import numpy

import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
from chainer import variable


_numpy_supports_0d_bool_index = \
    numpy.lib.NumpyVersion(numpy.__version__) >= '1.13.0'


class GetItem(function_node.FunctionNode):

    """Function that slices array and extract elements."""

    def __init__(self, slices):
        if isinstance(slices, list):
            if all([isinstance(s, int) for s in slices]):
                slices = slices,
            slices = tuple(slices)
        elif not isinstance(slices, tuple):
            slices = slices,

        if chainer.is_debug():
            n_ellipses = 0
            for s in slices:
                if s is Ellipsis:
                    n_ellipses += 1
            if n_ellipses > 1:
                raise ValueError('Only one Ellipsis is allowed')

        self.slices = slices

    def check_type_forward(self, in_types):
        type_check.expect(in_types.size() == 1)

    def forward(self, xs):
        return utils.force_array(xs[0][self.slices]),

    def backward(self, indexes, gy):
        return GetItemGrad(
            self.slices, self.inputs[0].shape, self.inputs[0].dtype).apply(gy)


class GetItemGrad(function_node.FunctionNode):

    def __init__(self, slices, in_shape, in_dtype):
        self.slices = slices
        self._in_shape = in_shape
        self._in_dtype = in_dtype

    def forward(self, inputs):
        gy, = inputs
        xp = cuda.get_array_module(*inputs)
        gx = xp.zeros(self._in_shape, self._in_dtype)
        if xp is numpy:
            try:
                numpy.add.at(gx, self.slices, gy)
            except IndexError:
                done = False
                # In numpy<1.13, 0-dim boolean index is only supported by
                # arr.__getitem__ for 0-dim arr.
                if not _numpy_supports_0d_bool_index and len(self.slices) == 1:
                    idx = numpy.asanyarray(self.slices[0])
                    if idx.dtype == numpy.dtype(bool):
                        numpy.add.at(gx[None], idx[None], gy)
                        done = True

                if not done:
                    msg = '''
GetItem does not support backward for this slices. The slices argument is not
supported by numpy.add.at, while it is supported by numpy.ndarray.__getitem__.

Please report this error to the issue tracker with the stack trace,
the information of your environment, and your script:
https://github.com/chainer/chainer/issues/new.
'''
                    raise IndexError(msg)
        else:
            gx.scatter_add(self.slices, inputs[0])
        return gx,

    def backward(self, indexes, ggx):
        return GetItem(self.slices).apply(ggx)


def get_item(x, slices):
    """Extract elements from array with specified shape, axes and offsets.

    Args:
        x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
        :class:`cupy.ndarray`): A variable to be sliced.
        slices (int, slice, Ellipsis, None, integer array-like, boolean\
        array-like or tuple of them):
            An object to specify the selection of elements.

    Returns:
        A :class:`~chainer.Variable` object which contains sliced array of
        ``x``.

    .. note::

        It only supports types that are supported by CUDA's atomicAdd when
        an integer array is included in ``slices``.
        The supported types are ``numpy.float32``, ``numpy.int32``,
        ``numpy.uint32``, ``numpy.uint64`` and ``numpy.ulonglong``.

    .. note::

        It does not support ``slices`` that contains multiple boolean arrays.

    .. note::

       See NumPy document for details of `indexing
       <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_.

    .. admonition:: Example

        >>> x = np.arange(12).reshape((2, 2, 3))
        >>> x
        array([[[ 0,  1,  2],
                [ 3,  4,  5]],
        <BLANKLINE>
               [[ 6,  7,  8],
                [ 9, 10, 11]]])
        >>> F.get_item(x, 0)
        variable([[0, 1, 2],
                  [3, 4, 5]])
        >>> F.get_item(x, (0, 0, slice(0, 2, 1)))  # equals x[0, 0, 0:2:1]
        variable([0, 1])
        >>> F.get_item(x, (Ellipsis, 2))  # equals x[..., 2]
        variable([[ 2,  5],
                  [ 8, 11]])
        >>> F.get_item(x, (1, np.newaxis, 1, 0))  # equals x[1, None, 1, 0]
        variable([9])

    """
    return GetItem(slices).apply((x,))[0]


def install_variable_get_item():
    variable.Variable.__getitem__ = get_item

@@ -1811,20 +1811,19 @@
 ndex is 
-only
+not
  support
@@ -1825,18 +1825,18 @@
 pported 
-by
+in
 %0A       
@@ -1846,16 +1846,83 @@
       # 
+numpy.add.at and it's supported for 0-dim arr in%0A                # 
 arr.__ge
@@ -1928,30 +1928,16 @@
 etitem__
- for 0-dim arr
 .%0A      
@@ -2065,24 +2065,24 @@
 .slices%5B0%5D)%0A
-
             
@@ -2124,16 +2124,161 @@
 (bool):%0A
+                        # Convert the array and the mask to 1-dim.%0A                        # numpy.add.at with them is supported in older numpy.%0A
         

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