import os
import sys
import time

import numpy as np
import pytest

import aesara
from aesara.compile.function import function
from aesara.compile.sharedvalue import shared
from aesara.configdefaults import config
from aesara.gradient import NullTypeGradError, UndefinedGrad, grad, zero_grad
from aesara.sandbox import rng_mrg
from aesara.sandbox.rng_mrg import MRG_RandomStream, mrg_uniform
from aesara.scan.basic import scan
from aesara.tensor.basic import as_tensor_variable, cast
from aesara.tensor.math import sum as at_sum
from aesara.tensor.random.utils import RandomStream
from aesara.tensor.type import iscalar, ivector, lmatrix, matrix, scalar, vector
from tests import unittest_tools as utt


# TODO: test MRG_RandomStream
# Partly done in test_consistency_randomstreams

# TODO: test optimizer mrg_random_make_inplace


# Results generated by Java code using L'Ecuyer et al.'s code, with:
# main seed: [12345]*6 (default)
# 12 streams
# 7 substreams for each stream
# 5 samples drawn from each substream
java_samples = np.loadtxt(
    os.path.join(
        os.path.split(aesara.__file__)[0], "sandbox", "samples_MRG31k3p_12_7_5.txt"
    )
)


def test_deterministic():
    seed = utt.fetch_seed()
    sample_size = (10, 20)

    R = MRG_RandomStream(seed=seed)
    u = R.uniform(size=sample_size)
    f = function([], u)

    fsample1 = f()
    fsample2 = f()
    assert not np.allclose(fsample1, fsample2)

    R2 = MRG_RandomStream(seed=seed)
    u2 = R2.uniform(size=sample_size)
    g = function([], u2)
    gsample1 = g()
    gsample2 = g()
    assert np.allclose(fsample1, gsample1)
    assert np.allclose(fsample2, gsample2)


def test_consistency_randomstreams():
    # Verify that the random numbers generated by MRG_RandomStream
    # are the same as the reference (Java) implementation by L'Ecuyer et al.
    seed = 12345
    n_samples = 5
    n_streams = 12
    n_substreams = 7

    samples = []
    rng = MRG_RandomStream(seed=seed)
    for i in range(n_streams):
        stream_samples = []
        u = rng.uniform(size=(n_substreams,), nstreams=n_substreams)
        f = function([], u)
        for j in range(n_samples):
            s = f()
            stream_samples.append(s)
        stream_samples = np.array(stream_samples)
        stream_samples = stream_samples.T.flatten()
        samples.append(stream_samples)

    samples = np.array(samples).flatten()
    assert np.allclose(samples, java_samples)


def test_get_substream_rstates():
    with config.change_flags(compute_test_value="raise"):
        n_streams = 100

        dtype = "float32"
        rng = MRG_RandomStream(
            np.random.default_rng(utt.fetch_seed()).integers(2147462579)
        )

        rng.get_substream_rstates(n_streams, dtype)


def test_consistency_cpu_serial():
    # Verify that the random numbers generated by mrg_uniform, serially,
    # are the same as the reference (Java) implementation by L'Ecuyer et al.

    seed = 12345
    n_samples = 5
    n_streams = 12
    n_substreams = 7

    samples = []
    curr_rstate = np.array([seed] * 6, dtype="int32")

    for i in range(n_streams):
        stream_rstate = curr_rstate.copy()
        for j in range(n_substreams):
            rstate = shared(np.array([stream_rstate.copy()], dtype="int32"))
            new_rstate, sample = rng_mrg.mrg_uniform.new(
                rstate, ndim=None, dtype=config.floatX, size=(1,)
            )
            # Not really necessary, just mimicking
            # rng_mrg.MRG_RandomStream' behavior
            sample.rstate = rstate
            sample.update = (rstate, new_rstate)

            rstate.default_update = new_rstate
            f = function([], sample)
            for k in range(n_samples):
                s = f()
                samples.append(s)

            # next substream
            stream_rstate = rng_mrg.ff_2p72(stream_rstate)

        # next stream
        curr_rstate = rng_mrg.ff_2p134(curr_rstate)

    samples = np.array(samples).flatten()
    assert np.allclose(samples, java_samples)


def test_consistency_cpu_parallel():
    # Verify that the random numbers generated by mrg_uniform, in parallel,
    # are the same as the reference (Java) implementation by L'Ecuyer et al.

    seed = 12345
    n_samples = 5
    n_streams = 12
    n_substreams = 7  # 7 samples will be drawn in parallel

    samples = []
    curr_rstate = np.array([seed] * 6, dtype="int32")

    for i in range(n_streams):
        stream_samples = []
        rstate = [curr_rstate.copy()]
        for j in range(1, n_substreams):
            rstate.append(rng_mrg.ff_2p72(rstate[-1]))
        rstate = np.asarray(rstate)
        rstate = shared(rstate)

        new_rstate, sample = rng_mrg.mrg_uniform.new(
            rstate, ndim=None, dtype=config.floatX, size=(n_substreams,)
        )
        # Not really necessary, just mimicking
        # rng_mrg.MRG_RandomStream' behavior
        sample.rstate = rstate
        sample.update = (rstate, new_rstate)

        rstate.default_update = new_rstate
        f = function([], sample)

        for k in range(n_samples):
            s = f()
            stream_samples.append(s)

        samples.append(np.array(stream_samples).T.flatten())

        # next stream
        curr_rstate = rng_mrg.ff_2p134(curr_rstate)

    samples = np.array(samples).flatten()
    assert np.allclose(samples, java_samples)


def check_basics(
    f,
    steps,
    sample_size,
    prefix="",
    allow_01=False,
    inputs=None,
    target_avg=0.5,
    target_std=None,
    mean_rtol=0.01,
    std_tol=0.01,
):
    if inputs is None:
        inputs = []
    dt = 0.0
    avg_var = 0.0

    for i in range(steps):
        t0 = time.time()
        ival = f(*inputs)
        assert ival.shape == sample_size
        dt += time.time() - t0
        ival = np.asarray(ival)
        if i == 0:
            mean = np.array(ival, copy=True)
            avg_var = np.mean((ival - target_avg) ** 2)
            min_ = ival.min()
            max_ = ival.max()
        else:
            alpha = 1.0 / (1 + i)
            mean = alpha * ival + (1 - alpha) * mean
            avg_var = alpha * np.mean((ival - target_avg) ** 2) + (1 - alpha) * avg_var
            min_ = min(min_, ival.min())
            max_ = max(max_, ival.max())
        if not allow_01:
            assert min_ > 0
            assert max_ < 1

    if hasattr(target_avg, "shape"):  # looks if target_avg is an array
        diff = np.mean(abs(mean - target_avg))
        # print prefix, 'mean diff with mean', diff
        assert np.all(
            diff < mean_rtol * (1 + abs(target_avg))
        ), f"bad mean? {mean} {target_avg}"
    else:
        # if target_avg is a scalar, then we can do the mean of
        # `mean` to get something more precise
        mean = np.mean(mean)
        # print prefix, 'mean', mean
        assert abs(mean - target_avg) < mean_rtol * (
            1 + abs(target_avg)
        ), f"bad mean? {mean:f} {target_avg:f}"

    std = np.sqrt(avg_var)
    # print prefix, 'var', avg_var
    # print prefix, 'std', std
    if target_std is not None:
        assert abs(std - target_std) < std_tol * (
            1 + abs(target_std)
        ), f"bad std? {std:f} {target_std:f} {std_tol:f}"
    # print prefix, 'time', dt
    # print prefix, 'elements', steps * sample_size[0] * sample_size[1]
    # print prefix, 'samples/sec', steps * sample_size[0] * sample_size[1] / dt
    # print prefix, 'min', min_, 'max', max_


@pytest.mark.slow
def test_uniform():
    # TODO: test param low, high
    # TODO: test size=None
    # TODO: test ndim!=size.ndim
    # TODO: test bad seed
    # TODO: test size=Var, with shape that change from call to call
    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (10, 100)
        steps = 50
    else:
        sample_size = (500, 50)
        steps = int(1e3)

    x = matrix()
    for size, const_size, var_input, input in [
        (sample_size, sample_size, [], []),
        (x.shape, sample_size, [x], [np.zeros(sample_size, dtype=config.floatX)]),
        (
            (x.shape[0], sample_size[1]),
            sample_size,
            [x],
            [np.zeros(sample_size, dtype=config.floatX)],
        ),
        # test empty size (scalar)
        ((), (), [], []),
    ]:

        # TEST CPU IMPLEMENTATION
        # The python and C implementation are tested with DebugMode
        x = matrix()
        R = MRG_RandomStream(234)
        # Note: we specify `nstreams` to avoid a warning.
        # TODO Look for all occurrences of `guess_n_streams` and `30 * 256`
        # for such situations: it would be better to instead filter the
        # warning using the warning module.
        u = R.uniform(size=size, nstreams=rng_mrg.guess_n_streams(size, warn=False))
        f = function(var_input, u)
        assert any(
            isinstance(node.op, mrg_uniform) for node in f.maker.fgraph.toposort()
        )
        f(*input)

        # Increase the number of steps if sizes implies only a few samples
        if np.prod(const_size) < 10:
            steps_ = steps * 100
        else:
            steps_ = steps
        check_basics(f, steps_, const_size, prefix="mrg cpu", inputs=input)

        RR = RandomStream(234)

        uu = RR.uniform(size=size)
        ff = function(var_input, uu)
        # It's not our problem if numpy generates 0 or 1
        check_basics(
            ff, steps_, const_size, prefix="numpy", allow_01=True, inputs=input
        )


def test_broadcastable():
    R = MRG_RandomStream(234)
    x = matrix()
    size1 = (10, 1)
    size2 = (x.shape[0], 1)
    pvals_1 = np.random.uniform(0, 1, size=size1)
    pvals_1 = pvals_1 / sum(pvals_1)
    pvals_2 = R.uniform(size=size2)
    pvals_2 = pvals_2 / at_sum(pvals_2)

    for distribution in [
        R.uniform,
        R.normal,
        R.truncated_normal,
        R.binomial,
        R.multinomial,
        R.multinomial_wo_replacement,
    ]:
        # multinomial or multinomial_wo_replacement does not support "size" argument,
        # the sizes of them are implicitly defined with "pvals" argument.
        if distribution in [R.multinomial, R.multinomial_wo_replacement]:
            # check when all dimensions are constant
            uu = distribution(pvals=pvals_1)
            assert uu.broadcastable == (False, True)

            # check when some dimensions are aesara variables
            uu = distribution(pvals=pvals_2)
            assert uu.broadcastable == (False, True)
        else:
            # check when all dimensions are constant
            uu = distribution(size=size1)
            assert uu.broadcastable == (False, True)

            # check when some dimensions are aesara variables
            uu = distribution(size=size2)
            assert uu.broadcastable == (False, True)


def check_binomial(mean, size, const_size, var_input, input, steps, rtol):
    R = MRG_RandomStream(234)
    u = R.binomial(size=size, p=mean)
    f = function(var_input, u)
    f(*input)

    # Increase the number of steps if sizes implies only a few samples
    if np.prod(const_size) < 10:
        steps_ = steps * 100
    else:
        steps_ = steps
    check_basics(
        f,
        steps_,
        const_size,
        prefix="mrg  cpu",
        inputs=input,
        allow_01=True,
        target_avg=mean,
        mean_rtol=rtol,
    )

    RR = RandomStream(234)

    uu = RR.binomial(1, mean, size=size)
    ff = function(var_input, uu)
    # It's not our problem if numpy generates 0 or 1
    check_basics(
        ff,
        steps_,
        const_size,
        prefix="numpy",
        allow_01=True,
        inputs=input,
        target_avg=mean,
        mean_rtol=rtol,
    )


@pytest.mark.slow
def test_binomial():
    # TODO: test size=None, ndim=X
    # TODO: test size=X, ndim!=X.ndim
    # TODO: test random seed in legal value(!=0 and other)
    # TODO: test sample_size not a multiple of guessed #streams
    # TODO: test size=Var, with shape that change from call to call
    # we test size in a tuple of int and a tensor.shape.
    # we test the param p with int.

    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (10, 50)
        steps = 50
        rtol = 0.02
    else:
        sample_size = (500, 50)
        steps = int(1e3)
        rtol = 0.01

    x = matrix()
    for mean in [0.1, 0.5]:
        for size, const_size, var_input, input in [
            (sample_size, sample_size, [], []),
            (x.shape, sample_size, [x], [np.zeros(sample_size, dtype=config.floatX)]),
            # test empty size (scalar)
            ((), (), [], []),
        ]:
            check_binomial(mean, size, const_size, var_input, input, steps, rtol)


@pytest.mark.slow
def test_normal0():
    steps = 50
    std = 2.0
    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (25, 30)
        default_rtol = 0.02
    else:
        sample_size = (999, 50)
        default_rtol = 0.01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = matrix()

    test_cases = [
        (sample_size, sample_size, [], [], -5.0, default_rtol, default_rtol),
        (
            x.shape,
            sample_size,
            [x],
            [np.zeros(sample_size, dtype=config.floatX)],
            -5.0,
            default_rtol,
            default_rtol,
        ),
        # test odd value
        (
            x.shape,
            sample_size_odd,
            [x],
            [np.zeros(sample_size_odd, dtype=config.floatX)],
            -5.0,
            default_rtol,
            default_rtol,
        ),
        (
            sample_size,
            sample_size,
            [],
            [],
            np.arange(np.prod(sample_size), dtype="float32").reshape(sample_size),
            10.0 * std / np.sqrt(steps),
            default_rtol,
        ),
        # test empty size (scalar)
        ((), (), [], [], -5.0, default_rtol, 0.02),
        # test with few samples at the same time
        ((1,), (1,), [], [], -5.0, default_rtol, 0.02),
        ((3,), (3,), [], [], -5.0, default_rtol, 0.02),
    ]

    for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
        R = MRG_RandomStream(234)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.normal(
            size=size,
            avg=avg,
            std=std,
            nstreams=rng_mrg.guess_n_streams(size, warn=False),
        )
        f = function(var_input, n)
        f(*input)

        # Increase the number of steps if size implies only a few samples
        if np.prod(const_size) < 10:
            steps_ = steps * 50
        else:
            steps_ = steps
        check_basics(
            f,
            steps_,
            const_size,
            target_avg=avg,
            target_std=std,
            prefix="mrg ",
            allow_01=True,
            inputs=input,
            mean_rtol=rtol,
            std_tol=std_tol,
        )

        sys.stdout.flush()

        RR = RandomStream(235)

        nn = RR.normal(avg, std, size=size)
        ff = function(var_input, nn)

        check_basics(
            ff,
            steps_,
            const_size,
            target_avg=avg,
            target_std=std,
            prefix="numpy ",
            allow_01=True,
            inputs=input,
            mean_rtol=rtol,
        )


@pytest.mark.slow
def test_normal_truncation():
    # just a copy of test_normal0 with extra bound check
    steps = 50
    std = 2.0
    # standard deviation is slightly less than for a regular Gaussian
    # constant taken from scipy.stats.truncnorm.std(a=-2, b=2, loc=0., scale=1.)
    target_std = 0.87962566103423978 * std

    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (25, 30)
        default_rtol = 0.02
    else:
        sample_size = (999, 50)
        default_rtol = 0.01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = matrix()

    test_cases = [
        (sample_size, sample_size, [], [], -5.0, default_rtol, default_rtol),
        (
            x.shape,
            sample_size,
            [x],
            [np.zeros(sample_size, dtype=config.floatX)],
            -5.0,
            default_rtol,
            default_rtol,
        ),
        # test odd value
        (
            x.shape,
            sample_size_odd,
            [x],
            [np.zeros(sample_size_odd, dtype=config.floatX)],
            -5.0,
            default_rtol,
            default_rtol,
        ),
        (
            sample_size,
            sample_size,
            [],
            [],
            np.arange(np.prod(sample_size), dtype="float32").reshape(sample_size),
            10.0 * std / np.sqrt(steps),
            default_rtol,
        ),
        # test empty size (scalar)
        ((), (), [], [], -5.0, default_rtol, 0.02),
        # test with few samples at the same time
        ((1,), (1,), [], [], -5.0, default_rtol, 0.02),
        ((3,), (3,), [], [], -5.0, default_rtol, 0.02),
    ]

    for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
        R = MRG_RandomStream(234)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.normal(
            size=size,
            avg=avg,
            std=std,
            truncate=True,
            nstreams=rng_mrg.guess_n_streams(size, warn=False),
        )
        f = function(var_input, n)

        # check if truncated at 2*std
        samples = f(*input)
        assert np.all(avg + 2 * std - samples >= 0), "bad upper bound? {} {}".format(
            samples,
            avg + 2 * std,
        )
        assert np.all(samples - (avg - 2 * std) >= 0), "bad lower bound? {} {}".format(
            samples,
            avg - 2 * std,
        )

        # Increase the number of steps if size implies only a few samples
        if np.prod(const_size) < 10:
            steps_ = steps * 50
        else:
            steps_ = steps
        check_basics(
            f,
            steps_,
            const_size,
            target_avg=avg,
            target_std=target_std,
            prefix="mrg ",
            allow_01=True,
            inputs=input,
            mean_rtol=rtol,
            std_tol=std_tol,
        )

        sys.stdout.flush()


@pytest.mark.slow
def test_truncated_normal():
    # just a copy of test_normal0 for truncated normal
    steps = 50
    std = 2.0

    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (25, 30)
        default_rtol = 0.02
    else:
        sample_size = (999, 50)
        default_rtol = 0.01
    sample_size_odd = (sample_size[0], sample_size[1] - 1)
    x = matrix()

    test_cases = [
        (sample_size, sample_size, [], [], -5.0, default_rtol, default_rtol),
        (
            x.shape,
            sample_size,
            [x],
            [np.zeros(sample_size, dtype=config.floatX)],
            -5.0,
            default_rtol,
            default_rtol,
        ),
        # test odd value
        (
            x.shape,
            sample_size_odd,
            [x],
            [np.zeros(sample_size_odd, dtype=config.floatX)],
            -5.0,
            default_rtol,
            default_rtol,
        ),
        (
            sample_size,
            sample_size,
            [],
            [],
            np.arange(np.prod(sample_size), dtype="float32").reshape(sample_size),
            10.0 * std / np.sqrt(steps),
            default_rtol,
        ),
        # test empty size (scalar)
        ((), (), [], [], -5.0, default_rtol, 0.02),
        # test with few samples at the same time
        ((1,), (1,), [], [], -5.0, default_rtol, 0.02),
        ((3,), (3,), [], [], -5.0, default_rtol, 0.02),
    ]

    for size, const_size, var_input, input, avg, rtol, std_tol in test_cases:
        R = MRG_RandomStream(234)
        # Note: we specify `nstreams` to avoid a warning.
        n = R.truncated_normal(
            size=size,
            avg=avg,
            std=std,
            nstreams=rng_mrg.guess_n_streams(size, warn=False),
        )
        f = function(var_input, n)

        # Increase the number of steps if size implies only a few samples
        if np.prod(const_size) < 10:
            steps_ = steps * 60
        else:
            steps_ = steps
        check_basics(
            f,
            steps_,
            const_size,
            target_avg=avg,
            target_std=std,
            prefix="mrg ",
            allow_01=True,
            inputs=input,
            mean_rtol=rtol,
            std_tol=std_tol,
        )

        sys.stdout.flush()


def basic_multinomialtest(
    f, steps, sample_size, target_pvals, n_samples, prefix="", mean_rtol=0.04
):

    dt = 0.0
    avg_pvals = np.zeros(target_pvals.shape, dtype=config.floatX)

    for i in range(steps):
        t0 = time.time()
        ival = f()
        assert ival.shape == sample_size
        assert np.all(np.sum(ival, axis=1) == n_samples)
        dt += time.time() - t0
        avg_pvals += ival
    avg_pvals /= steps * n_samples

    assert np.mean(abs(avg_pvals - target_pvals)) < mean_rtol

    # print("random?[:10]\n", np.asarray(f()[:10]))
    # print(prefix, "mean", avg_pvals)
    # # < mean_rtol, 'bad mean? %s %s' % (str(avg_pvals), str(target_pvals))
    # print(np.mean(abs(avg_pvals - target_pvals)))
    # print(prefix, "time", dt)
    # print(prefix, "elements", steps * np.prod(target_pvals.shape))
    # print(prefix, "samples/sec", steps * np.prod(target_pvals.shape) / dt)


def test_multinomial():
    steps = 100

    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (49, 5)
    else:
        sample_size = (450, 6)

    pvals = np.asarray(np.random.uniform(size=sample_size))
    pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
    R = MRG_RandomStream(234)
    # Note: we specify `nstreams` to avoid a warning.
    m = R.multinomial(pvals=pvals, dtype=config.floatX, nstreams=30 * 256)
    f = function([], m)
    f()
    basic_multinomialtest(f, steps, sample_size, pvals, n_samples=1, prefix="mrg ")


def test_multinomial_n_samples():
    if (
        config.mode in ("DEBUG_MODE", "DebugMode", "FAST_COMPILE")
        or config.mode == "Mode"
        and config.linker in ["py"]
    ):
        sample_size = (49, 5)
    else:
        sample_size = (450, 6)

    pvals = np.asarray(np.random.uniform(size=sample_size))
    pvals = np.apply_along_axis(lambda row: row / np.sum(row), 1, pvals)
    R = MRG_RandomStream(234)

    for n_samples, steps in zip([5, 10, 100, 1000], [20, 10, 1, 1]):
        m = R.multinomial(
            pvals=pvals, n=n_samples, dtype=config.floatX, nstreams=30 * 256
        )
        f = function([], m)
        basic_multinomialtest(f, steps, sample_size, pvals, n_samples, prefix="mrg ")
        sys.stdout.flush()


class TestMRG:
    def test_bad_size(self):

        R = MRG_RandomStream(234)

        for size in [
            (0, 100),
            (-1, 100),
            (1, 0),
        ]:

            with pytest.raises(ValueError):
                R.uniform(size)
            with pytest.raises(ValueError):
                R.binomial(size)
            with pytest.raises(ValueError):
                R.multinomial(size, 1, [])
            with pytest.raises(ValueError):
                R.normal(size)
            with pytest.raises(ValueError):
                R.truncated_normal(size)


def test_multiple_rng_aliasing():
    # Test that when we have multiple random number generators, we do not alias
    # the state_updates member. `state_updates` can be useful when attempting to
    # copy the (random) state between two similar aesara graphs. The test is
    # meant to detect a previous bug where state_updates was initialized as a
    # class-attribute, instead of the __init__ function.

    rng1 = MRG_RandomStream(1234)
    rng2 = MRG_RandomStream(2392)
    assert rng1.state_updates is not rng2.state_updates


def test_random_state_transfer():
    # Test that random state can be transferred from one aesara graph to another.

    class Graph:
        def __init__(self, seed=123):
            self.rng = MRG_RandomStream(seed)
            self.y = self.rng.uniform(size=(1,))

    g1 = Graph(seed=123)
    f1 = function([], g1.y)
    g2 = Graph(seed=987)
    f2 = function([], g2.y)

    g2.rng.rstate = g1.rng.rstate
    for (su1, su2) in zip(g1.rng.state_updates, g2.rng.state_updates):
        su2[0].set_value(su1[0].get_value())

    np.testing.assert_array_almost_equal(f1(), f2(), decimal=6)


@pytest.mark.parametrize(
    "mode",
    [
        "FAST_RUN",
        "FAST_COMPILE",
    ],
)
def test_gradient_scan(mode):
    aesara_rng = MRG_RandomStream(10)
    w = shared(np.ones(1, dtype="float32"))

    def one_step(x):
        return x + aesara_rng.uniform((1,), dtype="float32") * w

    x = vector(dtype="float32")
    values, updates = scan(one_step, outputs_info=x, n_steps=10)
    gw = grad(at_sum(values[-1]), w)
    f = function([x], gw, mode=mode)
    assert np.allclose(
        f(np.arange(1, dtype=np.float32)),
        np.array([0.13928187], dtype=np.float32),
        rtol=1e6,
    )


def test_simple_shared_mrg_random():
    aesara_rng = MRG_RandomStream(10)

    values, updates = scan(
        lambda: aesara_rng.uniform((2,), -1, 1),
        [],
        [],
        [],
        n_steps=5,
        truncate_gradient=-1,
        go_backwards=False,
    )
    my_f = function([], values, updates=updates, allow_input_downcast=True)

    # Just check for run-time errors
    my_f()
    my_f()


def test_multMatVect():
    A1 = lmatrix("A1")
    s1 = ivector("s1")
    m1 = iscalar("m1")
    A2 = lmatrix("A2")
    s2 = ivector("s2")
    m2 = iscalar("m2")

    g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
    f0 = function([A1, s1, m1, A2, s2, m2], g0)

    i32max = np.iinfo(np.int32).max
    rng = np.random.default_rng(utt.fetch_seed())
    A1 = rng.integers(0, i32max, (3, 3)).astype("int64")
    s1 = rng.integers(0, i32max, 3).astype("int32")
    m1 = np.asarray(rng.integers(i32max), dtype="int32")
    A2 = rng.integers(0, i32max, (3, 3)).astype("int64")
    s2 = rng.integers(0, i32max, 3).astype("int32")
    m2 = np.asarray(rng.integers(i32max), dtype="int32")

    f0.input_storage[0].storage[0] = A1
    f0.input_storage[1].storage[0] = s1
    f0.input_storage[2].storage[0] = m1
    f0.input_storage[3].storage[0] = A2
    f0.input_storage[4].storage[0] = s2
    f0.input_storage[5].storage[0] = m2

    r_a1 = rng_mrg.matVecModM(A1, s1, m1)
    r_a2 = rng_mrg.matVecModM(A2, s2, m2)
    f0.fn()
    r_b = f0.output_storage[0].value

    assert np.allclose(r_a1, r_b[:3])
    assert np.allclose(r_a2, r_b[3:])


def test_seed_fn():
    idx = ivector()

    for new_seed, same in [(234, True), (None, True), (23, False)]:
        random = MRG_RandomStream(234)
        fn1 = function([], random.uniform((2, 2), dtype="float32"))
        fn2 = function([], random.uniform((3, 3), nstreams=2, dtype="float32"))
        fn3 = function([idx], random.uniform(idx, nstreams=3, ndim=1, dtype="float32"))

        fn1_val0 = fn1()
        fn1_val1 = fn1()
        assert not np.allclose(fn1_val0, fn1_val1)
        fn2_val0 = fn2()
        fn2_val1 = fn2()
        assert not np.allclose(fn2_val0, fn2_val1)
        fn3_val0 = fn3([4])
        fn3_val1 = fn3([4])
        assert not np.allclose(fn3_val0, fn3_val1)
        assert fn1_val0.size == 4
        assert fn2_val0.size == 9

        random.seed(new_seed)

        fn1_val2 = fn1()
        fn1_val3 = fn1()
        fn2_val2 = fn2()
        fn2_val3 = fn2()
        fn3_val2 = fn3([4])
        fn3_val3 = fn3([4])
        assert np.allclose(fn1_val0, fn1_val2) == same
        assert np.allclose(fn1_val1, fn1_val3) == same
        assert np.allclose(fn2_val0, fn2_val2) == same
        assert np.allclose(fn2_val1, fn2_val3) == same
        assert np.allclose(fn3_val0, fn3_val2) == same
        assert np.allclose(fn3_val1, fn3_val3) == same


def rng_mrg_overflow(sizes, fct, mode, should_raise_error):
    for size in sizes:
        y = fct(size=size)
        f = function([], y, mode=mode)
        if should_raise_error:
            with pytest.raises(ValueError):
                f()
        else:
            f()


@pytest.mark.slow
def test_overflow_cpu():
    # run with AESARA_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32
    rng = MRG_RandomStream(np.random.default_rng(utt.fetch_seed()).integers(1234))
    fct = rng.uniform
    with config.change_flags(compute_test_value="off"):
        # should raise error as the size overflows
        sizes = [
            (2**31,),
            (2**32,),
            (
                2**15,
                2**16,
            ),
            (2, 2**15, 2**15),
        ]
        rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=True)
    # should not raise error
    sizes = [(2**5,), (2**5, 2**5), (2**5, 2**5, 2**5)]
    rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=False)
    # should support int32 sizes
    sizes = [(np.int32(2**10),), (np.int32(2), np.int32(2**10), np.int32(2**10))]
    rng_mrg_overflow(sizes, fct, config.mode, should_raise_error=False)


def test_undefined_grad():
    srng = MRG_RandomStream(seed=1234)

    # checking uniform distribution
    low = scalar()
    out = srng.uniform((), low=low)
    with pytest.raises(NullTypeGradError):
        grad(out, low)

    high = scalar()
    out = srng.uniform((), low=0, high=high)
    with pytest.raises(NullTypeGradError):
        grad(out, high)

    out = srng.uniform((), low=low, high=high)
    with pytest.raises(NullTypeGradError):
        grad(out, (low, high))

    # checking binomial distribution
    prob = scalar()
    out = srng.binomial((), p=prob)
    with pytest.raises(NullTypeGradError):
        grad(out, prob)

    # checking multinomial distribution
    prob1 = scalar()
    prob2 = scalar()
    p = [as_tensor_variable([prob1, 0.5, 0.25])]
    out = srng.multinomial(size=None, pvals=p, n=4)[0]
    with pytest.raises(NullTypeGradError):
        grad(at_sum(out), prob1)

    p = [as_tensor_variable([prob1, prob2])]
    out = srng.multinomial(size=None, pvals=p, n=4)[0]
    with pytest.raises(NullTypeGradError):
        grad(at_sum(out), (prob1, prob2))

    # checking choice
    p = [as_tensor_variable([prob1, prob2, 0.1, 0.2])]
    out = srng.choice(a=None, size=1, p=p, replace=False)[0]
    with pytest.raises(NullTypeGradError):
        grad(out[0], (prob1, prob2))

    p = [as_tensor_variable([prob1, prob2])]
    out = srng.choice(a=None, size=1, p=p, replace=False)[0]
    with pytest.raises(NullTypeGradError):
        grad(out[0], (prob1, prob2))

    p = [as_tensor_variable([prob1, 0.2, 0.3])]
    out = srng.choice(a=None, size=1, p=p, replace=False)[0]
    with pytest.raises(NullTypeGradError):
        grad(out[0], prob1)

    # checking normal distribution
    avg = scalar()
    out = srng.normal((), avg=avg)
    with pytest.raises(NullTypeGradError):
        grad(out, avg)

    std = scalar()
    out = srng.normal((), avg=0, std=std)
    with pytest.raises(NullTypeGradError):
        grad(out, std)

    out = srng.normal((), avg=avg, std=std)
    with pytest.raises(NullTypeGradError):
        grad(out, (avg, std))

    # checking truncated normal distribution
    avg = scalar()
    out = srng.truncated_normal((), avg=avg)
    with pytest.raises(NullTypeGradError):
        grad(out, avg)

    std = scalar()
    out = srng.truncated_normal((), avg=0, std=std)
    with pytest.raises(NullTypeGradError):
        grad(out, std)

    out = srng.truncated_normal((), avg=avg, std=std)
    with pytest.raises(NullTypeGradError):
        grad(out, (avg, std))


def test_f16_nonzero(mode=None, op_to_check=rng_mrg.mrg_uniform):
    srng = MRG_RandomStream(seed=utt.fetch_seed())
    m = srng.uniform(size=(1000, 1000), dtype="float16")
    assert m.dtype == "float16", m.type
    f = function([], m, mode=mode)
    assert any(isinstance(n.op, op_to_check) for n in f.maker.fgraph.apply_nodes)
    m_val = f()
    assert np.all((0 < m_val) & (m_val < 1))


@pytest.mark.slow
def test_target_parameter():
    srng = MRG_RandomStream()
    pvals = np.array([[0.98, 0.01, 0.01], [0.01, 0.49, 0.50]])

    def basic_target_parameter_test(x):
        f = function([], x)
        assert isinstance(f(), np.ndarray)

    basic_target_parameter_test(srng.uniform((3, 2), target="cpu"))
    basic_target_parameter_test(srng.normal((3, 2), target="cpu"))
    basic_target_parameter_test(srng.truncated_normal((3, 2), target="cpu"))
    basic_target_parameter_test(srng.binomial((3, 2), target="cpu"))
    basic_target_parameter_test(
        srng.multinomial(pvals=pvals.astype("float32"), target="cpu")
    )
    basic_target_parameter_test(
        srng.choice(p=pvals.astype("float32"), replace=False, target="cpu")
    )
    basic_target_parameter_test(
        srng.multinomial_wo_replacement(pvals=pvals.astype("float32"), target="cpu")
    )


@config.change_flags(compute_test_value="off")
def test_undefined_grad_opt():
    # Make sure that undefined grad get removed in optimized graph.
    random = MRG_RandomStream(
        np.random.default_rng(utt.fetch_seed()).integers(1, 2147462579)
    )
    pvals = shared(np.random.random((10, 20)).astype(config.floatX))
    pvals = pvals / pvals.sum(axis=1)
    pvals = zero_grad(pvals)
    samples = random.multinomial(pvals=pvals, n=1)
    samples = cast(samples, pvals.dtype)
    samples = zero_grad(samples)
    cost = at_sum(samples + pvals)
    grad_out = grad(cost, samples)
    f = function([], grad_out)
    assert not any(
        isinstance(node.op, UndefinedGrad) for node in f.maker.fgraph.apply_nodes
    )
