x1, **kwargs):
+        return cumsum(x1, **kwargs)
+
     def prod(*args, **kwargs):
         """
         Returns the prod along a given axis.

dpnp/dpnp_iface_indexing.py

@@ -404,7 +404,7 @@ def put(input, ind, v, mode='raise'):
             pass
         elif type(ind) != type(v):
             pass
-        elif numpy.max(ind) > input.size:
+        elif numpy.max(ind) >= input.size or numpy.min(ind) + input.size < 0:
             pass
         else:
             return dpnp_put(input, ind, v)

dpnp/linalg/dpnp_iface_linalg.py

@@ -382,21 +382,23 @@ def norm(input, ord=None, axis=None, keepdims=False):
         Norm of the matrix or vector(s).
     """
 
-    is_input_dparray = isinstance(input, dparray)
-
-    if not use_origin_backend(input) and is_input_dparray:
-        if keepdims is not False:
-            checker_throw_value_error("norm", "keepdims", keepdims, False)
-        if not isinstance(axis, int) and not isinstance(axis, tuple) and axis is not None:
-            raise TypeError("'axis' must be None, an integer or a tuple of integers")
-
-        result = dpnp_norm(input, ord=ord, axis=axis)
+    if not use_origin_backend(input):
+        if not isinstance(input, dparray):
+            pass
+        elif not isinstance(axis, int) and not isinstance(axis, tuple) and axis is not None:
+            pass
+        elif keepdims is not False:
+            pass
+        elif ord not in [None, 0, 3, 'fro', 'f']:
+            pass
+        else:
+            result = dpnp_norm(input, ord=ord, axis=axis)
 
-        # scalar returned
-        if result.shape == (1,) and axis is None:
-            return result.dtype.type(result[0])
+            # scalar returned
+            if result.shape == (1,) and axis is None:
+                return result.dtype.type(result[0])
 
-        return result
+            return result
 
     return call_origin(numpy.linalg.norm, input, ord, axis, keepdims)