# 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 Geometric 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 Geometric distribution.
    """
    def __init__(self):
        super(Prob, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_pmf():
    """
    Test pmf.
    """
    geom_benchmark = stats.geom(0.7)
    expect_pmf = geom_benchmark.pmf([0, 1, 2, 3, 4]).astype(np.float32)
    pdf = Prob()
    x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.float32), dtype=dtype.float32)
    output = pdf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_pmf) < tol).all()

class LogProb(nn.Cell):
    """
    Test class: log probability of Geometric distribution.
    """
    def __init__(self):
        super(LogProb, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_log_likelihood():
    """
    Test log_pmf.
    """
    geom_benchmark = stats.geom(0.7)
    expect_logpmf = geom_benchmark.logpmf([1, 2, 3, 4, 5]).astype(np.float32)
    logprob = LogProb()
    x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.int32), dtype=dtype.float32)
    output = logprob(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_logpmf) < tol).all()

class KL(nn.Cell):
    """
    Test class: kl_loss between Geometric distributions.
    """
    def __init__(self):
        super(KL, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

    @ms_function
    def construct(self, x_):
        return self.g('kl_loss', 'Geometric', x_)

def test_kl_loss():
    """
    Test kl_loss.
    """
    probs1_a = 0.7
    probs1_b = 0.5
    probs0_a = 1 - probs1_a
    probs0_b = 1 - probs1_b
    expect_kl_loss = np.log(probs1_a / probs1_b) + (probs0_a / probs1_a) * np.log(probs0_a / probs0_b)
    kl_loss = KL()
    output = kl_loss(Tensor([probs1_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 Geometric distribution.
    """
    def __init__(self):
        super(Basics, self).__init__()
        self.g = nn.Geometric([0.5, 0.5], dtype=dtype.int32)

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

def test_basics():
    """
    Test mean/standard deviation/mode.
    """
    basics = Basics()
    mean, sd, mode = basics()
    expect_mean = [1.0, 1.0]
    expect_sd = np.sqrt(np.array([0.5, 0.5]) / np.square(np.array([0.5, 0.5])))
    expect_mode = [0.0, 0.0]
    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: log probability of bernoulli distribution.
    """
    def __init__(self, shape, seed=0):
        super(Sampling, self).__init__()
        self.g = nn.Geometric([0.7, 0.5], seed=seed, dtype=dtype.int32)
        self.shape = shape

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

def test_sample():
    """
    Test sample.
    """
    shape = (2, 3)
    sample = Sampling(shape)
    output = sample()
    assert output.shape == (2, 3, 2)

class CDF(nn.Cell):
    """
    Test class: cdf of Geometric distribution.
    """
    def __init__(self):
        super(CDF, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_cdf():
    """
    Test cdf.
    """
    geom_benchmark = stats.geom(0.7)
    expect_cdf = geom_benchmark.cdf([0, 1, 2, 3, 4]).astype(np.float32)
    x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.int32), dtype=dtype.float32)
    cdf = CDF()
    output = cdf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()

class LogCDF(nn.Cell):
    """
    Test class: log cdf of Geometric distribution.
    """
    def __init__(self):
        super(LogCDF, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_logcdf():
    """
    Test log_cdf.
    """
    geom_benchmark = stats.geom(0.7)
    expect_logcdf = geom_benchmark.logcdf([1, 2, 3, 4, 5]).astype(np.float32)
    x_ = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.int32), dtype=dtype.float32)
    logcdf = LogCDF()
    output = logcdf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()

class SF(nn.Cell):
    """
    Test class: survial funciton of Geometric distribution.
    """
    def __init__(self):
        super(SF, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_survival():
    """
    Test survival function.
    """
    geom_benchmark = stats.geom(0.7)
    expect_survival = geom_benchmark.sf([0, 1, 2, 3, 4]).astype(np.float32)
    x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.int32), dtype=dtype.float32)
    sf = SF()
    output = sf(x_)
    tol = 1e-6
    assert (np.abs(output.asnumpy() - expect_survival) < tol).all()

class LogSF(nn.Cell):
    """
    Test class: log survial funciton of Geometric distribution.
    """
    def __init__(self):
        super(LogSF, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_log_survival():
    """
    Test log_survival function.
    """
    geom_benchmark = stats.geom(0.7)
    expect_logsurvival = geom_benchmark.logsf([0, 1, 2, 3, 4]).astype(np.float32)
    x_ = Tensor(np.array([-1, 0, 1, 2, 3]).astype(np.float32), dtype=dtype.float32)
    log_sf = LogSF()
    output = log_sf(x_)
    tol = 5e-6
    assert (np.abs(output.asnumpy() - expect_logsurvival) < tol).all()

class EntropyH(nn.Cell):
    """
    Test class: entropy of Geometric distribution.
    """
    def __init__(self):
        super(EntropyH, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

def test_entropy():
    """
    Test entropy.
    """
    geom_benchmark = stats.geom(0.7)
    expect_entropy = geom_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 Geometric distributions.
    """
    def __init__(self):
        super(CrossEntropy, self).__init__()
        self.g = nn.Geometric(0.7, dtype=dtype.int32)

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

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