"""
  Questions and notes about scan that should be answered :

   * Scan seems to do copies of every input variable. Is that needed?
   answer : probably not, but it doesn't hurt also ( what we copy is
   aesara variables, which just cary information about the type / dimension
   of the data)

   * There is some of scan functionality that is not well documented
"""

import os
import pickle
import shutil
import sys
import timeit
from collections import OrderedDict
from tempfile import mkdtemp

import numpy as np
import pytest

import aesara.tensor as at
from aesara.compile.debugmode import DebugMode
from aesara.compile.function import function
from aesara.compile.function.pfunc import rebuild_collect_shared
from aesara.compile.mode import Mode, get_default_mode, get_mode
from aesara.compile.monitormode import MonitorMode
from aesara.compile.sharedvalue import shared
from aesara.configdefaults import config
from aesara.gradient import NullTypeGradError, Rop, disconnected_grad, grad, hessian
from aesara.graph.basic import Apply, ancestors, equal_computations
from aesara.graph.fg import FunctionGraph
from aesara.graph.op import Op
from aesara.graph.opt import MergeOptimizer
from aesara.graph.utils import MissingInputError
from aesara.misc.safe_asarray import _asarray
from aesara.raise_op import assert_op
from aesara.scan.basic import scan
from aesara.scan.op import Scan
from aesara.scan.utils import until
from aesara.tensor.math import all as at_all
from aesara.tensor.math import dot, mean, sigmoid
from aesara.tensor.math import sum as at_sum
from aesara.tensor.math import tanh
from aesara.tensor.nnet import categorical_crossentropy
from aesara.tensor.random import normal
from aesara.tensor.random.utils import RandomStream
from aesara.tensor.shape import Shape_i, reshape, specify_shape
from aesara.tensor.sharedvar import SharedVariable
from aesara.tensor.subtensor import Subtensor
from aesara.tensor.type import (
    dcol,
    dmatrix,
    dscalar,
    dvector,
    fmatrix,
    fscalar,
    ftensor3,
    fvector,
    imatrix,
    iscalar,
    ivector,
    lscalar,
    lvector,
    matrix,
    scalar,
    tensor3,
    vector,
)
from tests import unittest_tools as utt
from tests.tensor.nnet.test_basic import softmax_graph


if config.mode == "FAST_COMPILE":
    mode_with_opt = get_mode("FAST_RUN")
else:
    mode_with_opt = get_default_mode()
if config.mode in ("DEBUG_MODE", "DebugMode"):
    mode_nodebug = get_mode("FAST_RUN")
else:
    mode_nodebug = mode_with_opt


type_eps = {"float64": 1e-7, "float32": 3e-3}


class multiple_outputs_numeric_grad:
    """WRITEME"""

    def __init__(self, f, pt, ndarray_mask=None, eps=None):
        """
        Return the gradient of f at pt.

        This function computes the gradient by a one-sided finite differences
        of a fixed step size (eps).

        It is assumed that f(...) will return a scalar.
        :param eps: the stepsize for the finite differencing. None means
        input dtype-dependent. See `type_eps`.
        """

        def prod(inputs):
            rval = 1
            for i in inputs:
                rval *= i
            return rval

        if not isinstance(pt, (list, tuple)):
            pt = [pt]

        # This mask tells us if we are dealing with an ndarray input or
        # something else ( a random state ? ) with which we shouldn't really
        # mess up
        if not ndarray_mask:
            ndarray_mask = [True for x in pt]

        dtype_eps = type_eps["float64"]

        for i, p in enumerate(pt):
            if ndarray_mask[i]:
                pt[i] = np.array(p)
                _eps = type_eps[str(pt[i].dtype)]
                if _eps > dtype_eps:
                    dtype_eps = _eps

        self.ndarray_mask = ndarray_mask
        # '''
        # Compute clean output:
        f_x = f(*pt)
        gx = []
        # now iterate over the elements of x and call f on those + delta x
        for i in range(len(pt)):
            if ndarray_mask[i]:
                # It is a ndarray that we can tweak
                if eps:
                    _eps = eps
                else:
                    _eps = dtype_eps
                if pt[i].ndim:
                    _g = []
                    # it has several dimensions:
                    for pos in range(prod(pt[i].shape)):
                        t = pt[i].copy()
                        t = t.flatten()
                        t[pos] += _eps
                        t = t.reshape(pt[i].shape)
                        f_eps = f(*(pt[:i] + [t] + pt[i + 1 :]))
                        _g.append(np.asarray((f_eps - f_x) / _eps))
                    gx.append(np.asarray(_g).reshape(pt[i].shape))
                else:
                    t = np.array(pt[i] + _eps)
                    f_eps = f(*(pt[:i] + [t] + pt[i + 1 :]))
                    gx.append(np.asarray((f_eps - f_x) / _eps))
        self.gx = gx

    @staticmethod
    def abs_rel_err(a, b, eps=1.0e-10):
        """
        Return a small number when a and b are close, relative to how big they are
        """
        return abs(a - b) / (abs(a) + abs(b) + eps)

    def max_err(self, _g_pt):
        """
        Return the biggest relative error between g_pt and self.gx
        """
        g_pt = []
        for i in range(len(_g_pt)):
            if self.ndarray_mask[i]:
                g_pt.append(_g_pt[i])
            elif isinstance(_g_pt[i], np.ndarray):
                assert np.all(_g_pt[i] == 0)
        if len(g_pt) != len(self.gx):
            raise ValueError("argument has wrong number of elements", len(g_pt))
        errs = []

        for i, (a, b) in enumerate(zip(g_pt, self.gx)):
            if a.shape != b.shape:
                raise ValueError(
                    "argument element %i has wrong shape %s"
                    % (i, str((a.shape, b.shape)))
                )
            errs.append(np.max(multiple_outputs_numeric_grad.abs_rel_err(a, b)))
        if np.all(np.isfinite(errs)):
            return np.max(errs), np.argmax(errs)
        else:
            return np.inf, 0


# TODO: Test this function, and if it works,
# use it with the normal verify_grad rather than the
# copy-and-pasted one above.
# Also - add a reference to this technique in the
# verify_grad method so that other ops with multiple outputs can be tested.
# DONE - rp
def scan_project_sum(*args, **kwargs):
    rng = RandomStream(123)
    scan_outputs, updates = scan(*args, **kwargs)
    if not isinstance(scan_outputs, (list, tuple)):
        scan_outputs = [scan_outputs]
    # we should ignore the random-state updates so that
    # the uniform numbers are the same every evaluation and on every call
    rng.add_default_updates = False
    factors = [rng.uniform(0.1, 0.9, size=s.shape) for s in scan_outputs]
    # Random values (?)
    return (sum((s * f).sum() for s, f in zip(scan_outputs, factors)), updates)


def asarrayX(value):
    return _asarray(value, dtype=config.floatX)


def clone_optimized_graph(f):
    maker_ins = [x for x in f.maker.fgraph.inputs if not isinstance(x, SharedVariable)]
    inps, outs, _ = rebuild_collect_shared(
        f.maker.fgraph.outputs, maker_ins, copy_inputs_over=False
    )
    ins = [x for x in inps if not isinstance(x, SharedVariable)]
    return (ins, outs)


def grab_scan_node(output):
    if output.owner is None:
        return None
    if output.owner.op.__class__.__name__ == "Scan":
        return [output.owner]
    rval = []
    for i in output.owner.inputs:
        ri = grab_scan_node(i)
        if ri is not None:
            rval += ri
    if rval is []:
        return None
    else:
        return rval


def scan_nodes_from_fct(fct):
    nodes = fct.maker.fgraph.toposort()
    scan_nodes = [n for n in nodes if isinstance(n.op, Scan)]
    return scan_nodes


class TestScan:
    @pytest.mark.parametrize(
        "rng_type",
        [
            np.random.default_rng,
            np.random.RandomState,
        ],
    )
    def test_inner_graph_cloning(self, rng_type):
        r"""Scan should remove the updates-providing special properties on `RandomType`\s."""

        inner_inner_rng = shared(rng_type(), name="inner_inner_rng")

        y = shared(np.array(1.0, dtype=config.floatX), name="y")
        y.default_update = y + 1

        z_rng = shared(rng_type(), name="z_rng")
        z = normal(0, 1, rng=z_rng, name="z")

        z_rng_update = z.owner.outputs[0]
        z_rng_update.name = "z_rng_update"
        z_rng.default_update = z_rng_update

        inner_rng = None

        def inner_fn(x):
            inner_rng = shared(rng_type(), name="inner_rng")
            inner_rng.default_update = inner_inner_rng
            inner_inner_rng.default_update = inner_rng

            r = normal(x, rng=inner_rng)
            return r + y + z, z

        out, out_updates = scan(
            inner_fn,
            outputs_info=[at.as_tensor(0.0, dtype=config.floatX), None],
            n_steps=4,
        )

        assert not hasattr(inner_rng, "default_update")
        assert hasattr(inner_inner_rng, "default_update")
        assert hasattr(y, "default_update")
        assert hasattr(z_rng, "default_update")

        out_fn = function([], out, mode=Mode(optimizer=None))
        res, z_res = out_fn()
        assert len(set(res)) == 4
        assert len(set(z_res)) == 1

    def test_clone(self):

        a = vector()
        output, _ = scan(fn=lambda x: x**2, sequences=[a])

        scan_op = output.owner.op
        assert isinstance(scan_op, Scan)

        scan_op_clone = scan_op.clone()
        assert scan_op_clone is not scan_op
        assert scan_op_clone.fgraph is not scan_op.fgraph
        assert scan_op_clone.fgraph.outputs != scan_op.fgraph.outputs
        assert equal_computations(scan_op_clone.fgraph.outputs, scan_op.fgraph.outputs)

    @pytest.mark.skipif(
        isinstance(get_default_mode(), DebugMode),
        reason="This test fails in DebugMode, because it is not yet picklable.",
    )
    def test_pickling(self):
        """
        generator network, only one output , type scalar ; no sequence or
        non sequence arguments
        """

        def f_pow2(x_tm1):
            return 2 * x_tm1

        state = scalar("state")
        n_steps = iscalar("nsteps")
        output, updates = scan(
            f_pow2,
            [],
            state,
            [],
            n_steps=n_steps,
            truncate_gradient=-1,
            go_backwards=False,
        )
        _my_f = function(
            [state, n_steps], output, updates=updates, allow_input_downcast=True
        )

        origdir = os.getcwd()
        tmpdir = None
        try:
            tmpdir = mkdtemp()
            os.chdir(tmpdir)

            with open("tmp_scan_test_pickle.pkl", "wb") as f_out:
                pickle.dump(_my_f, f_out, protocol=-1)
            with open("tmp_scan_test_pickle.pkl", "rb") as f_in:
                my_f = pickle.load(f_in)
        finally:
            # Get back to the original dir, and delete the temporary one.
            os.chdir(origdir)
            if tmpdir is not None:
                shutil.rmtree(tmpdir)

        rng = np.random.default_rng(utt.fetch_seed())
        state = rng.uniform()
        steps = 5

        numpy_values = np.array([state * (2 ** (k + 1)) for k in range(steps)])
        aesara_values = my_f(state, steps)
        utt.assert_allclose(numpy_values, aesara_values)

    def test_inner_storage_leak(self):
        """
        Test that the inner input_storage and output_storage are
        properly cleared
        """

        def f_pow2(x_tm1):
            return 2 * x_tm1

        state = scalar("state")
        n_steps = iscalar("nsteps")
        output, updates = scan(f_pow2, [], state, [], n_steps=n_steps)

        f = function(
            [state, n_steps], output, updates=updates, allow_input_downcast=True
        )

        scan_node = [
            node for node in f.maker.fgraph.toposort() if isinstance(node.op, Scan)
        ]

        assert len(scan_node) == 1
        scan_node = scan_node[0]

        # Make sure they start out as None
        assert all(i.value is None for i in scan_node.op.fn.input_storage)
        assert all(o.value is None for o in scan_node.op.fn.output_storage)

        rng = np.random.default_rng(utt.fetch_seed())
        state = rng.uniform()
        steps = 5

        f(state, steps)

        # And that they stay that way
        assert all(i.value is None for i in scan_node.op.fn.input_storage)
        assert all(o.value is None for o in scan_node.op.fn.output_storage)

    @pytest.mark.parametrize("mode", [Mode(linker="py"), Mode(linker="cvm")])
    @pytest.mark.parametrize(
        "x_init",
        [
            scalar("x"),
            iscalar("x"),
        ],
    )
    def test_no_step(self, mode, x_init):
        """We expect an empty output array when ``n_steps == 0``."""

        def f_pow(x_tm1):
            return 2 * x_tm1

        n_steps = iscalar("n_steps")
        values, _ = scan(f_pow, outputs_info=(x_init,), n_steps=n_steps)

        update_fn = function((x_init, n_steps), values, mode=mode)

        res = update_fn(1.0, 0)
        exp_res = np.array([], dtype=values.dtype)
        assert np.array_equal(res, exp_res)
        assert res.dtype == exp_res.dtype

    @pytest.mark.parametrize(
        "mode", [Mode(linker="py", optimizer=None), Mode(linker="cvm", optimizer=None)]
    )
    @pytest.mark.parametrize(
        "x",
        [
            vector("x"),
            ivector("x"),
        ],
    )
    @pytest.mark.parametrize(
        "x_init",
        [
            scalar("x"),
            iscalar("x"),
        ],
    )
    def test_no_steps_sit_sot(self, mode, x, x_init):
        """We expect an empty output array when scanning over an empty sequence."""

        def inner_fn(x_seq, x_i):
            return 2 * x_i

        with config.change_flags(mode=mode):
            values, _ = scan(inner_fn, outputs_info=(x_init,), sequences=x)
            values_fn = function((x_init, x), values)

        assert isinstance(values.owner.inputs[0].owner.op, Scan)

        x_val = np.array([], dtype=x.dtype)
        x_init_val = 1.0

        res = values_fn(x_init_val, x_val)
        exp_res = np.array([], dtype=values.dtype)

        assert np.array_equal(res, exp_res)
        assert res.dtype == exp_res.dtype

    @pytest.mark.parametrize(
        "mode", [Mode(linker="py", optimizer=None), Mode(linker="cvm", optimizer=None)]
    )
    @pytest.mark.parametrize(
        "x",
        [
            vector("x"),
            ivector("x"),
        ],
    )
    def test_no_steps_nit_sot(self, mode, x):
        """We expect an empty output array when scanning over an empty sequence."""

        def inner_fn(x_i):
            return 2 * x_i

        with config.change_flags(mode=mode):
            values, _ = scan(inner_fn, sequences=x)
            values_fn = function((x,), values)

        assert isinstance(values.owner.op, Scan)

        x_val = np.array([], dtype=x.dtype)

        res = values_fn(x_val)
        exp_res = np.array([], dtype=values.dtype)

        assert np.array_equal(res, exp_res)
        assert res.dtype == exp_res.dtype

    def test_only_nonseq_inputs(self):
        # Compile the Aesara function
        n_steps = 2
        inp = matrix()
        broadcasted_inp, _ = scan(lambda x: x, non_sequences=[inp], n_steps=n_steps)
        out = broadcasted_inp.sum()
        gr = grad(out, inp)
        fun = function([inp], [broadcasted_inp, gr])

        # Execute the Aesara function and compare outputs to the expected outputs
        inputs = np.array([[1, 2], [3, 4]], dtype=config.floatX)
        expected_out1 = np.repeat(inputs[None], n_steps, axis=0)
        expected_out2 = np.ones(inputs.shape, dtype="int8") * n_steps

        out1, out2 = fun(inputs)
        utt.assert_allclose(out1, expected_out1)
        utt.assert_allclose(out2, expected_out2)

    def test_one_sequence_one_output_weights(self):
        """
        simple rnn, one input, one state, weights for each; input/state
        are vectors, weights are scalars
        """

        def f_rnn(u_t, x_tm1, W_in, W):
            return u_t * W_in + x_tm1 * W

        u = vector("u")
        x0 = scalar("x0")
        W_in = scalar("win")
        W = scalar("w")

        output, updates = scan(
            f_rnn,
            u,
            x0,
            [W_in, W],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        f2 = function(
            [u, x0, W_in, W], output, updates=updates, allow_input_downcast=True
        )
        # get random initial values
        rng = np.random.default_rng(utt.fetch_seed())
        v_u = rng.uniform(-5.0, 5.0, size=(4,))
        v_x0 = rng.uniform()
        W = rng.uniform()
        W_in = rng.uniform()

        # compute the output in numpy
        v_out = np.zeros((4,))
        v_out[0] = v_u[0] * W_in + v_x0 * W
        for step in range(1, 4):
            v_out[step] = v_u[step] * W_in + v_out[step - 1] * W
        aesara_values = f2(v_u, v_x0, W_in, W)
        utt.assert_allclose(aesara_values, v_out)

    def test_one_sequence_one_output_weights_shared(self):
        """
        simple rnn, one input, one state, weights for each; input/state
        are vectors, weights are scalars; using shared variables
        """
        rng = np.random.default_rng(utt.fetch_seed())
        u = vector("u")
        x0 = scalar("x0")
        W_in = shared(asarrayX(rng.uniform()), name="w_in")
        W = shared(asarrayX(rng.uniform()), name="w")

        def f_rnn_shared(u_t, x_tm1, tmp_W_in, tmp_W):
            return u_t * tmp_W_in + x_tm1 * tmp_W

        output, updates = scan(
            f_rnn_shared,
            u,
            x0,
            [W_in, W],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )
        f3 = function([u, x0], output, updates=updates, allow_input_downcast=True)
        # get random initial values

        v_u = rng.uniform(-5.0, 5.0, size=(4,))
        v_x0 = rng.uniform()
        # compute the output i numpy
        v_out = np.zeros((4,))
        v_out[0] = v_u[0] * W_in.get_value() + v_x0 * W.get_value()
        for step in range(1, 4):
            v_out[step] = v_u[step] * W_in.get_value() + v_out[step - 1] * W.get_value()

        aesara_values = f3(v_u, v_x0)
        assert np.allclose(aesara_values, v_out)

    def test_oinp_iinp_iout_oout_mappings(self):
        """
        Test the mapping produces by
        ScanOp.get_oinp_iinp_iout_oout_mappings()
        """

        rng = RandomStream(123)

        def inner_fct(seq, mitsot, sitsot, nitsot, nseq):
            random_scalar = rng.uniform((1,))[0]
            total = seq + mitsot + sitsot + nitsot + nseq + random_scalar
            return total, total, total

        # Assemble a scan with one sequence, one mitsot, one sitsot, one nitsot
        # a non-sequence and a random state to test the mappings.
        seq = [vector()]
        non_seq = [scalar()]
        outputs_info = [
            dict(initial=vector(), taps=[-3, -1]),
            scalar(),
            None,
        ]

        scan_outputs, _ = scan(
            fn=inner_fct,
            sequences=seq,
            outputs_info=outputs_info,
            non_sequences=non_seq,
        )

        # Compare the mappings with the expected values
        scan_node = scan_outputs[0].owner.inputs[0].owner
        mappings = scan_node.op.get_oinp_iinp_iout_oout_mappings()

        assert mappings["inner_inp_from_outer_inp"] == {
            0: [],
            1: [0],
            2: [1, 2],
            3: [3],
            4: [4],
            5: [],
            6: [5],
        }
        assert mappings["inner_out_from_outer_inp"] == {
            0: [],
            1: [],
            2: [0],
            3: [1],
            4: [3],
            5: [2],
            6: [],
        }
        assert mappings["outer_out_from_outer_inp"] == {
            0: -1,
            1: -1,
            2: 0,
            3: 1,
            4: 3,
            5: 2,
            6: -1,
        }

        assert mappings["outer_inp_from_inner_inp"] == {
            0: 1,
            1: 2,
            2: 2,
            3: 3,
            4: 4,
            5: 6,
        }
        assert mappings["inner_out_from_inner_inp"] == {
            0: [],
            1: [0],
            2: [0],
            3: [1],
            4: [3],
            5: [],
        }
        assert mappings["outer_out_from_inner_inp"] == {
            0: -1,
            1: 0,
            2: 0,
            3: 1,
            4: 3,
            5: -1,
        }

        assert mappings["outer_inp_from_inner_out"] == {0: 2, 1: 3, 2: 5, 3: 4}
        assert mappings["inner_inp_from_inner_out"] == {
            0: [1, 2],
            1: [3],
            2: [],
            3: [4],
        }
        assert mappings["outer_out_from_inner_out"] == {0: 0, 1: 1, 2: 2, 3: 3}

        assert mappings["outer_inp_from_outer_out"] == {0: 2, 1: 3, 2: 5, 3: 4}
        assert mappings["inner_inp_from_outer_out"] == {
            0: [1, 2],
            1: [3],
            2: [],
            3: [4],
        }
        assert mappings["inner_out_from_outer_out"] == {0: [0], 1: [1], 2: [2], 3: [3]}

    def test_using_taps_sequence(self):
        # this test refers to a bug reported by Nicolas
        # Boulanger-Lewandowski June 6th
        x = dvector()
        y, updates = scan(
            lambda x: [x], sequences=dict(input=x, taps=[-1]), outputs_info=[None]
        )
        inp = np.arange(5).astype("float64")
        rval = function([x], y, updates=updates)(inp)
        assert np.all(rval == inp[:-1])

    def test_output_only(self):
        def f_rnn(u_t):
            return u_t + 3

        u = vector("u")

        outputs, updates = scan(
            f_rnn, u, [], [], n_steps=None, truncate_gradient=-1, go_backwards=False
        )

        f2 = function([u], outputs, updates=updates, allow_input_downcast=True)
        rng = np.random.default_rng(utt.fetch_seed())

        v_u = rng.uniform(-5.0, 5.0, size=(5,))
        numpy_result = v_u + 3
        aesara_result = f2(v_u)
        utt.assert_allclose(aesara_result, numpy_result)

    def test_backwards(self):
        def f_rnn(u_t, x_tm1, W_in, W):
            return u_t * W_in + x_tm1 * W

        u = vector("u")
        x0 = scalar("x0")
        W_in = scalar("win")
        W = scalar("w")

        output, updates = scan(
            f_rnn,
            u,
            x0,
            [W_in, W],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=True,
        )

        f2 = function(
            [u, x0, W_in, W], output, updates=updates, allow_input_downcast=True
        )
        # get random initial values
        rng = np.random.default_rng(utt.fetch_seed())
        v_u = rng.uniform(-5.0, 5.0, size=(4,))
        v_x0 = rng.uniform()
        W = rng.uniform()
        W_in = rng.uniform()

        # compute the output in numpy
        v_out = np.zeros((4,))
        v_out[0] = v_u[3] * W_in + v_x0 * W
        for step in range(1, 4):
            v_out[step] = v_u[3 - step] * W_in + v_out[step - 1] * W

        aesara_values = f2(v_u, v_x0, W_in, W)
        utt.assert_allclose(aesara_values, v_out)

    def test_output_padding(self):
        """
        Scan outputs are usually lists, whose entries correspond to the
        intermediate result. When n_steps=1, some extra machinery is
        required in order to mimic this interface. Scan thus calls
        tensor.shape_padleft on the inner function outputs.

        However, this is not the proper behavior for shared variables,
        they should not be padded in any way

        This unit test addresses the bug fix of changeset ba7157e95cb1.
        """

        a = vector()
        init_a = vector()
        b = shared(np.random.default_rng(utt.fetch_seed()).random((5, 4)))

        def inner_func(a):
            return a + 1, OrderedDict([(b, 2 * b)])

        out, updates = scan(
            inner_func, outputs_info=[OrderedDict([("initial", init_a)])], n_steps=1
        )
        out = out[-1]
        assert out.type.ndim == a.type.ndim
        assert updates[b].type.ndim == b.type.ndim

        out, updates = scan(inner_func, outputs_info=[init_a], n_steps=1)
        assert out.type.ndim == a.type.ndim + 1
        assert updates[b].type.ndim == b.type.ndim

    def test_sequence_dict(self):
        """Test that we can specify sequences as a dictionary with only the ``"input"`` key."""

        def incr(s):
            return s + 1

        x = vector("x")
        sx, upx = scan(fn=incr, sequences=[{"input": x}])

        scan_seqs = sx.owner.op.outer_seqs(sx.owner.inputs)

        assert len(scan_seqs) == 1
        assert x in ancestors(scan_seqs)

    def test_hash(self):
        x = vector()
        y = vector()
        scan1, updates = scan(lambda _x: _x + 1, x)
        scan2, updates = scan(lambda _x: _x + 1, y)
        assert scan1.owner.op == scan2.owner.op
        assert hash(scan1.owner.op) == hash(scan2.owner.op)

    def test_can_merge(self):
        """Make sure that equivalent `Scan` nodes can be merged."""

        x = vector("x")
        y = vector("y")
        c = scalar("c")

        scan_a, _ = scan(lambda x, y, c: x + y + c, sequences=[x, y], non_sequences=[c])
        scan_b, _ = scan(lambda x, y, c: x + y + c, sequences=[x, y], non_sequences=[c])
        scan_c, _ = scan(lambda x, y, c: x + y + c, sequences=[y, x], non_sequences=[c])

        assert scan_b is not scan_a
        assert scan_c is not scan_a

        g = FunctionGraph([x, y, c], [2 * scan_a, 2 * scan_b, 2 * scan_c], clone=False)
        MergeOptimizer().optimize(g)
        scan_a_out, scan_b_out, scan_c_out = g.outputs

        assert scan_a_out is scan_b_out
        assert scan_c_out is not scan_a_out

    def test_using_negative_taps_sequence(self):
        # This test refers to a bug reported on github on May 22 2015 by
        # user june-qijun
        def lp(x, x2):
            return x

        x = fvector("x")
        res, upd = scan(lp, sequences=dict(input=x, taps=[-2, -1]))
        f = function([x], res, updates=upd)

        output = f([1, 2, 3, 4, 5])
        expected_output = np.array([1, 2, 3], dtype="float32")
        utt.assert_allclose(output, expected_output)

    def test_shared_arguments_with_updates(self):
        rng = np.random.default_rng(utt.fetch_seed())

        vW1 = asarrayX(rng.random((2, 3)))
        vW2 = asarrayX(rng.random((3, 2)))
        vu1 = asarrayX(rng.random((3, 2)))
        vu2 = asarrayX(rng.random((3, 3)))
        vy0 = asarrayX(rng.random((3, 2)))
        vy1 = asarrayX(rng.random((2)))
        vu1 = asarrayX(rng.random((3, 2)))

        W1 = shared(vW1, "W1")
        W2 = shared(vW2, "W2")
        u1 = shared(vu1, "u1")
        y1 = shared(vy1, "y1")

        def f(u1_t, u2_t, y0_tm3, y0_tm2, y0_tm1, y1_tm1):
            y0_t = dot(dot(u1_t, W1), W2) + 0.1 * y0_tm1 + 0.33 * y0_tm2 + 0.17 * y0_tm3
            y1_t = dot(u2_t, W2) + y1_tm1
            y2_t = dot(u1_t, W1)
            nwW1 = W1 + 0.1
            nwW2 = W2 + 0.05
            # return outputs followed by a list of updates
            return ([y0_t, y1_t, y2_t], [(W1, nwW1), (W2, nwW2)])

        u2 = matrix("u2")
        y0 = matrix("y0")
        outputs, updates = scan(
            f,
            [u1, u2],
            [dict(initial=y0, taps=[-3, -2, -1]), y1, None],
            [],
            n_steps=None,
            go_backwards=False,
            truncate_gradient=-1,
        )

        f10 = function([u2, y0], outputs, updates=updates, allow_input_downcast=True)
        allstuff = f10(vu2, vy0)
        aesara_y0, aesara_y1, aesara_y2 = allstuff

        # do things in numpy
        numpy_y0 = np.zeros((6, 2))
        numpy_y1 = np.zeros((4, 2))
        numpy_y2 = np.zeros((3, 3))
        numpy_y0[:3] = vy0
        numpy_y1[0] = vy1
        numpy_W1 = vW1.copy()
        numpy_W2 = vW2.copy()
        for idx in range(3):
            numpy_y0[idx + 3] = (
                np.dot(np.dot(vu1[idx, :], numpy_W1), numpy_W2)
                + 0.1 * numpy_y0[idx + 2]
                + 0.33 * numpy_y0[idx + 1]
                + 0.17 * numpy_y0[idx]
            )
            numpy_y1[idx + 1] = np.dot(vu2[idx, :], numpy_W2) + numpy_y1[idx]
            numpy_y2[idx] = np.dot(vu1[idx, :], numpy_W1)
            numpy_W1 = numpy_W1 + 0.1
            numpy_W2 = numpy_W2 + 0.05

        utt.assert_allclose(aesara_y0, numpy_y0[3:])
        utt.assert_allclose(aesara_y1, numpy_y1[1:])
        utt.assert_allclose(aesara_y2, numpy_y2)
        utt.assert_allclose(W1.get_value(), numpy_W1)
        utt.assert_allclose(W2.get_value(), numpy_W2)

    def test_simple_shared_random(self):
        aesara_rng = RandomStream(utt.fetch_seed())

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

        rng_seed = np.random.SeedSequence(utt.fetch_seed())
        (rng_seed,) = rng_seed.spawn(1)
        rng = aesara_rng.rng_ctor(rng_seed)

        numpy_v = np.zeros((10, 2))
        for i in range(10):
            numpy_v[i] = rng.uniform(-1, 1, size=(2,))

        aesara_v = my_f()
        utt.assert_allclose(aesara_v, numpy_v[:5, :])
        aesara_v = my_f()
        utt.assert_allclose(aesara_v, numpy_v[5:, :])

    def test_only_shared_no_input_no_output(self):
        rng = np.random.default_rng(utt.fetch_seed())
        v_state = asarrayX(rng.uniform())
        state = shared(v_state, "vstate")

        def f_2():
            return OrderedDict([(state, 2 * state)])

        n_steps = iscalar("nstep")
        output, updates = scan(
            f_2, [], [], [], n_steps=n_steps, truncate_gradient=-1, go_backwards=False
        )
        this_f = function([n_steps], output, updates=updates, allow_input_downcast=True)
        n_steps = 3
        this_f(n_steps)
        numpy_state = v_state * (2 ** (n_steps))
        utt.assert_allclose(state.get_value(), numpy_state)

    def test_random_as_input_to_scan(self):
        trng = RandomStream(123)

        x = matrix("x")
        y = trng.binomial(1, x, size=x.shape)
        z, updates = scan(lambda a: a, non_sequences=y, n_steps=2)

        f = function([x], [y, z], updates=updates, allow_input_downcast=True)

        rng = np.random.default_rng(utt.fetch_seed())
        nx = rng.uniform(size=(10, 10))
        ny1, nz1 = f(nx)
        ny2, nz2 = f(nx)

        utt.assert_allclose([ny1, ny1], nz1)
        utt.assert_allclose([ny2, ny2], nz2)
        assert not np.allclose(ny1, ny2)

    def test_shared_updates(self):
        X = shared(np.array(1))

        out, updates = scan(
            lambda: OrderedDict([(X, (X + 1))]),
            outputs_info=[],
            non_sequences=[],
            sequences=[],
            n_steps=10,
        )

        f = function([], [], updates=updates)
        f()
        assert X.get_value() == 11

    def test_shared_memory_aliasing_updates(self):
        x = shared(np.array(1))
        y = shared(np.array(1))

        out, updates = scan(
            lambda: OrderedDict([(x, x + 1), (y, x)]),
            outputs_info=[],
            non_sequences=[],
            sequences=[],
            n_steps=10,
        )

        f = function([], [], updates=updates)
        f()
        assert not np.may_share_memory(x.container.storage[0], y.container.storage[0])

        assert x.get_value() != y.get_value()

    def test_while(self):
        x = vector("x")

        def lambda_fn(x_t):
            return x_t + 1, until(x_t > 3)

        o, _ = scan(lambda_fn, x)
        f = function([x], o)
        vx = np.zeros((50,), dtype=config.floatX)
        vx[23] = 4
        out = f(vx)
        assert len(out) == 24

    def test_while_infer_shape(self):
        x = vector("x")

        def lambda_fn(x_t):
            return x_t + 1, until(x_t > 3)

        o, _ = scan(lambda_fn, x)

        f = function([x], o.shape[0], mode=mode_with_opt)
        vx = np.zeros((50,), dtype=config.floatX)
        vx[23] = 4
        out = f(vx)
        assert out == 24

    def test_infer_shape_nsteps_smaller_seq_length(self):
        x = vector("x")
        [o1, o2], _ = scan(
            lambda x, y: (x + 1, y + x),
            sequences=x,
            outputs_info=[None, x[0]],
            n_steps=20,
        )

        f = function([x], [o1.shape[0], o2.shape[0]], mode=mode_with_opt)

        vx = np.ones((30,), dtype=config.floatX)
        o1, o2 = f(vx)
        assert o1 == 20
        assert o2 == 20
        assert not any(x for x in f.maker.fgraph.toposort() if isinstance(x.op, Scan))

    def test_strict_mode(self):
        w = np.array([[-1, 2], [3, -4]]).astype(config.floatX)
        w_ = shared(w)
        x0_ = vector(name="x0", dtype=config.floatX)

        def _scan_loose(x):
            return dot(x, w_)

        with pytest.raises(MissingInputError):
            scan(_scan_loose, sequences=[], outputs_info=[x0_], n_steps=10, strict=True)

    def test_monitor_mode(self):
        """Test that it is possible to pass an instance of `MonitorMode` to the inner function."""
        k = iscalar("k")
        A = vector("A")

        # Build a MonitorMode that counts how many values are greater than 10
        def detect_large_outputs(fgraph, i, node, fn):
            for output in fn.outputs:
                if isinstance(output[0], np.ndarray):
                    detect_large_outputs.large_count += (output[0] > 10).sum()

        detect_large_outputs.large_count = 0

        mode = MonitorMode(post_func=detect_large_outputs)

        # Symbolic description of the result
        result, updates = scan(
            fn=lambda prior_result, A: prior_result * A,
            outputs_info=at.ones_like(A),
            non_sequences=A,
            n_steps=k,
            mode=mode,
        )

        final_result = result[-1]

        f = function(inputs=[A, k], outputs=final_result, updates=updates)
        f(np.asarray([2, 3, 0.1, 0, 1], dtype=config.floatX), 4)

        # There should be 3 outputs greater than 10: prior_result[0] at step 3,
        # and prior_result[1] at steps 2 and 3.
        if config.mode in ["DEBUG_MODE", "DebugMode"]:
            # DebugMode will run all the intermediate nodes, so we
            # should expect a multiple of 3, not exactly 3.
            assert detect_large_outputs.large_count % 3 == 0

        else:
            assert detect_large_outputs.large_count == 3

    def test_inner_grad(self):
        x = vector("x")
        A = matrix("A")
        fc1 = shared(0.5, name="fc1")
        fc2 = shared(0.9, name="fc2")
        y = fc1 * dot(x * x, dot(A, x))
        y.name = "y"
        gy = grad(y, x)
        gy.name = "gy"
        hy, updates = scan(
            lambda i, gy, x: grad(gy[i] * fc2, x),
            sequences=at.arange(gy.shape[0]),
            non_sequences=[gy, x],
        )

        f = function([x, A], hy, allow_input_downcast=True)
        vx = np.array([1.0, 1.0], dtype=config.floatX)
        vA = np.array([[1.0, 1.0], [1.0, 0.0]], dtype=config.floatX)
        vR = np.array([[3.6, 1.8], [1.8, 0.9]], dtype=config.floatX)
        out = f(vx, vA)

        utt.assert_allclose(out, vR)

    @pytest.mark.parametrize(
        "mode", [Mode(linker="cvm", optimizer=None), Mode(linker="cvm")]
    )
    def test_sequence_is_scan(self, mode):
        """Make sure that a `Scan` can be used as a sequence input to another `Scan`."""
        x0 = scalar("x0")
        scan_1, _ = scan(lambda x: x + 1, outputs_info={"initial": x0}, n_steps=10)
        scan_2, _ = scan(lambda x: x + 1, sequences=[scan_1])

        with config.change_flags(mode=mode):
            scan_2_fn = function([x0], scan_2)

        scan_2_val = scan_2_fn(0.0)
        exp_res = np.arange(1, 11) + 1.0

        assert np.array_equal(scan_2_val, exp_res)

    def test_grad_sitsot(self):
        def get_sum_of_grad(inp):

            scan_outputs, updates = scan(
                fn=lambda x: x * 2, outputs_info=[inp], n_steps=5
            )

            # Take the gradient of each output wrt its corresponding initial
            # state
            return grad(scan_outputs.sum(), inp).sum()

        # Call verify_grad to ensure the correctness of the second gradients
        floatX = config.floatX
        inputs_test_values = [
            np.random.default_rng(utt.fetch_seed()).random(3).astype(floatX)
        ]
        utt.verify_grad(get_sum_of_grad, inputs_test_values)

    def test_grad_mitsot(self):
        def inner_fct(mitsot_m2, sitsot):
            total = mitsot_m2 + sitsot
            output = total**1.02
            return output, output

        def get_sum_of_grad(input0, input1):
            outputs_info = [dict(initial=input0, taps=[-2]), input1]

            scan_outputs, updates = scan(
                fn=inner_fct, outputs_info=outputs_info, n_steps=3
            )

            # Take the gradient of each output wrt its corresponding initial
            # state
            gradients = [
                grad(scan_outputs[0].sum(), input0),
                grad(scan_outputs[1].sum(), input1),
            ]

            return gradients[0].sum() + gradients[1].sum()

        # Call verify_grad to ensure the correctness of the second gradients
        floatX = config.floatX
        rng = np.random.default_rng(utt.fetch_seed())
        inputs_test_values = [
            rng.random((2, 3)).astype(floatX),
            rng.random(3).astype(floatX),
        ]

        utt.verify_grad(get_sum_of_grad, inputs_test_values, rng=rng)

    def test_connection_pattern(self):
        """Test `Scan.connection_pattern` in the presence of recurrent outputs with multiple taps."""

        def fn(a_m2, a_m1, b_m2, b_m1):
            return a_m1, b_m1

        a0 = shared(np.arange(2))
        b0 = shared(np.arange(2))

        (a, b), _ = scan(
            fn,
            outputs_info=[
                {"initial": a0, "taps": [-2, -1]},
                {"initial": b0, "taps": [-2, -1]},
            ],
            n_steps=2,
        )

        grad(a[-1], a0)

        # Also validate that the mappings outer_inp_from_outer_out and
        # outer_inp_from_inner_inp produce the correct results
        scan_node = a.owner.inputs[0].owner

        var_mappings = scan_node.op.get_oinp_iinp_iout_oout_mappings()
        result = var_mappings["outer_inp_from_outer_out"]
        expected_result = {0: 1, 1: 2}
        assert result == expected_result

        result = var_mappings["outer_inp_from_inner_inp"]
        expected_result = {0: 1, 1: 1, 2: 2, 3: 2}
        assert result == expected_result

    def test_connection_pattern_multiple_mitmot(self):
        """
        This tests for a crash in connection_pattern() when a scan node
        has more than one mitmot (multiple input taps as well as
        multiple output taps) output
        """

        x = matrix()
        seq = vector()

        def inner_fct(seq, state_old, state_current):
            state_next = state_old * 2 + state_current + seq
            return state_next

        out, _ = scan(
            inner_fct, sequences=seq, outputs_info={"initial": x, "taps": [-2, -1]}
        )

        g_out = grad(out.sum(), [seq, x])

        scan_node = g_out[0].owner.inputs[1].owner.inputs[1].owner.inputs[0].owner
        scan_node.op.connection_pattern(scan_node)

        # Also validate that the mappings outer_inp_from_outer_out and
        # outer_inp_from_inner_inp produce the correct results
        scan_node = out.owner.inputs[0].owner

        var_mappings = scan_node.op.get_oinp_iinp_iout_oout_mappings()
        result = var_mappings["outer_inp_from_outer_out"]
        expected_result = {0: 2}
        assert result == expected_result

        result = var_mappings["outer_inp_from_inner_inp"]
        expected_result = {0: 1, 1: 2, 2: 2}
        assert result == expected_result

    def test_grad_grad_mitsot_sitsot(self):
        """
        Test for an index error when taking the second derivative through a
        `Scan` node with one sitsot and one mitsot.
        """

        def inner_fct(mitsot_m2, mitsot_m1, sitsot):
            total = mitsot_m2 + mitsot_m1 + sitsot
            output = total**1.05
            return output, output

        inputs = [matrix(), vector()]
        outputs_info = [dict(initial=inputs[0], taps=[-2, -1]), inputs[1]]

        scan_outputs, updates = scan(fn=inner_fct, outputs_info=outputs_info, n_steps=5)

        # Take the gradient of each output wrt its corresponding initial state
        gradients = [
            grad(scan_outputs[0].sum(), inputs[0]),
            grad(scan_outputs[1].sum(), inputs[1]),
        ]

        # Take the gradient of the sum of gradients wrt the inputs
        sum_of_grads = sum(g.sum() for g in gradients)
        grad(sum_of_grads, inputs[0])

    def test_grad_dtype_change(self):
        x = fscalar("x")
        y = fscalar("y")
        c = iscalar("c")

        def inner_fn(cond, x, y):
            new_cond = at.cast(at.switch(cond, x, y), "int32")
            new_x = at.switch(cond, sigmoid(y * x), x)
            new_y = at.switch(cond, y, sigmoid(x))
            return new_cond, new_x, new_y

        values, _ = scan(
            inner_fn,
            outputs_info=[c, x, y],
            n_steps=10,
            truncate_gradient=-1,
            go_backwards=False,
        )
        gX, gY = grad(values[1].sum(), [x, y])
        f = function([c, x, y], [gX, gY], allow_input_downcast=True)
        # Check for runtime errors
        # TODO FIXME: Make this a real test and assert something.
        f(np.int32(0), np.float32(1.0), np.float32(0.5))

    def test_grad_one_output(self):
        def f_rnn(u_t, x_tm1, W_in, W):
            return u_t * W_in + x_tm1 * W

        u = vector("u")
        x0 = scalar("x0")
        W_in = scalar("W_in")
        W = scalar("W")

        cost, updates = scan_project_sum(
            f_rnn,
            u,
            x0,
            [W_in, W],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )
        gu, gx0, gW_in, gW = grad(cost, [u, x0, W_in, W])
        grad_fn = function(
            [u, x0, W_in, W],
            [gu, gx0, gW_in, gW],
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )
        cost_fn = function(
            [u, x0, W_in, W],
            cost,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )

        # get random initial values
        rng = np.random.default_rng(utt.fetch_seed())
        v_u = np.array(rng.uniform(-0.5, 0.5, size=(10,)), dtype=config.floatX)
        v_x0 = np.array(rng.uniform(), dtype=config.floatX)
        W = np.array(rng.uniform(), dtype=config.floatX)
        W_in = np.array(rng.uniform(), dtype=config.floatX)

        analytic_grad = grad_fn(v_u, v_x0, W_in, W)

        num_grad = multiple_outputs_numeric_grad(cost_fn, [v_u, v_x0, W_in, W])
        max_err, max_err_pos = num_grad.max_err(analytic_grad)
        assert max_err <= 1e-2

    def test_grad_multiple_outs(self):
        rng = np.random.default_rng(utt.fetch_seed())
        vW_in2 = asarrayX(rng.uniform(-0.1, 0.1, size=(2,)))
        vW = asarrayX(rng.uniform(-0.1, 0.1, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-0.1, 0.1, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-0.1, 0.1, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-0.1, 0.1, size=(7, 2)))
        v_u2 = asarrayX(rng.uniform(-0.1, 0.1, size=(7,)))
        v_x0 = asarrayX(rng.uniform(0.1, 0.1, size=(2,)))
        v_y0 = asarrayX(rng.uniform())

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = vector("u2")
        x0 = vector("x0")
        y0 = scalar("y0")

        def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, W_in1):
            return [
                dot(u1_t, W_in1) + u2_t * W_in2 + dot(x_tm1, W),
                dot(x_tm1, W_out),
            ]

        cost, updates = scan_project_sum(
            f_rnn_cmpl,
            [u1, u2],
            [x0, y0],
            W_in1,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )
        # y0 is actually not used in the computation of the cost
        params = [u1, u2, x0, y0, W_in1]
        gparams = grad(cost, params, disconnected_inputs="ignore")

        grad_fn = function(
            [u1, u2, x0, y0, W_in1],
            gparams,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )
        cost_fn = function(
            [u1, u2, x0, y0, W_in1],
            cost,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )

        num_grad = multiple_outputs_numeric_grad(
            cost_fn, [v_u1, v_u2, v_x0, v_y0, vW_in1]
        )
        analytic_grad = grad_fn(v_u1, v_u2, v_x0, v_y0, vW_in1)
        max_err, max_err_pos = num_grad.max_err(analytic_grad)
        assert max_err <= 1e-2

    def test_grad_multiple_outs_taps(self):
        n = 5
        rng = np.random.default_rng(utt.fetch_seed())

        vW_in2 = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
        vW = asarrayX(rng.uniform(-0.2, 0.2, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-0.2, 0.2, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-0.2, 0.2, size=(n, 2)))
        v_u2 = asarrayX(rng.uniform(-0.2, 0.2, size=(n + 2, 2)))
        v_x0 = asarrayX(rng.uniform(0.2, 0.2, size=(2,)))

        v_y0 = asarrayX(rng.uniform(size=(3,)))

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = matrix("u2")
        x0 = vector("x0")
        y0 = vector("y0")

        W_in1.tag.test_value = vW_in1
        u1.tag.test_value = v_u1
        u2.tag.test_value = v_u2
        x0.tag.test_value = v_x0
        y0.tag.test_value = v_y0

        def f_rnn_cmpl(u1_t, u2_tm1, u2_t, u2_tp1, x_tm1, y_tm1, y_tm3, W_in1):
            return [
                dot(u1_t, W_in1) + (u2_t + u2_tm1 * u2_tp1) * W_in2 + dot(x_tm1, W),
                (y_tm1 + y_tm3) * dot(x_tm1, W_out),
                dot(u1_t, W_in1),
            ]

        # We change the compute_test_value[_opt] flag to run the
        # assert in Scan.grad() of the new scan input sequence related
        # to outer_mitsot_outs, outer_sitsot_outs and
        # outer_nitsot_outs. This allow to test an old Scan bug.
        with config.change_flags(mode=Mode("cvm", optimizer=None)):
            cost, updates = scan_project_sum(
                f_rnn_cmpl,
                [u1, dict(input=u2, taps=[-1, 0, 1])],
                [x0, dict(initial=y0, taps=[-1, -3]), None],
                W_in1,
                n_steps=None,
                truncate_gradient=-1,
                go_backwards=False,
            )
            params = [u1, u2, x0, y0, W_in1]
            gparams = grad(cost, params)

            cost_fn = function(
                [u1, u2, x0, y0, W_in1],
                cost,
                updates=updates,
                no_default_updates=True,
                allow_input_downcast=True,
            )
            grad_fn = function(
                [u1, u2, x0, y0, W_in1],
                gparams,
                updates=updates,
                no_default_updates=True,
                allow_input_downcast=True,
            )

        num_grad = multiple_outputs_numeric_grad(
            cost_fn, [v_u1, v_u2, v_x0, v_y0, vW_in1]
        )

        analytic_grad = grad_fn(v_u1, v_u2, v_x0, v_y0, vW_in1)
        max_err, max_err_pos = num_grad.max_err(analytic_grad)
        assert max_err <= 1e-2

    @pytest.mark.slow
    def test_grad_multiple_outs_taps_backwards(self):
        """
        This test is special because when the inner-graph compilation is set to
        "fast_compile", and the `Loop` `VM` is used, its inner-graph will
        reallocate storage cells, which is a good test for correct, direct
        storage use in `Scan.perform`.

        TODO: Create a much more direct test.
        """
        n = 5
        rng = np.random.default_rng(utt.fetch_seed())
        vW_in2 = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
        vW = asarrayX(rng.uniform(-0.2, 0.2, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-0.2, 0.2, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-0.2, 0.2, size=(n, 2)))
        v_u2 = asarrayX(rng.uniform(-0.2, 0.2, size=(n + 2, 2)))
        v_x0 = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
        v_y0 = asarrayX(rng.uniform(size=(3,)))

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = matrix("u2")
        x0 = vector("x0")
        y0 = vector("y0")

        def f_rnn_cmpl(u1_t, u2_tm1, u2_t, u2_tp1, x_tm1, y_tm1, y_tm3, W_in1):
            return [
                dot(u1_t, W_in1) + (u2_t + u2_tm1 * u2_tp1) * W_in2 + dot(x_tm1, W),
                (y_tm1 + y_tm3) * dot(x_tm1, W_out),
            ]

        cost, updates = scan_project_sum(
            f_rnn_cmpl,
            [u1, dict(input=u2, taps=[-1, 0, 1])],
            [x0, dict(initial=y0, taps=[-1, -3])],
            W_in1,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=True,
        )
        params = [u1, u2, x0, y0, W_in1]
        gparams = grad(cost, params)
        grad_fn = function(
            [u1, u2, x0, y0, W_in1],
            gparams,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )
        cost_fn = function(
            [u1, u2, x0, y0, W_in1],
            cost,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )

        num_grad = multiple_outputs_numeric_grad(
            cost_fn, [v_u1, v_u2, v_x0, v_y0, vW_in1]
        )

        analytic_grad = grad_fn(v_u1, v_u2, v_x0, v_y0, vW_in1)
        max_err, max_err_pos = num_grad.max_err(analytic_grad)
        assert max_err <= 1e-2

    def test_grad_multiple_outs_some_uncomputable(self):
        rng = np.random.default_rng(utt.fetch_seed())
        vW_in = asarrayX(rng.uniform(-3.0, 3.0, size=(2, 2)))
        v_u = asarrayX(rng.uniform(-3.0, 3.0, size=(5, 2)))
        v_u2 = np.array([1, 3, 4, 6, 8], dtype="int32")
        v_x0 = asarrayX(rng.uniform(-3.0, 3.0, size=(2,)))

        W_in = matrix("win")
        u = matrix("u1")
        u2 = ivector("u2")
        x0 = vector("x0", dtype=config.floatX)

        def f_rnn_cmpl(u_t, u2_t, x_tm1, W_in):
            trng1 = RandomStream(123)
            x_t = (
                at.cast(u2_t, config.floatX)
                + dot(u_t, W_in)
                + x_tm1
                + trng1.uniform(low=-1.1, high=1.1, dtype=config.floatX)
            )
            return x_t, 2 * u2_t

        cost, updates = scan_project_sum(
            f_rnn_cmpl,
            [u, u2],
            [x0, None],
            W_in,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )
        params = [u, u2, x0, W_in]
        gparams = grad(cost, params)
        grad_fn = function(
            [u, u2, x0, W_in],
            gparams,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )
        cost_fn = function(
            [u, u2, x0, W_in],
            cost,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )

        def reset_rng_fn(fn, *args):
            # TODO: Get rid of all this `expanded_inputs` nonsense
            for idx, arg in enumerate(fn.maker.expanded_inputs):
                if arg.value and isinstance(arg.value.data, np.random.Generator):
                    obj = fn.maker.expanded_inputs[idx].value
                    obj.data = np.random.default_rng(123)
                    fn.maker.expanded_inputs[idx].value = obj
            return fn(*args)

        def reset_rng_cost_fn(*args):
            return reset_rng_fn(cost_fn, *args)

        def reset_rng_grad_fn(*args):
            return reset_rng_fn(grad_fn, *args)

        num_grad = multiple_outputs_numeric_grad(
            reset_rng_cost_fn,
            [v_u, v_u2, v_x0, vW_in],
            ndarray_mask=[True, False, True, True],
        )
        analytic_grad = reset_rng_grad_fn(v_u, v_u2, v_x0, vW_in)
        max_err, max_err_pos = num_grad.max_err(analytic_grad)
        assert max_err <= 1e-2

        # Also validate that the mappings outer_inp_from_outer_out and
        # outer_inp_from_inner_inp produce the correct results
        scan_node = list(updates.values())[0].owner

        var_mappings = scan_node.op.get_oinp_iinp_iout_oout_mappings()
        result = var_mappings["outer_inp_from_outer_out"]
        expected_result = {0: 3, 1: 5, 2: 4}
        assert result == expected_result

        result = var_mappings["outer_inp_from_inner_inp"]
        expected_result = {0: 1, 1: 2, 2: 3, 3: 4, 4: 6}
        assert result == expected_result

    def test_grad_multiple_outs_some_truncate(self):
        rng = np.random.default_rng(utt.fetch_seed())
        vW_in = asarrayX(rng.uniform(-0.1, 0.1, size=(2, 2)))
        v_u = asarrayX(rng.uniform(-0.1, 0.1, size=(5, 2)))
        v_x0 = asarrayX(rng.uniform(-0.1, 0.1, size=(2,)))

        W_in = matrix("win")
        u = matrix("u1")
        x0 = vector("x0")
        # trng  = RandomStream(
        #                                               utt.fetch_seed())

        def f_rnn_cmpl(u_t, x_tm1, W_in):
            trng1 = RandomStream(123)
            rnd_nb = trng1.uniform(-0.1, 0.1)
            x_t = dot(u_t, W_in) + x_tm1 + rnd_nb
            x_t = at.cast(x_t, dtype=config.floatX)
            return x_t

        cost, updates = scan_project_sum(
            f_rnn_cmpl,
            u,
            x0,
            W_in,
            n_steps=None,
            truncate_gradient=3,
            go_backwards=False,
        )
        params = [u, x0, W_in]
        gparams = grad(cost, params)

        grad_fn = function(
            [u, x0, W_in],
            gparams,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )
        cost_fn = function(
            [u, x0, W_in],
            cost,
            updates=updates,
            no_default_updates=True,
            allow_input_downcast=True,
        )

        def reset_rng_fn(fn, *args):
            # TODO: Get rid of all this `expanded_inputs` nonsense
            for idx, arg in enumerate(fn.maker.expanded_inputs):
                if arg.value and isinstance(arg.value.data, np.random.Generator):
                    obj = fn.maker.expanded_inputs[idx].value
                    obj.data = np.random.default_rng(123)
                    fn.maker.expanded_inputs[idx].value = obj
            out = fn(*args)
            return out

        def reset_rng_cost_fn(*args):
            return reset_rng_fn(cost_fn, *args)

        def reset_rng_grad_fn(*args):
            return reset_rng_fn(grad_fn, *args)

        multiple_outputs_numeric_grad(reset_rng_cost_fn, [v_u, v_x0, vW_in])

        analytic_grad = reset_rng_grad_fn(v_u, v_x0, vW_in)
        utt.assert_allclose(analytic_grad[0][:2], np.zeros((2, 2)))

    def test_grad_wrt_shared(self):
        x1 = shared(3.0)
        x1.name = "x1"
        x2 = vector("x2")
        y, updates = scan(lambda v: at.cast(v * x1, config.floatX), sequences=x2)
        m = grad(y.sum(), x1)

        f = function([x2], m, allow_input_downcast=True)
        utt.assert_allclose(f([2, 3]), 5)

    def test_inner_grad_wrt_shared(self):
        x1 = scalar("x1")
        x2 = shared(np.array([1, 2, 3, 4, 5]), name="x2")
        K = x2 * x1

        out, updates = scan(
            lambda i, v: grad(K[i], v),
            sequences=at.arange(K.shape[0]),
            non_sequences=x1,
        )
        f = function([x1], out, allow_input_downcast=True)

        assert np.all(f(3.0) != 0.0)

    def test_grad_duplicate_outputs(self):
        """
        This test validates that taking the gradient of a scan, in which
        multiple outputs are the same aesara variable, works.
        """

        def inner_fct(inp1, inp2, inp3):
            total = inp1 + inp2 + inp3
            return total, total

        # Assemble the scan
        seq = matrix()
        out_init = matrix()
        non_seq = vector()

        outputs_info = [None, dict(initial=out_init, taps=[-3])]

        scan_outputs, _ = scan(
            fn=inner_fct,
            sequences=seq,
            outputs_info=outputs_info,
            non_sequences=non_seq,
        )

        # Attempt to take various gradients
        g_output0 = grad(scan_outputs[0].sum(), [seq, out_init, non_seq])
        g_output1 = grad(scan_outputs[1].sum(), [seq, out_init, non_seq])

        # Compile the function
        fct = function([seq, out_init, non_seq], g_output0 + g_output1)

        # Run the function and validate the outputs
        dtype = config.floatX
        seq_value = (
            np.random.default_rng(utt.fetch_seed()).random((10, 3)).astype(dtype)
        )
        out_init_value = (
            np.random.default_rng(utt.fetch_seed()).random((3, 3)).astype(dtype)
        )
        non_seq_value = np.random.default_rng(utt.fetch_seed()).random(3).astype(dtype)

        outputs = fct(seq_value, out_init_value, non_seq_value)

        expected_g_seq = np.array(
            [
                [4, 4, 4],
                [3, 3, 3],
                [3, 3, 3],
                [3, 3, 3],
                [2, 2, 2],
                [2, 2, 2],
                [2, 2, 2],
                [1, 1, 1],
                [1, 1, 1],
                [1, 1, 1],
            ]
        )
        expected_g_out_init = expected_g_seq[:3]
        expected_g_non_seq = np.array([22, 22, 22])

        utt.assert_allclose(outputs[0], expected_g_seq)
        utt.assert_allclose(outputs[1], expected_g_out_init)
        utt.assert_allclose(outputs[2], expected_g_non_seq)
        utt.assert_allclose(outputs[3], expected_g_seq)
        utt.assert_allclose(outputs[4], expected_g_out_init)
        utt.assert_allclose(outputs[5], expected_g_non_seq)

    def test_grad_duplicate_outputs_connection_pattern(self):
        """
        This test checks for a crash in scan.connection_pattern when taking
        the grad of a scan with certain combinations of outputs.
        """

        def inner_fct(inp1, inp2, inp3, inp4, inp5, inp6):
            total = inp1 + inp2 + inp3 + inp4 + inp5 + inp6
            return total, total, total, total, total, total

        # Assemble the scan
        out_init = [vector(), vector(), matrix(), matrix()]

        outputs_info = [
            None,
            None,
            out_init[0],
            out_init[1],
            dict(initial=out_init[2], taps=[-2, -1]),
            dict(initial=out_init[3], taps=[-2, -1]),
        ]

        scan_outputs, _ = scan(fn=inner_fct, outputs_info=outputs_info, n_steps=10)

        grad(scan_outputs[0].sum(), out_init[1])

        # Validate the connection pattern is as it should be
        node = scan_outputs[0].owner
        connection_pattern = node.op.connection_pattern(node)
        expected_connection_pattern = [
            [(j in [1, 2, 3, 4]) for i in range(6)] for j in range(7)
        ]

        assert connection_pattern == expected_connection_pattern

    def test_grad_multiple_seqs_different_nsteps(self):
        """
        This test assures that we clip the sequences to n_steps before
        computing the gradient (so that when we reverse them we actually
        get the right values in
        """
        c = vector("c")
        x = scalar("x")
        _max_coefficients_supported = 1000
        full_range = at.arange(_max_coefficients_supported)
        components, updates = scan(
            fn=lambda coeff, power, free_var: coeff * (free_var**power),
            outputs_info=None,
            sequences=[c, full_range],
            non_sequences=x,
        )
        P = components.sum()
        dP = grad(P, x)
        tf = function([c, x], dP)
        assert tf([1.0, 2.0, -3.0, 4.0], 2.0) == 38

    def test_grad_of_grad_of_state(self):
        """
        This tests ensures that we can compute gradients through cost
        defines in terms of gradients of scan
        """
        c = vector("c")
        x = scalar("x")
        _max_coefficients_supported = 1000
        full_range = at.arange(_max_coefficients_supported)
        components, updates = scan(
            fn=lambda coeff, power, free_var: coeff * (free_var**power),
            outputs_info=None,
            sequences=[c, full_range],
            non_sequences=x,
        )
        P = components.sum()
        dP = grad(P, x).sum()
        ddP = grad(dP, x)
        tf = function([c, x], ddP)
        assert tf([1.0, 2.0, -3.0, 4.0], 2.0) == 42

    def test_grad_multiple_taps_state(self):
        def onestep(xdl, xprev, w):
            xnew = w + xprev
            return xnew

        xinit = tensor3("xinit")
        w = matrix("w")
        (xseq, updates) = scan(
            n_steps=10,
            fn=onestep,
            outputs_info=[dict(initial=xinit, taps=[-4, -1])],
            non_sequences=w,
        )
        loss = (xseq[-1] ** 2).sum()
        cost_fn = function(
            [xinit, w], loss, no_default_updates=True, allow_input_downcast=True
        )

        gw, gx = grad(loss, [w, xinit])
        grad_fn = function([xinit, w], [gx, gw], allow_input_downcast=True)
        rng = np.random.default_rng(utt.fetch_seed())
        # If numbers are small, the gradients with respect to x are small
        # and the numeric differentiation becomes unstable.
        # To fix this issue, ensure we are sampling numbers larger in
        # absolute value than 1.
        v_x = np.array(rng.uniform(1.0, 3.0, size=(5, 2, 2)), dtype=config.floatX)
        # Make some entries negative.
        pos = rng.uniform(0.0, 1, size=(5, 2, 2)) < 0.5
        v_x[pos] = -1 * v_x[pos]
        v_w = np.array(rng.uniform(1.0, 3.0, size=(2, 2)), dtype=config.floatX)
        pos = rng.uniform(0.0, 1.0, size=(2, 2)) < 0.5
        v_w[pos] = -1 * v_w[pos]
        analytic_grad = grad_fn(v_x, v_w)
        num_grad = multiple_outputs_numeric_grad(cost_fn, [v_x, v_w])
        max_err, max_err_pos = num_grad.max_err(analytic_grad)
        assert max_err <= 1e-2

    def test_grad_numeric_shared(self):
        shared_var = shared(np.float32(1.0))

        def inner_fn():
            return [], OrderedDict([(shared_var, shared_var + np.float32(1.0))])

        _, updates = scan(
            inner_fn, n_steps=10, truncate_gradient=-1, go_backwards=False
        )
        cost = list(updates.values())[0]
        g_sh = grad(cost, shared_var)
        fgrad = function([], g_sh)
        assert fgrad() == 1

    def test_R_op(self):
        seed = utt.fetch_seed()
        rng = np.random.default_rng(seed)
        floatX = config.floatX
        v_u = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
        v_W = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
        v_h0 = np.array(rng.uniform(size=(5,)), dtype=floatX)

        v_eu = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
        v_eW = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
        v_eh0 = np.array(rng.uniform(size=(5,)), dtype=floatX)

        def rnn_fn(_u, _y, _W):
            sl_o = tanh(dot(_W, (_u + _y)))
            return sl_o

        u = matrix("U")
        h0 = vector("h0")
        W = matrix("W")

        _u = specify_shape(u, v_u.shape)
        _u.name = "_U"
        _h0 = specify_shape(h0, v_h0.shape)
        _h0.name = "_h0"
        _W = specify_shape(W, v_W.shape)
        _W.name = "_W"

        o, _ = scan(
            rnn_fn, sequences=_u, outputs_info=_h0, non_sequences=_W, name="rnn_fn"
        )
        o = o[-1]
        eu = matrix("eu")
        eh0 = vector("eh0")
        eW = matrix("eW")

        nwo_u = Rop(o, _u, eu)
        nwo_h0 = Rop(o, _h0, eh0)
        nwo_W = Rop(o, _W, eW)
        fn_rop = function(
            [u, h0, W, eu, eh0, eW], [nwo_u, nwo_h0, nwo_W], on_unused_input="ignore"
        )

        n2o_u, _ = scan(
            lambda i, o, u, h0, W, eu: (grad(o[i], u) * eu).sum(),
            sequences=at.arange(o.shape[0]),
            non_sequences=[o, u, h0, W, eu],
            name="jacobU",
        )

        n2o_h0, _ = scan(
            lambda i, o, u, h0, W, eh0: (grad(o[i], h0) * eh0).sum(),
            sequences=at.arange(o.shape[0]),
            non_sequences=[o, u, h0, W, eh0],
            name="jacobh",
        )

        n2o_W, _ = scan(
            lambda i, o, u, h0, W, eW: (grad(o[i], W) * eW).sum(),
            sequences=at.arange(o.shape[0]),
            non_sequences=[o, u, h0, W, eW],
            name="jacobW",
        )

        fn_test = function(
            [u, h0, W, eu, eh0, eW], [n2o_u, n2o_h0, n2o_W], on_unused_input="ignore"
        )

        vnu, vnh0, vnW = fn_rop(v_u, v_h0, v_W, v_eu, v_eh0, v_eW)
        tnu, tnh0, tnW = fn_test(v_u, v_h0, v_W, v_eu, v_eh0, v_eW)

        utt.assert_allclose(vnu, tnu, atol=1e-6)
        utt.assert_allclose(vnh0, tnh0, atol=1e-6)
        utt.assert_allclose(vnW, tnW, atol=1e-6)

    @pytest.mark.slow
    def test_R_op_2(self):
        seed = utt.fetch_seed()
        rng = np.random.default_rng(seed)
        floatX = config.floatX
        v_u = np.array(rng.uniform(size=(3, 5)) - 0.5, dtype=floatX)
        v_W = np.array(rng.uniform(size=(5, 5)) - 0.5, dtype=floatX)
        v_h0 = np.array(rng.uniform(size=(5,)) - 0.5, dtype=floatX)

        v_eu = np.array(rng.uniform(size=(3, 5)) - 0.5, dtype=floatX)
        v_eW = np.array(rng.uniform(size=(5, 5)) - 0.5, dtype=floatX)
        v_eh0 = np.array(rng.uniform(size=(5,)) - 0.5, dtype=floatX)

        def rnn_fn(_u, _y, _W):

            srng = RandomStream(seed)
            tmp_val = (
                _u + _y + srng.uniform(size=v_h0.shape) * np.asarray(1e-6, dtype=floatX)
            )
            sl_o = tanh(dot(_W, tmp_val))
            return sl_o, tmp_val

        u = matrix("U")
        h0 = vector("h0")
        W = matrix("W")

        _u = specify_shape(u, v_u.shape)
        _u.name = "_U"
        _h0 = specify_shape(h0, v_h0.shape)
        _h0.name = "_h0"
        _W = specify_shape(W, v_W.shape)
        _W.name = "_W"

        [o, _], _ = scan(
            rnn_fn,
            sequences=_u,
            outputs_info=[_h0, None],
            non_sequences=_W,
            name="rnn_fn",
        )
        o = o[-1]
        eu = matrix("eu")
        eh0 = vector("eh0")
        eW = matrix("eW")

        nwo_u = Rop(o, _u, eu)
        nwo_h0 = Rop(o, _h0, eh0)
        nwo_W = Rop(o, _W, eW)
        fn_rop = function(
            [u, h0, W, eu, eh0, eW], [nwo_u, nwo_h0, nwo_W, o], on_unused_input="ignore"
        )
        vnu, vnh0, vnW, vno = fn_rop(v_u, v_h0, v_W, v_eu, v_eh0, v_eW)

        n2o_u, _ = scan(
            lambda i, o, u, h0, W, eu: (grad(o[i], u) * eu).sum(),
            sequences=at.arange(o.shape[0]),
            non_sequences=[o, u, h0, W, eu],
            name="jacobU",
        )

        n2o_h0, _ = scan(
            lambda i, o, u, h0, W, eh0: (grad(o[i], h0) * eh0).sum(),
            sequences=at.arange(o.shape[0]),
            non_sequences=[o, u, h0, W, eh0],
            name="jacobh",
        )

        n2o_W, _ = scan(
            lambda i, o, u, h0, W, eW: (grad(o[i], W) * eW).sum(),
            sequences=at.arange(o.shape[0]),
            non_sequences=[o, u, h0, W, eW],
            name="jacobW",
        )

        fn_test = function(
            [u, h0, W, eu, eh0, eW], [n2o_u, n2o_h0, n2o_W, o], on_unused_input="ignore"
        )

        tnu, tnh0, tnW, tno = fn_test(v_u, v_h0, v_W, v_eu, v_eh0, v_eW)
        utt.assert_allclose(vnu, tnu, atol=1e-6)
        utt.assert_allclose(vnh0, tnh0, atol=1e-6)
        utt.assert_allclose(vnW, tnW, atol=2e-6)

    def test_R_op_mitmot(self):
        # this test is a copy paste from the script given by Justin Bayer to
        # reproduce this bug
        # We have 2 parameter groups with the following shapes.
        W1shape = (1, 3)
        W2shape = (3, 3)

        n_pars = 1 * 3 + 3 * 3

        # Allocate big parameter array.
        pars = shared(np.empty(n_pars))

        # Assign slices.
        W1 = pars[:3].reshape(W1shape)
        W2 = pars[3:].reshape(W2shape)

        # Define recurrent model. We are using a model where each input is a
        # tensor
        # of shape (T, B, D) where T is the number of timesteps, B is the
        # number of
        # sequences iterated over in parallel and D is the dimensionality of
        # each
        # item at a timestep.

        inpt = tensor3("inpt")
        target = tensor3("target")

        # Make these flat in order to be able to use dot products instead of
        # tensordot,
        # which is slower.
        inpt_flat = inpt.reshape((inpt.shape[0] * inpt.shape[1], inpt.shape[2]))
        hidden_flat = dot(inpt_flat, W1)
        hidden = hidden_flat.reshape((inpt.shape[0], inpt.shape[1], 3))

        transfer = sigmoid

        hidden_rec, _ = scan(
            lambda x, h_tm1: transfer(dot(h_tm1, W2) + x),
            sequences=hidden,
            outputs_info=[at.zeros_like(hidden[0])],
        )

        hidden_rec.reshape(
            (hidden_rec.shape[0] * hidden_rec.shape[1], hidden_rec.shape[2])
        )

        cost = ((hidden_rec - target) ** 2).mean()
        d_cost_wrt_pars = grad(cost, pars)

        p = dvector()
        Rop(d_cost_wrt_pars, pars, p)


@pytest.mark.skipif(
    not config.cxx, reason="G++ not available, so we need to skip this test."
)
@pytest.mark.parametrize(
    "mode", [Mode(linker="c|py", optimizer=None), Mode(linker="cvm", optimizer=None)]
)
def test_cvm_exception_handling(mode):
    class MyOp(Op):
        def make_node(self, input):
            return Apply(self, [input], [vector()])

        def perform(self, node, inputs, outputs):
            raise Exception("blah")

        # def c_code(self, node, name, inputs, outputs, sub):
        #     fail = sub["fail"]
        #     return f"""
        #     PyErr_SetString(PyExc_Exception, "blah");
        #     {fail};
        #     """

    myop = MyOp()

    def scan_fn():
        return myop(at.as_tensor(1))

    res, _ = scan(scan_fn, n_steps=4, mode=mode)

    res_fn = function([], res, mode=mode)

    with pytest.raises(Exception, match="blah"):
        res_fn()


@pytest.mark.skipif(
    not config.cxx, reason="G++ not available, so we need to skip this test."
)
def test_cython_performance():

    # This implicitly confirms that the Cython version is being used
    from aesara.scan import scan_perform_ext  # noqa: F401

    # Python usually out-performs Aesara below 100 iterations
    N = 200
    n_timeit = 50

    M = -1 / np.arange(1, 11).astype(config.floatX)
    r = np.arange(N * 10).astype(config.floatX).reshape(N, 10)

    def f_py():
        py_out = np.empty((N, 10), dtype=config.floatX)
        py_out[0] = r[0]
        for i in range(1, py_out.shape[0]):
            py_out[i] = r[i] + M * py_out[i - 1]
        return py_out[1:]

    py_res = f_py()

    s_r = at.as_tensor_variable(r, dtype=config.floatX)
    s_y, updates = scan(
        fn=lambda ri, rii, M: ri + M * rii,
        sequences=[s_r[1:]],
        non_sequences=[at.as_tensor_variable(M, dtype=config.floatX)],
        outputs_info=s_r[0],
        mode=Mode(linker="cvm", optimizer="fast_run"),
    )
    assert not updates

    f_cvm = function([], s_y, mode="FAST_RUN")
    f_cvm.trust_input = True

    # Make sure we're actually computing a `Scan`
    assert any(isinstance(node.op, Scan) for node in f_cvm.maker.fgraph.apply_nodes)

    cvm_res = f_cvm()

    # Make sure the results are the same between the two implementations
    assert np.allclose(cvm_res, py_res)

    python_duration = timeit.timeit(lambda: f_py(), number=n_timeit)
    cvm_duration = timeit.timeit(lambda: f_cvm(), number=n_timeit)
    print(f"python={python_duration}, cvm={cvm_duration}")

    assert cvm_duration <= python_duration


@config.change_flags(mode="FAST_COMPILE", compute_test_value="raise")
def test_compute_test_values():
    """Verify that test values can be used with scan."""
    x = vector("x")
    x.tag.test_value = np.ones(3, dtype=config.floatX)

    y = shared(np.arange(3, dtype=config.floatX), name="y")

    z, updates = scan(fn=lambda u, v: u + v, sequences=[x, y])

    assert not updates

    z_grad = grad(z.sum(), x)

    assert np.array_equal(z_grad.tag.test_value, np.r_[1.0, 1.0, 1.0])

    # Use `non_sequences` this time
    y = shared(np.arange(9, dtype=config.floatX).reshape(3, 3), name="y")

    z, updates = scan(fn=lambda u, v: u + v, sequences=[x], non_sequences=[y])

    assert not updates

    z_grad = grad(z.sum(), x)

    assert np.array_equal(z_grad.tag.test_value, np.r_[9.0, 9.0, 9.0])


@pytest.mark.xfail(reason="NominalVariables don't support test values")
def test_compute_test_value_grad():
    """
    See https://groups.google.com/d/msg/theano-users/fAP3i2CbskQ/3OgBf4yjqiQJ
    """
    WEIGHT = np.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1], dtype="float32")

    with config.change_flags(compute_test_value="raise", exception_verbosity="high"):
        W_flat = fvector(name="W")
        W_flat.tag.test_value = WEIGHT
        W = W_flat.reshape((2, 2, 3))

        outputs_mi = at.as_tensor_variable(np.asarray(0, dtype="float32"))
        outputs_mi.tag.test_value = np.asarray(0, dtype="float32")

        def loss_mi(mi, sum_mi, W):
            outputs_ti = at.as_tensor_variable(np.asarray(0, dtype="float32"))
            outputs_ti.tag.test_value = np.asarray(0, dtype="float32")

            def loss_ti(ti, sum_ti, mi, W):
                return W.sum().sum().sum() + sum_ti

            result_ti, _ = scan(
                fn=loss_ti,
                outputs_info=outputs_ti,
                sequences=at.arange(W.shape[1], dtype="int32"),
                non_sequences=[mi, W],
            )
            lossmi = result_ti[-1]
            return sum_mi + lossmi

        result_mi, _ = scan(
            fn=loss_mi,
            outputs_info=outputs_mi,
            sequences=at.arange(W.shape[0], dtype="int32"),
            non_sequences=[W],
        )

        loss = result_mi[-1]
        grad(loss, W_flat)


@pytest.mark.xfail(reason="NominalVariables don't support test values")
def test_compute_test_value_grad_cast():
    """Test for test values when variables have to be casted.

    See https://groups.google.com/d/topic/theano-users/o4jK9xDe5WI/discussion
    """
    with config.change_flags(compute_test_value="raise"):
        h = matrix("h")
        h.tag.test_value = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=config.floatX)

        w = shared(
            np.random.default_rng(utt.fetch_seed())
            .random((4, 3))
            .astype(config.floatX),
            name="w",
        )

        outputs, _ = scan(
            lambda i, h, w: (dot(h[i], w), i),
            outputs_info=[None, 0],
            non_sequences=[h, w],
            n_steps=3,
        )

        grad(outputs[0].sum(), w)


def test_constant_folding_n_steps():
    # The following code used to crash at revision 2060b8f, in the constant
    # folding optimization step.
    res, _ = scan(
        lambda x: x * 2,
        outputs_info=at.ones(()),
        # The constant `n_steps` was causing the crash.
        n_steps=10,
    )
    with config.change_flags(on_opt_error="raise"):
        function([], res)()


def test_outputs_taps_check():
    """Checks that errors are raised with bad output_info taps."""
    x = fvector("x")
    y = fvector("y")

    def f(x, y):
        return [x]

    outputs_info = {"initial": y, "taps": [0]}
    with pytest.raises(ValueError):
        scan(f, x, outputs_info)
    outputs_info = {"initial": y, "taps": [-1, -1]}
    with pytest.raises(ValueError):
        scan(f, x, outputs_info)


def test_inconsistent_broadcast_error():
    x = tensor3()
    initial_x = at.constant(np.zeros((1, 10)))
    y, updates = scan(
        fn=lambda x, prev_x: x + prev_x,
        sequences=x,
        outputs_info=[dict(initial=initial_x)],
    )
    # Error, because the broadcast patterns are inconsistent.
    with pytest.raises(TypeError):
        grad(y.sum(), x)


def test_missing_input_error():
    c = shared(0.0)
    inc = scalar("inc")

    def count_up():
        return at.zeros(()), {c: c + inc}

    with pytest.raises(MissingInputError):
        _, updates = scan(count_up, n_steps=20)


class TestGradUntil:
    def setup_method(self):
        self.x = vector(name="x")
        self.threshold = scalar(name="threshold", dtype="int64")
        self.seq = np.arange(15, dtype=config.floatX)
        self.numpy_output = self.seq[:7] ** 2
        z = np.zeros(8, dtype=config.floatX)
        self.numpy_gradient = 2 * np.concatenate([self.seq[:7], z], axis=0)

    def test_grad_until(self):
        r, _ = scan(
            lambda x, u: (x * x, until(x > u)),
            sequences=self.x,
            non_sequences=[self.threshold],
        )
        g = grad(r.sum(), self.x)
        f = function([self.x, self.threshold], [r, g])
        aesara_output, aesara_gradient = f(self.seq, 5)

        utt.assert_allclose(aesara_output, self.numpy_output)
        utt.assert_allclose(aesara_gradient, self.numpy_gradient)

    def test_grad_until_ndim_greater_one(self):
        def tile_array(inp):
            n_cols = 5
            return np.tile(inp.reshape((-1, 1)), (1, n_cols))

        X = matrix(name="x")
        arr = tile_array(self.seq)
        r, _ = scan(
            lambda x, u: (x * x, until(at_all(x > u))),
            sequences=X,
            non_sequences=[self.threshold],
        )
        g = grad(r.sum(), X)
        f = function([X, self.threshold], [r, g])
        aesara_output, aesara_gradient = f(arr, 5)

        utt.assert_allclose(aesara_output, tile_array(self.numpy_output))
        utt.assert_allclose(aesara_gradient, tile_array(self.numpy_gradient))

    def test_grad_until_and_truncate(self):
        n = 3
        r, _ = scan(
            lambda x, u: (x * x, until(x > u)),
            sequences=self.x,
            non_sequences=[self.threshold],
            truncate_gradient=n,
        )
        g = grad(r.sum(), self.x)
        f = function([self.x, self.threshold], [r, g])
        aesara_output, aesara_gradient = f(self.seq, 5)

        self.numpy_gradient[: 7 - n] = 0
        utt.assert_allclose(aesara_output, self.numpy_output)
        utt.assert_allclose(aesara_gradient, self.numpy_gradient)

    def test_grad_until_and_truncate_sequence_taps(self):
        n = 3
        r, _ = scan(
            lambda x, y, u: (x * y, until(y > u)),
            sequences=dict(input=self.x, taps=[-2, 0]),
            non_sequences=[self.threshold],
            truncate_gradient=n,
        )
        g = grad(r.sum(), self.x)
        f = function([self.x, self.threshold], [r, g])
        aesara_output, aesara_gradient = f(self.seq, 6)

        # Gradient computed by hand:
        numpy_grad = np.array([0, 0, 0, 5, 6, 10, 4, 5, 0, 0, 0, 0, 0, 0, 0])
        numpy_grad = numpy_grad.astype(config.floatX)
        utt.assert_allclose(aesara_gradient, numpy_grad)


def test_mintap_onestep():
    seq = ivector("seq")

    def accum(seq_t, prev_sum):
        new_sum = prev_sum + seq_t
        return new_sum

    rs, updates = scan(
        fn=accum, sequences={"input": seq, "taps": [2]}, outputs_info=0, n_steps=1
    )

    f = function(inputs=[seq], outputs=rs)
    res = f(np.arange(20).astype("int32"))
    assert res == 2


def test_inner_get_vector_length():
    """Make sure we can handle/preserve fixed shape terms when cloning the body of a `Scan`."""

    rng_at = RandomStream()

    s1 = lscalar("s1")
    s2 = lscalar("s2")
    size_at = at.as_tensor([s1, s2])

    def scan_body(size):
        # `size` will be cloned and replaced with an ownerless `TensorVariable`.
        # This will cause `RandomVariable.infer_shape` to fail, because it expects
        # `get_vector_length` to work on all `size` arguments.
        return rng_at.normal(0, 1, size=size)

    res, _ = scan(
        scan_body,
        non_sequences=[size_at],
        n_steps=10,
        strict=True,
    )

    assert isinstance(res.owner.op, Scan)

    # Make sure the `size` in `scan_body` is a plain `Variable` instance
    # carrying no information with which we can derive its length
    size_clone = res.owner.op.inner_inputs[1]
    assert size_clone.owner is None

    # Make sure the cloned `size` maps to the original `size_at`
    inner_outer_map = res.owner.op.get_oinp_iinp_iout_oout_mappings()
    outer_input_idx = inner_outer_map["outer_inp_from_inner_inp"][1]
    original_size = res.owner.inputs[outer_input_idx]
    assert original_size == size_at

    with config.change_flags(on_opt_error="raise", on_shape_error="raise"):
        res_fn = function([size_at], res.shape)

    assert np.array_equal(res_fn((1, 2)), (10, 1, 2))

    # Second case has an empty size non-sequence
    size_at = at.as_tensor([], dtype=np.int64)

    res, _ = scan(
        scan_body,
        non_sequences=[size_at],
        n_steps=10,
        strict=True,
    )

    assert isinstance(res.owner.op, Scan)
    with config.change_flags(on_opt_error="raise", on_shape_error="raise"):
        res_fn = function([], res.shape)

    assert np.array_equal(res_fn(), (10,))

    # Third case has a constant size non-sequence
    size_at = at.as_tensor([3], dtype=np.int64)

    res, _ = scan(
        scan_body,
        non_sequences=[size_at],
        n_steps=10,
        strict=True,
    )

    assert isinstance(res.owner.op, Scan)
    with config.change_flags(on_opt_error="raise", on_shape_error="raise"):
        res_fn = function([], res.shape)

    assert np.array_equal(res_fn(), (10, 3))


@config.change_flags(mode=Mode("cvm", None))
def test_profile_info():

    from aesara.scan.utils import ScanProfileStats

    z, updates = scan(fn=lambda u: u + 1, sequences=[at.arange(10)], profile=True)

    assert isinstance(z.owner.op, Scan)
    fn = z.owner.op.fn

    assert isinstance(fn.profile, ScanProfileStats)
    assert fn.profile.name == "scan_fn"

    # Set the `ScanProfileStats` name
    z, updates = scan(
        fn=lambda u: u + 1, sequences=[at.arange(10)], profile="profile_name"
    )

    assert isinstance(z.owner.op, Scan)
    fn = z.owner.op.fn

    assert isinstance(fn.profile, ScanProfileStats)
    assert fn.profile.name == "profile_name"

    # Use an existing profile object
    profile = fn.profile
    z, updates = scan(fn=lambda u: u + 1, sequences=[at.arange(10)], profile=profile)

    assert isinstance(z.owner.op, Scan)
    fn = z.owner.op.fn

    assert fn.profile is profile

    assert not profile.apply_time
    assert profile.callcount == 0
    assert profile.nbsteps == 0
    assert profile.call_time == 0.0
    assert fn.vm.call_times == [0.0]
    assert fn.vm.call_counts == [0]

    z_fn = function([], z)

    _ = z_fn()

    # assert profile.vm_call_time > 0
    assert profile.callcount == 1
    assert profile.nbsteps == 10
    assert profile.call_time > 0

    # Confirm that `VM.update_profile` was called
    assert profile.apply_time
    assert fn.vm.call_times == [0.0]
    assert fn.vm.call_counts == [0]


class TestExamples:
    """Miscellaneous example-based tests with unnecessarily complicated setups and/or no background information.

    TODO FIXME: These tests need to be rewritten, explained, and/or removed.
    """

    def test_gibbs_chain(self):
        rng = np.random.default_rng(utt.fetch_seed())
        v_W = np.array(rng.random((20, 30)) - 0.5, dtype="float32")
        v_vsample = np.array(
            rng.binomial(
                1,
                0.5,
                size=(3, 20),
            ),
            dtype="float32",
        )
        v_bvis = np.array(rng.random((20)) - 0.5, dtype="float32")
        v_bhid = np.array(rng.random((30)) - 0.5, dtype="float32")
        W = shared(v_W, "vW")
        bhid = shared(v_bhid, "vbhid")
        bvis = shared(v_bvis, "vbvis")
        vsample = matrix(dtype="float32")
        trng = RandomStream(utt.fetch_seed())

        def f(vsample_tm1):
            hmean_t = sigmoid(dot(vsample_tm1, W) + bhid)
            hsample_t = at.cast(
                trng.binomial(1, hmean_t, size=hmean_t.shape), dtype="float32"
            )
            vmean_t = sigmoid(dot(hsample_t, W.T) + bvis)
            return at.cast(
                trng.binomial(1, vmean_t, size=vmean_t.shape), dtype="float32"
            )

        aesara_vsamples, updates = scan(
            f, [], vsample, [], n_steps=10, truncate_gradient=-1, go_backwards=False
        )

        my_f = function(
            [vsample], aesara_vsamples[-1], updates=updates, allow_input_downcast=True
        )

        rng_seed = np.random.SeedSequence(utt.fetch_seed())
        (rng_seed_1, rng_seed_2) = rng_seed.spawn(2)
        nrng1 = trng.rng_ctor(rng_seed_1)
        nrng2 = trng.rng_ctor(rng_seed_2)

        def numpy_implementation(vsample):
            for idx in range(10):
                hmean = 1.0 / (1.0 + np.exp(-(np.dot(vsample, v_W) + v_bhid)))
                hsample = np.array(
                    nrng1.binomial(1, hmean, size=hmean.shape), dtype="float32"
                )
                vmean = 1.0 / (1.0 + np.exp(-(np.dot(hsample, v_W.T) + v_bvis)))
                vsample = np.array(
                    nrng2.binomial(1, vmean, size=vmean.shape), dtype="float32"
                )

            return vsample

        t_result = my_f(v_vsample)
        n_result = numpy_implementation(v_vsample)
        utt.assert_allclose(t_result, n_result)

    def test_reordering(self):
        """Test re-ordering of inputs.

        some rnn with multiple outputs and multiple inputs; other
        dimension instead of scalars/vectors

        TODO FIXME: Reformulate this test so that it doesn't use an
        unnecessarily complicated graph (i.e. an entire RNN model)
        """
        rng = np.random.default_rng(utt.fetch_seed())

        vW_in2 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
        vW = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-0.5, 0.5, size=(3, 2)))
        v_u2 = asarrayX(rng.uniform(-0.5, 0.5, size=(3,)))
        v_x0 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
        v_y0 = asarrayX(rng.uniform(size=(3,)))

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = vector("u2")
        x0 = vector("x0")
        y0 = vector("y0")

        def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, y_tm3, W_in1):
            return [
                y_tm3 + 1,
                y_tm3 + 2,
                dot(u1_t, W_in1) + u2_t * W_in2 + dot(x_tm1, W),
                y_tm1 + dot(x_tm1, W_out),
            ]

        outputs, updates = scan(
            f_rnn_cmpl,
            [u1, u2],
            [None, None, x0, dict(initial=y0, taps=[-1, -3])],
            W_in1,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        f4 = function(
            [u1, u2, x0, y0, W_in1], outputs, updates=updates, allow_input_downcast=True
        )

        # compute the values in numpy
        v_x = np.zeros((3, 2), dtype=config.floatX)
        v_y = np.zeros((3,), dtype=config.floatX)
        v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + np.dot(v_x0, vW)
        v_y[0] = np.dot(v_x0, vWout) + v_y0[2]
        for i in range(1, 3):
            v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + np.dot(v_x[i - 1], vW)
            v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]

        (aesara_dump1, aesara_dump2, aesara_x, aesara_y) = f4(
            v_u1, v_u2, v_x0, v_y0, vW_in1
        )

        utt.assert_allclose(aesara_x, v_x)
        utt.assert_allclose(aesara_y, v_y)

    def test_scan_as_tensor_on_gradients(self):
        to_scan = dvector("to_scan")
        seq = dmatrix("seq")
        f1 = dscalar("f1")

        def scanStep(prev, seq, f1):
            return prev + f1 * seq

        scanned, _ = scan(
            fn=scanStep, sequences=[seq], outputs_info=[to_scan], non_sequences=[f1]
        )
        function(inputs=[to_scan, seq, f1], outputs=scanned, allow_input_downcast=True)

        t_grad = grad(scanned.sum(), wrt=[to_scan, f1], consider_constant=[seq])
        function(inputs=[to_scan, seq, f1], outputs=t_grad, allow_input_downcast=True)

    def caching_nsteps_by_scan_op(self):
        W = matrix("weights")
        initial = vector("initial")
        inpt = matrix("inpt")

        def one_step(x_t, h_tm1, W):
            expr = dot(h_tm1, W) + x_t
            return expr

        expr, _ = scan(
            fn=one_step, sequences=[inpt], outputs_info=[initial], non_sequences=[W]
        )

        v1 = shared(np.ones(5, dtype=config.floatX))
        v2 = shared(np.ones((5, 5), dtype=config.floatX))
        shapef = function([W], expr, givens=OrderedDict([(initial, v1), (inpt, v2)]))
        # First execution to cache n_steps
        shapef(np.ones((5, 5), dtype=config.floatX))

        cost = expr.sum()
        d_cost_wrt_W = grad(cost, [W])
        f = function(
            [W, inpt],
            d_cost_wrt_W,
            givens=OrderedDict([(initial, shared(np.zeros(5)))]),
        )

        rval = np.asarray([[5187989] * 5] * 5, dtype=config.floatX)
        arg1 = np.ones((5, 5), dtype=config.floatX)
        arg2 = np.ones((10, 5), dtype=config.floatX)
        utt.assert_allclose(f(arg1, arg2), rval)

    def test_use_scan_direct_output(self):
        """
        This test looks for a crash that happened when directly using the
        recurrent output of a scan node instead of taking the result
        returned by the scan() function
        """

        # Obtain a compilation mode that will cause the test to fail if an
        # exception occurs in the optimization process
        with config.change_flags(on_opt_error="raise"):
            mode = get_default_mode()

        x = scalar()
        seq = vector()
        outputs_info = [x, at.zeros_like(x)]
        (out1, out2), updates = scan(
            lambda a, b, c: (a + b, b + c),
            sequences=seq,
            outputs_info=outputs_info,
            mode=mode,
        )

        # Obtain a reference to the scan outputs before the subtensor and
        # compile a function with them as outputs
        assert isinstance(out1.owner.op, Subtensor)
        assert isinstance(out2.owner.op, Subtensor)
        out1_direct = out1.owner.inputs[0]
        out2_direct = out2.owner.inputs[0]
        fct = function([x, seq], [out1_direct[:-1], out2_direct[:-1]], mode=mode)

        # Test the function to ensure valid outputs
        init_value = 5.0
        seq_value = np.arange(4, dtype=config.floatX)
        output1, output2 = fct(init_value, seq_value)

        expected_output1 = [init_value]
        expected_output2 = [0]
        for i in seq_value[:-1]:
            expected_output2.append(expected_output1[-1] + expected_output2[-1])
            expected_output1.append(expected_output1[-1] + i)

        utt.assert_allclose(output1, expected_output1)
        utt.assert_allclose(output2, expected_output2)

    def test_use_scan_direct_output2(self):
        """
        This test looks for a crash that happened when directly using the
        recurrent output of a scan node associated with a state with a
        state with broadcastable dimensions
        """

        x = dcol()
        seq = dcol()
        outputs_info = [x, at.zeros_like(x)]
        (out1, out2), updates = scan(
            lambda a, b, c: (a + b, a + c), sequences=seq, outputs_info=outputs_info
        )

        # Obtain a reference to the scan outputs before the subtensor and
        # compile a function with them as outputs
        assert isinstance(out1.owner.op, Subtensor)
        assert isinstance(out2.owner.op, Subtensor)
        out1_direct = out1.owner.inputs[0]
        out2_direct = out2.owner.inputs[0]
        fct = function([x, seq], [out1_direct, out2_direct])

        # Test that the function returns valid outputs
        x_val = np.arange(0, 4)[:, None]
        seq_val = np.arange(4, 8)[:, None]

        out1, out2 = fct(x_val, seq_val)

        expected_out1 = np.zeros((5, 4, 1))
        expected_out2 = np.zeros((5, 4, 1))
        for i in range(4):
            expected_out2[i + 1] = expected_out2[i] + seq_val[i]
        for i in range(5):
            expected_out1[i] = expected_out2[i] + x_val

        utt.assert_allclose(out1, expected_out1)
        utt.assert_allclose(out2, expected_out2)

    def test_same(self):
        x = fmatrix("x")

        mem_val = np.zeros((2,), dtype="float32")
        memory = shared(mem_val)
        W = shared(
            np.random.default_rng(utt.fetch_seed()).random((5, 2)).astype("float32")
        )

        def f(inp, mem):
            i = at.join(0, inp, mem)
            d = dot(i, W)
            return d, d

        outs, updts = scan(
            f, sequences=[x], non_sequences=[], outputs_info=[None, memory]
        )

        f = function([x], outs[0])
        f2 = function([x], outs[1])

        x_val = np.random.default_rng(utt.fetch_seed()).random((4, 3)).astype("float32")

        f_vals = f(x_val)
        memory.set_value(mem_val)
        f2_vals = f2(x_val)
        utt.assert_allclose(f_vals, f2_vals)

    def test_eliminate_seqs(self):
        U = vector("U")
        sh = shared(asarrayX(2.0))
        x1 = vector("x1")
        x2 = scalar("x2")

        def rec_fn(*args):
            u_t = args[0]
            return [
                (u_t + 1, u_t + 2, u_t + 3),  # mitsot  # sitsot  # nitsot
                {sh: u_t + 4},
            ]  # shared

        [X1, X2, X3], updates = scan(
            rec_fn,
            U,
            [dict(initial=x1, taps=[-1, -3]), x2, None],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )
        f = function(
            [U, x1, x2],
            [X1, X2, X3],
            updates=updates,
            mode=Mode(linker="py"),
            allow_input_downcast=True,
        )
        rng = np.random.default_rng(utt.fetch_seed())
        v_u = asarrayX(rng.uniform(size=(5,)))
        outs = f(v_u, [0, 0, 0], 0)
        utt.assert_allclose(outs[0], v_u + 1)
        utt.assert_allclose(outs[1], v_u + 2)
        utt.assert_allclose(outs[2], v_u + 3)
        utt.assert_allclose(sh.get_value(), v_u[-1] + 4)

    def test_eliminate_nonseqs(self):
        W = scalar("W")
        sh = shared(asarrayX(2.0))
        x1 = vector("x1")
        x2 = scalar("x2")

        def rec_fn(*args):
            w = args[-1]
            return [
                (w + 1.0, w + 2.0, w + 3.0),  # mitsot  # sitsot  # nitsot
                {sh: w + 4.0},
            ]  # shared

        [X1, X2, X3], updates = scan(
            rec_fn,
            [],
            [dict(initial=x1, taps=[-1, -3]), x2, None],
            W,
            n_steps=5,
            truncate_gradient=-1,
            go_backwards=False,
        )
        f = function(
            [W, x1, x2],
            [X1, X2, X3],
            updates=updates,
            mode=Mode(linker="py"),
            allow_input_downcast=True,
        )
        rng = np.random.default_rng(utt.fetch_seed())
        v_w = asarrayX(rng.uniform())
        outs = f(v_w, [0, 0, 0], 0)
        utt.assert_allclose(outs[0], v_w + 1)
        utt.assert_allclose(outs[1], v_w + 2)
        utt.assert_allclose(outs[2], v_w + 3)
        utt.assert_allclose(sh.get_value(), v_w + 4)

    def test_seq_tap_bug_jeremiah(self):
        inp = np.arange(10).reshape(-1, 1).astype(config.floatX)
        exp_out = np.zeros((10, 1)).astype(config.floatX)
        exp_out[4:] = inp[:-4]

        def onestep(x, x_tm4):
            return x, x_tm4

        seq = matrix()
        initial_value = shared(np.zeros((4, 1), dtype=config.floatX))
        outputs_info = [OrderedDict([("initial", initial_value), ("taps", [-4])]), None]
        results, updates = scan(fn=onestep, sequences=seq, outputs_info=outputs_info)

        f = function([seq], results[1])
        assert np.all(exp_out == f(inp))

    def test_shared_borrow(self):
        """
        This tests two things. The first is a bug occurring when scan wrongly
        used the borrow flag. The second thing it that Scan's infer_shape()
        method will be able to remove the Scan node from the graph in this
        case.
        """

        inp = np.arange(10).reshape(-1, 1).astype(config.floatX)
        exp_out = np.zeros((10, 1)).astype(config.floatX)
        exp_out[4:] = inp[:-4]

        def onestep(x, x_tm4):
            return x, x_tm4

        seq = matrix()
        initial_value = shared(np.zeros((4, 1), dtype=config.floatX))
        outputs_info = [OrderedDict([("initial", initial_value), ("taps", [-4])]), None]
        results, _ = scan(fn=onestep, sequences=seq, outputs_info=outputs_info)
        sharedvar = shared(np.zeros((1, 1), dtype=config.floatX))
        updates = OrderedDict([(sharedvar, results[0][-1:])])

        f = function([seq], results[1], updates=updates)

        # This fails if scan uses wrongly the borrow flag
        assert np.all(exp_out == f(inp))

        # This fails if Scan's infer_shape() is unable to remove the Scan
        # node from the graph.
        f_infershape = function([seq], results[1].shape, mode="FAST_RUN")
        scan_nodes_infershape = scan_nodes_from_fct(f_infershape)
        assert len(scan_nodes_infershape) == 0

    def test_memory_reuse_with_outputs_as_inputs(self):
        """
        Test the memory pre-allocation feature in scan for the following cases:
         - An output of the inner graph is also an input of the inner graph
         - An output of the inner graph is not an input in the unoptimized
           graph but it could becomes the case in the optimized graph due to
           the optimizations.
         - An output of the inner graph is obtained through a view op on an
           input of the inner graph and the view op is removed by the
           optimization process
         - An output of the inner graph is obtained through a view op on an
           input of the inner graph and the view op is NOT removed by the
           optimization process
         - An output of the inner graph is not obtained through any of the
           previously mentioned cases (standard case)

        """

        def inner_fn(tap_m3, tap_m2, tap_m1):
            return (
                tap_m2,
                (tap_m1 * 1),
                disconnected_grad(tap_m2),
                assert_op(tap_m2, 1),
                tap_m3 + tap_m2 + tap_m1,
            )

        init = matrix()
        outputs_info = [None, None, None, None, dict(initial=init, taps=[-3, -2, -1])]

        out, _ = scan(inner_fn, outputs_info=outputs_info, n_steps=3)
        fct = function([init], out)

        # Compare obtained outputs with expected outputs
        outputs = fct(np.arange(9, dtype=config.floatX).reshape(3, 3))

        states = np.array(
            [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 12, 15], [18, 23, 28], [33, 42, 51]],
            dtype=config.floatX,
        )
        expected_outputs = [
            states[1:4],
            states[2:5],
            states[1:4],
            states[1:4],
            states[3:6],
        ]

        utt.assert_allclose(outputs, expected_outputs)

    @pytest.mark.slow
    def test_hessian_bug_grad_grad_two_scans(self):
        # Bug reported by Bitton Tenessi
        # NOTE : The test to reproduce the bug reported by Bitton Tenessi
        # was modified from its original version to be faster to run.

        W = fvector(name="W")
        n_steps = iscalar(name="Nb_steps")

        def loss_outer(sum_outer, W):
            def loss_inner(sum_inner, W):

                return sum_inner + (W**2).sum()

            result_inner, _ = scan(
                fn=loss_inner,
                outputs_info=at.as_tensor_variable(np.asarray(0, dtype=np.float32)),
                non_sequences=[W],
                n_steps=1,
            )
            return sum_outer + result_inner[-1]

        # Also test return_list for that case.
        result_outer, _ = scan(
            fn=loss_outer,
            outputs_info=at.as_tensor_variable(np.asarray(0, dtype=np.float32)),
            non_sequences=[W],
            n_steps=n_steps,
            return_list=True,
        )

        cost = result_outer[0][-1]
        H = hessian(cost, W)
        print(".", file=sys.stderr)
        f = function([W, n_steps], H)
        f(np.ones((8,), dtype="float32"), 1)

    def test_grad_connectivity_matrix(self):
        def inner_fn(x_tm1, y_tm1, z_tm1):
            x_tm1.name = "x"
            y_tm1.name = "y"
            z_tm1.name = "z"
            return x_tm1**2, y_tm1, x_tm1 + 1

        x0 = vector("X")
        y0 = vector("y0")
        z0 = vector("Z")
        [x, y, z], _ = scan(inner_fn, outputs_info=[x0, y0, z0], n_steps=10)
        cost = (x + y + z).sum()

        grad(cost, x0)  # defined
        grad(cost, y0)  # defined

        with pytest.raises(ValueError):
            grad(cost, z0)
        cost = x.sum()
        with pytest.raises(ValueError):
            grad(cost, y0)

    def test_disconnected_gradient(self):
        v = vector("v")
        m = matrix("m")
        u0 = at.zeros((7,))

        [u, m2], _ = scan(lambda _, u: [u, v], sequences=m, outputs_info=[u0, None])
        # This used to raise an exception with older versions because for a
        # disconnected gradient a non disconnected type was returned
        grad((m * m2).sum(), v)

    def test_disconnected_gradient2(self):
        v = vector("v")
        m = matrix("m")
        u0 = at.zeros((7,))

        [u, m2], _ = scan(
            lambda x, u: [x + u, u + v], sequences=m, outputs_info=[u0, None]
        )
        # This used to raise an exception with older versions because
        # scan could not detect the connection between `m2` and `x`
        grad(m2.sum(), m)

    def test_disconnected_gradient3(self):
        """
        This tests for a crash that would occur sometimes when taking the
        gradient through a scan with a non-recurrent output which would
        receive a disconnected gradient
        """

        v = dvector("v")

        def step(seq):
            out1 = seq + 1
            out2 = out1 + 1
            return out1, out2

        [out1, out2], _ = scan(step, sequences=v)
        gv = grad(out2.sum(), [v])
        f = function([v], gv)

        # Ensure the output of the function is valid
        output = f(np.random.default_rng(utt.fetch_seed()).random(5))
        utt.assert_allclose(output, np.ones(5))

    def test_grad_bug_disconnected_input(self):
        W = shared(np.zeros((3, 3)), name="W")
        v = ivector(name="v")
        y, _ = scan(lambda i, W: W[i], sequences=v, outputs_info=None, non_sequences=W)

        # This used to raise an exception
        f = function([v], grad(y.sum(), W))
        utt.assert_allclose(f([1, 2]), [[0, 0, 0], [1, 1, 1], [1, 1, 1]])

    def test_grad_find_input(self):
        w = shared(np.array(0, dtype="float32"), name="w")
        init = fscalar("init")

        out, _ = scan(
            fn=lambda prev: w,
            outputs_info=init,
            n_steps=2,
        )
        grad(out[-1], w)

    def test_using_taps_input_output(self):
        """
        simple rnn, one input, one state, weights for each; input/state are
        vectors, weights are scalars; using shared variables and past
        taps (sequences and outputs)
        """
        rng = np.random.default_rng(utt.fetch_seed())
        vW = asarrayX(rng.uniform())
        vW_in = asarrayX(rng.uniform())
        vu = asarrayX(rng.uniform(-5.0, 5.0, size=(4,)))
        vx0 = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))

        u = vector("u")
        x0 = vector("x0")
        W_in = shared(vW_in, name="w_in")
        W = shared(vW, name="w")

        def f_rnn_shared(u_tm2, x_tm1, x_tm2):
            return u_tm2 * W_in + x_tm1 * W + x_tm2

        outputs, updates = scan(
            f_rnn_shared,
            dict(input=u, taps=-2),
            dict(initial=x0, taps=[-1, -2]),
            [],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        f7 = function([u, x0], outputs, updates=updates, allow_input_downcast=True)
        aesara_out = f7(vu, vx0)

        # compute output in numpy
        # a bit of explaining:
        # due to the definition of sequences taps in scan, v_0[0] is
        # actually v_0[-2], and v_0[1] is v_0[-1]. The values v_0[2]
        # and v_0[3] do not get used ( because you do not use v_0[t]
        # in scan) which might seem strange, but then again why not use
        # v_0[t] instead of v_0[t-2] in a real application ??
        # also vx0[0] corresponds to vx0[-2], vx0[1] to vx0[-1]
        numpy_out = np.zeros((2,))
        numpy_out[0] = vu[0] * vW_in + vx0[1] * vW + vx0[0]
        numpy_out[1] = vu[1] * vW_in + numpy_out[0] * vW + vx0[1]
        utt.assert_allclose(numpy_out, aesara_out)

    def test_past_future_taps_shared(self):
        """
        simple rnn, one input, one state, weights for each; input/state are
        vectors, weights are scalars; using shared variables and past
        taps (sequences and outputs) and future taps for sequences
        """
        rng = np.random.default_rng(utt.fetch_seed())
        vW = asarrayX(rng.uniform())
        vW_in = asarrayX(rng.uniform())
        vu = asarrayX(rng.uniform(-5.0, 5.0, size=(6,)))
        vx0 = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))

        u = vector("u")
        x0 = vector("x0")
        W_in = shared(vW_in, name="w_in")
        W = shared(vW, name="w")

        def f_rnn_shared(u_tm2, u_tp2, x_tm1, x_tm2):
            return (u_tm2 + u_tp2) * W_in + x_tm1 * W + x_tm2

        output, updates = scan(
            f_rnn_shared,
            dict(input=u, taps=[-2, 2]),
            dict(initial=x0, taps=[-1, -2]),
            [],
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        f8 = function([u, x0], output, updates=updates, allow_input_downcast=True)
        aesara_out = f8(vu, vx0)
        # compute output in numpy
        numpy_out = np.zeros(2)
        # think of vu[0] as vu[-2], vu[4] as vu[2]
        # and vx0[0] as vx0[-2], vx0[1] as vx0[-1]
        numpy_out[0] = (vu[0] + vu[4]) * vW_in + vx0[1] * vW + vx0[0]
        numpy_out[1] = (vu[1] + vu[5]) * vW_in + numpy_out[0] * vW + vx0[1]
        utt.assert_allclose(numpy_out, aesara_out)

    def test_generator_one_output_scalar(self):
        """
        generator network, only one output , type scalar ; no sequence or
        non sequence arguments
        """

        def f_pow2(x_tm1):
            return 2 * x_tm1

        state = scalar("state")
        n_steps = iscalar("nsteps")
        # Test return_list at the same time.
        output, updates = scan(
            f_pow2,
            [],
            state,
            [],
            n_steps=n_steps,
            truncate_gradient=-1,
            return_list=True,
            go_backwards=False,
        )
        my_f = function(
            [state, n_steps], output, updates=updates, allow_input_downcast=True
        )

        rng = np.random.default_rng(utt.fetch_seed())
        state = rng.uniform()
        steps = 5

        numpy_values = np.array([state * (2 ** (k + 1)) for k in range(steps)])
        aesara_values = my_f(state, steps)
        utt.assert_allclose(numpy_values, aesara_values[0])

    def test_default_value_broadcasted(self):
        def floatx(X):
            return np.asarray(X, dtype=config.floatX)

        def init_weights(shape, name):
            return shared(
                floatx(np.random.default_rng(utt.fetch_seed()).random(shape) * 0.1),
                name,
            )

        X = matrix("X")
        in_size = 2
        out_size = 4
        W_x = init_weights((in_size, out_size), "W_x")

        def _active(x, pre_h):
            x = reshape(x, (1, in_size))
            pre_h = dot(x, W_x)
            return pre_h

        value, scan_updates = scan(
            _active,
            sequences=X,
            outputs_info=[at.alloc(floatx(0.0), 1, out_size)],
        )
        cost = mean(value)
        gW_x = grad(cost, W_x)
        updates = [(W_x, W_x - 0.1 * gW_x)]
        f = function([X], outputs=cost, updates=updates)
        f(np.random.default_rng(utt.fetch_seed()).random((10, in_size)).astype(X.dtype))

    def test_condition_hidden_inp(self):
        max_value = scalar("max_value")
        n_steps = iscalar("n_steps")

        def accum(prev_value, step):
            new_value = prev_value + step
            new_step = step + 1
            condition = until(new_value > max_value)
            return [new_value, new_step], condition

        rs, updates = scan(fn=accum, outputs_info=[0, 0], n_steps=n_steps)

        f = function(inputs=[max_value, n_steps], outputs=rs)

        _sum, total_steps = f(100, 100)

    @pytest.mark.skip(
        reason="This test fails because not typed outputs_info "
        "are always given the smallest dtype. There is "
        "no upcast of outputs_info in scan for now.",
    )
    def test_outputs_info_not_typed(self):
        # This was ticket 766

        coefficients = vector("coefficients")
        x = scalar("x")
        max_coefficients_supported = 10000

        # Generate the components of the polynomial
        full_range = at.arange(max_coefficients_supported)
        components, updates = scan(
            fn=lambda coeff, power, free_var: coeff * (free_var**power),
            sequences=[coefficients, full_range],
            non_sequences=x,
        )
        polynomial1 = components.sum()
        polynomial2, updates = scan(
            fn=lambda coeff, power, prev, free_var: prev + coeff * (free_var**power),
            outputs_info=at.constant(0, dtype="floatX"),
            sequences=[coefficients, full_range],
            non_sequences=x,
        )

        # python int
        polynomial3, updates = scan(
            fn=lambda coeff, power, prev, free_var: prev + coeff * (free_var**power),
            outputs_info=0,
            sequences=[coefficients, full_range],
            non_sequences=x,
        )

        # python float
        polynomial4, updates = scan(
            fn=lambda coeff, power, prev, free_var: prev + coeff * (free_var**power),
            outputs_info=0.0,
            sequences=[coefficients, full_range],
            non_sequences=x,
        )

        calculate_polynomial = function(
            inputs=[coefficients, x],
            outputs=[polynomial1, polynomial2[-1], polynomial3[-1], polynomial4[-1]],
        )

        test_coeff = np.asarray([1, 0, 2], dtype=config.floatX)
        # This will be tested by DEBUG_MODE
        out = calculate_polynomial(test_coeff, 3)
        assert out[0] == 19
        assert out[1] == 19
        assert out[2] == 19
        assert out[4] == 19
        # 19.0

    def test_crash_nonseq_grad(self):
        """
        This crashed during the grad operation and this tests validates that it
        now raises a NullTypeGradError instead because the gradient relies on
        the intermediary states of the random number generators used in the
        test. The test case was modified from the original for simplicity
        """

        rand_stream = RandomStream()
        inp = matrix()
        norm_inp = inp / at_sum(inp, axis=0)

        def unit_dropout(out_idx):
            def stochastic_pooling(in_idx):
                # sample the input matrix for each column according to the
                # column values
                pvals = norm_inp.T
                sample = rand_stream.multinomial(1, pvals)
                return inp + sample

            pooled, updates_inner = scan(
                fn=stochastic_pooling, sequences=at.arange(inp.shape[0])
            )

            # randomly add stuff to units
            rand_nums = rand_stream.binomial(1, 0.5, size=pooled.shape)
            return pooled + rand_nums, updates_inner

        out, updates_outer = scan(unit_dropout, sequences=[at.arange(inp.shape[0])])

        with pytest.raises(NullTypeGradError):
            grad(out.sum(), inp)

    def test_bugFunctioProvidesIntermediateNodesAsInputs(self):
        # made it CPU friendly
        V = ftensor3("INPUT")
        orig = fmatrix("PARAM")
        # = gpu_from_host(orig)  # <-- this doesn't work
        W = orig + 2  # <-- has same effect but it works on CPU as well
        # W = T.fmatrix('PARAM') # <-- this line works

        def one_step(v, W):
            o = v + 1 + W.sum()  # <-- this doesn't work
            # o = v + 1  # <-- this line works
            return o

        OS, updates = scan(
            fn=one_step, sequences=V, outputs_info=[None], non_sequences=[W]
        )

        O = OS.sum() + W.sum()

        # This bug manifests itself by not allowing the function to compile,
        # so if it compiles it means the test pass
        function([V, W], O)

    @pytest.mark.xfail(
        reason="This is a questionable situation that needs consideration."
    )
    def test_infershape_seq_shorter_nsteps(self):
        x = vector("x")
        [o1, o2], _ = scan(
            lambda x, y: (x + 1, y + x),
            sequences=x,
            outputs_info=[None, x[0]],
            n_steps=20,
        )

        f = function([x], [o1, o2], mode=mode_with_opt)
        f_shape = function([x], [o1.shape[0], o2.shape[0]], mode=mode_with_opt)

        vx = np.ones((10,), dtype=config.floatX)

        with pytest.raises(
            ValueError,
            match=r".*Sequence 0 has shape \(10,\) but the Scan's required number of steps is 20.*",
        ):
            out1, out2 = f(vx)

        # Even though the graph is nonsense, this could be correct somehow?
        out_shape1, out_shape2 = f_shape(vx)
        assert out_shape1 == 10
        assert out_shape2 == 10
        assert not any(
            x for x in f_shape.maker.fgraph.toposort() if isinstance(x.op, Scan)
        )

    def test_infer_shape_remove_stuff(self):
        """
        The following test will fail in DebugMode if there are
        some problems in Scan.infer_shape
        """
        x = vector("x")

        def lm(m):
            trng = RandomStream(utt.fetch_seed())
            return [
                2 * m + trng.uniform(-1.1, 1.1, dtype=config.floatX),
                m + trng.uniform(size=[3]),
            ]

        [o1, o2], updates = scan(
            lm,
            sequences=x,
            n_steps=None,
            truncate_gradient=-1,
            name="forward",
            go_backwards=False,
        )
        go1 = grad(o1.mean(), wrt=x)
        f = function(
            [x], go1, updates=updates, allow_input_downcast=True, mode=mode_with_opt
        )
        assert np.allclose(f([1, 2, 3]), 2.0 / 3)

        topo = f.maker.fgraph.toposort()
        # this new assert is here to test if scan_merging works ..
        nb_scan = len([n for n in topo if isinstance(n.op, Scan)])
        assert nb_scan == 1
        nb_shape_i = len([n for n in topo if isinstance(n.op, Shape_i)])
        if config.mode != "FAST_COMPILE":
            assert nb_shape_i == 1

    def test_return_steps(self):
        rng = np.random.default_rng(utt.fetch_seed())

        vW_in2 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
        vW = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-0.5, 0.5, size=(8, 2)))
        v_u2 = asarrayX(rng.uniform(-0.5, 0.5, size=(8,)))
        v_x0 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
        v_y0 = asarrayX(rng.uniform(size=(3,)))

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = vector("u2")
        x0 = vector("x0")
        y0 = vector("y0")

        def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, y_tm3, W_in1):
            return [
                y_tm3 + 1,
                dot(u1_t, W_in1) + u2_t * W_in2 + dot(x_tm1, W),
                y_tm1 + dot(x_tm1, W_out),
            ]

        rval, updates = scan(
            f_rnn_cmpl,
            [u1, u2],
            [None, dict(initial=x0), dict(initial=y0, taps=[-1, -3])],
            W_in1,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        outputs = []
        outputs += [rval[0][-3:]]
        outputs += [rval[1][-2:]]
        outputs += [rval[2][-4:]]
        f4 = function(
            [u1, u2, x0, y0, W_in1], outputs, updates=updates, allow_input_downcast=True
        )

        # compute the values in numpy
        v_x = np.zeros((8, 2), dtype=config.floatX)
        v_y = np.zeros((8,), dtype=config.floatX)
        v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + np.dot(v_x0, vW)
        v_y[0] = np.dot(v_x0, vWout) + v_y0[2]

        for i in range(1, 8):
            v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + np.dot(v_x[i - 1], vW)
            v_y[i] = np.dot(v_x[i - 1], vWout) + v_y[i - 1]

        (aesara_dump, aesara_x, aesara_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)

        utt.assert_allclose(aesara_x, v_x[-2:])
        utt.assert_allclose(aesara_y, v_y[-4:])

    def test_until_random_infer_shape(self):
        """
        Test for a crash in scan.infer_shape when using both
        an until condition and random sampling in the inner function.
        """

        x = scalar()
        srng = RandomStream(0)

        def inner_fct(previous_val):
            new_val = previous_val + srng.uniform()
            condition = until(previous_val > 5)
            return new_val, condition

        out, updates = scan(inner_fct, outputs_info=x, n_steps=10)

        g_out = grad(out.sum(), x)
        fct = function([x], [out, g_out])

        for i in range(-5, 5):
            output, g_output = fct(i)
            assert len(output) == g_output

    @pytest.mark.xfail(reason="Not supported/implemented yet")
    def test_not_working_infer_shape(self):
        """
        Ensure that the shape inference can remove the Scan node in the
        case of a complicated inner graph involving sequences and recurrent
        states
        """

        seq = lvector()
        sitsot_init = lscalar()
        mitsot_init = lvector()

        def step(seq1, sitsot_m1, mitsot_m2, mitsot_m1):
            # Every iteration, the sitsot state decreases and the mitsot state
            # increases such that their total value remains identical. This
            # is because this value will be used as the shape of a nitsot
            # output and the outputs of every iteration need to have the same
            # shape
            diff = mitsot_m1 + seq1
            next_mitsot_val = mitsot_m2 + diff
            next_sitsot_val = sitsot_m1 - diff
            nitsot_out = at.alloc(
                np.asarray(0.0, "float32"), next_mitsot_val + next_sitsot_val
            )
            return next_sitsot_val, next_mitsot_val, nitsot_out

        out, updates = scan(
            fn=step,
            sequences=seq,
            outputs_info=[
                sitsot_init,
                {"initial": mitsot_init, "taps": [-2, -1]},
                None,
            ],
            n_steps=5,
        )

        f = function([seq, sitsot_init, mitsot_init], out[2].shape)
        assert not any(isinstance(x.op, Scan) for x in f.maker.fgraph.toposort())

    def test_multiple_inputs_multiple_outputs(self):
        """
        some rnn with multiple outputs and multiple inputs; other
        dimension instead of scalars/vectors
        """
        rng = np.random.default_rng(utt.fetch_seed())
        vW_in2 = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))
        vW = asarrayX(rng.uniform(-5.0, 5.0, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-5.0, 5.0, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-5.0, 5.0, size=(3, 2)))
        v_u2 = asarrayX(rng.uniform(-5.0, 5.0, size=(3,)))
        v_x0 = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))
        v_y0 = asarrayX(rng.uniform())

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = vector("u2")
        x0 = vector("x0")
        y0 = scalar("y0")

        def f_rnn_cmpl(u1_t, u2_t, x_tm1, y_tm1, W_in1):
            return [
                dot(u1_t, W_in1) + u2_t * W_in2 + dot(x_tm1, W),
                dot(x_tm1, W_out),
            ]

        outputs, updates = scan(
            f_rnn_cmpl,
            [u1, u2],
            [x0, y0],
            W_in1,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        f4 = function(
            [u1, u2, x0, y0, W_in1], outputs, updates=updates, allow_input_downcast=True
        )

        # compute the values in numpy
        v_x = np.zeros((3, 2), dtype=config.floatX)
        v_y = np.zeros((3,), dtype=config.floatX)
        v_x[0] = np.dot(v_u1[0], vW_in1) + v_u2[0] * vW_in2 + np.dot(v_x0, vW)
        v_y[0] = np.dot(v_x0, vWout)
        for i in range(1, 3):
            v_x[i] = np.dot(v_u1[i], vW_in1) + v_u2[i] * vW_in2 + np.dot(v_x[i - 1], vW)
            v_y[i] = np.dot(v_x[i - 1], vWout)

        (aesara_x, aesara_y) = f4(v_u1, v_u2, v_x0, v_y0, vW_in1)
        utt.assert_allclose(aesara_x, v_x)
        utt.assert_allclose(aesara_y, v_y)

    def test_multiple_outs_taps(self):
        l = 5
        rng = np.random.default_rng(utt.fetch_seed())

        vW_in2 = asarrayX(rng.uniform(-2.0, 2.0, size=(2,)))
        vW = asarrayX(rng.uniform(-2.0, 2.0, size=(2, 2)))
        vWout = asarrayX(rng.uniform(-2.0, 2.0, size=(2,)))
        vW_in1 = asarrayX(rng.uniform(-2.0, 2.0, size=(2, 2)))
        v_u1 = asarrayX(rng.uniform(-2.0, 2.0, size=(l, 2)))
        v_u2 = asarrayX(rng.uniform(-2.0, 2.0, size=(l + 2, 2)))
        v_x0 = asarrayX(rng.uniform(-2.0, 2.0, size=(2,)))
        v_y0 = asarrayX(rng.uniform(size=(3,)))

        W_in2 = shared(vW_in2, name="win2")
        W = shared(vW, name="w")
        W_out = shared(vWout, name="wout")
        W_in1 = matrix("win")
        u1 = matrix("u1")
        u2 = matrix("u2")
        x0 = vector("x0")
        y0 = vector("y0")

        def f_rnn_cmpl(u1_t, u2_tm1, u2_t, u2_tp1, x_tm1, y_tm1, y_tm3, W_in1):
            return [
                dot(u1_t, W_in1) + (u2_t + u2_tm1 * u2_tp1) * W_in2 + dot(x_tm1, W),
                (y_tm1 + y_tm3) * dot(x_tm1, W_out),
                dot(u1_t, W_in1),
            ]

        outputs, updates = scan(
            f_rnn_cmpl,
            [u1, dict(input=u2, taps=[-1, 0, 1])],
            [x0, dict(initial=y0, taps=[-1, -3]), None],
            W_in1,
            n_steps=None,
            truncate_gradient=-1,
            go_backwards=False,
        )

        f = function(
            [u1, u2, x0, y0, W_in1], outputs, updates=updates, allow_input_downcast=True
        )

        f(v_u1, v_u2, v_x0, v_y0, vW_in1)

        ny0 = np.zeros((5, 2))
        ny1 = np.zeros((5,))
        ny2 = np.zeros((5, 2))
        ny0[0] = (
            np.dot(v_u1[0], vW_in1)
            + (v_u2[1] + v_u2[0] * v_u2[2]) * vW_in2
            + np.dot(v_x0, vW)
        )

        ny1[0] = (v_y0[2] + v_y0[0]) * np.dot(v_x0, vWout)
        ny2[0] = np.dot(v_u1[0], vW_in1)

        ny0[1] = (
            np.dot(v_u1[1], vW_in1)
            + (v_u2[2] + v_u2[1] * v_u2[3]) * vW_in2
            + np.dot(ny0[0], vW)
        )

        ny1[1] = (ny1[0] + v_y0[1]) * np.dot(ny0[0], vWout)
        ny2[1] = np.dot(v_u1[1], vW_in1)

        ny0[2] = (
            np.dot(v_u1[2], vW_in1)
            + (v_u2[3] + v_u2[2] * v_u2[4]) * vW_in2
            + np.dot(ny0[1], vW)
        )
        ny1[2] = (ny1[1] + v_y0[2]) * np.dot(ny0[1], vWout)
        ny2[2] = np.dot(v_u1[2], vW_in1)

        ny0[3] = (
            np.dot(v_u1[3], vW_in1)
            + (v_u2[4] + v_u2[3] * v_u2[5]) * vW_in2
            + np.dot(ny0[2], vW)
        )

        ny1[3] = (ny1[2] + ny1[0]) * np.dot(ny0[2], vWout)
        ny2[3] = np.dot(v_u1[3], vW_in1)

        ny0[4] = (
            np.dot(v_u1[4], vW_in1)
            + (v_u2[5] + v_u2[4] * v_u2[6]) * vW_in2
            + np.dot(ny0[3], vW)
        )

        ny1[4] = (ny1[3] + ny1[1]) * np.dot(ny0[3], vWout)
        ny2[4] = np.dot(v_u1[4], vW_in1)

        # TODO FIXME: What is this testing?  At least assert something.

    def test_grad_two_scans(self):

        # data input & output
        x = tensor3("x")
        t = imatrix("t")

        # forward pass
        W = shared(
            np.random.default_rng(utt.fetch_seed()).random((2, 2)).astype("float32"),
            name="W",
            borrow=True,
        )

        def forward_scanner(x_t):
            a2_t = dot(x_t, W)
            y_t = softmax_graph(a2_t)
            return y_t

        y, _ = scan(fn=forward_scanner, sequences=x, outputs_info=[None])

        # loss function
        def error_scanner(y_t, t_t):
            return mean(categorical_crossentropy(y_t, t_t))

        L, _ = scan(fn=error_scanner, sequences=[y, t], outputs_info=[None])
        L = mean(L)

        # backward pass
        grad(L, [W])

    def _grad_mout_helper(self, n_iters, mode):
        rng = np.random.default_rng(utt.fetch_seed())
        n_hid = 3
        n_in = 1
        n_out = 1

        W_hh_v = asarrayX(rng.uniform(-1, 1, size=(n_hid, n_hid)))
        h0_v = asarrayX(rng.uniform(-1, 1, size=(2, n_hid)))
        b_h_v = asarrayX(rng.uniform(-0.01, 0.01, size=(n_hid)))
        W_ih_v = asarrayX(rng.uniform(-1, 1, size=(n_in, n_hid)))
        W_ho_v = asarrayX(rng.uniform(-1, 1, size=(n_hid, n_out)))
        b_o_v = asarrayX(rng.uniform(-0.01, 0.01, size=(n_out)))

        # parameters of the rnn
        b_h = shared(b_h_v, name="b_h")
        h0 = shared(h0_v, name="h0")
        W_ih = shared(W_ih_v, name="W_ih")
        W_hh = shared(W_hh_v, name="W_hh")
        W_ho = shared(W_ho_v, name="W_ho")
        b_o = shared(b_o_v, name="b_o")
        params = [W_ih, W_hh, b_h, W_ho, b_o, h0]

        # first dimension is time
        x = matrix()

        # sequences: x_t
        # prior results: h_tm2, h_tm1
        # non-sequences: W_ih, W_hh, W_ho, b_h
        def one_step(x_t, h_tm2, h_tm1, W_ih, W_hh, b_h, W_ho, b_o):
            h_t = tanh(dot(x_t, W_ih) + dot(h_tm2, W_hh) + b_h)
            y_t = dot(h_t, W_ho) + b_o
            return [h_t, y_t]

        # hidden and outputs of the entire sequence
        [h, y], _ = scan(
            fn=one_step,
            sequences=dict(input=x),
            # corresponds to the return type of one_step
            outputs_info=[dict(initial=h0, taps=[-2, -1]), None],
            non_sequences=[W_ih, W_hh, b_h, W_ho, b_o],
            mode=mode,
        )

        # target values
        t = matrix()

        # learning rate
        lr = asarrayX(0.1)
        learning_rate = shared(lr)

        cost = (0.5 * ((y - t) ** 2.0).mean()) + (0.5 * (y.std() - t.std()) ** 2.0)

        gparams = grad(cost, params)
        updates = [
            (param, param - gparam * learning_rate)
            for param, gparam in zip(params, gparams)
        ]
        learn_rnn_fn = function(inputs=[x, t], outputs=cost, updates=updates, mode=mode)
        function(inputs=[x], outputs=y, mode=mode)

        # artificial data
        x_v = np.arange(0.0, 10.49, 0.21, dtype=config.floatX)
        x_v = x_v.reshape(len(x_v), 1)
        s_v = np.sin(x_v)
        t_v = np.roll(s_v, -1)[:-1]
        s_v = s_v[:-1]
        for i in range(n_iters):
            cost = learn_rnn_fn(s_v, t_v)
        # pred = eval_rnn_fn(s_v)
        return cost

    def test_grad_multiple_outs_some_disconnected(self):
        final_cost = self._grad_mout_helper(100, mode_nodebug)
        assert final_cost < 0.02, final_cost

    def test_grad_multiple_outs_some_disconnected_2(self):
        # This is to try the network in DEBUG_MODE, but not fully
        # train it since that would take 3 hours

        # TODO FIXME: This won't work in debug mode due to an `Elemwise` bug.
        # with config.change_flags(mode="DebugMode"):
        # Also, the purpose of this test is not clear.
        self._grad_mout_helper(1, None)


@pytest.mark.parametrize(
    "fn, sequences, outputs_info, non_sequences, n_steps, op_check",
    [
        # sequences
        (
            lambda a_t: 2 * a_t,
            [at.arange(10)],
            [{}],
            [],
            None,
            lambda op: op.info.n_seqs > 0,
        ),
        # nit-sot
        (
            lambda: at.as_tensor(2.0),
            [],
            [{}],
            [],
            3,
            lambda op: op.info.n_nit_sot > 0,
        ),
        # nit-sot, non_seq
        (
            lambda c: at.as_tensor(2.0) * c,
            [],
            [{}],
            [scalar("c", dtype="floatX")],
            3,
            lambda op: op.info.n_nit_sot > 0 and op.info.n_non_seqs > 0,
        ),
        # sit-sot
        (
            lambda a_tm1: 2 * a_tm1,
            [],
            [{"initial": at.as_tensor(0.0, dtype="floatX"), "taps": [-1]}],
            [],
            3,
            lambda op: op.info.n_sit_sot > 0,
        ),
        # sit-sot, while
        (
            lambda a_tm1: (a_tm1 + 1, until(a_tm1 > 2)),
            [],
            [{"initial": at.as_tensor(1, dtype=np.int64), "taps": [-1]}],
            [],
            3,
            lambda op: op.info.n_sit_sot > 0,
        ),
        # nit-sot, shared input/output
        (
            lambda: RandomStream().normal(0, 1, name="a"),
            [],
            [{}],
            [],
            3,
            lambda op: op.info.n_shared_outs > 0,
        ),
        # mit-sot (that's also a type of sit-sot)
        (
            lambda a_tm1: 2 * a_tm1,
            [],
            [{"initial": at.as_tensor([0.0, 1.0], dtype="floatX"), "taps": [-2]}],
            [],
            6,
            lambda op: op.info.n_mit_sot > 0,
        ),
        # mit-sot
        (
            lambda a_tm1, b_tm1: (2 * a_tm1, 2 * b_tm1),
            [],
            [
                {"initial": at.as_tensor(0.0, dtype="floatX"), "taps": [-1]},
                {"initial": at.as_tensor(0.0, dtype="floatX"), "taps": [-1]},
            ],
            [],
            10,
            lambda op: op.info.n_mit_sot > 0,
        ),
        # TODO: mit-mot (can't be created through the `scan` interface)
    ],
)
def test_ScanInfo_totals(fn, sequences, outputs_info, non_sequences, n_steps, op_check):
    res, _ = scan(
        fn,
        sequences=sequences,
        outputs_info=outputs_info,
        non_sequences=non_sequences,
        n_steps=n_steps,
        strict=True,
    )

    if isinstance(res, list):
        res = res[0]

    if not isinstance(res.owner.op, Scan):
        res = res.owner.inputs[0]

    scan_op = res.owner.op
    assert isinstance(scan_op, Scan)

    _ = op_check(scan_op)

    assert scan_op.info.n_outer_inputs == len(res.owner.inputs)
    assert scan_op.info.n_outer_outputs == len(res.owner.outputs)
    assert scan_op.info.n_inner_inputs == len(res.owner.op.inner_inputs)
    assert scan_op.info.n_inner_outputs == len(res.owner.op.inner_outputs)


@pytest.mark.parametrize("linker_mode", ["cvm", "py"])
def test_output_storage_reuse(linker_mode):
    """Make sure that outer-output storage is correctly initialized when it's non-``None``/empty."""

    if linker_mode == "cvm":
        # This implicitly confirms that the Cython version is being used
        from aesara.scan import scan_perform_ext  # noqa: F401

    mode = Mode(linker=linker_mode, optimizer=None)

    def fn(n):
        """
        Since the following inner-`Scan` is nested, its outer-output storage
        will be non-``None`` after the second outer-`Scan` iteration, and all
        subsequent iterations will use the previous outer-output storage.  Due
        to the ``n_step`` changes, the shape of the outer-inputs array that's
        allocated for the lagged/sit-sot ``z`` results should differ from the
        shape of the previously allocated outer-output array. Since the
        outer-output arrays are initialized using the outer-input arrays, the
        shape difference needs to be handled correctly.
        """
        s_in_y, _ = scan(
            fn=lambda z: (z + 1, until(z > 2)),
            outputs_info=[
                {"taps": [-1], "initial": at.as_tensor(0.0, dtype=np.float64)}
            ],
            mode=mode,
            n_steps=n - 1,
            allow_gc=False,
        )

        return s_in_y.sum()

    s_y, updates = scan(
        fn=fn,
        outputs_info=[None],
        sequences=[at.as_tensor([3, 2, 1], dtype=np.int64)],
        mode=mode,
        allow_gc=False,
    )

    f_cvm = function([], s_y, mode=mode)

    res = f_cvm()

    assert np.array_equal(res, np.array([3, 1, 0]))
