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"""
Interface of the searching function 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

"""


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

import dpnp
import numpy


__all__ = [
    'argmax',
    'argmin',
    'searchsorted',
    'where'
]


def argmax(x1, axis=None, out=None):
    """
    Returns the indices of the maximum values along an axis.

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

    Limitations
    -----------
    Input array is supported as :obj:`dpnp.ndarray`.
    Otherwise the function will be executed sequentially on CPU.
    Parameter ``axis`` is supported only with default value ``None``.
    Parameter ``out`` is supported only with default value ``None``.
    Input array data types are limited by supported DPNP :ref:`Data types`.

    See Also
    --------
    :obj:`dpnp.argmin` : Returns the indices of the minimum values along an axis.
    :obj:`dpnp.amax` : The maximum value along a given axis.
    :obj:`dpnp.unravel_index` : Convert a flat index into an index tuple.
    :obj:`dpnp.take_along_axis` : Apply ``np.expand_dims(index_array, axis)``
                                  from argmax to an array as if by calling max.

    Notes
    -----
    In case of multiple occurrences of the maximum values, the indices
    corresponding to the first occurrence are returned.

    Examples
    --------
    >>> import dpnp as np
    >>> a = np.arange(6).reshape((2, 3)) + 10
    >>> a.shape
    (2, 3)
    >>> [i for i in a]
    [10, 11, 12, 13, 14, 15]
    >>> np.argmax(a)
    5

    """

    x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
    if x1_desc:
        if axis is not None:
            pass
        elif out is not None:
            pass
        else:
            result_obj = dpnp_argmax(x1_desc).get_pyobj()
            result = dpnp.convert_single_elem_array_to_scalar(result_obj)

            return result

    return call_origin(numpy.argmax, x1, axis, out)


def argmin(x1, axis=None, out=None):
    """
    Returns the indices of the minimum values along an axis.

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

    Limitations
    -----------
    Input array is supported as :obj:`dpnp.ndarray`.
    Otherwise the function will be executed sequentially on CPU.
    Parameter ``axis`` is supported only with default value ``None``.
    Parameter ``out`` is supported only with default value ``None``.
    Input array data types are limited by supported DPNP :ref:`Data types`.

    See Also
    --------
    :obj:`dpnp.argmax` : Returns the indices of the maximum values along an axis.
    :obj:`dpnp.amin` : The minimum value along a given axis.
    :obj:`dpnp.unravel_index` : Convert a flat index into an index tuple.
    :obj:`dpnp.take_along_axis` : Apply ``np.expand_dims(index_array, axis)``
                                  from argmin to an array as if by calling min.

    Notes
    -----
    In case of multiple occurrences of the minimum values, the indices
    corresponding to the first occurrence are returned.

    Examples
    --------
    >>> import dpnp as np
    >>> a = np.arange(6).reshape((2, 3)) + 10
    >>> a.shape
    (2, 3)
    >>> [i for i in a]
    [10, 11, 12, 13, 14, 15]
    >>> np.argmin(a)
    0

    """

    x1_desc = dpnp.get_dpnp_descriptor(x1, copy_when_nondefault_queue=False)
    if x1_desc:
        if axis is not None:
            pass
        elif out is not None:
            pass
        else:
            result_obj = dpnp_argmin(x1_desc).get_pyobj()
            result = dpnp.convert_single_elem_array_to_scalar(result_obj)

            return result

    return call_origin(numpy.argmin, x1, axis, out)


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

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

    """

    return call_origin(numpy.where, a, v, side, sorter)


def where(condition, x=None, y=None):
    """
    Find indices where elements should be inserted to maintain order.

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

    """

    return call_origin(numpy.where, condition, x, y)
