import pickle
import re
from copy import deepcopy

import numpy as np
import pytest
import scipy.stats as stats

import pytensor.tensor as pt
from pytensor import function, shared
from pytensor.compile.mode import Mode
from pytensor.compile.sharedvalue import SharedVariable
from pytensor.configdefaults import config
from pytensor.graph.basic import Constant, Variable, graph_inputs
from pytensor.graph.fg import FunctionGraph
from pytensor.graph.op import get_test_value
from pytensor.graph.replace import clone_replace
from pytensor.graph.rewriting.db import RewriteDatabaseQuery
from pytensor.tensor import ones, stack
from pytensor.tensor.random.basic import (
    ChoiceWithoutReplacement,
    MvNormalRV,
    PermutationRV,
    _gamma,
    bernoulli,
    beta,
    betabinom,
    binomial,
    broadcast_shapes,
    categorical,
    cauchy,
    chisquare,
    choice,
    dirichlet,
    exponential,
    gamma,
    gengamma,
    geometric,
    gumbel,
    halfcauchy,
    halfnormal,
    hypergeometric,
    integers,
    invgamma,
    laplace,
    logistic,
    lognormal,
    multinomial,
    multivariate_normal,
    nbinom,
    normal,
    pareto,
    permutation,
    poisson,
    rayleigh,
    standard_normal,
    t,
    triangular,
    truncexpon,
    uniform,
    vonmises,
    wald,
    weibull,
)
from pytensor.tensor.rewriting.shape import ShapeFeature
from pytensor.tensor.type import iscalar, scalar, tensor, vector
from tests.unittest_tools import create_pytensor_param


rewrites_query = RewriteDatabaseQuery(include=[None], exclude=["cxx_only", "BlasOpt"])
py_mode = Mode("py", rewrites_query)


def fixed_scipy_rvs(rvs_name):
    """Create a SciPy sampling function compatible with the `test_fn` argument of `compare_sample_values`."""

    def _rvs(*args, size=None, **kwargs):
        res = getattr(stats, rvs_name).rvs(*args, size=size, **kwargs)
        res = np.broadcast_to(
            res,
            size
            if size is not None
            else broadcast_shapes(*[np.shape(a) for a in args]),
        )
        return res

    return _rvs


def compare_sample_values(rv, *params, rng=None, test_fn=None, **kwargs):
    """Test for equivalence between `RandomVariable` and NumPy/other samples.

    An equivalently named method on a NumPy RNG object will be used, unless
    `test_fn` is specified.

    """
    if rng is None:
        rng = np.random.default_rng()

    if test_fn is None:
        name = getattr(rv, "name", None)

        if name is None:
            name = rv.__name__

        def test_fn(*args, random_state=None, **kwargs):
            return getattr(random_state, name)(*args, **kwargs)

    param_vals = [get_test_value(p) if isinstance(p, Variable) else p for p in params]
    kwargs_vals = {
        k: get_test_value(v) if isinstance(v, Variable) else v
        for k, v in kwargs.items()
    }

    pt_rng = shared(rng, borrow=True)

    numpy_res = np.asarray(
        test_fn(*param_vals, random_state=deepcopy(rng), **kwargs_vals)
    )

    pytensor_res = rv(*params, rng=pt_rng, **kwargs)

    assert pytensor_res.type.numpy_dtype.kind == numpy_res.dtype.kind

    numpy_shape = np.shape(numpy_res)
    numpy_bcast = [s == 1 for s in numpy_shape]
    np.testing.assert_array_equal(pytensor_res.type.broadcastable, numpy_bcast)

    fn_inputs = [
        i
        for i in graph_inputs([pytensor_res])
        if not isinstance(i, Constant | SharedVariable)
    ]
    pytensor_fn = function(fn_inputs, pytensor_res, mode=py_mode)

    pytensor_res_val = pytensor_fn()

    assert pytensor_res_val.flags.writeable

    np.testing.assert_array_equal(pytensor_res_val.shape, numpy_res.shape)

    np.testing.assert_allclose(pytensor_res_val, numpy_res)


@pytest.mark.parametrize(
    "l, u, size",
    [
        (np.array(10, dtype=config.floatX), np.array(20, dtype=config.floatX), None),
        (np.array(10, dtype=config.floatX), np.array(20, dtype=config.floatX), []),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(20, dtype=config.floatX),
            None,
        ),
    ],
)
def test_uniform_samples(l, u, size):
    compare_sample_values(uniform, l, u, size=size)


def test_uniform_default_args():
    compare_sample_values(uniform)


@pytest.mark.parametrize(
    "left, mode, right, size",
    [
        (
            np.array(10, dtype=config.floatX),
            np.array(12, dtype=config.floatX),
            np.array(20, dtype=config.floatX),
            None,
        ),
        (
            np.array(10, dtype=config.floatX),
            np.array(12, dtype=config.floatX),
            np.array(20, dtype=config.floatX),
            [],
        ),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(12, dtype=config.floatX),
            np.array(20, dtype=config.floatX),
            None,
        ),
    ],
)
def test_triangular_samples(left, mode, right, size):
    compare_sample_values(triangular, left, mode, right, size=size)


@pytest.mark.parametrize(
    "a, b, size",
    [
        (np.array(0.5, dtype=config.floatX), np.array(0.5, dtype=config.floatX), None),
        (np.array(0.5, dtype=config.floatX), np.array(0.5, dtype=config.floatX), []),
        (
            np.full((1, 2), 0.5, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_beta_samples(a, b, size):
    compare_sample_values(beta, a, b, size=size)


M_pt = iscalar("M")
M_pt.tag.test_value = 3
sd_pt = scalar("sd")
sd_pt.tag.test_value = np.array(1.0, dtype=config.floatX)


@pytest.mark.parametrize(
    "M, sd, size",
    [
        (pt.as_tensor_variable(np.array(1.0, dtype=config.floatX)), sd_pt, None),
        (
            pt.as_tensor_variable(np.array(1.0, dtype=config.floatX)),
            sd_pt,
            (M_pt,),
        ),
        (
            pt.as_tensor_variable(np.array(1.0, dtype=config.floatX)),
            sd_pt,
            (2, M_pt),
        ),
        (pt.zeros((M_pt,)), sd_pt, None),
        (pt.zeros((M_pt,)), sd_pt, (M_pt,)),
        (pt.zeros((M_pt,)), sd_pt, (2, M_pt)),
        (pt.zeros((M_pt,)), pt.ones((M_pt,)), None),
        (pt.zeros((M_pt,)), pt.ones((M_pt,)), (2, M_pt)),
        (
            create_pytensor_param(
                np.array([[-1, 20], [300, -4000]], dtype=config.floatX)
            ),
            create_pytensor_param(np.array([[1e-6, 2e-6]], dtype=config.floatX)),
            (3, 2, 2),
        ),
        (
            create_pytensor_param(np.array([1], dtype=config.floatX)),
            create_pytensor_param(np.array([10], dtype=config.floatX)),
            (1, 2),
        ),
    ],
)
def test_normal_infer_shape(M, sd, size):
    rv = normal(M, sd, size=size)
    size_pt = rv.owner.op.size_param(rv.owner)
    rv_shape = list(normal._infer_shape(size_pt, [M, sd], None))

    all_args = (M, sd, *(() if size is None else size))
    fn_inputs = [
        i
        for i in graph_inputs([a for a in all_args if isinstance(a, Variable)])
        if not isinstance(i, Constant | SharedVariable)
    ]
    pytensor_fn = function(
        fn_inputs, [pt.as_tensor(o) for o in [*rv_shape, rv]], mode=py_mode
    )

    *rv_shape_val, rv_val = pytensor_fn(
        *[
            i.tag.test_value
            for i in fn_inputs
            if not isinstance(i, SharedVariable | Constant)
        ]
    )

    assert tuple(rv_shape_val) == tuple(rv_val.shape)


@config.change_flags(compute_test_value="raise")
def test_normal_ShapeFeature():
    M_pt = iscalar("M")
    M_pt.tag.test_value = 3
    sd_pt = scalar("sd")
    sd_pt.tag.test_value = np.array(1.0, dtype=config.floatX)

    d_rv = normal(pt.ones((M_pt,)), sd_pt, size=(2, M_pt))
    d_rv.tag.test_value

    fg = FunctionGraph(
        [i for i in graph_inputs([d_rv]) if not isinstance(i, Constant)],
        [d_rv],
        clone=False,
        features=[ShapeFeature()],
    )
    s1, s2 = fg.shape_feature.shape_of[d_rv]

    assert get_test_value(s1) == get_test_value(d_rv).shape[0]
    assert get_test_value(s2) == get_test_value(d_rv).shape[1]


@pytest.mark.parametrize(
    "mean, sigma, size",
    [
        (np.array(100, dtype=config.floatX), np.array(1e-2, dtype=config.floatX), None),
        (np.array(100, dtype=config.floatX), np.array(1e-2, dtype=config.floatX), []),
        (
            np.full((1, 2), 100, dtype=config.floatX),
            np.array(1e-2, dtype=config.floatX),
            None,
        ),
    ],
)
def test_normal_samples(mean, sigma, size):
    compare_sample_values(normal, mean, sigma, size=size)


def test_normal_default_args():
    compare_sample_values(standard_normal)


@pytest.mark.parametrize(
    "mean, sigma, size",
    [
        (np.array(100, dtype=config.floatX), np.array(1e-2, dtype=config.floatX), None),
        (np.array(100, dtype=config.floatX), np.array(1e-2, dtype=config.floatX), []),
        (
            np.full((1, 2), 100, dtype=config.floatX),
            np.array(1e-2, dtype=config.floatX),
            None,
        ),
    ],
)
def test_halfnormal_samples(mean, sigma, size):
    compare_sample_values(
        halfnormal, mean, sigma, size=size, test_fn=fixed_scipy_rvs("halfnorm")
    )


@pytest.mark.parametrize(
    "mean, sigma, size",
    [
        (np.array(10, dtype=config.floatX), np.array(1e-2, dtype=config.floatX), None),
        (np.array(10, dtype=config.floatX), np.array(1e-2, dtype=config.floatX), []),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(1e-2, dtype=config.floatX),
            None,
        ),
    ],
)
def test_lognormal_samples(mean, sigma, size):
    compare_sample_values(lognormal, mean, sigma, size=size)


@pytest.mark.parametrize(
    "a, b, size",
    [
        (np.array(0.5, dtype=config.floatX), np.array(0.5, dtype=config.floatX), None),
        (np.array(0.5, dtype=config.floatX), np.array(0.5, dtype=config.floatX), []),
        (
            np.full((1, 2), 0.5, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_gamma_samples(a, b, size):
    compare_sample_values(
        _gamma,
        a,
        b,
        size=size,
    )


def test_gamma_deprecation_wrapper_fn():
    out = gamma(5.0, scale=0.5, size=(5,))
    assert out.type.shape == (5,)
    assert out.owner.inputs[-1].eval() == 0.5

    with pytest.warns(FutureWarning, match="Gamma rate argument is deprecated"):
        out = gamma([5.0, 10.0], 2.0, size=None)
    assert out.type.shape == (2,)
    assert out.owner.inputs[-1].eval() == 0.5

    with pytest.raises(ValueError, match="Must specify scale"):
        gamma(5.0)

    with pytest.raises(ValueError, match="Cannot specify both rate and scale"):
        gamma(5.0, rate=2.0, scale=0.5)


@pytest.mark.parametrize(
    "df, size",
    [
        (np.array(2, dtype=config.floatX), None),
        (np.array(2, dtype=config.floatX), []),
        (np.full((1, 2), 2, dtype=np.int64), None),
    ],
)
def test_chisquare_samples(df, size):
    compare_sample_values(chisquare, df, size=size, test_fn=fixed_scipy_rvs("chi2"))


@pytest.mark.parametrize(
    "scale, size",
    [
        (1, None),
        (2, []),
        (4, 100),
    ],
)
def test_rayleigh_samples(scale, size):
    compare_sample_values(
        rayleigh, scale=scale, size=size, test_fn=fixed_scipy_rvs("rayleigh")
    )


@pytest.mark.parametrize(
    "mu, beta, size",
    [
        (np.array(0, dtype=config.floatX), np.array(1, dtype=config.floatX), None),
        (np.array(0, dtype=config.floatX), np.array(1, dtype=config.floatX), []),
        (
            np.full((1, 2), 0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_gumbel_samples(mu, beta, size):
    compare_sample_values(
        gumbel, mu, beta, size=size, test_fn=fixed_scipy_rvs("gumbel_r")
    )


@pytest.mark.parametrize(
    "lam, size",
    [
        (np.array(10, dtype=config.floatX), None),
        (np.array(10, dtype=config.floatX), []),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            None,
        ),
    ],
)
def test_exponential_samples(lam, size):
    compare_sample_values(exponential, lam, size=size)


def test_exponential_default_args():
    compare_sample_values(exponential)


@pytest.mark.parametrize(
    "alpha, size",
    [
        (np.array(10, dtype=config.floatX), None),
        (np.array(10, dtype=config.floatX), []),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            None,
        ),
    ],
)
def test_weibull_samples(alpha, size):
    compare_sample_values(weibull, alpha, size=size)


@pytest.mark.parametrize(
    "loc, scale, size",
    [
        (np.array(2, dtype=config.floatX), np.array(0.5, dtype=config.floatX), None),
        (np.array(2, dtype=config.floatX), np.array(0.5, dtype=config.floatX), []),
        (
            np.full((1, 2), 2, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_logistic_samples(loc, scale, size):
    compare_sample_values(logistic, loc, scale, size=size)


def test_logistic_default_args():
    compare_sample_values(logistic)


@pytest.mark.parametrize(
    "mu, kappa, size",
    [
        (
            np.array(np.pi, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
        (np.array(np.pi, dtype=config.floatX), np.array(0.5, dtype=config.floatX), []),
        (
            np.full((1, 2), np.pi, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_vonmises_samples(mu, kappa, size):
    compare_sample_values(vonmises, mu, kappa, size=size)


@pytest.mark.parametrize(
    "alpha, scale, size",
    [
        (np.array(0.5, dtype=config.floatX), np.array(3.0, dtype=config.floatX), None),
        (np.array(0.5, dtype=config.floatX), np.array(5.0, dtype=config.floatX), []),
        (
            np.full((1, 2), 0.5, dtype=config.floatX),
            np.array([0.5, 1.0], dtype=config.floatX),
            None,
        ),
    ],
)
def test_pareto_samples(alpha, scale, size):
    pareto_test_fn = fixed_scipy_rvs("pareto")

    def test_fn(shape, scale, **kwargs):
        return pareto_test_fn(shape, scale=scale, **kwargs)

    compare_sample_values(pareto, alpha, scale, size=size, test_fn=test_fn)


def mvnormal_test_fn(mean=None, cov=None, size=None, random_state=None):
    rng = random_state if random_state is not None else np.random.default_rng()

    if size is None:
        size = np.broadcast_shapes(mean.shape[:-1], cov.shape[:-2])

    mean = np.broadcast_to(mean, (*size, *mean.shape[-1:]))
    cov = np.broadcast_to(cov, (*size, *cov.shape[-2:]))

    @np.vectorize(signature="(n),(n,n)->(n)")
    def vec_mvnormal(mean, cov):
        return rng.multivariate_normal(mean, cov, method="cholesky")

    return vec_mvnormal(mean, cov)


@pytest.mark.parametrize(
    "mu, cov, size",
    [
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            None,
        ),
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            [1],
        ),
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            [4],
        ),
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            [4, 1],
        ),
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            [4, 1, 1],
        ),
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            [1, 4, 1],
        ),
        (
            np.array([0], dtype=config.floatX),
            np.eye(1, dtype=config.floatX),
            [1, 5, 8],
        ),
        (
            np.array([0, 1, 2], dtype=config.floatX),
            np.diag(
                np.array([1, 10, 100], dtype=config.floatX),
            ),
            None,
        ),
        (
            np.array([0, 1, 2], dtype=config.floatX),
            np.stack(
                [
                    np.eye(3, dtype=config.floatX),
                    np.eye(3, dtype=config.floatX) * 10.0,
                ]
            ),
            [2, 3, 2],
        ),
        (
            np.array([[0, 1, 2], [4, 5, 6]], dtype=config.floatX),
            np.diag(
                np.array([1, 10, 100], dtype=config.floatX),
            ),
            None,
        ),
        (
            np.array([[0, 1, 2], [4, 5, 6]], dtype=config.floatX),
            np.stack(
                [
                    np.eye(3, dtype=config.floatX),
                    np.eye(3, dtype=config.floatX) * 10.0,
                ]
            ),
            [2, 3, 2, 2],
        ),
        (
            np.array([[0], [10], [100]], dtype=config.floatX),
            np.eye(1, dtype=config.floatX) * 1e-6,
            [2, 3, 3],
        ),
    ],
)
@pytest.mark.skipif(
    config.floatX == "float32",
    reason="Draws are only strictly equal to numpy in float64",
)
def test_mvnormal_samples(mu, cov, size):
    compare_sample_values(
        multivariate_normal, mu, cov, size=size, test_fn=mvnormal_test_fn
    )


def test_mvnormal_no_default_args():
    with pytest.raises(
        TypeError, match="missing 2 required positional arguments: 'mean' and 'cov'"
    ):
        multivariate_normal()


def test_mvnormal_impl_catches_incompatible_size():
    with pytest.raises(ValueError, match="operands could not be broadcast together "):
        multivariate_normal.rng_fn(
            np.random.default_rng(),
            np.zeros((3, 2)),
            np.broadcast_to(np.eye(2), (3, 2, 2)),
            size=(4,),
        )


@config.change_flags(compute_test_value="raise")
def test_mvnormal_ShapeFeature():
    M_pt = iscalar("M")
    M_pt.tag.test_value = 2

    d_rv = multivariate_normal(pt.ones((M_pt,)), pt.eye(M_pt), size=2)

    fg = FunctionGraph(
        [i for i in graph_inputs([d_rv]) if not isinstance(i, Constant)],
        [d_rv],
        clone=False,
        features=[ShapeFeature()],
    )

    s1, s2 = fg.shape_feature.shape_of[d_rv]

    assert get_test_value(s1) == 2
    assert M_pt in graph_inputs([s2])

    # Test broadcasted shapes
    mean = tensor(dtype=config.floatX, shape=(1, None))
    mean.tag.test_value = np.array([[0, 1, 2]], dtype=config.floatX)

    test_covar = np.diag(np.array([1, 10, 100], dtype=config.floatX))
    test_covar = np.stack([test_covar, test_covar * 10.0])
    cov = pt.as_tensor(test_covar).type()
    cov.tag.test_value = test_covar

    d_rv = multivariate_normal(mean, cov, size=[2, 3, 2])

    fg = FunctionGraph(
        outputs=[d_rv],
        clone=False,
        features=[ShapeFeature()],
    )

    s1, s2, s3, s4 = fg.shape_feature.shape_of[d_rv]

    assert s1.get_test_value() == 2
    assert s2.get_test_value() == 3
    assert s3.get_test_value() == 2
    assert s4.get_test_value() == 3


def create_mvnormal_cov_decomposition_method_test(mode):
    @pytest.mark.parametrize("psd", (True, False))
    @pytest.mark.parametrize("method", ("cholesky", "svd", "eigh"))
    def test_mvnormal_cov_decomposition_method(method, psd):
        mean = 2 ** np.arange(3)
        if psd:
            cov = [
                [1, 0.5, -1],
                [0.5, 2, 0],
                [-1, 0, 3],
            ]
        else:
            cov = [
                [1, 0.5, 0],
                [0.5, 2, 0],
                [0, 0, 0],
            ]
        rng = shared(np.random.default_rng(675))
        draws = MvNormalRV(method=method)(mean, cov, rng=rng, size=(10_000,))
        assert draws.owner.op.method == method

        # JAX doesn't raise errors at runtime
        if not psd and method == "cholesky":
            if mode == "JAX":
                # JAX doesn't raise errors at runtime, instead it returns nan
                np.isnan(draws.eval(mode=mode)).all()
            else:
                with pytest.raises(np.linalg.LinAlgError):
                    draws.eval(mode=mode)

        else:
            draws_eval = draws.eval(mode=mode)
            np.testing.assert_allclose(np.mean(draws_eval, axis=0), mean, rtol=0.02)
            np.testing.assert_allclose(np.cov(draws_eval, rowvar=False), cov, atol=0.1)

    return test_mvnormal_cov_decomposition_method


test_mvnormal_cov_decomposition_method = create_mvnormal_cov_decomposition_method_test(
    None
)


@pytest.mark.parametrize(
    "alphas, size",
    [
        (np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX), None),
        (
            np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX),
            (10, 3),
        ),
        (
            np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX),
            (10, 2, 3),
        ),
    ],
)
def test_dirichlet_samples(alphas, size):
    def dirichlet_test_fn(mean=None, cov=None, size=None, random_state=None):
        if size is None:
            size = ()
        return dirichlet.rng_fn(random_state, alphas, size)

    compare_sample_values(dirichlet, alphas, size=size, test_fn=dirichlet_test_fn)


def test_dirichlet_rng():
    alphas = np.array([[100, 1, 1], [1, 100, 1], [1, 1, 100]], dtype=config.floatX)

    with pytest.raises(ValueError, match="operands could not be broadcast together"):
        # The independent dimension's shape cannot be broadcasted from (3,) to (10, 2)
        dirichlet.rng_fn(None, alphas, size=(10, 2))

    with pytest.raises(
        ValueError, match="input operand has more dimensions than allowed"
    ):
        # One of the independent dimension's shape is missing from size
        # (i.e. should be `(1, 3)`)
        dirichlet.rng_fn(None, np.broadcast_to(alphas, (1, 3, 3)), size=(3,))


M_pt = iscalar("M")
M_pt.tag.test_value = 3


@pytest.mark.parametrize(
    "M, size",
    [
        (pt.ones((M_pt,)), None),
        (pt.ones((M_pt,)), (M_pt + 1,)),
        (pt.ones((M_pt,)), (2, M_pt)),
        (pt.ones((M_pt, M_pt + 1)), None),
        (pt.ones((M_pt, M_pt + 1)), (M_pt + 2, M_pt)),
        (pt.ones((M_pt, M_pt + 1)), (2, M_pt + 2, M_pt + 3, M_pt)),
    ],
)
def test_dirichlet_infer_shape(M, size):
    rv = dirichlet(M, size=size)
    size_pt = rv.owner.op.size_param(rv.owner)
    rv_shape = list(dirichlet._infer_shape(size_pt, [M], None))

    all_args = (M, *(() if size is None else size))
    fn_inputs = [
        i
        for i in graph_inputs([a for a in all_args if isinstance(a, Variable)])
        if not isinstance(i, Constant | SharedVariable)
    ]
    pytensor_fn = function(
        fn_inputs, [pt.as_tensor(o) for o in [*rv_shape, rv]], mode=py_mode
    )

    *rv_shape_val, rv_val = pytensor_fn(
        *[
            i.tag.test_value
            for i in fn_inputs
            if not isinstance(i, SharedVariable | Constant)
        ]
    )

    assert tuple(rv_shape_val) == tuple(rv_val.shape)


@config.change_flags(compute_test_value="raise")
def test_dirichlet_ShapeFeature():
    """Make sure `RandomVariable.infer_shape` works with `ShapeFeature`."""
    M_pt = iscalar("M")
    M_pt.tag.test_value = 2
    N_pt = iscalar("N")
    N_pt.tag.test_value = 3

    d_rv = dirichlet(pt.ones((M_pt, N_pt)), name="Gamma")

    fg = FunctionGraph(
        outputs=[d_rv],
        clone=False,
        features=[ShapeFeature()],
    )

    s1, s2 = fg.shape_feature.shape_of[d_rv]

    assert M_pt in graph_inputs([s1])
    assert N_pt in graph_inputs([s2])


@pytest.mark.parametrize(
    "lam, size",
    [
        (np.array(10, dtype=np.int64), None),
        (np.array(10, dtype=np.int64), []),
        (
            np.full((1, 2), 10, dtype=np.int64),
            None,
        ),
    ],
)
def test_poisson_samples(lam, size):
    compare_sample_values(poisson, lam, size=size)


def test_poisson_default_args():
    compare_sample_values(poisson)


@pytest.mark.parametrize(
    "p, size",
    [
        (np.array(0.1, dtype=config.floatX), None),
        (np.array(0.1, dtype=config.floatX), []),
        (
            np.full((1, 2), 0.1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_geometric_samples(p, size):
    compare_sample_values(geometric, p, size=size)


@pytest.mark.parametrize(
    "ngood, nbad, nsample, size",
    [
        (
            np.array(10, dtype=np.int64),
            np.array(20, dtype=np.int64),
            np.array(5, dtype=np.int64),
            None,
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(20, dtype=np.int64),
            np.array(5, dtype=np.int64),
            [],
        ),
        (
            np.full((1, 2), 10, dtype=np.int64),
            np.array(20, dtype=np.int64),
            np.array(5, dtype=np.int64),
            None,
        ),
    ],
)
def test_hypergeometric_samples(ngood, nbad, nsample, size):
    compare_sample_values(hypergeometric, ngood, nbad, nsample, size=size)


@pytest.mark.parametrize(
    "loc, scale, size",
    [
        (np.array(10, dtype=config.floatX), np.array(0.1, dtype=config.floatX), None),
        (
            np.array([[0]], dtype=config.floatX),
            np.array([[1]], dtype=config.floatX),
            None,
        ),
        (np.array(10, dtype=config.floatX), np.array(0.1, dtype=config.floatX), []),
        (np.array(10, dtype=config.floatX), np.array(0.1, dtype=config.floatX), [2, 3]),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(0.1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_cauchy_samples(loc, scale, size):
    compare_sample_values(
        cauchy, loc, scale, size=size, test_fn=fixed_scipy_rvs("cauchy")
    )


def test_cauchy_default_args():
    compare_sample_values(cauchy, test_fn=stats.cauchy.rvs)


@pytest.mark.parametrize(
    "loc, scale, size",
    [
        (np.array(10, dtype=config.floatX), np.array(0.1, dtype=config.floatX), None),
        (np.array(10, dtype=config.floatX), np.array(0.1, dtype=config.floatX), []),
        (np.array(10, dtype=config.floatX), np.array(0.1, dtype=config.floatX), [2, 3]),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(0.1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_halfcauchy_samples(loc, scale, size):
    compare_sample_values(
        halfcauchy, loc, scale, size=size, test_fn=fixed_scipy_rvs("halfcauchy")
    )


def test_halfcauchy_default_args():
    compare_sample_values(halfcauchy, test_fn=stats.halfcauchy.rvs)


@pytest.mark.parametrize(
    "loc, scale, size",
    [
        (np.array(2, dtype=config.floatX), np.array(1, dtype=config.floatX), None),
        (np.array(2, dtype=config.floatX), np.array(1, dtype=config.floatX), []),
        (np.array(2, dtype=config.floatX), np.array(1, dtype=config.floatX), [2, 3]),
        (
            np.full((1, 2), 2, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_invgamma_samples(loc, scale, size):
    compare_sample_values(
        invgamma,
        loc,
        scale,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: invgamma.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "mean, scale, size",
    [
        (np.array(10, dtype=config.floatX), np.array(1, dtype=config.floatX), None),
        (np.array(10, dtype=config.floatX), np.array(1, dtype=config.floatX), []),
        (np.array(10, dtype=config.floatX), np.array(1, dtype=config.floatX), [2, 3]),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_wald_samples(mean, scale, size):
    compare_sample_values(wald, mean, scale, size=size)


@pytest.mark.parametrize(
    "b, loc, scale, size",
    [
        (
            np.array(5, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
        (
            np.array(5, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            [],
        ),
        (
            np.array(5, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 5, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_truncexpon_samples(b, loc, scale, size):
    compare_sample_values(
        truncexpon,
        b,
        loc,
        scale,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: truncexpon.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "df, loc, scale, size",
    [
        (
            np.array(2, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
        (
            np.array(2, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            [],
        ),
        (
            np.array(2, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 5, dtype=config.floatX),
            np.array(0, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            None,
        ),
    ],
)
def test_t_samples(df, loc, scale, size):
    compare_sample_values(
        t,
        df,
        loc,
        scale,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: t.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "p, size",
    [
        (
            np.array(0.5, dtype=config.floatX),
            None,
        ),
        (
            np.array(0.5, dtype=config.floatX),
            [],
        ),
        (
            np.array(0.5, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_bernoulli_samples(p, size):
    compare_sample_values(
        bernoulli,
        p,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: bernoulli.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "loc, scale, size",
    [
        (
            np.array(10, dtype=config.floatX),
            np.array(5, dtype=config.floatX),
            None,
        ),
        (
            np.array(10, dtype=config.floatX),
            np.array(5, dtype=config.floatX),
            [],
        ),
        (
            np.array(10, dtype=config.floatX),
            np.array(5, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 10, dtype=config.floatX),
            np.array(5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_laplace_samples(loc, scale, size):
    compare_sample_values(laplace, loc, scale, size=size)


@pytest.mark.parametrize(
    "M, p, size",
    [
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            [],
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_binomial_samples(M, p, size):
    compare_sample_values(binomial, M, p, size=size)


@pytest.mark.parametrize(
    "M, p, size",
    [
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            [],
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_nbinom_samples(M, p, size):
    compare_sample_values(
        nbinom,
        M,
        p,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: nbinom.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "M, a, p, size",
    [
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            [],
        ),
        (
            np.array(10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 10, dtype=np.int64),
            np.array(0.5, dtype=config.floatX),
            np.array(0.5, dtype=config.floatX),
            None,
        ),
    ],
)
def test_betabinom_samples(M, a, p, size):
    compare_sample_values(
        betabinom,
        M,
        a,
        p,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: betabinom.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "alpha, p, lambd, size",
    [
        (
            np.array(2, dtype=config.floatX),
            np.array(3, dtype=config.floatX),
            np.array(5, dtype=config.floatX),
            None,
        ),
        (
            np.array(1, dtype=config.floatX),
            np.array(1, dtype=config.floatX),
            np.array(10, dtype=config.floatX),
            [],
        ),
        (
            np.array(2, dtype=config.floatX),
            np.array(2, dtype=config.floatX),
            np.array(10, dtype=config.floatX),
            [2, 3],
        ),
        (
            np.full((1, 2), 2, dtype=config.floatX),
            np.array(2, dtype=config.floatX),
            np.array(10, dtype=config.floatX),
            None,
        ),
    ],
)
def test_gengamma_samples(alpha, p, lambd, size):
    compare_sample_values(
        gengamma,
        alpha,
        p,
        lambd,
        size=size,
        test_fn=lambda *args, size=None, random_state=None, **kwargs: gengamma.rng_fn(
            random_state, *((*args, size))
        ),
    )


@pytest.mark.parametrize(
    "M, p, size, test_fn",
    [
        (
            np.array(10, dtype=np.int64),
            np.array([0.7, 0.3], dtype=config.floatX),
            None,
            None,
        ),
        (
            np.array(10, dtype=np.int64),
            np.array([0.7, 0.3], dtype=config.floatX),
            [],
            None,
        ),
        (
            np.array(10, dtype=np.int64),
            np.array([0.7, 0.3], dtype=config.floatX),
            [2, 3],
            None,
        ),
        (
            np.full((1, 2), 10, dtype=np.int64),
            np.array([0.7, 0.3], dtype=config.floatX),
            None,
            lambda *args, size=None, random_state=None, **kwargs: multinomial.rng_fn(
                random_state, *((*args, size))
            ),
        ),
        (
            np.array([10, 20], dtype=np.int64),
            np.array([[0.999, 0.001], [0.001, 0.999]], dtype=config.floatX),
            None,
            lambda *args, **kwargs: np.array([[10, 0], [0, 20]]),
        ),
        (
            np.array([10, 20], dtype=np.int64),
            np.array([[0.999, 0.001], [0.001, 0.999]], dtype=config.floatX),
            (3, 2),
            lambda *args, **kwargs: np.stack([np.array([[10, 0], [0, 20]])] * 3),
        ),
    ],
)
def test_multinomial_samples(M, p, size, test_fn):
    rng = np.random.default_rng(1234)
    compare_sample_values(
        multinomial,
        M,
        p,
        size=size,
        test_fn=test_fn,
        rng=rng,
    )


def test_multinomial_rng():
    test_M = np.array([10, 20], dtype=np.int64)
    test_p = np.array([[0.999, 0.001], [0.001, 0.999]], dtype=config.floatX)

    with pytest.raises(ValueError, match="operands could not be broadcast together"):
        # The independent dimension's shape cannot be broadcasted from (2,) to (1,)
        multinomial.rng_fn(None, test_M, test_p, size=(1,))

    with pytest.raises(
        ValueError, match="input operand has more dimensions than allowed"
    ):
        # One of the independent dimension's shape is missing from size
        # (i.e. should be `(5, 2)`)
        multinomial.rng_fn(None, np.broadcast_to(test_M, (5, 2)), test_p, size=(2,))


@pytest.mark.parametrize(
    "p, size, test_fn",
    [
        (
            np.array([100000, 1, 1], dtype=config.floatX),
            None,
            lambda *args, **kwargs: np.array(0, dtype=np.int64),
        ),
        (
            np.array(
                [[100000, 1, 1], [1, 100000, 1], [1, 1, 100000]], dtype=config.floatX
            ),
            (10, 3),
            lambda *args, **kwargs: np.tile(np.arange(3).astype(np.int64), (10, 1)),
        ),
        (
            np.array(
                [[100000, 1, 1], [1, 100000, 1], [1, 1, 100000]], dtype=config.floatX
            ),
            (10, 2, 3),
            lambda *args, **kwargs: np.tile(np.arange(3).astype(np.int64), (10, 2, 1)),
        ),
        (
            np.full((4, 1, 3), [100000, 1, 1], dtype=config.floatX),
            (4, 2),
            lambda *args, **kwargs: np.zeros((4, 2), dtype=np.int64),
        ),
    ],
)
def test_categorical_samples(p, size, test_fn):
    p = p / p.sum(axis=-1, keepdims=True)
    rng = np.random.default_rng(232)

    compare_sample_values(
        categorical,
        p,
        size=size,
        test_fn=test_fn,
        rng=rng,
    )


def test_categorical_basic():
    p = np.array([[100000, 1, 1], [1, 100000, 1], [1, 1, 100000]], dtype=config.floatX)
    p = p / p.sum(axis=-1)

    rng = np.random.default_rng()

    with pytest.raises(ValueError):
        # The independent dimension of p has shape=(3,) which cannot be
        # broadcasted to (10,)
        categorical.rng_fn(rng, p, size=(10,))

    msg = re.escape("`size` is incompatible with the shape of `p`")
    with pytest.raises(ValueError, match=msg):
        # The independent dimension of p has shape=(3,) which cannot be
        # broadcasted to (1,)
        categorical.rng_fn(rng, p, size=(1,))

    with pytest.raises(ValueError, match=msg):
        # The independent dimensions of p have shape=(1, 3) which cannot be
        # broadcasted to (3,)
        categorical.rng_fn(rng, p[None], size=(3,))


def test_integers_samples():
    with pytest.raises(TypeError):
        integers(10, rng=shared(np.random.RandomState()))

    rng = np.random.default_rng(2313)
    compare_sample_values(integers, 10, None, rng=rng)
    compare_sample_values(integers, 0, 1, rng=rng)
    compare_sample_values(integers, 0, 1, size=[3], rng=rng)
    compare_sample_values(integers, [0, 1, 2], 5, rng=rng)
    compare_sample_values(integers, [0, 1, 2], 5, size=[3, 3], rng=rng)
    compare_sample_values(integers, [0], [5], size=[1], rng=rng)
    compare_sample_values(integers, pt.as_tensor_variable([-1]), [1], size=[1], rng=rng)
    compare_sample_values(
        integers,
        pt.as_tensor_variable([-1]),
        [1],
        size=pt.as_tensor_variable([1]),
        rng=rng,
    )


def test_choice_samples():
    compare_sample_values(choice, np.asarray(5))
    compare_sample_values(choice, np.asarray([5]))
    compare_sample_values(choice, np.array([1.0, 5.0], dtype=config.floatX))
    compare_sample_values(choice, np.asarray([5]), 3)

    compare_sample_values(choice, np.array([[1, 2], [3, 4]]))
    compare_sample_values(choice, np.array([[1, 2], [3, 4]]), p=[0.4, 0.6])

    compare_sample_values(choice, [1, 2, 3], 1)

    compare_sample_values(
        choice, [1, 2, 3], 1, p=pt.as_tensor([1 / 3.0, 1 / 3.0, 1 / 3.0])
    )

    # p must be 1-dimensional.
    # TODO: The exception is raised at runtime but could be raised at compile
    # time in some situations using static shape analysis.
    with pytest.raises(ValueError):
        rng = np.random.default_rng()
        rng_pt = shared(rng, borrow=True)
        choice(a=[1, 2], p=pt.as_tensor([[0.1, 0.9], [0.3, 0.7]]), rng=rng_pt).eval()

    compare_sample_values(choice, [1, 2, 3], (10, 2), replace=True)
    compare_sample_values(choice, pt.as_tensor_variable([1, 2, 3]), 2, replace=True)


def test_choice_scalar_size():
    np.testing.assert_array_equal(
        choice([[1, 2, 3]], size=(), replace=True).eval(), [1, 2, 3]
    )
    np.testing.assert_array_equal(
        choice([[1, 2, 3]], size=(), replace=False).eval(), [1, 2, 3]
    )


def test_permutation_samples():
    compare_sample_values(
        permutation,
        np.asarray(5),
        test_fn=lambda x, random_state=None: random_state.permutation(x.item()),
    )
    compare_sample_values(permutation, [1, 2, 3])
    compare_sample_values(permutation, [[1, 2], [3, 4]])
    compare_sample_values(permutation, np.array([1.0, 2.0, 3.0], dtype=config.floatX))


def test_permutation_shape():
    assert tuple(permutation(5).shape.eval()) == (5,)
    assert tuple(permutation(np.arange(5)).shape.eval()) == (5,)
    assert tuple(permutation(np.arange(10).reshape(2, 5)).shape.eval()) == (2, 5)
    assert tuple(permutation(5, size=(2, 3)).shape.eval()) == (2, 3, 5)
    assert tuple(permutation(np.arange(5), size=(2, 3)).shape.eval()) == (2, 3, 5)


def batched_unweighted_choice_without_replacement_tester(mode="FAST_RUN"):
    """Test unweighted choice without replacement with batched ndims.

    This has no corresponding in numpy, but is supported for consistency within the
    RandomVariable API.

    It can be triggered by manual buiding the Op or during automatic vectorization.
    """
    rng = shared(np.random.default_rng())

    # Batched a implicit size
    rv_op = ChoiceWithoutReplacement(
        signature="(a0,a1),(1)->(s0,a1)",
        dtype="int64",
    )

    a = np.arange(3 * 5 * 2).reshape((3, 5, 2))
    core_shape = (4,)
    rv = rv_op(a, core_shape, rng=rng)
    assert rv.type.shape == (3, 4, 2)
    draws = rv.eval(mode=mode)

    for i in range(3):
        draw = draws[i]
        assert np.unique(draw).size == 8
        assert np.all((draw >= i * 10) & (draw < (i + 1) * 10))

    # Explicit size broadcasts beyond a
    rv_op = ChoiceWithoutReplacement(
        signature="(a0,a1),(2)->(s0,s1,a1)",
        dtype="int64",
    )

    core_shape = (4, 1)
    rv = rv_op(a, core_shape, size=(2, 3), rng=rng)
    assert rv.type.shape == (2, 3, 4, 1, 2)
    draws = rv.eval(mode=mode)

    for j in range(2):
        for i in range(3):
            draw = draws[j, i]
            assert np.unique(draw).size == 8
            assert np.all((draw >= i * 10) & (draw < (i + 1) * 10))


def batched_weighted_choice_without_replacement_tester(mode="FAST_RUN"):
    """Test weighted choice without replacement with batched ndims.

    This has no corresponding in numpy, but is supported for consistency within the
    RandomVariable API.

    It can be triggered by manual buiding the Op or during automatic vectorization.
    """
    rng = shared(np.random.default_rng())

    rv_op = ChoiceWithoutReplacement(
        signature="(a0,a1),(a0),(1)->(s0,a1)",
        dtype="int64",
    )

    # Batched a implicit size
    a = np.arange(4 * 5 * 2).reshape((4, 5, 2))
    p = np.array([0.0, 0.25, 0.25, 0.25, 0.25])
    core_shape = (3,)
    rv = rv_op(a, p, core_shape, rng=rng)
    assert rv.type.shape == (4, 3, 2)
    draws = rv.eval(mode=mode)

    for i in range(4):
        draw = draws[i].ravel()
        assert np.unique(draw).size == 6
        # The first two entries after each step of 10 have zero probability
        assert np.all((draw >= i * 10 + 2) & (draw < (i + 1) * 10))

    # p and a are batched
    # Test implicit arange
    rv_op = ChoiceWithoutReplacement(
        signature="(),(a),(2)->(s0,s1)",
        dtype="int64",
    )
    a = 6
    p = np.array(
        [
            # Only even numbers allowed
            [1 / 3, 0.0, 1 / 3, 0.0, 1 / 3, 0.0],
            # Only odd numbers allowed
            [0.0, 1 / 3, 0.0, 1 / 3, 0.0, 1 / 3],
        ]
    )
    core_shape = (3, 1)
    rv = rv_op(a, p, core_shape, rng=rng)
    assert rv.type.shape == (2, 3, 1)
    draws = rv.eval(mode=mode)

    for i in range(2):
        draw = np.asarray(draws[i].ravel())
        assert set(draw) == set(range(i, 6, 2))

    # Size broadcasts beyond a
    rv_op = ChoiceWithoutReplacement(
        signature="(a0,a1),(a0),(1)->(s0,a1)",
        dtype="int64",
    )
    a = np.arange(4 * 5 * 2).reshape((4, 5, 2))
    p = np.array([0.0, 0.25, 0.25, 0.25, 0.25])
    core_shape = (3,)
    rv = rv_op(a, p, core_shape, size=(5, 1, 4))
    assert rv.type.shape == (5, 1, 4, 3, 2)
    draws = rv.eval(mode=mode)

    for j in range(5):
        for i in range(4):
            draw = draws[j, 0, i].ravel()
            assert np.unique(draw).size == 6
            # The first two entries after each step of 10 have zero probability
            assert np.all((draw >= i * 10 + 2) & (draw < (i + 1) * 10))


def batched_permutation_tester(mode="FAST_RUN"):
    """Test permutation with batched ndims.

    This has no corresponding in numpy, but is supported for consistency within the
    RandomVariable API.

    It can be triggered by manual buiding the Op or during automatic vectorization.
    """

    rng = shared(np.random.default_rng())

    rv_op = PermutationRV(ndim_supp=2, ndims_params=[2], dtype="int64")
    x = np.arange(5 * 3 * 2).reshape((5, 3, 2))

    # Batched x and implicit size
    rv = rv_op(x, rng=rng)
    assert rv.type.shape == (5, 3, 2)

    draws = rv.eval(mode=mode)
    for i in range(5):
        assert set(np.asarray(draws[i].ravel())) == set(range(i * 6, (i + 1) * 6))

    # Size broadcasts beyond x
    rv = rv_op(x, size=(4, 5), rng=rng)
    assert rv.type.shape == (4, 5, 3, 2)
    draws = rv.eval(mode=mode)
    for j in range(4):
        for i in range(5):
            assert set(np.asarray(draws[j, i].ravel())) == set(
                range(i * 6, (i + 1) * 6)
            )


@pytest.mark.parametrize(
    "batch_dims_tester",
    [
        batched_unweighted_choice_without_replacement_tester,
        batched_weighted_choice_without_replacement_tester,
        batched_permutation_tester,
    ],
)
def test_unnatural_batched_dims(batch_dims_tester):
    "Tests for RVs that don't have natural batch dims in Numpy API."
    batch_dims_tester()


@config.change_flags(compute_test_value="off")
def test_pickle():
    # This is an interesting `Op` case, because it has a conditional dtype
    sample_a = choice(5, replace=False, size=(2, 3))

    a_pkl = pickle.dumps(sample_a)
    a_unpkl = pickle.loads(a_pkl)

    assert a_unpkl.owner.op._props() == sample_a.owner.op._props()


def test_rebuild():
    x = vector(shape=(50,))
    x_test = np.zeros((50,), dtype=config.floatX)
    y = normal(size=x.shape)
    assert y.type.shape == (50,)
    assert y.shape.eval({x: x_test}) == (50,)
    assert y.eval({x: x_test}).shape == (50,)

    x_new = vector(shape=(100,))
    x_new_test = np.zeros((100,), dtype=config.floatX)
    y_new = clone_replace(y, {x: x_new}, rebuild_strict=False)
    assert y_new.type.shape == (100,)
    assert y_new.shape.eval({x_new: x_new_test}) == (100,)
    assert y_new.eval({x_new: x_new_test}).shape == (100,)


def test_categorical_join_p_static_shape():
    """Regression test against a bug caused by misreading a numpy.bool_"""
    p = ones(3) / 3
    prob = stack([p, 1 - p], axis=-1)
    assert prob.type.shape == (3, 2)
    x = categorical(p=prob)
    assert x.type.shape == (3,)
