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

import pytensor
import pytensor.tensor as pt
import pytensor.tensor.random.basic as ptr
from pytensor import clone_replace
from pytensor.compile.function import function
from pytensor.compile.sharedvalue import shared
from pytensor.tensor.random.basic import RandomVariable
from pytensor.tensor.random.type import RandomType
from pytensor.tensor.random.utils import RandomStream
from tests.link.jax.test_basic import compare_jax_and_py, jax_mode
from tests.tensor.random.test_basic import (
    batched_permutation_tester,
    batched_unweighted_choice_without_replacement_tester,
    batched_weighted_choice_without_replacement_tester,
    create_mvnormal_cov_decomposition_method_test,
)


jax = pytest.importorskip("jax")


from pytensor.link.jax.dispatch.random import numpyro_available  # noqa: E402


def compile_random_function(*args, mode="JAX", **kwargs):
    with pytest.warns(
        UserWarning, match=r"The RandomType SharedVariables \[.+\] will not be used"
    ):
        return function(*args, mode=mode, **kwargs)


def test_random_RandomStream():
    """Two successive calls of a compiled graph using `RandomStream` should
    return different values.

    """
    srng = RandomStream(seed=123)
    out = srng.normal() - srng.normal()

    fn = compile_random_function([], out, mode=jax_mode)
    jax_res_1 = fn()
    jax_res_2 = fn()

    assert not np.array_equal(jax_res_1, jax_res_2)


@pytest.mark.parametrize("rng_ctor", (np.random.default_rng,))
def test_random_updates(rng_ctor):
    original_value = rng_ctor(seed=98)
    rng = shared(original_value, name="original_rng", borrow=False)
    next_rng, x = pt.random.normal(name="x", rng=rng).owner.outputs

    f = compile_random_function([], [x], updates={rng: next_rng}, mode=jax_mode)
    assert f() != f()

    # Check that original rng variable content was not overwritten when calling jax_typify
    assert all(
        a == b if not isinstance(a, np.ndarray) else np.array_equal(a, b)
        for a, b in zip(
            rng.get_value().bit_generator.state,
            original_value.bit_generator.state,
            strict=True,
        )
    )


@pytest.mark.parametrize("noise_first", (False, True))
def test_replaced_shared_rng_storage_order(noise_first):
    # Test that replacing the RNG variable in the linker does not cause
    # a disalignment between the compiled graph and the storage_map.

    mu = pytensor.shared(np.array(1.0), name="mu")
    rng = pytensor.shared(np.random.default_rng(123))
    next_rng, noise = pt.random.normal(rng=rng).owner.outputs

    if noise_first:
        out = noise * mu
    else:
        out = mu * noise

    updates = {
        mu: pt.grad(out, mu),
        rng: next_rng,
    }
    f = compile_random_function([], [out], updates=updates, mode="JAX")

    # The bug was found when noise used to be the first input of the fgraph
    # If this changes, the test may need to be tweaked to keep the save coverage
    assert isinstance(
        f.input_storage[1 - noise_first].type, RandomType
    ), "Test may need to be tweaked"

    # Confirm that input_storage type and fgraph input order are aligned
    for storage, fgrapn_input in zip(
        f.input_storage, f.maker.fgraph.inputs, strict=True
    ):
        assert storage.type == fgrapn_input.type

    assert mu.get_value() == 1
    f()
    assert mu.get_value() != 1


def test_replaced_shared_rng_storage_ordering_equality():
    """Test case described in issue #314.

    This happened when we tried to update the input storage after we clone the shared RNG.
    We used to call `input_storage.index(old_input_storage)` which would fail when the input_storage contained
    numpy arrays before the RNG value, which would fail the equality check.

    """
    pt_rng = RandomStream(1)

    batchshape = (3, 1, 4, 4)
    inp_shared = pytensor.shared(
        np.zeros(batchshape, dtype="float64"), name="inp_shared"
    )

    inp = pt.tensor4(dtype="float64", name="inp")
    inp_update = inp + pt_rng.normal(size=inp.shape, loc=5, scale=1e-5)

    # This function replaces inp by input_shared in the update expression
    # This is what caused the RNG to appear later than inp_shared in the input_storage

    fn = compile_random_function(
        inputs=[],
        outputs=[],
        updates={inp_shared: inp_update},
        givens={inp: inp_shared},
        mode="JAX",
    )
    fn()
    np.testing.assert_allclose(inp_shared.get_value(), 5, rtol=1e-3)
    fn()
    np.testing.assert_allclose(inp_shared.get_value(), 10, rtol=1e-3)


@pytest.mark.parametrize(
    "rv_op, dist_params, base_size, cdf_name, params_conv",
    [
        (
            ptr.beta,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "beta",
            lambda *args: args,
        ),
        (
            ptr.cauchy,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "cauchy",
            lambda *args: args,
        ),
        (
            ptr.exponential,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
            ],
            (2,),
            "expon",
            lambda *args: (0, args[0]),
        ),
        (
            ptr._gamma,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([0.5, 3.0], dtype=np.float64),
                ),
            ],
            (2,),
            "gamma",
            lambda a, b: (a, 0.0, b),
        ),
        (
            ptr.gumbel,
            [
                (
                    pt.lvector(),
                    np.array([1, 2], dtype=np.int64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "gumbel_r",
            lambda *args: args,
        ),
        (
            ptr.laplace,
            [
                (pt.dvector(), np.array([1.0, 2.0], dtype=np.float64)),
                (pt.dscalar(), np.array(1.0, dtype=np.float64)),
            ],
            (2,),
            "laplace",
            lambda *args: args,
        ),
        (
            ptr.logistic,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "logistic",
            lambda *args: args,
        ),
        (
            ptr.lognormal,
            [
                (
                    pt.lvector(),
                    np.array([0, 0], dtype=np.int64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "lognorm",
            lambda mu, sigma: (sigma, 0, np.exp(mu)),
        ),
        (
            ptr.normal,
            [
                (
                    pt.lvector(),
                    np.array([1, 2], dtype=np.int64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "norm",
            lambda *args: args,
        ),
        (
            ptr.pareto,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([2.0, 10.0], dtype=np.float64),
                ),
            ],
            (2,),
            "pareto",
            lambda shape, scale: (shape, 0.0, scale),
        ),
        (
            ptr.poisson,
            [
                (
                    pt.dvector(),
                    np.array([100000.0, 200000.0], dtype=np.float64),
                ),
            ],
            (2,),
            "poisson",
            lambda *args: args,
        ),
        (
            ptr.integers,
            [
                (
                    pt.lscalar(),
                    np.array(0, dtype=np.int64),
                ),
                (  # high-value necessary since test on cdf
                    pt.lscalar(),
                    np.array(1000, dtype=np.int64),
                ),
            ],
            (),
            "randint",
            lambda *args: args,
        ),
        (
            ptr.standard_normal,
            [],
            (2,),
            "norm",
            lambda *args: args,
        ),
        (
            ptr.t,
            [
                (
                    pt.dscalar(),
                    np.array(2.0, dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dscalar(),
                    np.array(1.0, dtype=np.float64),
                ),
            ],
            (2,),
            "t",
            lambda *args: args,
        ),
        (
            ptr.uniform,
            [
                (
                    pt.dvector(),
                    np.array([1.0, 2.0], dtype=np.float64),
                ),
                (
                    pt.dscalar(),
                    np.array(1000.0, dtype=np.float64),
                ),
            ],
            (2,),
            "uniform",
            lambda *args: args,
        ),
        (
            ptr.halfnormal,
            [
                (
                    pt.dvector(),
                    np.array([-1.0, 200.0], dtype=np.float64),
                ),
                (
                    pt.dscalar(),
                    np.array(1000.0, dtype=np.float64),
                ),
            ],
            (2,),
            "halfnorm",
            lambda *args: args,
        ),
        (
            ptr.invgamma,
            [
                (
                    pt.dvector(),
                    np.array([10.4, 2.8], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([3.4, 7.3], dtype=np.float64),
                ),
            ],
            (2,),
            "invgamma",
            lambda a, b: (a, 0, b),
        ),
        (
            ptr.chisquare,
            [
                (
                    pt.dvector(),
                    np.array([2.4, 4.9], dtype=np.float64),
                ),
            ],
            (2,),
            "chi2",
            lambda *args: args,
        ),
        (
            ptr.gengamma,
            [
                (
                    pt.dvector(),
                    np.array([10.4, 2.8], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([3.4, 7.3], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([0.9, 2.0], dtype=np.float64),
                ),
            ],
            (2,),
            "gengamma",
            lambda alpha, p, lambd: (alpha / p, p, 0, lambd),
        ),
        (
            ptr.wald,
            [
                (
                    pt.dvector(),
                    np.array([10.4, 2.8], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([4.5, 2.0], dtype=np.float64),
                ),
            ],
            (2,),
            "invgauss",
            # https://stackoverflow.com/a/48603469
            lambda mean, scale: (mean / scale, 0, scale),
        ),
        pytest.param(
            ptr.vonmises,
            [
                (
                    pt.dvector(),
                    np.array([-0.5, 1.3], dtype=np.float64),
                ),
                (
                    pt.dvector(),
                    np.array([5.5, 13.0], dtype=np.float64),
                ),
            ],
            (2,),
            "vonmises",
            lambda mu, kappa: (kappa, mu),
            marks=pytest.mark.skipif(
                not numpyro_available, reason="VonMises dispatch requires numpyro"
            ),
        ),
    ],
)
def test_random_RandomVariable(rv_op, dist_params, base_size, cdf_name, params_conv):
    """The JAX samplers are not one-to-one with NumPy samplers so we
    need to use a statistical test to make sure that the transpilation
    is correct.

    Parameters
    ----------
    rv_op
        The transpiled `RandomVariable` `Op`.
    dist_params
        The parameters passed to the op.
    """
    dist_params, test_values = (
        zip(*dist_params, strict=True) if dist_params else ([], [])
    )
    rng = shared(np.random.default_rng(29403))
    g = rv_op(*dist_params, size=(10000, *base_size), rng=rng)
    g_fn = compile_random_function(dist_params, g, mode=jax_mode)
    samples = g_fn(*test_values)

    bcast_dist_args = np.broadcast_arrays(*test_values)

    for idx in np.ndindex(*base_size):
        cdf_params = params_conv(*(arg[idx] for arg in bcast_dist_args))
        test_res = stats.cramervonmises(
            samples[(Ellipsis, *idx)], cdf_name, args=cdf_params
        )
        assert not np.isnan(test_res.statistic)
        assert test_res.pvalue > 0.01


@pytest.mark.parametrize(
    "rv_fn",
    [
        lambda param_that_implies_size: ptr.normal(
            loc=0, scale=pt.exp(param_that_implies_size)
        ),
        lambda param_that_implies_size: ptr.exponential(
            scale=pt.exp(param_that_implies_size)
        ),
        lambda param_that_implies_size: ptr.gamma(
            shape=1, scale=pt.exp(param_that_implies_size)
        ),
        lambda param_that_implies_size: ptr.t(
            df=3, loc=param_that_implies_size, scale=1
        ),
    ],
)
def test_size_implied_by_broadcasted_parameters(rv_fn):
    # We need a parameter with untyped shapes to test broadcasting does not result in identical draws
    param_that_implies_size = pt.matrix("param_that_implies_size", shape=(None, None))

    rv = rv_fn(param_that_implies_size)
    draws = rv.eval({param_that_implies_size: np.zeros((2, 2))}, mode=jax_mode)

    assert draws.shape == (2, 2)
    assert np.unique(draws).size == 4


@pytest.mark.parametrize("size", [(), (4,)])
def test_random_bernoulli(size):
    rng = shared(np.random.default_rng(123))
    g = pt.random.bernoulli(0.5, size=(1000, *size), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), 0.5, 1)


def test_random_mvnormal():
    rng = shared(np.random.default_rng(123))

    mu = np.ones(4)
    cov = np.eye(4)
    g = pt.random.multivariate_normal(mu, cov, size=(10000,), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), mu, atol=0.1)


test_mvnormal_cov_decomposition_method = create_mvnormal_cov_decomposition_method_test(
    "JAX"
)


@pytest.mark.parametrize(
    "parameter, size",
    [
        (np.ones(4), ()),
        (np.ones(4), (2, 4)),
    ],
)
def test_random_dirichlet(parameter, size):
    rng = shared(np.random.default_rng(123))
    g = pt.random.dirichlet(parameter, size=(1000, *size), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), 0.5, 1)


def test_random_choice():
    # `replace=True` and `p is None`
    rng = shared(np.random.default_rng(123))
    g = pt.random.choice(np.arange(4), size=10_000, rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    assert samples.shape == (10_000,)
    # Elements are picked at equal frequency
    np.testing.assert_allclose(np.mean(samples == 3), 0.25, 2)

    # `replace=True` and `p is not None`
    rng = shared(np.random.default_rng(123))
    g = pt.random.choice(4, p=np.array([0.0, 0.5, 0.0, 0.5]), size=(5, 2), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    assert samples.shape == (5, 2)
    # Only odd numbers are picked
    assert np.all(samples % 2 == 1)

    # `replace=False` and `p is None`
    rng = shared(np.random.default_rng(123))
    g = pt.random.choice(np.arange(100), replace=False, size=(2, 49), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    assert samples.shape == (2, 49)
    # Elements are unique
    assert len(np.unique(samples)) == 98

    # `replace=False` and `p is not None`
    rng = shared(np.random.default_rng(123))
    g = pt.random.choice(
        8,
        p=np.array([0.25, 0, 0.25, 0, 0.25, 0, 0.25, 0]),
        size=3,
        rng=rng,
        replace=False,
    )
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    assert samples.shape == (3,)
    # Elements are unique
    assert len(np.unique(samples)) == 3
    # Only even numbers are picked
    assert np.all(samples % 2 == 0)


def test_random_categorical():
    rng = shared(np.random.default_rng(123))
    g = pt.random.categorical(0.25 * np.ones(4), size=(10000, 4), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    assert samples.shape == (10000, 4)
    np.testing.assert_allclose(samples.mean(axis=0), 6 / 4, 1)

    # Test zero probabilities
    g = pt.random.categorical([0, 0.5, 0, 0.5], size=(1000,), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    assert samples.shape == (1000,)
    assert np.all(samples % 2 == 1)


def test_random_permutation():
    array = np.arange(4)
    rng = shared(np.random.default_rng(123))
    g = pt.random.permutation(array, rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    permuted = g_fn()
    with pytest.raises(AssertionError):
        np.testing.assert_allclose(array, permuted)


@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 JAX API."""
    batch_dims_tester(mode="JAX")


def test_random_geometric():
    rng = shared(np.random.default_rng(123))
    p = np.array([0.3, 0.7])
    g = pt.random.geometric(p, size=(10_000, 2), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), 1 / p, rtol=0.1)
    np.testing.assert_allclose(samples.std(axis=0), np.sqrt((1 - p) / p**2), rtol=0.1)


def test_negative_binomial():
    rng = shared(np.random.default_rng(123))
    n = np.array([10, 40])
    p = np.array([0.3, 0.7])
    g = pt.random.negative_binomial(n, p, size=(10_000, 2), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), n * (1 - p) / p, rtol=0.1)
    np.testing.assert_allclose(
        samples.std(axis=0), np.sqrt(n * (1 - p) / p**2), rtol=0.1
    )


@pytest.mark.skipif(not numpyro_available, reason="Binomial dispatch requires numpyro")
def test_binomial():
    rng = shared(np.random.default_rng(123))
    n = np.array([10, 40])
    p = np.array([0.3, 0.7])
    g = pt.random.binomial(n, p, size=(10_000, 2), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), n * p, rtol=0.1)
    np.testing.assert_allclose(samples.std(axis=0), np.sqrt(n * p * (1 - p)), rtol=0.1)


@pytest.mark.skipif(
    not numpyro_available, reason="BetaBinomial dispatch requires numpyro"
)
def test_beta_binomial():
    rng = shared(np.random.default_rng(123))
    n = np.array([10, 40])
    a = np.array([1.5, 13])
    b = np.array([0.5, 9])
    g = pt.random.betabinom(n, a, b, size=(10_000, 2), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), n * a / (a + b), rtol=0.1)
    np.testing.assert_allclose(
        samples.std(axis=0),
        np.sqrt((n * a * b * (a + b + n)) / ((a + b) ** 2 * (a + b + 1))),
        rtol=0.1,
    )


@pytest.mark.skipif(
    not numpyro_available, reason="Multinomial dispatch requires numpyro"
)
def test_multinomial():
    rng = shared(np.random.default_rng(123))
    n = np.array([10, 40])
    p = np.array([[0.3, 0.7, 0.0], [0.1, 0.4, 0.5]])
    g = pt.random.multinomial(n, p, size=(10_000, 2), rng=rng)
    g_fn = compile_random_function([], g, mode="JAX")
    samples = g_fn()
    np.testing.assert_allclose(samples.mean(axis=0), n[..., None] * p, rtol=0.1)
    np.testing.assert_allclose(
        samples.std(axis=0), np.sqrt(n[..., None] * p * (1 - p)), rtol=0.1
    )


@pytest.mark.skipif(not numpyro_available, reason="VonMises dispatch requires numpyro")
def test_vonmises_mu_outside_circle():
    # Scipy implementation does not behave as PyTensor/NumPy for mu outside the unit circle
    # We test that the random draws from the JAX dispatch work as expected in these cases
    rng = shared(np.random.default_rng(123))
    mu = np.array([-30, 40])
    kappa = np.array([100, 10])
    g = pt.random.vonmises(mu, kappa, size=(10_000, 2), rng=rng)
    g_fn = compile_random_function([], g, mode=jax_mode)
    samples = g_fn()
    np.testing.assert_allclose(
        samples.mean(axis=0), (mu + np.pi) % (2.0 * np.pi) - np.pi, rtol=0.1
    )

    # Circvar only does the correct thing in more recent versions of Scipy
    # https://github.com/scipy/scipy/pull/5747
    # np.testing.assert_allclose(
    #     stats.circvar(samples, axis=0),
    #     1 - special.iv(1, kappa) / special.iv(0, kappa),
    #     rtol=0.1,
    # )

    # For now simple compare with std from numpy draws
    rng = np.random.default_rng(123)
    ref_samples = rng.vonmises(mu, kappa, size=(10_000, 2))
    np.testing.assert_allclose(
        np.std(samples, axis=0), np.std(ref_samples, axis=0), rtol=0.1
    )


def test_random_unimplemented():
    """Compiling a graph with a non-supported `RandomVariable` should
    raise an error.

    """

    class NonExistentRV(RandomVariable):
        name = "non-existent"
        signature = "->()"
        dtype = "floatX"

        def __call__(self, size=None, **kwargs):
            return super().__call__(size=size, **kwargs)

        def rng_fn(cls, rng, size):
            return 0

    nonexistentrv = NonExistentRV()
    rng = shared(np.random.default_rng(123))
    out = nonexistentrv(rng=rng)

    with pytest.raises(NotImplementedError):
        with pytest.warns(
            UserWarning, match=r"The RandomType SharedVariables \[.+\] will not be used"
        ):
            compare_jax_and_py([], [out], [])


def test_random_custom_implementation():
    """We can register a JAX implementation for user-defined `RandomVariable`s"""

    class CustomRV(RandomVariable):
        name = "non-existent"
        signature = "->()"
        dtype = "floatX"

        def __call__(self, size=None, **kwargs):
            return super().__call__(size=size, **kwargs)

        def rng_fn(cls, rng, size):
            return 0

    from pytensor.link.jax.dispatch.random import jax_sample_fn

    @jax_sample_fn.register(CustomRV)
    def jax_sample_fn_custom(op, node):
        def sample_fn(rng, size, dtype, *parameters):
            return (rng, 0)

        return sample_fn

    nonexistentrv = CustomRV()
    rng = shared(np.random.default_rng(123))
    out = nonexistentrv(rng=rng)
    with pytest.warns(
        UserWarning, match=r"The RandomType SharedVariables \[.+\] will not be used"
    ):
        compare_jax_and_py([], [out], [])


def test_random_concrete_shape():
    """JAX should compile when a `RandomVariable` is passed a concrete shape.

    There are three quantities that JAX considers as concrete:
    1. Constants known at compile time;
    2. The shape of an array.
    3. `static_argnums` parameters
    This test makes sure that graphs with `RandomVariable`s compile when the
    `size` parameter satisfies either of these criteria.

    """
    rng = shared(np.random.default_rng(123))
    x_pt = pt.dmatrix()
    out = pt.random.normal(0, 1, size=x_pt.shape, rng=rng)
    jax_fn = compile_random_function([x_pt], out, mode=jax_mode)
    assert jax_fn(np.ones((2, 3))).shape == (2, 3)


def test_random_concrete_shape_from_param():
    rng = shared(np.random.default_rng(123))
    x_pt = pt.dmatrix()
    out = pt.random.normal(x_pt, 1, rng=rng)
    jax_fn = compile_random_function([x_pt], out, mode=jax_mode)
    assert jax_fn(np.ones((2, 3))).shape == (2, 3)


def test_random_concrete_shape_subtensor():
    """JAX should compile when a concrete value is passed for the `size` parameter.

    This test ensures that the `DimShuffle` `Op` used by PyTensor to turn scalar
    inputs into 1d vectors is replaced by an `Op` that turns concrete scalar
    inputs into tuples of concrete values using the `jax_size_parameter_as_tuple`
    rewrite.

    JAX does not accept scalars as `size` or `shape` arguments, so this is a
    slight improvement over their API.

    """
    rng = shared(np.random.default_rng(123))
    x_pt = pt.dmatrix()
    out = pt.random.normal(0, 1, size=x_pt.shape[1], rng=rng)
    jax_fn = compile_random_function([x_pt], out, mode=jax_mode)
    assert jax_fn(np.ones((2, 3))).shape == (3,)


def test_random_concrete_shape_subtensor_tuple():
    """JAX should compile when a tuple of concrete values is passed for the `size` parameter.

    This test ensures that the `MakeVector` `Op` used by PyTensor to turn tuple
    inputs into 1d vectors is replaced by an `Op` that turns a tuple of concrete
    scalar inputs into tuples of concrete values using the
    `jax_size_parameter_as_tuple` rewrite.

    """
    rng = shared(np.random.default_rng(123))
    x_pt = pt.dmatrix()
    out = pt.random.normal(0, 1, size=(x_pt.shape[0],), rng=rng)
    jax_fn = compile_random_function([x_pt], out, mode=jax_mode)
    assert jax_fn(np.ones((2, 3))).shape == (2,)


@pytest.mark.xfail(
    reason="`size_pt` should be specified as a static argument", strict=True
)
def test_random_concrete_shape_graph_input():
    rng = shared(np.random.default_rng(123))
    size_pt = pt.scalar()
    out = pt.random.normal(0, 1, size=size_pt, rng=rng)
    jax_fn = compile_random_function([size_pt], out, mode=jax_mode)
    assert jax_fn(10).shape == (10,)


def test_constant_shape_after_graph_rewriting():
    size = pt.vector("size", shape=(2,), dtype=int)
    x = pt.random.normal(size=size)
    assert x.type.shape == (None, None)

    with pytest.raises(TypeError):
        compile_random_function([size], x)([2, 5])

    # Rebuild with strict=False so output type is not updated
    # This reflects cases where size is constant folded during rewrites but the RV node is not recreated
    new_x = clone_replace(x, {size: pt.constant([2, 5])}, rebuild_strict=True)
    assert new_x.type.shape == (None, None)
    assert compile_random_function([], new_x)().shape == (2, 5)

    # Rebuild with strict=True, so output type is updated
    # This uses a different path in the dispatch implementation
    new_x = clone_replace(x, {size: pt.constant([2, 5])}, rebuild_strict=False)
    assert new_x.type.shape == (2, 5)
    assert compile_random_function([], new_x)().shape == (2, 5)
