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#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
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#       http://www.apache.org/licenses/LICENSE-2.0
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import warnings

import numpy as np
import numpy.testing as npt
import pytest

import pymc as pm

from pymc.blocking import DictToArrayBijection, RaveledVars
from pymc.pytensorf import floatX
from pymc.step_methods.hmc import HamiltonianMC
from pymc.step_methods.hmc.base_hmc import BaseHMC
from tests import models
from tests.helpers import RVsAssignmentStepsTester, StepMethodTester


class TestStepHamiltonianMC(StepMethodTester):
    @pytest.mark.parametrize(
        "step_fn, draws",
        [
            (lambda C, _: HamiltonianMC(scaling=C, is_cov=True, blocked=False), 1000),
            (lambda C, _: HamiltonianMC(scaling=C, is_cov=True), 1000),
        ],
    )
    def test_step_continuous(self, step_fn, draws):
        self.step_continuous(step_fn, draws)


class TestRVsAssignmentHamiltonianMC(RVsAssignmentStepsTester):
    @pytest.mark.parametrize("step, step_kwargs", [(HamiltonianMC, {})])
    def test_continuous_steps(self, step, step_kwargs):
        self.continuous_steps(step, step_kwargs)


def test_leapfrog_reversible():
    n = 3
    np.random.seed(42)
    start, model, _ = models.non_normal(n)
    size = sum(start[n.name].size for n in model.value_vars)
    scaling = floatX(np.random.rand(size))

    class HMC(BaseHMC):
        def _hamiltonian_step(self, *args, **kwargs):
            pass

    step = HMC(vars=model.value_vars, model=model, scaling=scaling)

    astart = DictToArrayBijection.map(start)
    p = RaveledVars(floatX(step.potential.random()), astart.point_map_info)
    q = floatX(np.random.randn(size))
    start = step.integrator.compute_state(p, q)
    for epsilon in [0.01, 0.1]:
        for n_steps in [1, 2, 3, 4, 20]:
            state = start
            for _ in range(n_steps):
                state = step.integrator.step(epsilon, state)
            for _ in range(n_steps):
                state = step.integrator.step(-epsilon, state)
            npt.assert_allclose(state.q.data, start.q.data, rtol=1e-5)
            npt.assert_allclose(state.p.data, start.p.data, rtol=1e-5)


def test_nuts_tuning():
    with pm.Model():
        pm.Normal("mu", mu=0, sigma=1)
        step = pm.NUTS()
        with warnings.catch_warnings():
            warnings.filterwarnings("ignore", ".*number of samples.*", UserWarning)
            idata = pm.sample(
                10, step=step, tune=5, discard_tuned_samples=False, progressbar=False, chains=1
            )

    assert not step.tune
    ss_tuned = idata.warmup_sample_stats["step_size"][0, -1]
    ss_posterior = idata.sample_stats["step_size"][0, :]
    np.testing.assert_array_equal(ss_posterior, ss_tuned)
