12069407815

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Merge c42a5a5b8 into 4e021720c

Pull Request Pull Request #2148: Update the scope of third party tests with the latest changes

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98.53

/dpnp/dpnp_array.py

# -*- coding: utf-8 -*-
# *****************************************************************************
# Copyright (c) 2016-2024, Intel Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# - Redistributions of source code must retain the above copyright notice,
#   this list of conditions and the following disclaimer.
# - Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
# THE POSSIBILITY OF SUCH DAMAGE.
# *****************************************************************************

import dpctl.tensor as dpt
from dpctl.tensor._numpy_helper import AxisError

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 further 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=None,
        buffer=None,
        offset=0,
        strides=None,
        order="C",
        device=None,
        usm_type="device",
        sycl_queue=None,
    ):
        if order is None:
            order = "C"

        if buffer is not None:
            buffer = dpnp.get_usm_ndarray(buffer)

            if dtype is None:
                dtype = buffer.dtype
        else:
            buffer = usm_type

        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=buffer,
            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()

    @property
    def mT(self):
        """
        View of the matrix transposed array.

        The matrix transpose is the transpose of the last two dimensions, even
        if the array is of higher dimension.

        Raises
        ------
        ValueError
            If the array is of dimension less than 2.

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

        >>> a = np.arange(8).reshape((2, 2, 2))
        >>> a
        array([[[0, 1],
                [2, 3]],
               [[4, 5],
                [6, 7]]])
        >>> a.mT
        array([[[0, 2],
                [1, 3]],
               [[4, 6],
                [5, 7]]])

        """

        if self.ndim < 2:
            raise ValueError("matrix transpose with ndim < 2 is undefined")

        return dpnp_array._create_from_usm_ndarray(self._array_obj.mT)

    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):
        r"""Return ``\|self\|``."""
        return dpnp.abs(self)

    def __add__(self, other):
        """Return ``self+value``."""
        return dpnp.add(self, other)

    def __and__(self, other):
        """Return ``self&value``."""
        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):
        """``True`` if self else ``False``."""
        return self._array_obj.__bool__()

    # '__class__',
    # `__class_getitem__`,

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

    # '__contains__',

    def __copy__(self):
        """
        Used if :func:`copy.copy` is called on an array. Returns a copy of the array.

        Equivalent to ``a.copy(order="K")``.

        """
        return self.copy(order="K")

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

    def __dlpack__(
        self, *, stream=None, max_version=None, dl_device=None, copy=None
    ):
        """
        Produces DLPack capsule.

        Parameters
        ----------
        stream : {:class:`dpctl.SyclQueue`, None}, optional
            Execution queue to synchronize with. If ``None``, synchronization
            is not performed.
            Default: ``None``.
        max_version {tuple of ints, None}, optional
            The maximum DLPack version the consumer (caller of ``__dlpack__``)
            supports. As ``__dlpack__`` may not always return a DLPack capsule
            with version `max_version`, the consumer must verify the version
            even if this argument is passed.
            Default: ``None``.
        dl_device {tuple, None}, optional:
            The device the returned DLPack capsule will be placed on. The
            device must be a 2-tuple matching the format of
            ``__dlpack_device__`` method, an integer enumerator representing
            the device type followed by an integer representing the index of
            the device.
            Default: ``None``.
        copy {bool, None}, optional:
            Boolean indicating whether or not to copy the input.

            * If `copy` is ``True``, the input will always be copied.
            * If ``False``, a ``BufferError`` will be raised if a copy is
              deemed necessary.
            * If ``None``, a copy will be made only if deemed necessary,
              otherwise, the existing memory buffer will be reused.

            Default: ``None``.

        Raises
        ------
        MemoryError:
            when host memory can not be allocated.
        DLPackCreationError:
            when array is allocated on a partitioned SYCL device, or with
            a non-default context.
        BufferError:
            when a copy is deemed necessary but `copy` is ``False`` or when
            the provided `dl_device` cannot be handled.

        """

        return self._array_obj.__dlpack__(
            stream=stream,
            max_version=max_version,
            dl_device=dl_device,
            copy=copy,
        )

    def __dlpack_device__(self):
        """
        Gives a tuple (``device_type``, ``device_id``) corresponding to
        ``DLDevice`` entry in ``DLTensor`` in DLPack protocol.

        The tuple describes the non-partitioned device where the array has been
        allocated, or the non-partitioned parent device of the allocation
        device.

        Raises
        ------
        DLPackCreationError:
            when the ``device_id`` could not be determined.

        """

        return self._array_obj.__dlpack_device__()

    def __eq__(self, other):
        """Return ``self==value``."""
        return dpnp.equal(self, other)

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

    def __floordiv__(self, other):
        """Return ``self//value``."""
        return dpnp.floor_divide(self, other)

    # '__format__',

    def __ge__(self, other):
        """Return ``self>=value``."""
        return dpnp.greater_equal(self, other)

    # '__getattribute__',

    def __getitem__(self, key):
        """Return ``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

    # '__getstate__',

    def __gt__(self, other):
        """Return ``self>value``."""
        return dpnp.greater(self, other)

    # '__hash__',

    def __iadd__(self, other):
        """Return ``self+=value``."""
        dpnp.add(self, other, out=self)
        return self

    def __iand__(self, other):
        """Return ``self&=value``."""
        dpnp.bitwise_and(self, other, out=self)
        return self

    def __ifloordiv__(self, other):
        """Return ``self//=value``."""
        dpnp.floor_divide(self, other, out=self)
        return self

    def __ilshift__(self, other):
        """Return ``self<<=value``."""
        dpnp.left_shift(self, other, out=self)
        return self

    def __imatmul__(self, other):
        """Return ``self@=value``."""

        """
        Unlike `matmul(a, b, out=a)` we ensure that the result is not broadcast
        if the result without `out` would have less dimensions than `a`.
        Since the signature of matmul is '(n?,k),(k,m?)->(n?,m?)' this is the
        case exactly when the second operand has both core dimensions.
        We have to enforce this check by passing the correct `axes=`.
        """
        if self.ndim == 1:
            axes = [(-1,), (-2, -1), (-1,)]
        else:
            axes = [(-2, -1), (-2, -1), (-2, -1)]

        try:
            dpnp.matmul(self, other, out=self, axes=axes)
        except AxisError:
            # AxisError should indicate that the axes argument didn't work out
            # which should mean the second operand not being 2 dimensional.
            raise ValueError(
                "inplace matrix multiplication requires the first operand to "
                "have at least one and the second at least two dimensions."
            )
        return self

    def __imod__(self, other):
        """Return ``self%=value``."""
        dpnp.remainder(self, other, out=self)
        return self

    def __imul__(self, other):
        """Return ``self*=value``."""
        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 ``~self``."""
        return dpnp.invert(self)

    def __ior__(self, other):
        """Return ``self|=value``."""
        dpnp.bitwise_or(self, other, out=self)
        return self

    def __ipow__(self, other):
        """Return ``self**=value``."""
        dpnp.power(self, other, out=self)
        return self

    def __irshift__(self, other):
        """Return ``self>>=value``."""
        dpnp.right_shift(self, other, out=self)
        return self

    def __isub__(self, other):
        """Return ``self-=value``."""
        dpnp.subtract(self, other, out=self)
        return self

    # '__iter__',

    def __itruediv__(self, other):
        """Return ``self/=value``."""
        dpnp.true_divide(self, other, out=self)
        return self

    def __ixor__(self, other):
        """Return ``self^=value``."""
        dpnp.bitwise_xor(self, other, out=self)
        return self

    def __le__(self, other):
        """Return ``self<=value``."""
        return dpnp.less_equal(self, other)

    def __len__(self):
        """Return ``len(self)``."""
        return self._array_obj.__len__()

    def __lshift__(self, other):
        """Return ``self<<value``."""
        return dpnp.left_shift(self, other)

    def __lt__(self, other):
        """Return ``self<value``."""
        return dpnp.less(self, other)

    def __matmul__(self, other):
        """Return ``self@value``."""
        return dpnp.matmul(self, other)

    def __mod__(self, other):
        """Return ``self%value``."""
        return dpnp.remainder(self, other)

    def __mul__(self, other):
        """Return ``self*value``."""
        return dpnp.multiply(self, other)

    def __ne__(self, other):
        """Return ``self!=value``."""
        return dpnp.not_equal(self, other)

    def __neg__(self):
        """Return ``-self``."""
        return dpnp.negative(self)

    # '__new__',

    def __or__(self, other):
        """Return ``self|value``."""
        return dpnp.bitwise_or(self, other)

    def __pos__(self):
        """Return ``+self``."""
        return dpnp.positive(self)

    def __pow__(self, other):
        """Return ``self**value``."""
        return dpnp.power(self, other)

    def __radd__(self, other):
        """Return ``value+self``."""
        return dpnp.add(other, self)

    def __rand__(self, other):
        """Return ``value&self``."""
        return dpnp.bitwise_and(other, self)

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

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

    def __rfloordiv__(self, other):
        """Return ``value//self``."""
        return dpnp.floor_divide(self, other)

    def __rlshift__(self, other):
        """Return ``value<<self``."""
        return dpnp.left_shift(other, self)

    def __rmatmul__(self, other):
        """Return ``value@self``."""
        return dpnp.matmul(other, self)

    def __rmod__(self, other):
        """Return ``value%self``."""
        return dpnp.remainder(other, self)

    def __rmul__(self, other):
        """Return ``value*self``."""
        return dpnp.multiply(other, self)

    def __ror__(self, other):
        """Return ``value|self``."""
        return dpnp.bitwise_or(other, self)

    def __rpow__(self, other):
        """Return ``value**self``."""
        return dpnp.power(other, self)

    def __rrshift__(self, other):
        """Return ``value>>self``."""
        return dpnp.right_shift(other, self)

    def __rshift__(self, other):
        """Return ``self>>value``."""
        return dpnp.right_shift(self, other)

    def __rsub__(self, other):
        """Return ``value-self``."""
        return dpnp.subtract(other, self)

    def __rtruediv__(self, other):
        """Return ``value/self``."""
        return dpnp.true_divide(other, self)

    def __rxor__(self, other):
        """Return ``value^self``."""
        return dpnp.bitwise_xor(other, self)

    # '__setattr__',

    def __setitem__(self, key, val):
        """Set ``self[key]`` to value."""
        key = _get_unwrapped_index_key(key)

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

        self._array_obj.__setitem__(key, val)

    # '__setstate__',
    # '__sizeof__',

    __slots__ = ("_array_obj",)

    def __str__(self):
        """Return ``str(self)``."""
        return self._array_obj.__str__()

    def __sub__(self, other):
        """Return ``self-value``."""
        return dpnp.subtract(self, other)

    # '__subclasshook__',

    def __truediv__(self, other):
        """Return ``self/value``."""
        return dpnp.true_divide(self, other)

    def __xor__(self, other):
        """Return ``self^value``."""
        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, *, keepdims=False):
        """
        Returns array of indices of the maximum values along the given axis.

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

        """

        return dpnp.argmax(self, axis=axis, out=out, keepdims=keepdims)

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

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

        """

        return dpnp.argmin(self, axis=axis, out=out, keepdims=keepdims)

    # 'argpartition',

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

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

        """
        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,
        device=None,
    ):
        """
        Copy the array with data type casting.

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

        Parameters
        ----------
        x1 : {dpnp.ndarray, usm_ndarray}
            Array data type casting.
        dtype : dtype
            Target data type.
        order : {"C", "F", "A", "K"}, optional
            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.
        casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
            Controls what kind of data casting may occur.
            Defaults to ``'unsafe'`` for backwards compatibility.

            - 'no' means the data types should not be cast at all.
            - 'equiv' means only byte-order changes are allowed.
            - 'safe' means only casts which can preserve values are allowed.
            - 'same_kind' means only safe casts or casts within a kind, like
              float64 to float32, are allowed.
            - 'unsafe' means any data conversions may be done.

        copy : {bool}, optional
            By default, ``astype`` always returns a newly allocated array. If
            this is set to ``False``, and the `dtype`, `order`, and `subok`
            requirements are satisfied, the input array is returned instead of
            a copy.
        device : {None, string, SyclDevice, SyclQueue}, optional
            An array API concept of device where the output array is created.
            The `device` can be ``None`` (the default), an OneAPI filter selector
            string, an instance of :class:`dpctl.SyclDevice` corresponding to
            a non-partitioned SYCL device, an instance of :class:`dpctl.SyclQueue`,
            or a `Device` object returned by
            :obj:`dpnp.dpnp_array.dpnp_array.device` property. Default: ``None``.

        Returns
        -------
        arr_t : dpnp.ndarray
            Unless `copy` is ``False`` and the other conditions for returning the input array
            are satisfied, `arr_t` is a new array of the same shape as the input array,
            with dtype, order given by dtype, order.

        Limitations
        -----------
        Parameter `subok` is supported with default value.
        Otherwise ``NotImplementedError`` exception will be raised.

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.array([1, 2, 2.5])
        >>> x
        array([1. , 2. , 2.5])
        >>> x.astype(int)
        array([1, 2, 2])

        """

        if subok is not True:
            raise NotImplementedError(
                f"subok={subok} is currently not supported"
            )

        return dpnp.astype(
            self, dtype, order=order, casting=casting, copy=copy, device=device
        )

    # '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.

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

        """

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

    def clip(self, min=None, max=None, out=None, **kwargs):
        """
        Clip (limit) the values in an array.

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

        """

        return dpnp.clip(self, min, max, out=out, **kwargs)

    def compress(self, condition, axis=None, out=None):
        """
        Select slices of an array along a given axis.

        Refer to :obj:`dpnp.compress` for full documentation.
        """

        return dpnp.compress(condition, self, axis=axis, out=out)

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

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

        """

        if not dpnp.issubdtype(self.dtype, dpnp.complexfloating):
            return self
        else:
            return dpnp.conjugate(self)

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

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

        """

        if not dpnp.issubdtype(self.dtype, dpnp.complexfloating):
            return self
        else:
            return dpnp.conjugate(self)

    def copy(self, order="C", device=None, usm_type=None, sycl_queue=None):
        """
        Return a copy of the array.

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

        Parameters
        ----------
        order : {"C", "F", "A", "K"}, optional
            Memory layout of the newly output array.
            Default: ``"C"``.
        device : {None, string, SyclDevice, SyclQueue}, optional
            An array API concept of device where the output array is created.
            The `device` can be ``None`` (the default), an OneAPI filter
            selector string, an instance of :class:`dpctl.SyclDevice`
            corresponding to a non-partitioned SYCL device, an instance of
            :class:`dpctl.SyclQueue`, or a `Device` object returned by
            :obj:`dpnp.dpnp_array.dpnp_array.device` property.
            Default: ``None``.
        usm_type : {None, "device", "shared", "host"}, optional
            The type of SYCL USM allocation for the output array.
            Default: ``None``.
        sycl_queue : {None, SyclQueue}, optional
            A SYCL queue to use for output array allocation and copying. The
            `sycl_queue` can be passed as ``None`` (the default), which means
            to get the SYCL queue from `device` keyword if present or to use
            a default queue.
            Default: ``None``.

        Returns
        -------
        out : dpnp.ndarray
            A copy of the array.

        See also
        --------
        :obj:`dpnp.copy` : Similar function with different default behavior
        :obj:`dpnp.copyto` : Copies values from one array to another.

        Notes
        -----
        This function is the preferred method for creating an array copy.
        The function :func:`dpnp.copy` is similar, but it defaults to using
        order ``"K"``.

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.array([[1, 2, 3], [4, 5, 6]], order='F')
        >>> y = x.copy()
        >>> x.fill(0)

        >>> x
        array([[0, 0, 0],
               [0, 0, 0]])

        >>> y
        array([[1, 2, 3],
               [4, 5, 6]])

        >>> y.flags['C_CONTIGUOUS']
        True

        """

        return dpnp.copy(
            self,
            order=order,
            device=device,
            usm_type=usm_type,
            sycl_queue=sycl_queue,
        )

    # 'ctypes',

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

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

        """

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

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

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

        """

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

    # 'data',

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

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

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

        Examples
        --------
        >>> import dpnp as np
        >>> a = np.arange(4).reshape(2,2)
        >>> a.diagonal()
        array([0, 3])

        """

        return dpnp.diagonal(self, offset=offset, axis1=axis1, axis2=axis2)

    def dot(self, b, out=None):
        """
        Dot product of two arrays.

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

        Examples
        --------
        >>> import dpnp as np
        >>> a = np.eye(2)
        >>> b = np.ones((2, 2)) * 2
        >>> a.dot(b)
        array([[2., 2.],
               [2., 2.]])

        This array method can be conveniently chained:

        >>> a.dot(b).dot(b)
        array([[8., 8.],
               [8., 8.]])
        """

        return dpnp.dot(self, b, 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.

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

        Parameters
        ----------
        value : {dpnp.ndarray, usm_ndarray, scalar}
            All elements of `a` will be assigned this value.

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

        """

        # lazy import avoids circular imports
        from .dpnp_algo.dpnp_fill import dpnp_fill

        dpnp_fill(self, 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.

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

        Parameters
        ----------
        order : {"C", "F"}, optional
            Read the elements using this index order, and place the elements
            into the reshaped array using this index order.

                - ``"C"`` means to read / write the elements using C-like index
                  order, with the last axis index changing fastest, back to the
                  first axis index changing slowest.
                - ``"F"`` means to read / write the elements using Fortran-like
                  index order, with the first index changing fastest, and the
                  last index changing slowest.

            Default: ``"C"``.

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

        See Also
        --------
        :obj:`dpnp.ravel` : Return a flattened array.
        :obj:`dpnp.flat` : A 1-D flat iterator over the array.

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

        """

        return self.reshape(-1, order=order, copy=True)

    # 'getfield',

    @property
    def imag(self):
        """
        The imaginary part of the array.

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

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.sqrt(np.array([1+0j, 0+1j]))
        >>> x.imag
        array([0.        , 0.70710677])

        """
        return dpnp_array._create_from_usm_ndarray(
            dpnp.get_usm_ndarray(self).imag
        )

    @imag.setter
    def imag(self, value):
        """
        Set the imaginary part of the array.

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

        Examples
        --------
        >>> import dpnp as np
        >>> a = np.array([1+2j, 3+4j, 5+6j])
        >>> a.imag = 9
        >>> a
        array([1.+9.j, 3.+9.j, 5.+9.j])

        """
        if dpnp.issubdtype(self.dtype, dpnp.complexfloating):
            dpnp.copyto(self._array_obj.imag, value)
        else:
            raise TypeError("array does not have imaginary part to set")

    def item(self, *args):
        """
        Copy an element of an array to a standard Python scalar and return it.

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

        Parameters
        ----------
        *args : {none, int, tuple of ints}
            - none: in this case, the method only works for arrays with
              one element (``a.size == 1``), which element is copied into a
              standard Python scalar object and returned.
            - int: this argument is interpreted as a flat index into the array,
              specifying which element to copy and return.
            - tuple of ints: functions as does a single int argument, except
              that the argument is interpreted as an nd-index into the array.

        Returns
        -------
        out : Standard Python scalar object
            A copy of the specified element of the array as a suitable Python scalar.

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

        >>> x = np.array(5)
        >>> x.item()
        5

        """

        # TODO: implement a more efficient way to avoid copying to host
        # for large arrays using `asnumpy()`
        return self.asnumpy().item(*args)

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

        return self._array_obj.itemsize

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

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

        """

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

    def mean(
        self, axis=None, dtype=None, out=None, keepdims=False, *, where=True
    ):
        """
        Returns the average of the array elements.

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

        """

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

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

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

        """

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

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

        return self._array_obj.nbytes

    @property
    def ndim(self):
        """
        Return the number of dimensions of an array.

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

        Returns
        -------
        number_of_dimensions : int
            The number of dimensions in `a`.

        See Also
        --------
        :obj:`dpnp.ndim` : Equivalent method for any array-like input.
        :obj:`dpnp.shape` : Return the shape of an array.
        :obj:`dpnp.ndarray.shape` : Return the shape of an array.

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.array([1, 2, 3])
        >>> x.ndim
        1
        >>> y = np.zeros((2, 3, 4))
        >>> y.ndim
        3

        """

        return self._array_obj.ndim

    def nonzero(self):
        """
        Return the indices of the elements that are non-zero.

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

        """

        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.

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

        """

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

    def put(self, indices, vals, /, *, axis=None, mode="wrap"):
        """
        Puts values of an array into another array along a given axis.

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

        """

        return dpnp.put(self, indices, vals, axis=axis, mode=mode)

    def ravel(self, order="C"):
        """
        Return a contiguous flattened array.

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

        """

        return dpnp.ravel(self, order=order)

    @property
    def real(self):
        """
        The real part of the array.

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

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.sqrt(np.array([1+0j, 0+1j]))
        >>> x.real
        array([1.        , 0.70710677])

        """

        if dpnp.issubdtype(self.dtype, dpnp.complexfloating):
            return dpnp_array._create_from_usm_ndarray(
                dpnp.get_usm_ndarray(self).real
            )
        return self

    @real.setter
    def real(self, value):
        """
        Set the real part of the array.

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

        Examples
        --------
        >>> import dpnp as np
        >>> a = np.array([1+2j, 3+4j, 5+6j])
        >>> a.real = 9
        >>> a
        array([9.+2.j, 9.+4.j, 9.+6.j])

        """

        dpnp.copyto(self._array_obj.real, value)

    def repeat(self, repeats, axis=None):
        """
        Repeat elements of an array.

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

        """

        return dpnp.repeat(self, repeats, axis=axis)

    def reshape(self, *shape, order="C", copy=None):
        """
        Returns an array containing the same data with a new shape.

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

        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(shape) == 1:
            shape = shape[0]
        return dpnp.reshape(self, shape, order=order, copy=copy)

    # 'resize',

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

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

        """

        return dpnp.around(self, decimals, out)

    def searchsorted(self, v, side="left", sorter=None):
        """
        Find indices where elements of `v` should be inserted in `a`
        to maintain order.

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

        """

        return dpnp.searchsorted(self, v, side=side, sorter=sorter)

    # 'setfield',
    # 'setflags',

    @property
    def shape(self):
        """
        Tuple of array dimensions.

        The shape property is usually used to get the current shape of an array,
        but may also be used to reshape the array in-place by assigning a tuple
        of array dimensions to it. Unlike :obj:`dpnp.reshape`, only non-negative
        values are supported to be set as new shape. Reshaping an array in-place
        will fail if a copy is required.

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

        Note
        ----
        Using :obj:`dpnp.ndarray.reshape` or :obj:`dpnp.reshape` is the
        preferred approach to set new shape of an array.

        See Also
        --------
        :obj:`dpnp.shape` : Equivalent getter function.
        :obj:`dpnp.reshape` : Function similar to setting `shape`.
        :obj:`dpnp.ndarray.reshape` : Method similar to setting `shape`.

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.array([1, 2, 3, 4])
        >>> x.shape
        (4,)
        >>> y = np.zeros((2, 3, 4))
        >>> y.shape
        (2, 3, 4)

        >>> y.shape = (3, 8)
        >>> y
        array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
               [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
               [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
        >>> y.shape = (3, 6)
        ...
        TypeError: Can not reshape array of size 24 into (3, 6)

        """

        return self._array_obj.shape

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

        Modifies array instance in-place by changing its metadata about the
        shape and the strides of the array, or raises `AttributeError`
        exception if in-place change is not possible.

        Whether the array can be reshape in-place depends on its strides. Use
        :obj:`dpnp.reshape` function which always succeeds to reshape the array
        by performing a copy if necessary.

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

        Parameters
        ----------
        newshape : {tuple, int}
            New shape. Only non-negative values are supported. The new shape
            may not lead to the change in the number of elements in the array.

        """

        self._array_obj.shape = newshape

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

        Returns
        -------
        element_count : int
            Number of elements in the array.

        See Also
        --------
        :obj:`dpnp.size` : Return the number of elements along a given axis.
        :obj:`dpnp.shape` : Return the shape of an array.
        :obj:`dpnp.ndarray.shape` : Return the shape of an array.

        Examples
        --------
        >>> import dpnp as np
        >>> x = np.zeros((3, 5, 2), dtype=np.complex64)
        >>> x.size
        30

        """

        return self._array_obj.size

    def sort(self, axis=-1, kind=None, order=None):
        """
        Sort an array in-place.

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

        Note
        ----
        `axis` in :obj:`dpnp.sort` could be integer or ``None``. If ``None``,
        the array is flattened before sorting. However, `axis` in
        :obj:`dpnp.ndarray.sort` can only be integer since it sorts an array
        in-place.

        Examples
        --------
        >>> import dpnp as np
        >>> a = np.array([[1,4],[3,1]])
        >>> a.sort(axis=1)
        >>> a
        array([[1, 4],
              [1, 3]])
        >>> a.sort(axis=0)
        >>> a
        array([[1, 1],
              [3, 4]])

        """

        if axis is None:
            raise TypeError(
                "'NoneType' object cannot be interpreted as an integer"
            )
        self[...] = dpnp.sort(self, axis=axis, kind=kind, order=order)

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

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

        """

        return dpnp.squeeze(self, axis)

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

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

        """

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

    @property
    def strides(self):
        """
        Returns memory displacement in array elements, upon unit
        change of respective index.

        For example, for strides ``(s1, s2, s3)`` and multi-index
        ``(i1, i2, i3)`` position of the respective element relative
        to zero multi-index element is ``s1*s1 + s2*i2 + s3*i3``.

        """

        return self._array_obj.strides

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

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

        """

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

    def swapaxes(self, axis1, axis2):
        """
        Interchange two axes of an array.

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

        """

        return dpnp.swapaxes(self, axis1=axis1, axis2=axis2)

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

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

        """

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

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

    def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):
        """
        Return the sum along diagonals of the array.

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

        """

        return dpnp.trace(
            self, offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out
        )

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

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

        Parameters
        ----------
        axes : None, tuple or list of ints, n ints, optional
            * ``None`` or no argument: reverses the order of the axes.
            * ``tuple or list of ints``: `i` in the `j`-th place in the
              tuple/list means that the array’s `i`-th axis becomes the
              transposed array’s `j`-th axis.
            * ``n ints``: same as an n-tuple/n-list of the same integers (this
              form is intended simply as a “convenience” alternative to the
              tuple form).

        Returns
        -------
        out : 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 np
        >>> a = np.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 = np.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, list)):
            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,
        *,
        where=True,
    ):
        """
        Returns the variance of the array elements, along given axis.

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

        """

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


# 'view'

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