# Copyright 2019 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 cases for exponential distribution"""
import numpy as np
from scipy import stats
import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.api import ms_function
from mindspore import dtype

context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")

class Prob(nn.Cell):
    """
    Test class: probability of Exponential distribution.
    """
    def __init__(self):
        super(Prob, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('prob', x_)

def test_pdf():
    """
    Test pdf.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_pdf = expon_benchmark.pdf([-1.0, 0.0, 1.0]).astype(np.float32)
    pdf = Prob()
    x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
    output = pdf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()

class LogProb(nn.Cell):
    """
    Test class: log probability of Exponential distribution.
    """
    def __init__(self):
        super(LogProb, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('log_prob', x_)

def test_log_likelihood():
    """
    Test log_pdf.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_logpdf = expon_benchmark.logpdf([0.5, 1.0, 2.0]).astype(np.float32)
    logprob = LogProb()
    x_ = Tensor(np.array([0.5, 1.0, 2.0]).astype(np.float32), dtype=dtype.float32)
    output = logprob(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()

class KL(nn.Cell):
    """
    Test class: kl_loss between Exponential distributions.
    """
    def __init__(self):
        super(KL, self).__init__()
        self.e = nn.Exponential([1.5], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('kl_loss', 'Exponential', x_)

def test_kl_loss():
    """
    Test kl_loss.
    """
    rate_a = 1.5
    rate_b = np.array([0.5, 2.0]).astype(np.float32)
    expect_kl_loss = np.log(rate_a) - np.log(rate_b) + rate_b / rate_a  - 1.0
    kl = KL()
    output = kl(Tensor(rate_b, dtype=dtype.float32))
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()

class Basics(nn.Cell):
    """
    Test class: mean/sd/mode of Exponential distribution.
    """
    def __init__(self):
        super(Basics, self).__init__()
        self.e = nn.Exponential([0.5], dtype=dtype.float32)

    @ms_function
    def construct(self):
        return self.e('mean'), self.e('sd'), self.e('mode')

def test_basics():
    """
    Test mean/standard/mode deviation.
    """
    basics = Basics()
    mean, sd, mode = basics()
    expect_mean = 2.
    expect_sd = 2.
    expect_mode = 0.
    tol = 1e-6
    assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
    assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
    assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()

class Sampling(nn.Cell):
    """
    Test class: sample of Exponential distribution.
    """
    def __init__(self, shape, seed=0):
        super(Sampling, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], seed=seed, dtype=dtype.float32)
        self.shape = shape

    @ms_function
    def construct(self, rate=None):
        return self.e('sample', self.shape, rate)

def test_sample():
    """
    Test sample.
    """
    shape = (2, 3)
    seed = 10
    rate = Tensor([1.0, 2.0, 3.0], dtype=dtype.float32)
    sample = Sampling(shape, seed=seed)
    output = sample(rate)
    assert output.shape == (2, 3, 3)

class CDF(nn.Cell):
    """
    Test class: cdf of Exponential distribution.
    """
    def __init__(self):
        super(CDF, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('cdf', x_)

def test_cdf():
    """
    Test cdf.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_cdf = expon_benchmark.cdf([-1.0, 0.0, 1.0]).astype(np.float32)
    cdf = CDF()
    x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
    output = cdf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()

class LogCDF(nn.Cell):
    """
    Test class: log_cdf of Exponential distribution.
    """
    def __init__(self):
        super(LogCDF, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('log_cdf', x_)

def test_log_cdf():
    """
    Test log_cdf.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_logcdf = expon_benchmark.logcdf([0.5, 1.0, 2.5]).astype(np.float32)
    logcdf = LogCDF()
    x_ = Tensor(np.array([0.5, 1.0, 2.5]).astype(np.float32), dtype=dtype.float32)
    output = logcdf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()

class SF(nn.Cell):
    """
    Test class: survival function of Exponential distribution.
    """
    def __init__(self):
        super(SF, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('survival_function', x_)

def test_survival():
    """
    Test survival function.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_survival = expon_benchmark.sf([-1.0, 0.0, 1.0]).astype(np.float32)
    survival = SF()
    x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
    output = survival(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_survival) < tol).all()

class LogSF(nn.Cell):
    """
    Test class: log survival function of Exponential distribution.
    """
    def __init__(self):
        super(LogSF, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        return self.e('log_survival', x_)

def test_log_survival():
    """
    Test log survival function.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_logsurvival = expon_benchmark.logsf([-1.0, 0.0, 1.0]).astype(np.float32)
    logsurvival = LogSF()
    x_ = Tensor(np.array([-1.0, 0.0, 1.0]).astype(np.float32), dtype=dtype.float32)
    output = logsurvival(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()

class EntropyH(nn.Cell):
    """
    Test class: entropy of Exponential distribution.
    """
    def __init__(self):
        super(EntropyH, self).__init__()
        self.e = nn.Exponential([[1.0], [0.5]], dtype=dtype.float32)

    @ms_function
    def construct(self):
        return self.e('entropy')

def test_entropy():
    """
    Test entropy.
    """
    expon_benchmark = stats.expon(scale=[[1.0], [2.0]])
    expect_entropy = expon_benchmark.entropy().astype(np.float32)
    entropy = EntropyH()
    output = entropy()
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()

class CrossEntropy(nn.Cell):
    """
    Test class: cross entropy between Exponential distribution.
    """
    def __init__(self):
        super(CrossEntropy, self).__init__()
        self.e = nn.Exponential([1.0], dtype=dtype.float32)

    @ms_function
    def construct(self, x_):
        entropy = self.e('entropy')
        kl_loss = self.e('kl_loss', 'Exponential', x_)
        h_sum_kl = entropy + kl_loss
        cross_entropy = self.e('cross_entropy', 'Exponential', x_)
        return h_sum_kl - cross_entropy

def test_cross_entropy():
    """
    Test cross_entropy.
    """
    cross_entropy = CrossEntropy()
    rate = Tensor([0.5], dtype=dtype.float32)
    diff = cross_entropy(rate)
    tol = 1e-6
    assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()
