# -*- coding: utf-8 -*-
# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import itertools
import platform
from functools import partial

import numpy as np
import pytest
from utils import opr_test

import megengine.amp as amp
import megengine.config as config
import megengine.core.ops.builtin as builtin
import megengine.core.tensor.dtype as dtype
import megengine.functional as F
import megengine.jit as jit
from megengine import Parameter, Tensor, is_cuda_available, tensor
from megengine.core._trace_option import use_symbolic_shape
from megengine.core.autodiff.grad import Grad
from megengine.core.tensor.utils import make_shape_tuple
from megengine.device import get_device_count
from megengine.module import LayerNorm


def test_where():
    maskv0 = np.array([[1, 0], [0, 1]], dtype=np.bool_)
    xv0 = np.array([[1, np.inf], [np.nan, 4]], dtype=np.float32)
    yv0 = np.array([[5, 6], [7, 8]], dtype=np.float32)

    maskv1 = np.array([[1, 0, 1], [1, 0, 0], [1, 1, 0]], dtype=np.bool_)
    xv1 = np.array([[1, np.inf, 2], [0, np.nan, 4], [1, 5, 7]], dtype=np.float32)
    yv1 = np.array([[5, 6, 9], [2, 7, 8], [2, 1, 9]], dtype=np.float32)

    cases = [
        {"input": [maskv0, xv0, yv0]},
        {"input": [maskv1, xv1, yv1]},
    ]
    opr_test(cases, F.where, ref_fn=np.where, test_trace=False)

    maskv2 = np.array([1, 1, 1], dtype=np.bool_)
    xv2 = np.array([1, 3, 2], dtype=np.float32)
    yv2 = np.array([5, 6, 9], dtype=np.float32)

    maskv3 = np.array([0, 0, 0], dtype=np.bool_)
    xv3 = np.array([1, 3, 2], dtype=np.float32)
    yv3 = np.array([5, 6, 9], dtype=np.float32)

    cases = [
        {"input": [maskv2, xv2, yv2]},
        {"input": [maskv3, xv3, yv3]},
    ]
    opr_test(cases, F.where, ref_fn=np.where, test_trace=False)


def test_dropout():
    from megengine.autodiff import GradManager
    from megengine.core._imperative_rt.ops import set_global_rng_seed

    def test_dropout_with_shape(shape, rate):
        data = tensor(np.ones(shape, dtype=np.float32))
        gm = GradManager().attach([data])
        with gm:
            out = F.nn.dropout(data, rate, training=True)
            gm.backward(out, tensor(np.ones(shape, dtype=np.float32)))
            assert not out.numpy().all()
            np.testing.assert_allclose(out.numpy(), data.grad.numpy(), 1e-7, 1e-7)

    def test_multiple_dropout(shape, rate):
        data = tensor(np.ones(shape, dtype=np.float32))
        gm = GradManager().attach([data])
        with gm:
            out1 = F.nn.dropout(data, rate, training=True)
            out2 = F.nn.dropout(out1, rate, training=True)
            out3 = F.nn.dropout(out2, rate, training=True)
            gm.backward(out3, tensor(np.ones(shape, dtype=np.float32)))
            np.testing.assert_allclose(out3.numpy(), data.grad.numpy(), 1e-7, 1e-7)

    def test_dropout_seed(shape, rate):
        data = tensor(np.random.randn(*shape), dtype="float32")
        set_global_rng_seed(111)
        out1 = F.nn.dropout(data, rate, training=True)
        out2 = 
F.nn.dropout(data, rate, training=True)