5649609617

# -*- coding: utf-8 -*-
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import dpctl.tensor as dpt
import numpy

import dpnp


def _get_unwrapped_index_key(key):
    """
    Get an unwrapped index key.

    Return a key where each nested instance of DPNP array is unwrapped into USM ndarray
    for futher processing in DPCTL advanced indexing functions.

    """

    if isinstance(key, tuple):
        if any(isinstance(x, dpnp_array) for x in key):
            # create a new tuple from the input key with unwrapped DPNP arrays
            return tuple(
                x.get_array() if isinstance(x, dpnp_array) else x for x in key
            )
    elif isinstance(key, dpnp_array):
        return key.get_array()
    return key


class dpnp_array:
    """
    Multi-dimensional array object.

    This is a wrapper around dpctl.tensor.usm_ndarray that provides
    methods to be compliant with original Numpy.

    """

    def __init__(
        self,
        shape,
        dtype="f8",
        buffer=None,
        offset=0,
        strides=None,
        order="C",
        device=None,
        usm_type="device",
        sycl_queue=None,
    ):
        if buffer is not None:
            if not isinstance(buffer, dpt.usm_ndarray):
                raise TypeError(
                    "Expected dpctl.tensor.usm_ndarray, got {}"
                    "".format(type(buffer))
                )
            if buffer.shape != shape:
                raise ValueError(
                    "Expected buffer.shape={}, got {}"
                    "".format(shape, buffer.shape)
                )
            self._array_obj = dpt.asarray(buffer, copy=False, order=order)
        else:
            sycl_queue_normalized = dpnp.get_normalized_queue_device(
                device=device, sycl_queue=sycl_queue
            )
            self._array_obj = dpt.usm_ndarray(
                shape,
                dtype=dtype,
                strides=strides,
                buffer=usm_type,
                offset=offset,
                order=order,
                buffer_ctor_kwargs={"queue": sycl_queue_normalized},
            )

    @property
    def __sycl_usm_array_interface__(self):
        return self._array_obj.__sycl_usm_array_interface__

    def get_array(self):
        """Get usm_ndarray object."""
        return self._array_obj

    @property
    def T(self):
        """View of the transposed array."""
        return self.transpose()

    def to_device(self, target_device):
        """Transfer array to target device."""

        return dpnp_array(
            shape=self.shape, buffer=self.get_array().to_device(target_device)
        )

    @property
    def sycl_queue(self):
        return self._array_obj.sycl_queue

    @property
    def sycl_device(self):
        return self._array_obj.sycl_device

    @property
    def sycl_context(self):
        return self._array_obj.sycl_context

    @property
    def device(self):
        return self._array_obj.device

    @property
    def usm_type(self):
        return self._array_obj.usm_type

    def __abs__(self):
        return dpnp.abs(self)

    def __add__(self, other):
        return dpnp.add(self, other)

    def __and__(self, other):
        return dpnp.bitwise_and(self, other)

    # '__array__',
    # '__array_finalize__',
    # '__array_function__',
    # '__array_interface__',
    # '__array_prepare__',
    # '__array_priority__',
    # '__array_struct__',
    # '__array_ufunc__',
    # '__array_wrap__',

    def __bool__(self):
        return self._array_obj.__bool__()

    # '__class__',

    def __complex__(self):
        return self._array_obj.__complex__()

    # '__contains__',
    # '__copy__',
    # '__deepcopy__',
    # '__delattr__',
    # '__delitem__',
    # '__dir__',
    # '__divmod__',
    # '__doc__',

    def __dlpack__(self, stream=None):
        return self._array_obj.__dlpack__(stream=stream)

    def __dlpack_device__(self):
        return self._array_obj.__dlpack_device__()

    def __eq__(self, other):
        return dpnp.equal(self, other)

    def __float__(self):
        return self._array_obj.__float__()

    # '__floordiv__',
    # '__format__',

    def __ge__(self, other):
        return dpnp.greater_equal(self, other)

    # '__getattribute__',

    def __getitem__(self, key):
        key = _get_unwrapped_index_key(key)

        item = self._array_obj.__getitem__(key)
        if not isinstance(item, dpt.usm_ndarray):
            raise RuntimeError(
                "Expected dpctl.tensor.usm_ndarray, got {}"
                "".format(type(item))
            )

        res = self.__new__(dpnp_array)
        res._array_obj = item

        return res

    def __gt__(self, other):
        return dpnp.greater(self, other)

    # '__hash__',

    def __iadd__(self, other):
        dpnp.add(self, other, out=self)
        return self

    def __iand__(self, other):
        dpnp.bitwise_and(self, other, out=self)
        return self

    # '__ifloordiv__',

    def __ilshift__(self, other):
        dpnp.left_shift(self, other, out=self)
        return self

    # '__imatmul__',
    # '__imod__',

    def __imul__(self, other):
        dpnp.multiply(self, other, out=self)
        return self

    def __index__(self):
        return self._array_obj.__index__()

    # '__init__',
    # '__init_subclass__',

    def __int__(self):
        return self._array_obj.__int__()

    def __invert__(self):
        return dpnp.invert(self)

    def __ior__(self, other):
        dpnp.bitwise_or(self, other, out=self)
        return self

    def __ipow__(self, other):
        dpnp.power(self, other, out=self)
        return self

    def __irshift__(self, other):
        dpnp.right_shift(self, other, out=self)
        return self

    def __isub__(self, other):
        dpnp.subtract(self, other, out=self)
        return self

    # '__iter__',

    def __itruediv__(self, other):
        dpnp.true_divide(self, other, out=self)
        return self

    def __ixor__(self, other):
        dpnp.bitwise_xor(self, other, out=self)
        return self

    def __le__(self, other):
        return dpnp.less_equal(self, other)

    def __len__(self):
        """Performs the operation __len__."""

        return self._array_obj.__len__()

    def __lshift__(self, other):
        return dpnp.left_shift(self, other)

    def __lt__(self, other):
        return dpnp.less(self, other)

    def __matmul__(self, other):
        return dpnp.matmul(self, other)

    def __mod__(self, other):
        return dpnp.remainder(self, other)

    def __mul__(self, other):
        return dpnp.multiply(self, other)

    def __ne__(self, other):
        return dpnp.not_equal(self, other)

    def __neg__(self):
        return dpnp.negative(self)

    # '__new__',

    def __or__(self, other):
        return dpnp.bitwise_or(self, other)

    # '__pos__',

    def __pow__(self, other):
        return dpnp.power(self, other)

    def __radd__(self, other):
        return dpnp.add(other, self)

    def __rand__(self, other):
        return dpnp.bitwise_and(other, self)

    # '__rdivmod__',
    # '__reduce__',
    # '__reduce_ex__',

    def __repr__(self):
        return dpt.usm_ndarray_repr(self._array_obj, prefix="array")

    # '__rfloordiv__',

    def __rlshift__(self, other):
        return dpnp.left_shift(other, self)

    def __rmatmul__(self, other):
        return dpnp.matmul(other, self)

    def __rmod__(self, other):
        return dpnp.remainder(other, self)

    def __rmul__(self, other):
        return dpnp.multiply(other, self)

    def __ror__(self, other):
        return dpnp.bitwise_or(other, self)

    def __rpow__(self, other):
        return dpnp.power(other, self)

    def __rrshift__(self, other):
        return dpnp.right_shift(other, self)

    def __rshift__(self, other):
        return dpnp.right_shift(self, other)

    def __rsub__(self, other):
        return dpnp.subtract(other, self)

    def __rtruediv__(self, other):
        return dpnp.true_divide(other, self)

    def __rxor__(self, other):
        return dpnp.bitwise_xor(other, self)

    # '__setattr__',

    def __setitem__(self, key, val):
        key = _get_unwrapped_index_key(key)

        if isinstance(val, dpnp_array):
            val = val.get_array()

        self._array_obj.__setitem__(key, val)

    # '__setstate__',
    # '__sizeof__',

    def __str__(self):
        """
        Output values from the array to standard output.

        Examples
        --------
        >>> print(a)
        [[ 136.  136.  136.]
         [ 272.  272.  272.]
         [ 408.  408.  408.]]

        """

        return self._array_obj.__str__()

    def __sub__(self, other):
        return dpnp.subtract(self, other)

    # '__subclasshook__',

    def __truediv__(self, other):
        return dpnp.true_divide(self, other)

    def __xor__(self, other):
        return dpnp.bitwise_xor(self, other)

    @staticmethod
    def _create_from_usm_ndarray(usm_ary: dpt.usm_ndarray):
        if not isinstance(usm_ary, dpt.usm_ndarray):
            raise TypeError(
                f"Expected dpctl.tensor.usm_ndarray, got {type(usm_ary)}"
            )
        res = dpnp_array.__new__(dpnp_array)
        res._array_obj = usm_ary
        return res

    def all(self, axis=None, out=None, keepdims=False, *, where=True):
        """
        Returns True if all elements evaluate to True.

        Refer to :obj:`dpnp.all` for full documentation.

        See Also
        --------
        :obj:`dpnp.all` : equivalent function

        """

        return dpnp.all(
            self, axis=axis, out=out, keepdims=keepdims, where=where
        )

    def any(self, axis=None, out=None, keepdims=False, *, where=True):
        """
        Returns True if any of the elements of `a` evaluate to True.

        Refer to :obj:`dpnp.any` for full documentation.

        See Also
        --------
        :obj:`dpnp.any` : equivalent function

        """

        return dpnp.any(
            self, axis=axis, out=out, keepdims=keepdims, where=where
        )

    def argmax(self, axis=None, out=None):
        """
        Returns array of indices of the maximum values along the given axis.

        Parameters
        ----------
        axis : {None, integer}
            If None, the index is into the flattened array, otherwise along
            the specified axis
        out : {None, array}, optional
            Array into which the result can be placed. Its type is preserved
            and it must be of the right shape to hold the output.

        Returns
        -------
        index_array : {integer_array}

        Examples
        --------
        >>> a = np.arange(6).reshape(2,3)
        >>> a.argmax()
        5
        >>> a.argmax(0)
        array([1, 1, 1])
        >>> a.argmax(1)
        array([2, 2])

        """
        return dpnp.argmax(self, axis, out)

    def argmin(self, axis=None, out=None):
        """
        Return array of indices to the minimum values along the given axis.

        Parameters
        ----------
        axis : {None, integer}
            If None, the index is into the flattened array, otherwise along
            the specified axis
        out : {None, array}, optional
            Array into which the result can be placed. Its type is preserved
            and it must be of the right shape to hold the output.

        Returns
        -------
        ndarray or scalar
            If multi-dimension input, returns a new ndarray of indices to the
            minimum values along the given axis.  Otherwise, returns a scalar
            of index to the minimum values along the given axis.

        """
        return dpnp.argmin(self, axis, out)

    # 'argpartition',

    def argsort(self, axis=-1, kind=None, order=None):
        """
        Return an ndarray of indices that sort the array along the specified axis.

        Parameters
        ----------
        axis : int, optional
            Axis along which to sort. If None, the default, the flattened array
            is used.
            ..  versionchanged:: 1.13.0
                Previously, the default was documented to be -1, but that was
                in error. At some future date, the default will change to -1, as
                originally intended.
                Until then, the axis should be given explicitly when
                ``arr.ndim > 1``, to avoid a FutureWarning.
        kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional
            The sorting algorithm used.
        order : list, optional
            When `a` is an array with fields defined, this argument specifies
            which fields to compare first, second, etc.  Not all fields need be
            specified.

        Returns
        -------
        index_array : ndarray, int
            Array of indices that sort `a` along the specified axis.
            In other words, ``a[index_array]`` yields a sorted `a`.

        See Also
        --------
        MaskedArray.sort : Describes sorting algorithms used.
        :obj:`dpnp.lexsort` : Indirect stable sort with multiple keys.
        :obj:`numpy.ndarray.sort` : Inplace sort.

        Notes
        -----
        See `sort` for notes on the different sorting algorithms.

        """
        return dpnp.argsort(self, axis, kind, order)

    def asnumpy(self):
        """
        Copy content of the array into :class:`numpy.ndarray` instance of the same shape and data type.

        Returns
        -------
        numpy.ndarray
            An instance of :class:`numpy.ndarray` populated with the array content.

        """

        return dpt.asnumpy(self._array_obj)

    def astype(self, dtype, order="K", casting="unsafe", subok=True, copy=True):
        """Copy the array with data type casting.

        Args:
            dtype: Target type.
            order ({'C', 'F', 'A', 'K'}): Row-major (C-style) or column-major (Fortran-style) order.
                When ``order`` is 'A', it uses 'F' if ``a`` is column-major and uses 'C' otherwise.
                And when ``order`` is 'K', it keeps strides as closely as possible.
            copy (bool): If it is False and no cast happens, then this method returns the array itself.
                Otherwise, a copy is returned.

        Returns:
            If ``copy`` is False and no cast is required, then the array itself is returned.
            Otherwise, it returns a (possibly casted) copy of the array.

        .. note::
           This method currently does not support `order``, `casting``, ``copy``, and ``subok`` arguments.

        .. seealso:: :meth:`numpy.ndarray.astype`

        """

        new_array = self.__new__(dpnp_array)
        new_array._array_obj = dpt.astype(
            self._array_obj, dtype, order=order, casting=casting, copy=copy
        )
        return new_array

    # 'base',
    # 'byteswap',

    def choose(input, choices, out=None, mode="raise"):
        """Construct an array from an index array and a set of arrays to choose from."""

        return dpnp.choose(input, choices, out, mode)

    # 'clip',
    # 'compress',

    def conj(self):
        """
        Complex-conjugate all elements.

        For full documentation refer to :obj:`numpy.ndarray.conj`.

        """

        if not dpnp.issubsctype(self.dtype, dpnp.complex_):
            return self
        else:
            return dpnp.conjugate(self)

    def conjugate(self):
        """
        Return the complex conjugate, element-wise.

        For full documentation refer to :obj:`numpy.ndarray.conjugate`.

        """

        if not dpnp.issubsctype(self.dtype, dpnp.complex_):
            return self
        else:
            return dpnp.conjugate(self)

    # 'copy',
    # 'ctypes',
    # 'cumprod',

    def cumsum(self, axis=None, dtype=None, out=None):
        """
        Return the cumulative sum of the elements along the given axis.

        See Also
        --------
        :obj:`dpnp.cumsum`

        """

        return dpnp.cumsum(self, axis=axis, dtype=dtype, out=out)

    # 'data',

    def diagonal(input, offset=0, axis1=0, axis2=1):
        """
        Return specified diagonals.

        See Also
        --------
        :obj:`dpnp.diagonal`

        """

        return dpnp.diagonal(input, offset, axis1, axis2)

    def dot(self, other, out=None):
        return dpnp.dot(self, other, out)

    @property
    def dtype(self):
        """Returns NumPy's dtype corresponding to the type of the array elements."""

        return self._array_obj.dtype

    # 'dump',
    # 'dumps',

    def fill(self, value):
        """
        Fill the array with a scalar value.

        Parameters
        ----------
        value : scalar
            All elements of `a` will be assigned this value.

        Examples
        --------
        >>> a = np.array([1, 2])
        >>> a.fill(0)
        >>> a
        array([0, 0])
        >>> a = np.empty(2)
        >>> a.fill(1)
        >>> a
        array([1.,  1.])

        """

        for i in range(self.size):
            self.flat[i] = value

    @property
    def flags(self):
        """Return information about the memory layout of the array."""

        return self._array_obj.flags

    @property
    def flat(self):
        """Return a flat iterator, or set a flattened version of self to value."""

        return dpnp.flatiter(self)

    def flatten(self, order="C"):
        """
        Return a copy of the array collapsed into one dimension.

        Parameters
        ----------
        order: {'C', 'F', 'A', 'K'}, optional
            'C' means to flatten in row-major (C-style) order.
            'F' means to flatten in column-major (Fortran- style) order.
            'A' means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise.
            'K' means to flatten a in the order the elements occur in memory. The default is 'C'.

        Returns
        -------
        out: ndarray
            A copy of the input array, flattened to one dimension.

        See Also
        --------
        :obj:`dpnp.ravel`, :obj:`dpnp.flat`

        """
        new_arr = self.__new__(dpnp_array)
        new_arr._array_obj = dpt.empty(
            self.shape,
            dtype=self.dtype,
            order=order,
            device=self._array_obj.sycl_device,
            usm_type=self._array_obj.usm_type,
            sycl_queue=self._array_obj.sycl_queue,
        )

        if self.size > 0:
            dpt._copy_utils._copy_from_usm_ndarray_to_usm_ndarray(
                new_arr._array_obj, self._array_obj
            )
            new_arr._array_obj = dpt.reshape(new_arr._array_obj, (self.size,))

        return new_arr

    # 'getfield',
    # 'imag',

    def item(self, id=None):
        """
        Copy an element of an array to a standard Python scalar and return it.

        For full documentation refer to :obj:`numpy.ndarray.item`.

        Examples
        --------
        >>> np.random.seed(123)
        >>> x = np.random.randint(9, size=(3, 3))
        >>> x
        array([[2, 2, 6],
               [1, 3, 6],
               [1, 0, 1]])
        >>> x.item(3)
        1
        >>> x.item(7)
        0
        >>> x.item((0, 1))
        2
        >>> x.item((2, 2))
        1

        """

        if id is None:
            if self.size != 1:
                raise ValueError(
                    "DPNP dparray::item(): can only convert an array of size 1 to a Python scalar"
                )
            else:
                id = 0

        return self.flat[id]

    # 'itemset',

    @property
    def itemsize(self):
        """Size of one array element in bytes."""

        return self._array_obj.itemsize

    def max(
        self,
        axis=None,
        out=None,
        keepdims=numpy._NoValue,
        initial=numpy._NoValue,
        where=numpy._NoValue,
    ):
        """Return the maximum along an axis."""

        return dpnp.max(self, axis, out, keepdims, initial, where)

    def mean(self, axis=None, **kwargs):
        """Returns the average of the array elements."""

        return dpnp.mean(self, axis=axis, **kwargs)

    def min(
        self,
        axis=None,
        out=None,
        keepdims=numpy._NoValue,
        initial=numpy._NoValue,
        where=numpy._NoValue,
    ):
        """Return the minimum along a given axis."""

        return dpnp.min(self, axis, out, keepdims, initial, where)

    @property
    def nbytes(self):
        """Total bytes consumed by the elements of the array."""

        return self._array_obj.nbytes

    @property
    def ndim(self):
        """Number of array dimensions."""

        return self._array_obj.ndim

    # 'newbyteorder',

    def nonzero(self):
        return dpnp.nonzero(self)

    def partition(self, kth, axis=-1, kind="introselect", order=None):
        """
        Return a partitioned copy of an array.

        Rearranges the elements in the array in such a way that the value of the
        element in kth position is in the position it would be in a sorted array.

        All elements smaller than the kth element are moved before this element and
        all equal or greater are moved behind it. The ordering of the elements in
        the two partitions is undefined.

        Refer to `dpnp.partition` for full documentation.

        See Also
        --------
        :obj:`dpnp.partition` : Return a partitioned copy of an array.

        Examples
        --------
        >>> import dpnp as np
        >>> a = np.array([3, 4, 2, 1])
        >>> a.partition(3)
        >>> a
        array([1, 2, 3, 4])

        """

        self._array_obj = dpnp.partition(
            self, kth, axis=axis, kind=kind, order=order
        ).get_array()

    def prod(
        self,
        axis=None,
        dtype=None,
        out=None,
        keepdims=False,
        initial=None,
        where=True,
    ):
        """
        Returns the prod along a given axis.

        .. seealso::
           :obj:`dpnp.prod` for full documentation,
           :meth:`dpnp.dparray.sum`

        """

        return dpnp.prod(self, axis, dtype, out, keepdims, initial, where)

    # 'ptp',
    # 'put',
    # 'ravel',
    # 'real',
    # 'repeat',

    def reshape(self, *sh, **kwargs):
        """
        Returns an array containing the same data with a new shape.

        For full documentation refer to :obj:`numpy.ndarray.reshape`.

        Returns
        -------
        y : dpnp.ndarray
            This will be a new view object if possible;
            otherwise, it will be a copy.

        See Also
        --------
        :obj:`dpnp.reshape` : Equivalent function.

        Notes
        -----
        Unlike the free function `dpnp.reshape`, this method on `ndarray` allows
        the elements of the shape parameter to be passed in as separate arguments.
        For example, ``a.reshape(10, 11)`` is equivalent to
        ``a.reshape((10, 11))``.

        """

        if len(sh) == 1:
            sh = sh[0]
        return dpnp.reshape(self, sh, **kwargs)

    # 'resize',

    def round(self, decimals=0, out=None):
        """
        Return array with each element rounded to the given number of decimals.

        .. seealso::
           :obj:`dpnp.around` for full documentation.

        """

        return dpnp.around(self, decimals, out)

    # 'searchsorted',
    # 'setfield',
    # 'setflags',

    @property
    def shape(self):
        """Lengths of axes. A tuple of numbers represents size of each dimention.

        Setter of this property involves reshaping without copy. If the array
        cannot be reshaped without copy, it raises an exception.

        .. seealso: :attr:`numpy.ndarray.shape`

        """

        return self._array_obj.shape

    @shape.setter
    def shape(self, newshape):
        """
        Set new lengths of axes.

        A tuple of numbers represents size of each dimention.
        It involves reshaping without copy. If the array cannot be reshaped without copy,
        it raises an exception.

        .. seealso: :attr:`numpy.ndarray.shape`

        """

        dpnp.reshape(self, newshape=newshape)

    @property
    def size(self):
        """Number of elements in the array."""

        return self._array_obj.size

    # 'sort',

    def squeeze(self, axis=None):
        """
        Remove single-dimensional entries from the shape of an array.

        .. seealso::
           :obj:`dpnp.squeeze` for full documentation

        """

        return dpnp.squeeze(self, axis)

    def std(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
        """Returns the variance of the array elements, along given axis.

        .. seealso::
           :obj:`dpnp.var` for full documentation,

        """

        return dpnp.std(self, axis, dtype, out, ddof, keepdims)

    @property
    def strides(self):
        """
        Get strides of an array.

        Returns memory displacement in array elements, upon unit
        change of respective index.

        E.g. for strides (s1, s2, s3) and multi-index (i1, i2, i3)

           a[i1, i2, i3] == (&a[0,0,0])[ s1*s1 + s2*i2 + s3*i3]

        """

        return self._array_obj.strides

    def sum(
        self,
        /,
        *,
        axis=None,
        dtype=None,
        keepdims=False,
        out=None,
        initial=0,
        where=True,
    ):
        """
        Returns the sum along a given axis.

        .. seealso::
           :obj:`dpnp.sum` for full documentation,
           :meth:`dpnp.dparray.sum`

        """

        return dpnp.sum(
            self,
            axis=axis,
            dtype=dtype,
            out=out,
            keepdims=keepdims,
            initial=initial,
            where=where,
        )

    # 'swapaxes',

    def take(self, indices, axis=None, out=None, mode="raise"):
        """
        Take elements from an array.

        For full documentation refer to :obj:`numpy.take`.

        """

        return dpnp.take(self, indices, axis, out, mode)

    # 'tobytes',
    # 'tofile',
    # 'tolist',
    # 'tostring',
    # 'trace',

    def transpose(self, *axes):
        """
        Returns a view of the array with axes transposed.

        For full documentation refer to :obj:`numpy.ndarray.transpose`.

        Returns
        -------
        y : dpnp.ndarray
            View of the array with its axes suitably permuted.

        See Also
        --------
            :obj:`dpnp.transpose` : Equivalent function.
            :obj:`dpnp.ndarray.ndarray.T` : Array property returning the array transposed.
            :obj:`dpnp.ndarray.reshape` : Give a new shape to an array without changing its data.

        Examples
        --------
        >>> import dpnp as dp
        >>> a = dp.array([[1, 2], [3, 4]])
        >>> a
        array([[1, 2],
               [3, 4]])
        >>> a.transpose()
        array([[1, 3],
               [2, 4]])
        >>> a.transpose((1, 0))
        array([[1, 3],
               [2, 4]])

        >>> a = dp.array([1, 2, 3, 4])
        >>> a
        array([1, 2, 3, 4])
        >>> a.transpose()
        array([1, 2, 3, 4])

        """

        ndim = self.ndim
        if ndim < 2:
            return self

        axes_len = len(axes)
        if axes_len == 1 and isinstance(axes[0], tuple):
            axes = axes[0]

        res = self.__new__(dpnp_array)
        if ndim == 2 and axes_len == 0:
            res._array_obj = self._array_obj.T
        else:
            if len(axes) == 0 or axes[0] is None:
                # self.transpose().shape == self.shape[::-1]
                # self.transpose(None).shape == self.shape[::-1]
                axes = tuple((ndim - x - 1) for x in range(ndim))

            res._array_obj = dpt.permute_dims(self._array_obj, axes)
        return res

    def var(self, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
        """
        Returns the variance of the array elements along given axis.

        Masked entries are ignored, and result elements which are not
        finite will be masked.

        Refer to `numpy.var` for full documentation.

        See Also
        --------
        :obj:`numpy.ndarray.var` : corresponding function for ndarrays
        :obj:`numpy.var` : Equivalent function

        """

        return dpnp.var(self, axis, dtype, out, ddof, keepdims)


# 'view'

IntelPython / dpnp / 5649609617

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