from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np  # type: ignore

import onnx
from ..base import Base
from . import expect


def scatter_nd_impl(data, indices, updates):
    # type: (np.ndarray, np.ndarray, np.ndarray) -> np.ndarray

    # Check tensor shapes
    assert indices.shape[-1] <= len(data.shape)
    assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1]:]

    # Compute output
    output = np.copy(data)
    for i in np.ndindex(indices.shape[:-1]):
        # NOTE: The order of iteration in this loop is not specified.
        # In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
        # This ensures that the output value does not depend on the iteration order.
        output[indices[i]] = updates[i]
    return output


class ScatterND(Base):

    @staticmethod
    def export_scatternd():  # type: () -> None
        node = onnx.helper.make_node(
            'ScatterND',
            inputs=['data', 'indices', 'updates'],
            outputs=['y'],
        )
        data = np.array(
            [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
        indices = np.array([[0], [2]], dtype=np.int64)
        updates = np.array(
            [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
        # Expecting output as np.array(
        #    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
        output = scatter_nd_impl(data, indices, updates)
        expect(node, inputs=[data, indices, updates], outputs=[output],
               name='test_scatternd')

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

import numpy as np  # type: ignore

import onnx
from ..base import Base
from . import expect


def scatter_nd_impl(data, indices, updates):
    # type: (np.ndarray, np.ndarray, np.ndarray) -> np.ndarray

    # Check tensor shapes
    assert indices.shape[-1] <= len(data.shape)
    assert updates.shape == indices.shape[:-1] + data.shape[indices.shape[-1]:]

    # Compute output
    output = np.copy(data)
    for i in np.ndindex(indices.shape[:-1]):
        # NOTE: The order of iteration in this loop is not specified.
        # In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2].
        # This ensures that the output value does not depend on the iteration order.
        output[indices[i]] = updates[i]
    return output


class ScatterND(Base):

    @staticmethod
    def export_scatternd():  # type: () -> None
        node = onnx.helper.make_node(
            'ScatterND',
            inputs=['data', 'indices', 'updates'],
            outputs=['y'],
        )
        data = np.array(
            [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
             [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
             [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
        indices = np.array([[0], [2]], dtype=np.int64)
        updates = np.array(
            [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
             [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]], dtype=np.float32)
        # Expecting output as np.array(
        #    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
        #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
        #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
        #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)
        output = scatter_nd_impl(data, indices, updates)
        expect(node, inputs=[data, indices, updates], outputs=[output],
               name='test_scatternd')

# type: ignore # type: (np.ndarray, np.ndarray, np.ndarray) -> np.ndarray # Check tensor shapes # Compute output # NOTE: The order of iteration in this loop is not specified. # In particular, indices should not have duplicate entries: that is, if idx1 != idx2, then indices[idx1] != indices[idx2]. # This ensures that the output value does not depend on the iteration order. # type: () -> None # Expecting output as np.array( #    [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]], #     [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]], #     [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]], #     [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]], dtype=np.float32)