# Copyright (c) 2022 PaddlePaddle 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.

import unittest

import config
import numpy as np
import parameterize
import scipy.stats

import paddle

paddle.enable_static()


@parameterize.place(config.DEVICES)
@parameterize.parameterize_cls(
    (parameterize.TEST_CASE_NAME, 'loc', 'scale'),
    [
        ('one-dim', parameterize.xrand((2,)), parameterize.xrand((2,))),
        ('multi-dim', parameterize.xrand((5, 5)), parameterize.xrand((5, 5))),
    ],
)
class TestLaplace(unittest.TestCase):
    def setUp(self):
        startup_program = paddle.static.Program()
        main_program = paddle.static.Program()
        executor = paddle.static.Executor(self.place)
        with paddle.static.program_guard(main_program, startup_program):
            loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
            scale = paddle.static.data(
                'scale', self.scale.shape, self.scale.dtype
            )
            self._dist = paddle.distribution.Laplace(loc=loc, scale=scale)
            self.sample_shape = (50000,)
            mean = self._dist.mean
            var = self._dist.variance
            stddev = self._dist.stddev
            entropy = self._dist.entropy()
            samples = self._dist.sample(self.sample_shape)
        fetch_list = [mean, var, stddev, entropy, samples]
        self.feeds = {'loc': self.loc, 'scale': self.scale}

        executor.run(startup_program)
        [
            self.mean,
            self.var,
            self.stddev,
            self.entropy,
            self.samples,
        ] = executor.run(main_program, feed=self.feeds, fetch_list=fetch_list)

    def test_mean(self):
        self.assertEqual(str(self.mean.dtype).split('.')[-1], self.scale.dtype)
        np.testing.assert_allclose(
            self.mean,
            self._np_mean(),
            rtol=config.RTOL.get(str(self.scale.dtype)),
            atol=config.ATOL.get(str(self.scale.dtype)),
        )

    def test_variance(self):
        self.assertEqual(str(self.var.dtype).split('.')[-1], self.scale.dtype)
        np.testing.assert_allclose(
            self.var,
            self._np_variance(),
            rtol=config.RTOL.get(str(self.scale.dtype)),
            atol=config.ATOL.get(str(self.scale.dtype)),
        )

    def test_stddev(self):
        self.assertEqual(
            str(self.stddev.dtype).split('.')[-1], self.scale.dtype
        )
        np.testing.assert_allclose(
            self.stddev,
            self._np_stddev(),
            rtol=config.RTOL.get(str(self.scale.dtype)),
            atol=config.ATOL.get(str(self.scale.dtype)),
        )

    def test_entropy(self):
        self.assertEqual(
            str(self.entropy.dtype).split('.')[-1], self.scale.dtype
        )

    def test_sample(self):

        self.assertEqual(self.samples.dtype, self.scale.dtype)
        self.assertEqual(
            tuple(self.samples.shape),
            tuple(self._dist._extend_shape(self.sample_shape)),
        )

        self.assertEqual(self.samples.shape, self.sample_shape + self.loc.shape)
        self.assertEqual(self.samples.shape, self.sample_shape + self.loc.shape)

        np.testing.assert_allclose(
            self.samples.mean(axis=0),
            scipy.stats.laplace.mean(self.loc, scale=self.scale),
            rtol=0.2,
            atol=0.0,
        )
        np.testing.assert_allclose(
            self.samples.var(axis=0),
            scipy.stats.laplace.var(self.loc, scale=self.scale),
            rtol=0.1,
            atol=0.0,
        )

    def _np_mean(self):
        return self.loc

    def _np_stddev(self):
        return (2**0.5) * self.scale

    def _np_variance(self):
        stddev = (2**0.5) * self.scale
        return np.power(stddev, 2)

    def _np_entropy(self):
        return scipy.stats.laplace.entropy(loc=self.loc, scale=self.scale)


@parameterize.place(config.DEVICES)
@parameterize.parameterize_cls(
    (parameterize.TEST_CASE_NAME, 'loc', 'scale', 'value'),
    [
        (
            'value-float',
            np.array([0.2, 0.3]),
            np.array([2.0, 3.0]),
            np.array([2.0, 5.0]),
        ),
        (
            'value-int',
            np.array([0.2, 0.3]),
            np.array([2.0, 3.0]),
            np.array([2, 5]),
        ),
        (
            'value-multi-dim',
            np.array([0.2, 0.3]),
            np.array([2.0, 3.0]),
            np.array([[4.0, 6], [8, 2]]),
        ),
    ],
)
class TestLaplacePDF(unittest.TestCase):
    def setUp(self):
        startup_program = paddle.static.Program()
        main_program = paddle.static.Program()
        executor = paddle.static.Executor(self.place)

        with paddle.static.program_guard(main_program, startup_program):
            loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
            scale = paddle.static.data(
                'scale', self.scale.shape, self.scale.dtype
            )
            value = paddle.static.data(
                'value', self.value.shape, self.value.dtype
            )
            self._dist = paddle.distribution.Laplace(loc=loc, scale=scale)
            prob = self._dist.prob(value)
            log_prob = self._dist.log_prob(value)
            cdf = self._dist.cdf(value)
            icdf = self._dist.icdf(value)
        fetch_list = [prob, log_prob, cdf, icdf]
        self.feeds = {'loc': self.loc, 'scale': self.scale, 'value': self.value}

        executor.run(startup_program)
        [self.prob, self.log_prob, self.cdf, self.icdf] = executor.run(
            main_program, feed=self.feeds, fetch_list=fetch_list
        )

    def test_prob(self):
        np.testing.assert_allclose(
            self.prob,
            scipy.stats.laplace.pdf(self.value, self.loc, self.scale),
            rtol=config.RTOL.get(str(self.loc.dtype)),
            atol=config.ATOL.get(str(self.loc.dtype)),
        )

    def test_log_prob(self):
        np.testing.assert_allclose(
            self.log_prob,
            scipy.stats.laplace.logpdf(self.value, self.loc, self.scale),
            rtol=config.RTOL.get(str(self.loc.dtype)),
            atol=config.ATOL.get(str(self.loc.dtype)),
        )

    def test_cdf(self):
        np.testing.assert_allclose(
            self.cdf,
            scipy.stats.laplace.cdf(self.value, self.loc, self.scale),
            rtol=config.RTOL.get(str(self.loc.dtype)),
            atol=config.ATOL.get(str(self.loc.dtype)),
        )

    def test_icdf(self):
        np.testing.assert_allclose(
            self.icdf,
            scipy.stats.laplace.ppf(self.value, self.loc, self.scale),
            rtol=config.RTOL.get(str(self.loc.dtype)),
            atol=config.ATOL.get(str(self.loc.dtype)),
        )


@parameterize.place(config.DEVICES)
@parameterize.parameterize_cls(
    (parameterize.TEST_CASE_NAME, 'loc1', 'scale1', 'loc2', 'scale2'),
    [
        (
            'kl',
            np.array([0.0]),
            np.array([1.0]),
            np.array([1.0]),
            np.array([0.5]),
        )
    ],
)
class TestLaplaceAndLaplaceKL(unittest.TestCase):
    def setUp(self):
        self.mp = paddle.static.Program()
        self.sp = paddle.static.Program()
        self.executor = paddle.static.Executor(self.place)

        with paddle.static.program_guard(self.mp, self.sp):
            loc1 = paddle.static.data('loc1', self.loc1.shape, self.loc1.dtype)
            scale1 = paddle.static.data(
                'scale1', self.scale1.shape, self.scale1.dtype
            )
            loc2 = paddle.static.data('loc2', self.loc2.shape, self.loc2.dtype)
            scale2 = paddle.static.data(
                'scale2', self.scale2.shape, self.scale2.dtype
            )
            self._dist_1 = paddle.distribution.Laplace(loc=loc1, scale=scale1)
            self._dist_2 = paddle.distribution.Laplace(loc=loc2, scale=scale2)
            self.feeds = {
                'loc1': self.loc1,
                'scale1': self.scale1,
                'loc2': self.loc2,
                'scale2': self.scale2,
            }

    def test_kl_divergence(self):
        with paddle.static.program_guard(self.mp, self.sp):
            out = paddle.distribution.kl_divergence(self._dist_1, self._dist_2)
            self.executor.run(self.sp)
            [out] = self.executor.run(
                self.mp, feed=self.feeds, fetch_list=[out]
            )
            np.testing.assert_allclose(out, self._np_kl(), atol=0, rtol=0.50)

    def _np_kl(self):
        x = np.linspace(
            scipy.stats.laplace.ppf(0.01), scipy.stats.laplace.ppf(0.99), 1000
        )
        d1 = scipy.stats.laplace.pdf(x, loc=0.0, scale=1.0)
        d2 = scipy.stats.laplace.pdf(x, loc=1.0, scale=0.5)
        return scipy.stats.entropy(d1, d2)


"""
# Note: Zero dimension of a Tensor is not supported by static graph mode of paddle;
# therefore, ks test below cannot be conducted temporarily.

@parameterize.place(config.DEVICES)
@parameterize.parameterize_cls(
    (parameterize.TEST_CASE_NAME, 'loc', 'scale', 'sample_shape'), [
    ('one-dim', np.array(4.0), np.array(3.0), np.array([3000]))])
class TestLaplaceKS(unittest.TestCase):
    def setUp(self):
        self.program = paddle.static.Program()
        self.executor = paddle.static.Executor(self.place)
        with paddle.static.program_guard(self.program):
            loc = paddle.static.data('loc', self.loc.shape,
                                        self.loc.dtype)
            scale = paddle.static.data('scale', self.scale.shape,
                                        self.scale.dtype)
            self.sample = paddle.static.data('sample_shape', self.sample_shape.shape,
                                        self.sample_shape.dtype)
            self._dist = paddle.distribution.Laplace(loc=loc, scale=scale)
            self.feeds = {'loc': self.loc, 'scale': self.scale, 'sample_shape': self.sample_shape}

    def test_sample(self):
        with paddle.static.program_guard(self.program):
            [sample_values] = self.executor.run(self.program,
                              feed=self.feeds,
                              fetch_list=self._dist.sample((3000,)))
            self.assertTrue(self._kstest(self.loc, self.scale, sample_values))

    def _kstest(self, loc, scale, samples):
        # Uses the Kolmogorov-Smirnov test for goodness of fit.
        ks, p_value = scipy.stats.kstest(
            samples,
            scipy.stats.laplace(loc, scale=scale).cdf)
        return ks < 0.02
"""

if __name__ == '__main__':
    unittest.main()
