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import re

import numpy as np
import pytensor
import pytensor.tensor as pt
import pytest
import scipy.stats as sp

import pymc as pm

from pymc import logp
from pymc.logprob import conditional_logp
from pymc.testing import assert_no_rvs


def test_argmax():
    """Test whether the logprob for ```pt.argmax``` is correctly rejected"""
    x = pt.random.normal(0, 1, size=(3,))
    x.name = "x"
    x_argmax = pt.argmax(x, axis=-1)
    x_max_value = pt.scalar("x_max_value", dtype=x_argmax.type.dtype)

    with pytest.raises(RuntimeError, match=re.escape("Logprob method not implemented for Argmax")):
        logp(x_argmax, x_max_value)


@pytest.mark.parametrize(
    "pt_op",
    [
        pt.max,
        pt.min,
    ],
)
def test_non_iid_fails(pt_op):
    """Test whether the logprob for ```pt.max``` or ```pt.min``` for non i.i.d is correctly rejected"""
    x = pm.Normal.dist([0, 1, 2, 3, 4], 1, shape=(5,))
    x.name = "x"
    x_m = pt_op(x, axis=-1)
    x_m_value = pt.scalar("x_value")
    with pytest.raises(RuntimeError, match=re.escape("Logprob method not implemented")):
        logp(x_m, x_m_value)


@pytest.mark.parametrize(
    "pt_op",
    [
        pt.max,
        pt.min,
    ],
)
def test_multivariate_rv_fails(pt_op):
    _alpha = pt.scalar()
    _k = pt.iscalar()
    x = pm.StickBreakingWeights.dist(_alpha, _k)
    x.name = "x"
    x_m = pt_op(x, axis=-1)
    x_m_value = pt.scalar("x_value")
    with pytest.raises(RuntimeError, match=re.escape("Logprob method not implemented")):
        logp(x_m, x_m_value)


@pytest.mark.parametrize(
    "pt_op",
    [
        pt.max,
        pt.min,
    ],
)
def test_categorical(pt_op):
    """Test whether the logprob for ```pt.max``` for unsupported distributions is correctly rejected"""
    x = pm.Categorical.dist([1, 1, 1, 1], shape=(5,))
    x.name = "x"
    x_m = pt_op(x, axis=-1)
    x_m_value = pt.scalar("x_value", dtype=x.type.dtype)
    with pytest.raises(RuntimeError, match=re.escape("Logprob method not implemented")):
        logp(x_m, x_m_value)


@pytest.mark.parametrize(
    "pt_op",
    [
        pt.max,
        pt.min,
    ],
)
def test_non_supp_axis(pt_op):
    """Test whether the logprob for ```pt.max``` for unsupported axis is correctly rejected"""
    x = pt.random.normal(0, 1, size=(3, 3))
    x.name = "x"
    x_m = pt_op(x, axis=-1)
    x_m_value = pt.vector("x_value")
    with pytest.raises(RuntimeError, match=re.escape("Logprob method not implemented")):
        x_max_logprob = logp(x_m, x_m_value)


@pytest.mark.parametrize(
    "shape, value, axis",
    [
        (3, 0.85, -1),
        (3, 0.01, 0),
        (2, 0.2, None),
        (4, 0.5, 0),
        ((3, 4), 0.9, None),
        ((3, 4), 0.75, (1, 0)),
    ],
)
def test_max_logprob(shape, value, axis):
    """Test whether the logprob for ```pt.max``` produces the corrected

    The fact that order statistics of i.i.d. uniform RVs ~ Beta is used here:
        U_1, \\dots, U_n \\stackrel{\text{i.i.d.}}{\\sim} \text{Uniform}(0, 1) \\Rightarrow U_{(k)} \\sim \text{Beta}(k, n + 1- k)
    for all 1<=k<=n
    """
    x = pt.random.uniform(0, 1, size=shape)
    x.name = "x"
    x_max = pt.max(x, axis=axis)
    x_max_value = pt.scalar("x_max_value")
    x_max_logprob = logp(x_max, x_max_value)

    assert_no_rvs(x_max_logprob)

    test_value = value

    n = np.prod(shape)
    beta_rv = pt.random.beta(n, 1, name="beta")
    beta_vv = beta_rv.clone()
    beta_rv_logprob = logp(beta_rv, beta_vv)

    np.testing.assert_allclose(
        beta_rv_logprob.eval({beta_vv: test_value}),
        (x_max_logprob.eval({x_max_value: test_value})),
        rtol=1e-06,
    )


@pytest.mark.parametrize(
    "shape, value, axis",
    [
        (3, 0.85, -1),
        (3, 0.01, 0),
        (2, 0.2, None),
        (4, 0.5, 0),
        ((3, 4), 0.9, None),
        ((3, 4), 0.75, (1, 0)),
    ],
)
def test_min_logprob(shape, value, axis):
    """Test whether the logprob for ```pt.mix``` produces the corrected
    The fact that order statistics of i.i.d. uniform RVs ~ Beta is used here:
        U_1, \\dots, U_n \\stackrel{\text{i.i.d.}}{\\sim} \text{Uniform}(0, 1) \\Rightarrow U_{(k)} \\sim \text{Beta}(k, n + 1- k)
    for all 1<=k<=n
    """
    x = pt.random.uniform(0, 1, size=shape)
    x.name = "x"
    x_min = pt.min(x, axis=axis)
    x_min_value = pt.scalar("x_min_value")
    x_min_logprob = logp(x_min, x_min_value)

    assert_no_rvs(x_min_logprob)

    test_value = value

    n = np.prod(shape)
    beta_rv = pt.random.beta(1, n, name="beta")
    beta_vv = beta_rv.clone()
    beta_rv_logprob = logp(beta_rv, beta_vv)

    np.testing.assert_allclose(
        beta_rv_logprob.eval({beta_vv: test_value}),
        (x_min_logprob.eval({x_min_value: test_value})),
        rtol=1e-06,
    )


def test_min_non_mul_elemwise_fails():
    """Test whether the logprob for ```pt.min``` for non-mul elemwise RVs is rejected correctly"""
    x = pt.log(pt.random.beta(0, 1, size=(3,)))
    x.name = "x"
    x_min = pt.min(x, axis=-1)
    x_min_value = pt.scalar("x_min_value")
    with pytest.raises(RuntimeError, match=re.escape("Logprob method not implemented")):
        logp(x_min, x_min_value)


@pytest.mark.parametrize(
    "mu, size, value, axis",
    [(2, 3, 1, -1), (2, 3, 1, 0), (1, 2, 2, None), (0, 4, 0, 0)],
)
def test_max_discrete(mu, size, value, axis):
    x = pm.Poisson.dist(name="x", mu=mu, size=size)
    x_max = pt.max(x, axis=axis)
    x_max_value = pt.scalar("x_max_value", dtype=x.type.dtype)
    x_max_logprob = logp(x_max, x_max_value)

    test_value = value

    n = size
    exp_rv = sp.poisson(mu).cdf(test_value) ** n
    exp_rv_prev = sp.poisson(mu).cdf(test_value - 1) ** n

    np.testing.assert_allclose(
        np.log(exp_rv - exp_rv_prev),
        (x_max_logprob.eval({x_max_value: test_value})),
        rtol=1e-06,
    )


@pytest.mark.parametrize(
    "mu, n, test_value, axis",
    [(2, 3, 1, -1), (2, 3, 1, 0), (1, 2, 2, None), (0, 4, 0, 0)],
)
def test_min_discrete(mu, n, test_value, axis):
    x = pm.Poisson.dist(name="x", mu=mu, size=(n,))
    x_min = pt.min(x, axis=axis)
    x_min_value = pt.scalar("x_min_value", dtype=x.type.dtype)
    x_min_logprob = logp(x_min, x_min_value)

    sf_before = 1 - sp.poisson(mu).cdf(test_value - 1)
    sf = 1 - sp.poisson(mu).cdf(test_value)

    expected_logp = np.log(sf_before**n - sf**n)

    np.testing.assert_allclose(
        x_min_logprob.eval({x_min_value: test_value}),
        expected_logp,
        rtol=1e-06,
    )


def test_min_max_bernoulli():
    p = 0.7
    q = 1 - p
    n = 3
    x = pm.Bernoulli.dist(name="x", p=p, shape=(n,))
    value = pt.scalar("value", dtype=int)

    max_logp_fn = pytensor.function([value], pm.logp(pt.max(x), value))
    np.testing.assert_allclose(max_logp_fn(0), np.log(q**n))
    np.testing.assert_allclose(max_logp_fn(1), np.log(1 - q**n))

    min_logp_fn = pytensor.function([value], pm.logp(pt.min(x), value))
    np.testing.assert_allclose(min_logp_fn(1), np.log(p**n))
    np.testing.assert_allclose(min_logp_fn(0), np.log(1 - p**n))


def test_non_measurable_max_grad():
    # Regression test for https://github.com/pymc-devs/pytensor/issues/711
    x = pt.random.normal(0, 1, size=(3,))
    max_x = x.max()
    y = pt.random.normal(max_x, 1)

    x_vv = x.type()
    y_vv = y.type()
    logp_terms = conditional_logp({x: x_vv, y: y_vv}).values()
    joint_logp = pt.sum([term.sum() for term in logp_terms])

    # Test that calling gradient does not raise a NotImplementedError
    pt.grad(joint_logp, x_vv)
