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"""
Interface of the Binary operations of the DPNP

Notes
-----
This module is a face or public interface file for the library
it contains:
 - Interface functions
 - documentation for the functions
 - The functions parameters check

"""


import numpy


from dpnp.dpnp_algo import *
from dpnp.dpnp_utils import *
import dpnp

__all__ = [
    'bitwise_and',
    'bitwise_or',
    'bitwise_xor',
    'invert',
    'bitwise_not',
    'left_shift',
    'right_shift',
]


def _check_nd_call(origin_func, dpnp_func, x1, x2, dtype=None, out=None, where=True, **kwargs):
    """Choose function to call based on input and call chosen fucntion."""

    x1_is_scalar = dpnp.isscalar(x1)
    x2_is_scalar = dpnp.isscalar(x2)
    x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
    x2_desc = dpnp.get_dpnp_descriptor(x2, copy_when_nondefault_queue=False)
    x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_strides=False, copy_when_nondefault_queue=False)
    x2_desc = dpnp.get_dpnp_descriptor(x2, copy_when_strides=False, copy_when_nondefault_queue=False)

    if x1_desc and x2_desc and not kwargs:
        if not x1_desc and not x1_is_scalar:
            pass
        elif not x2_desc and not x2_is_scalar:
            pass
        elif x1_is_scalar and x2_is_scalar:
            pass
        elif x1_desc and x1_desc.ndim == 0:
            pass
        elif x2_desc and x2_desc.ndim == 0:
            pass
        elif x1_desc and x2_desc and x1_desc.size != x2_desc.size:
            pass
        elif x1_desc and x2_desc and x1_desc.shape != x2_desc.shape:
            pass
        elif dtype is not None:
            pass
        elif out is not None:
            pass
        elif not where:
            pass
        else:
            out_desc = dpnp.get_dpnp_descriptor(out, copy_when_nondefault_queue=False) if out is not None else None
            return dpnp_func(x1_desc, x2_desc, dtype, out_desc, where).get_pyobj()

    return call_origin(origin_func, x1, x2, dtype=dtype, out=out, where=where, **kwargs)


def bitwise_and(x1, x2, dtype=None, out=None, where=True, **kwargs):
    """
    Compute the bit-wise AND of two arrays element-wise.

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

    Limitations
    -----------
    Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar.
    Parameters ``dtype``, ``out`` and ``where`` are supported with their default values.
    Sizes, shapes and data types of input arrays are supported to be equal.
    Keyword arguments ``kwargs`` are currently unsupported.
    Otherwise the function will be executed sequentially on CPU.
    Input data is supported as integer only.

    See Also
    --------
    :obj:`dpnp.logical_and` : Compute the truth value of ``x1`` AND ``x2`` element-wise.
    :obj:`dpnp.bitwise_or`: Compute the bit-wise OR of two arrays element-wise.
    :obj:`dpnp.bitwise_xor` : Compute the bit-wise XOR of two arrays element-wise.

    Examples
    --------
    >>> import dpnp as np
    >>> x1 = np.array([2, 5, 255])
    >>> x2 = np.array([3,14,16])
    >>> out = np.bitwise_and(x1, x2)
    >>> [i for i in out]
    [2, 4, 16]

    """
    return _check_nd_call(numpy.bitwise_and, dpnp_bitwise_and, x1, x2, dtype=dtype, out=out, where=where, **kwargs)


def bitwise_or(x1, x2, dtype=None, out=None, where=True, **kwargs):
    """
    Compute the bit-wise OR of two arrays element-wise.

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

    Limitations
    -----------
    Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar.
    Parameters ``dtype``, ``out`` and ``where`` are supported with their default values.
    Sizes, shapes and data types of input arrays are supported to be equal.
    Keyword arguments ``kwargs`` are currently unsupported.
    Otherwise the function will be executed sequentially on CPU.
    Input data is supported as integer only.

    See Also
    --------
    :obj:`dpnp.logical_or` : Compute the truth value of ``x1`` OR ``x2`` element-wise.
    :obj:`dpnp.bitwise_and`: Compute the bit-wise AND of two arrays element-wise.
    :obj:`dpnp.bitwise_xor` : Compute the bit-wise XOR of two arrays element-wise.

    Examples
    --------
    >>> import dpnp as np
    >>> x1 = np.array([2, 5, 255])
    >>> x2 = np.array([4, 4, 4])
    >>> out = np.bitwise_or(x1, x2)
    >>> [i for i in out]
    [6, 5, 255]

    """
    return _check_nd_call(numpy.bitwise_or, dpnp_bitwise_or, x1, x2, dtype=dtype, out=out, where=where, **kwargs)


def bitwise_xor(x1, x2, dtype=None, out=None, where=True, **kwargs):
    """
    Compute the bit-wise XOR of two arrays element-wise.

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

    Limitations
    -----------
    Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar.
    Parameters ``dtype``, ``out`` and ``where`` are supported with their default values.
    Sizes, shapes and data types of input arrays are supported to be equal.
    Keyword arguments ``kwargs`` are currently unsupported.
    Otherwise the function will be executed sequentially on CPU.
    Input data is supported as integer only.

    See Also
    --------
    :obj:`dpnp.logical_xor` : Compute the truth value of ``x1`` XOR `x2`, element-wise.
    :obj:`dpnp.bitwise_and`: Compute the bit-wise AND of two arrays element-wise.
    :obj:`dpnp.bitwise_or` : Compute the bit-wise OR of two arrays element-wise.

    Examples
    --------
    >>> import dpnp as np
    >>> x1 = np.array([31, 3])
    >>> x2 = np.array([5, 6])
    >>> out = np.bitwise_xor(x1, x2)
    >>> [i for i in out]
    [26, 5]

    """
    return _check_nd_call(numpy.bitwise_xor, dpnp_bitwise_xor, x1, x2, dtype=dtype, out=out, where=where, **kwargs)


def invert(x, **kwargs):
    """
    Compute bit-wise inversion, or bit-wise NOT, element-wise.

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

    Limitations
    -----------
    Parameters ``x`` is supported as :obj:`dpnp.ndarray`.
    Keyword arguments ``kwargs`` are currently unsupported.
    Otherwise the function will be executed sequentially on CPU.
    Input array ``x`` is supported as integer :obj:`dpnp.ndarray` only.

    See Also
    --------
    :obj:`dpnp.bitwise_and`: Compute the bit-wise AND of two arrays element-wise.
    :obj:`dpnp.bitwise_or` : Compute the bit-wise OR of two arrays element-wise.
    :obj:`dpnp.bitwise_xor` : Compute the bit-wise XOR of two arrays element-wise.
    :obj:`dpnp.logical_not` : Compute the truth value of NOT x element-wise.

    Examples
    --------
    >>> import dpnp as np
    >>> x = np.array([13])
    >>> out = np.invert(x)
    >>> out[0]
    -14

    """

    x1_desc = dpnp.get_dpnp_descriptor(x, copy_when_nondefault_queue=False)
    if x1_desc and not kwargs:
        return dpnp_invert(x1_desc).get_pyobj()

    return call_origin(numpy.invert, x, **kwargs)


bitwise_not = invert  # bitwise_not is an alias for invert


def left_shift(x1, x2, dtype=None, out=None, where=True, **kwargs):
    """
    Shift the bits of an integer to the left.

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

    Limitations
    -----------
    Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar.
    Parameters ``dtype``, ``out`` and ``where`` are supported with their default values.
    Sizes, shapes and data types of input arrays are supported to be equal.
    Keyword arguments ``kwargs`` are currently unsupported.
    Otherwise the function will be executed sequentially on CPU.
    Input data is supported as integer only.

    See Also
    --------
    :obj:`dpnp.right_shift` : Shift the bits of an integer to the right.

    Examples
    --------
    >>> import dpnp as np
    >>> x1 = np.array([5, 5, 5])
    >>> x2 = np.array([1, 2, 3])
    >>> out = np.left_shift(x1, x2)
    >>> [i for i in out]
    [10, 20, 40]

    """
    return _check_nd_call(numpy.left_shift, dpnp_left_shift, x1, x2, dtype=dtype, out=out, where=where, **kwargs)


def right_shift(x1, x2, dtype=None, out=None, where=True, **kwargs):
    """
    Shift the bits of an integer to the right.

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

    Limitations
    -----------
    Parameters ``x1`` and ``x2`` are supported as either :obj:`dpnp.ndarray` or scalar.
    Parameters ``dtype``, ``out`` and ``where`` are supported with their default values.
    Sizes, shapes and data types of input arrays are supported to be equal.
    Keyword arguments ``kwargs`` are currently unsupported.
    Otherwise the function will be executed sequentially on CPU.
    Input data is supported as integer only.

    See Also
    --------
    :obj:`dpnp.left_shift` : Shift the bits of an integer to the left.

    Examples
    --------
    >>> import dpnp as np
    >>> x1 = np.array([10, 10, 10])
    >>> x2 = np.array([1, 2, 3])
    >>> out = np.right_shift(x1, x2)
    >>> [i for i in out]
    [5, 2, 1]

    """
    return _check_nd_call(numpy.right_shift, dpnp_right_shift, x1, x2, dtype=dtype, out=out, where=where, **kwargs)
