"""
Copyright 2020 The OneFlow Authors. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from collections import OrderedDict

import numpy as np
import oneflow as flow
import oneflow.typing as oft

import test_global_storage
from test_util import GenArgList

func_config = flow.FunctionConfig()
func_config.default_data_type(flow.float)
func_config.default_logical_view(flow.scope.consistent_view())


def _check(test_case, data, segment_ids, out_shape, out):
    test_case.assertEqual(out.shape, out_shape)
    ref = np.zeros_like(out)
    for idx, i in np.ndenumerate(segment_ids):
        out_idx = list(idx)
        out_idx[-1] = i
        out_idx = tuple(out_idx)
        ref[out_idx] += data[idx]
    test_case.assertTrue(np.allclose(ref, out, atol=1e-5, rtol=1e-5))


def _check_bw(test_case, params, indices, out_shape, out):
    ref = np.zeros_like(out)
    for idx, i in np.ndenumerate(indices):
        in_idx = list(idx)
        in_idx[-1] = i
        in_idx = tuple(in_idx)
        ref[idx] += params[in_idx]
    test_case.assertTrue(np.array_equal(ref, out))


def _gen_segment_ids(out_shape, num_segments, segment_ids_shape):
    axis = len(segment_ids_shape) - 1
    return np.random.randint(
        low=0, high=out_shape[axis], size=segment_ids_shape, dtype=np.int32
    )


def _gen_data(out_shape, num_segments, segment_ids_shape):
    axis = len(segment_ids_shape) - 1
    data_shape = out_shape[0:axis] + (segment_ids_shape[axis],) + out_shape[axis + 1 :]
    return np.random.rand(*data_shape).astype(np.float32)


def _make_unsoted_segment_sum_fn(device, data, segment_ids, num_segments):
    flow.clear_default_session()

    @flow.global_function(type="train", function_config=func_config)
    def unsorted_batch_segment_sum_job(
        data: oft.Numpy.Placeholder(data.shape, dtype=flow.float),
        segment_ids: oft.Numpy.Placeholder(segment_ids.shape, dtype=flow.int32),
    ):
        with flow.scope.placement(device, "0:0"):
            x = flow.get_variable(
                "data",
                shape=data.shape,
                dtype=flow.float32,
                initializer=flow.constant_initializer(0),
            )
            data = x + data
            res = flow.math.unsorted_batch_segment_sum(
                data=data, segment_ids=segment_ids, num_segments=num_segments
            )
            flow.optimizer.SGD(
                flow.optimizer.PiecewiseConstantScheduler([], [1e-3]), momentum=0
            ).minimize(res)
            flow.watch_diff(x, test_global_storage.Setter("x_diff"))
            flow.watch_diff(res, test_global_storage.Setter("loss_diff"))
            return res

    return unsorted_batch_segment_sum_job(data, segment_ids)


def _run_test(test_case, device, out_shape, num_segments, segment_ids_shape):
    segment_ids = _gen_segment_ids(out_shape, num_segments, segment_ids_shape)
    data = _gen_data(out_shape, num_segments, segment_ids_shape)

    unsorted_batch_segment_sum_out = _make_unsoted_segment_sum_fn(
        device, data, segment_ids, num_segments
    ).get()
    out_ndarray = unsorted_batch_segment_sum_out.numpy()
    grad_in_ndarray = test_global_storage.Get("x_diff")
    grad_out_ndarray = test_global_storage.Get("loss_diff")
    check_point = flow.train.CheckPoint()
    check_point.init()

    _check(test_case, data, segment_ids, out_shape, out_ndarray)
    _check_bw(
        test_case, grad_out_ndarray, segment_ids, grad_in_ndarray.shape, grad_in_ndarray
    )


def test_unsorted_batch_segment_sum(test_case):
    arg_dict = OrderedDict()
    arg_dict["device_type"] = ["cpu", "gpu"]
    arg_dict["out_shape"] = [(2, 4, 7, 6)]
    arg_dict["num_segments"] = [7]
    arg_dict["segment_ids_shape"] = [(2, 4, 5)]
    for arg in GenArgList(arg_dict):
        _run_test(test_case, *arg)
