# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Test nn.Distribution.Normal.
"""
import numpy as np
import pytest

import mindspore.nn as nn
from mindspore import dtype
from mindspore import Tensor

def test_normal_shape_errpr():
    """
    Invalid shapes.
    """
    with pytest.raises(ValueError):
        nn.Normal([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)


def test_arguments():
    """
    args passing during initialization.
    """
    n = nn.Normal()
    assert isinstance(n, nn.Distribution)
    n = nn.Normal([3.0], [4.0], dtype=dtype.float32)
    assert isinstance(n, nn.Distribution)


class NormalProb(nn.Cell):
    """
    Normal distribution: initialize with mean/sd.
    """
    def __init__(self):
        super(NormalProb, self).__init__()
        self.normal = nn.Normal(3.0, 4.0, dtype=dtype.float32)

    def construct(self, value):
        prob = self.normal('prob', value)
        log_prob = self.normal('log_prob', value)
        cdf = self.normal('cdf', value)
        log_cdf = self.normal('log_cdf', value)
        sf = self.normal('survival_function', value)
        log_sf = self.normal('log_survival', value)
        return prob + log_prob + cdf + log_cdf + sf + log_sf

def test_normal_prob():
    """
    Test probability functions: passing value through construct.
    """
    net = NormalProb()
    value = Tensor([0.5, 1.0], dtype=dtype.float32)
    ans = net(value)
    assert isinstance(ans, Tensor)


class NormalProb1(nn.Cell):
    """
    Normal distribution: initialize without mean/sd.
    """
    def __init__(self):
        super(NormalProb1, self).__init__()
        self.normal = nn.Normal()

    def construct(self, value, mean, sd):
        prob = self.normal('prob', value, mean, sd)
        log_prob = self.normal('log_prob', value, mean, sd)
        cdf = self.normal('cdf', value, mean, sd)
        log_cdf = self.normal('log_cdf', value, mean, sd)
        sf = self.normal('survival_function', value, mean, sd)
        log_sf = self.normal('log_survival', value, mean, sd)
        return prob + log_prob + cdf + log_cdf + sf + log_sf

def test_normal_prob1():
    """
    Test probability functions: passing mean/sd, value through construct.
    """
    net = NormalProb1()
    value = Tensor([0.5, 1.0], dtype=dtype.float32)
    mean = Tensor([0.0], dtype=dtype.float32)
    sd = Tensor([1.0], dtype=dtype.float32)
    ans = net(value, mean, sd)
    assert isinstance(ans, Tensor)

class NormalKl(nn.Cell):
    """
    Test class: kl_loss of Normal distribution.
    """
    def __init__(self):
        super(NormalKl, self).__init__()
        self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
        self.n2 = nn.Normal(dtype=dtype.float32)

    def construct(self, mean_b, sd_b, mean_a, sd_a):
        kl1 = self.n1('kl_loss', 'Normal', mean_b, sd_b)
        kl2 = self.n2('kl_loss', 'Normal', mean_b, sd_b, mean_a, sd_a)
        return kl1 + kl2

def test_kl():
    """
    Test kl_loss.
    """
    net = NormalKl()
    mean_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    sd_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    mean_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
    sd_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
    ans = net(mean_b, sd_b, mean_a, sd_a)
    assert isinstance(ans, Tensor)

class NormalCrossEntropy(nn.Cell):
    """
    Test class: cross_entropy of Normal distribution.
    """
    def __init__(self):
        super(NormalCrossEntropy, self).__init__()
        self.n1 = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
        self.n2 = nn.Normal(dtype=dtype.float32)

    def construct(self, mean_b, sd_b, mean_a, sd_a):
        h1 = self.n1('cross_entropy', 'Normal', mean_b, sd_b)
        h2 = self.n2('cross_entropy', 'Normal', mean_b, sd_b, mean_a, sd_a)
        return h1 + h2

def test_cross_entropy():
    """
    Test cross entropy between Normal distributions.
    """
    net = NormalCrossEntropy()
    mean_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    sd_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
    mean_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
    sd_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
    ans = net(mean_b, sd_b, mean_a, sd_a)
    assert isinstance(ans, Tensor)

class NormalBasics(nn.Cell):
    """
    Test class: basic mean/sd function.
    """
    def __init__(self):
        super(NormalBasics, self).__init__()
        self.n = nn.Normal(3.0, 4.0, dtype=dtype.float32)

    def construct(self):
        mean = self.n('mean')
        sd = self.n('sd')
        mode = self.n('mode')
        entropy = self.n('entropy')
        return mean + sd + mode + entropy

def test_bascis():
    """
    Test mean/sd/mode/entropy functionality of Normal.
    """
    net = NormalBasics()
    ans = net()
    assert isinstance(ans, Tensor)
