"""This module provides utility functions for the `Scan` `Op`."""

import copy
import dataclasses
import logging
from collections import OrderedDict, namedtuple
from itertools import chain
from typing import TYPE_CHECKING, Callable, List, Optional, Sequence, Set, Tuple, Union
from typing import cast as type_cast

import numpy as np

from pytensor import scalar as aes
from pytensor import tensor as at
from pytensor.compile.profiling import ProfileStats
from pytensor.configdefaults import config
from pytensor.graph.basic import Constant, Variable, equal_computations, graph_inputs
from pytensor.graph.op import get_test_value
from pytensor.graph.replace import clone_replace
from pytensor.graph.type import HasDataType
from pytensor.graph.utils import TestValueError
from pytensor.tensor.basic import AllocEmpty, cast
from pytensor.tensor.subtensor import set_subtensor
from pytensor.tensor.var import TensorConstant


if TYPE_CHECKING:
    from pytensor.scan.op import ScanInfo

_logger = logging.getLogger("pytensor.scan.utils")


class InnerFunctionError(Exception):
    """An exception indicating that an error occurred in `Scan`'s inner function."""


def safe_new(
    x: Variable, tag: str = "", dtype: Optional[Union[str, np.dtype]] = None
) -> Variable:
    """Clone variables.

    Internal function that constructs a new variable from `x` with the same
    type, but with a different name (old name + tag). This function is used
    by `gradient`, or the R-op to construct new variables for the inputs of
    the inner graph such that there is no interference between the original
    graph and the newly constructed graph.

    """
    if hasattr(x, "name") and x.name is not None:
        nw_name = x.name + tag
    else:
        nw_name = None

    if isinstance(x, Constant):
        # TODO: Do something better about this
        assert isinstance(x.type, HasDataType)

        if dtype and x.type.dtype != dtype:
            casted_x = cast(x, dtype)
            nwx = type(x)(casted_x.type, x.data, x.name)
            nwx.tag = copy.copy(x.tag)
            return nwx
        else:
            return x
    # Note, `as_tensor_variable` will convert the `ScalarType` into a
    # `TensorScalar` that will require a `ScalarFromTensor` `Op`, making the
    # push-out optimization fail
    elif isinstance(x, aes.ScalarVariable):
        if dtype:
            nw_x = aes.get_scalar_type(dtype=dtype)()
        else:
            nw_x = x.type()
        nw_x.name = nw_name
        if config.compute_test_value != "off":
            # Copy test value, cast it if necessary
            try:
                x_test_value = get_test_value(x)
            except TestValueError:
                pass
            else:
                # This clause is executed if no exception was raised
                nw_x.tag.test_value = nw_x.type.filter(x_test_value)
        return nw_x
    else:
        try:
            x = at.as_tensor_variable(x)
        except TypeError:
            # This could happen for example for random states
            pass

    # Cast `x` if needed. If `x` has a test value, this will also cast it.
    if dtype:
        # TODO: Do something better about this
        assert isinstance(x.type, HasDataType)

        if x.type.dtype != dtype:
            x = cast(x, dtype)

    nw_x = x.type()
    nw_x.name = nw_name
    # Preserve test values so that the `compute_test_value` option can be used.
    # The test value is deep-copied to ensure there can be no interactions
    # between test values, due to inplace operations for instance. This may
    # not be the most efficient memory-wise, though.
    if config.compute_test_value != "off":
        try:
            nw_x.tag.test_value = copy.deepcopy(get_test_value(x))
        except TestValueError:
            pass

    return nw_x


class until:
    """
    Class used to encode the different things the inner function of scan can
    (or needs) to return.

    This class has to be used when scan needs to halt when a condition is
    met, otherwise the list of outputs and dictionary can directly be return
    as a tuple. The reason is that otherwise scan has no way to distinguish
    between the condition and the list of outputs ( unless we enforce and
    order, but since this was not impose up to know it can make quite a bit
    of code to fail).

    """

    def __init__(self, condition):
        self.condition = at.as_tensor_variable(condition)
        assert self.condition.ndim == 0


class ScanProfileStats(ProfileStats):
    show_sum = False
    callcount = 0
    nbsteps = 0
    call_time = 0.0

    def __init__(self, atexit_print=True, name=None, **kwargs):
        super().__init__(atexit_print, **kwargs)
        self.name = name

    def summary_globals(self, file):
        # Do nothing, we don't want to print extra global summary
        # here.
        pass

    def summary_function(self, file):
        # RP: every time we compile a function a ProfileStats is created for
        # that function. This means that every time a optimization replaces
        # some scan op, some orphane ProfileStats remains in the air ..
        # also even without any optimization, scan compiles a dummy function
        # that will produce a ProfileStats that will correspond to a
        # function that will never be called. Printing several empty
        # Function profiling is just extremely confusing
        if self.callcount == 0:
            return
        print("", file=file)

        if self.name is not None:
            print("Scan Op profiling (", self.name, ")", file=file)
        else:
            print("Scan Op profiling", file=file)
        print("==================", file=file)
        print(f"  Message: {self.message}", file=file)

        print(
            (
                f"  Time in {self.callcount} calls of the op (for a total of {self.nbsteps} "
                f"steps) {self.call_time:3}s"
            ),
            file=file,
        )
        print("", file=file)
        val = 0
        if self.call_time > 0:
            val = self.vm_call_time * 100 / self.call_time
        print(
            f"  Total time spent in calling the VM {self.vm_call_time:e}s ({val:.3f}%)",
            file=file,
        )
        val = 100
        if self.call_time > 0:
            val = 100.0 - self.vm_call_time * 100 / self.call_time
        print(
            f"  Total overhead (computing slices..) {self.call_time - self.vm_call_time:e}s ({val:.3f}%)",
            file=file,
        )
        print("", file=file)


def traverse(out, x, x_copy, d, visited=None):
    """
    Function used by scan to parse the tree and figure out which nodes
    it needs to replace.

    There are two options :
        1) x and x_copy or on host, then you would replace x with x_copy

    """
    # ``visited`` is a set of nodes that are already known and don't need to be
    # checked again, speeding up the traversal of multiply-connected graphs.
    # if a ``visited`` set is given, it will be updated in-place so the callee
    # knows which nodes we have seen.
    if visited is None:
        visited = set()
    if out in visited:
        return d
    visited.add(out)

    if out == x:
        # assert isinstance(x.type, GpuArrayType)
        # d[out] = GpuFromHost(x.type.context_name)(x_copy)
        # return d
        raise RuntimeError("Not supported")
    elif out.owner is None:
        return d
    else:
        for inp in out.owner.inputs:
            d = traverse(inp, x, x_copy, d, visited)
        return d


def expand_empty(tensor_var, size):
    """
    Transforms the shape of a tensor from (d1, d2 ... ) to ( d1+size, d2, ..)
    by adding uninitialized memory at the end of the tensor.

    """

    if size == 0:
        return tensor_var
    shapes = [tensor_var.shape[x] for x in range(tensor_var.ndim)]
    new_shape = [size + shapes[0]] + shapes[1:]
    empty = AllocEmpty(tensor_var.dtype)(*new_shape)

    ret = set_subtensor(empty[: shapes[0]], tensor_var)
    ret.tag.nan_guard_mode_check = False
    return ret


class Validator:
    """
    Check if variables can be expressed without using variables in invalid.

    Parameters
    ----------
    valid_equivalent
        Provides a dictionary mapping some invalid variables to valid ones that
        can be used instead.

    """

    def __init__(self, valid=None, invalid=None, valid_equivalent=None):
        if valid is None:
            valid = []
        if invalid is None:
            invalid = []
        if valid_equivalent is None:
            valid_equivalent = OrderedDict()

        # Nodes that are valid to have in the graph computing outputs
        self.valid = set(valid)

        # Nodes that are NOT valid to have in the graph computing outputs
        self.invalid = set(invalid)

        # Mapping from invalid variables to equivalent valid ones.
        self.valid_equivalent = valid_equivalent.copy()
        self.valid.update(list(valid_equivalent.values()))
        self.invalid.update(list(valid_equivalent.keys()))

    def check(self, out):
        """
        Go backwards in the graph, from out, and check if out is valid.

        If out is a valid node, (out, True) is returned.
        If out is not valid, but has an equivalent e, (e, False) is returned.
        If out is not valid and has no equivalent, None is returned.

        """

        def get_value(out):
            if out in self.valid:
                return out, True
            elif out in self.valid_equivalent:
                return self.valid_equivalent[out], False
            elif out in self.invalid:
                return None
            else:
                raise RuntimeError("This should not happen")

        q = [out]
        while q:
            out = q.pop()
            if out in self.valid:
                continue
            elif out in self.invalid:
                continue

            if out.owner is None:
                if isinstance(out, TensorConstant):
                    self.valid.add(out)
                    continue
                else:
                    # This is an input node and it has not been
                    # explicitly marked as invalid so we can use it
                    self.valid.add(out)
                    continue

            # Process the input if needed
            continue_while = False
            for inp in out.owner.inputs:
                if inp not in self.valid and inp not in self.invalid:
                    q.append(out)
                    q.extend(out.owner.inputs)
                    continue_while = True
                    break
            if continue_while:
                continue
            inputs = [get_value(i) for i in out.owner.inputs]

            # If some inputs are invalid without equivalent, so is out
            if None in inputs:
                self.invalid.add(out)
                continue

            # If some inputs are invalid with equivalent,
            # an equivalent out should be built and returned
            all_inputs = [inp for (inp, is_valid) in inputs]
            equiv_inputs = [inp for (inp, is_valid) in inputs if not is_valid]
            if equiv_inputs:
                cloned_node = out.owner.clone_with_new_inputs(all_inputs)
                cloned_out = cloned_node.outputs[out.index]
                self.invalid.add(out)
                self.valid.add(cloned_out)
                self.valid_equivalent[out] = cloned_out
                continue

            # All inputs are valid, so is out
            self.valid.add(out)

        return get_value(out)


def scan_can_remove_outs(op, out_idxs):
    """Look at all outputs defined by indices ``out_idxs`` and determines which can be removed.

    Returns
    -------
    two lists, the first one with the indices of outs that can be removed, the
    second with the outputs that can not be removed.

    """
    non_removable = [o for i, o in enumerate(op.inner_outputs) if i not in out_idxs]
    required_inputs = list(graph_inputs(non_removable))

    out_ins = []
    offset = op.info.n_seqs
    for idx, tap in enumerate(
        chain(
            op.info.mit_mot_in_slices,
            op.info.mit_sot_in_slices,
            op.info.sit_sot_in_slices,
        )
    ):
        n_ins = len(tap)
        out_ins += [op.inner_inputs[offset : offset + n_ins]]
        offset += n_ins
    out_ins += [[] for k in range(op.info.n_nit_sot)]
    out_ins += [[op.inner_inputs[offset + k]] for k in range(op.info.n_shared_outs)]

    added = True
    out_idxs_mask = [1 for idx in out_idxs]
    while added:
        added = False
        for pos, idx in enumerate(out_idxs):
            if out_idxs_mask[pos] and any(x in required_inputs for x in out_ins[idx]):
                # This output is required ..
                out_idxs_mask[pos] = 0
                required_inputs += list(graph_inputs([op.inner_outputs[idx]]))
                added = True

    required_outs = [x for i, x in enumerate(out_idxs) if out_idxs_mask[i] == 0]
    not_required = [x for i, x in enumerate(out_idxs) if out_idxs_mask[i] == 1]
    return (required_outs, not_required)


def compress_outs(op, not_required, inputs):
    """
    Helpful function that gets a Scan op, a list of indices indicating
    which outputs are not required anymore and should be removed, and
    a list of inputs to the apply node corresponding to the scan op and
    produces the list of inputs and outputs and the info dictionary where
    the indicated outputs are eliminated. Note that eliminating an output
    means removing its inputs from the inner function and from the
    node inputs, and changing the dictionary.

    """
    from pytensor.scan.op import ScanInfo

    op_info = op.info
    info = ScanInfo(
        n_seqs=op_info.n_seqs,
        mit_mot_in_slices=(),
        mit_mot_out_slices=(),
        mit_sot_in_slices=(),
        sit_sot_in_slices=(),
        n_nit_sot=0,
        n_shared_outs=0,
        n_non_seqs=0,
        as_while=op_info.as_while,
    )

    op_inputs = op.inner_inputs[: op_info.n_seqs]
    op_outputs = []
    node_inputs = inputs[: op_info.n_seqs + 1]
    map_old_new = OrderedDict()

    offset = 0
    ni_offset = op_info.n_seqs + 1
    i_offset = op_info.n_seqs
    o_offset = 0
    curr_pos = 0
    for idx in range(op_info.n_mit_mot):
        if offset + idx not in not_required:
            map_old_new[offset + idx] = curr_pos
            curr_pos += 1
            info = dataclasses.replace(
                info,
                mit_mot_in_slices=info.mit_mot_in_slices
                + (op_info.mit_mot_in_slices[idx],),
                mit_mot_out_slices=info.mit_mot_out_slices
                + (op_info.mit_mot_out_slices[idx],),
            )
            # input taps
            for jdx in op_info.mit_mot_in_slices[idx]:
                op_inputs += [op.inner_inputs[i_offset]]
                i_offset += 1
            # output taps
            for jdx in op_info.mit_mot_out_slices[idx]:
                op_outputs += [op.inner_outputs[o_offset]]
                o_offset += 1
            # node inputs
            node_inputs += [inputs[ni_offset + idx]]
        else:
            o_offset += len(op_info.mit_mot_out_slices[idx])
            i_offset += len(op_info.mit_mot_in_slices[idx])

    offset += op_info.n_mit_mot
    ni_offset += op_info.n_mit_mot

    for idx in range(op_info.n_mit_sot):
        if offset + idx not in not_required:
            map_old_new[offset + idx] = curr_pos
            curr_pos += 1
            info = dataclasses.replace(
                info,
                mit_sot_in_slices=info.mit_sot_in_slices
                + (op_info.mit_sot_in_slices[idx],),
            )
            # input taps
            for jdx in op_info.mit_sot_in_slices[idx]:
                op_inputs += [op.inner_inputs[i_offset]]
                i_offset += 1
            # output taps
            op_outputs += [op.inner_outputs[o_offset]]
            o_offset += 1
            # node inputs
            node_inputs += [inputs[ni_offset + idx]]
        else:
            o_offset += 1
            i_offset += len(op_info.mit_sot_in_slices[idx])

    offset += op_info.n_mit_sot
    ni_offset += op_info.n_mit_sot
    for idx in range(op_info.n_sit_sot):
        if offset + idx not in not_required:
            map_old_new[offset + idx] = curr_pos
            curr_pos += 1
            info = dataclasses.replace(
                info,
                sit_sot_in_slices=info.sit_sot_in_slices
                + (op_info.sit_sot_in_slices[idx],),
            )
            # input taps
            op_inputs += [op.inner_inputs[i_offset]]
            i_offset += 1
            # output taps
            op_outputs += [op.inner_outputs[o_offset]]
            o_offset += 1
            # node inputs
            node_inputs += [inputs[ni_offset + idx]]
        else:
            o_offset += 1
            i_offset += 1

    offset += op_info.n_sit_sot
    ni_offset += op_info.n_sit_sot
    nit_sot_ins = []
    for idx in range(op_info.n_nit_sot):
        if offset + idx not in not_required:
            map_old_new[offset + idx] = curr_pos
            curr_pos += 1
            info = dataclasses.replace(info, n_nit_sot=info.n_nit_sot + 1)
            op_outputs += [op.inner_outputs[o_offset]]
            o_offset += 1
            nit_sot_ins += [inputs[ni_offset + idx + op_info.n_shared_outs]]
        else:
            o_offset += 1

    offset += op_info.n_nit_sot
    shared_ins = []
    for idx in range(op_info.n_shared_outs):
        if offset + idx not in not_required:
            map_old_new[offset + idx] = curr_pos
            curr_pos += 1
            info = dataclasses.replace(info, n_shared_outs=info.n_shared_outs + 1)
            op_outputs += [op.inner_outputs[o_offset]]
            o_offset += 1
            op_inputs += [op.inner_inputs[i_offset]]
            i_offset += 1
            shared_ins += [inputs[ni_offset + idx]]
        else:
            o_offset += 1
            i_offset += 1
    node_inputs += shared_ins
    node_inputs += nit_sot_ins
    # other stuff
    op_inputs += op.inner_inputs[i_offset:]
    info = dataclasses.replace(info, n_non_seqs=len(op.inner_inputs[i_offset:]))
    node_inputs += inputs[ni_offset + op_info.n_shared_outs + op_info.n_nit_sot :]
    if op_info.as_while:
        op_outputs += [op.inner_outputs[o_offset]]
        map_old_new[o_offset] = len(op_outputs) - 1
        # map_old_new[len(op_outputs)-1] = o_offset

    return (op_inputs, op_outputs, info, node_inputs, map_old_new)


def reconstruct_graph(inputs, outputs, tag=None):
    """
    Different interface to clone, that allows you to pass inputs.
    Compared to clone, this method always replaces the inputs with
    new variables of the same type, and returns those (in the same
    order as the original inputs).

    """
    if tag is None:
        tag = ""
    nw_inputs = [safe_new(x, tag) for x in inputs]

    givens = {x: nw_x for nw_x, x in zip(nw_inputs, inputs)}
    nw_outputs = clone_replace(outputs, replace=givens)
    return (nw_inputs, nw_outputs)


FieldInfo = namedtuple(
    "FieldInfo", ("name", "agg_name", "index", "inner_index", "agg_index")
)


def safe_index(lst, x):
    try:
        return lst.index(x)
    except ValueError:
        return None


def default_filter_scanargs(x):
    return x.startswith("inner_") or x.startswith("outer_")


class ScanArgs:
    """Parses the inputs and outputs of `Scan` in an easy to manipulate format."""

    default_filter = default_filter_scanargs
    nested_list_fields = ("inner_in_mit_mot", "inner_in_mit_sot", "inner_out_mit_mot")

    def __init__(
        self,
        outer_inputs: Sequence[Variable],
        outer_outputs: Sequence[Variable],
        _inner_inputs: Sequence[Variable],
        _inner_outputs: Sequence[Variable],
        info: "ScanInfo",
        clone: Optional[bool] = True,
    ):
        self.n_steps = outer_inputs[0]
        self.as_while = info.as_while

        if clone:
            rval = reconstruct_graph(_inner_inputs, _inner_outputs, "")
        else:
            rval = (_inner_inputs, _inner_outputs)

        if self.as_while:
            self.cond = [rval[1][-1]]
            inner_outputs = rval[1][:-1]
        else:
            self.cond = []
            inner_outputs = rval[1]
        inner_inputs = rval[0]

        p = 1
        q = 0

        n_seqs = info.n_seqs
        self.outer_in_seqs = list(outer_inputs[p : p + n_seqs])
        self.inner_in_seqs = list(inner_inputs[q : q + n_seqs])
        p += n_seqs
        q += n_seqs

        n_mit_mot = info.n_mit_mot
        n_mit_sot = info.n_mit_sot

        self.mit_mot_in_slices = info.mit_mot_in_slices
        self.mit_sot_in_slices = info.mit_sot_in_slices
        self.sit_sot_in_slices = info.sit_sot_in_slices

        n_mit_mot_ins = sum(len(s) for s in self.mit_mot_in_slices)
        n_mit_sot_ins = sum(len(s) for s in self.mit_sot_in_slices)

        iimm = inner_inputs[q : q + n_mit_mot_ins]
        self.inner_in_mit_mot = []
        qq = 0
        for sl in self.mit_mot_in_slices:
            self.inner_in_mit_mot.append(iimm[qq : qq + len(sl)])
            qq += len(sl)
        q += n_mit_mot_ins

        iims = inner_inputs[q : q + n_mit_sot_ins]
        self.inner_in_mit_sot = []
        qq = 0
        for sl in self.mit_sot_in_slices:
            self.inner_in_mit_sot.append(iims[qq : qq + len(sl)])
            qq += len(sl)
        q += n_mit_sot_ins

        self.outer_in_mit_mot = list(outer_inputs[p : p + n_mit_mot])
        p += n_mit_mot
        self.outer_in_mit_sot = list(outer_inputs[p : p + n_mit_sot])
        p += n_mit_sot

        n_sit_sot = info.n_sit_sot
        self.outer_in_sit_sot = list(outer_inputs[p : p + n_sit_sot])
        self.inner_in_sit_sot = list(inner_inputs[q : q + n_sit_sot])
        p += n_sit_sot
        q += n_sit_sot

        n_shared_outs = info.n_shared_outs
        self.outer_in_shared = list(outer_inputs[p : p + n_shared_outs])
        self.inner_in_shared = list(inner_inputs[q : q + n_shared_outs])
        p += n_shared_outs
        q += n_shared_outs

        n_nit_sot = info.n_nit_sot
        self.outer_in_nit_sot = list(outer_inputs[p : p + n_nit_sot])
        p += n_nit_sot

        self.outer_in_non_seqs = list(outer_inputs[p:])
        self.inner_in_non_seqs = list(inner_inputs[q:])

        # now for the outputs
        p = 0
        q = 0

        self.mit_mot_out_slices = info.mit_mot_out_slices
        n_mit_mot_outs = info.n_mit_mot_outs
        self.outer_out_mit_mot = list(outer_outputs[p : p + n_mit_mot])
        iomm = list(inner_outputs[q : q + n_mit_mot_outs])
        self.inner_out_mit_mot: Tuple[List[Variable], ...] = ()
        qq = 0
        for sl in self.mit_mot_out_slices:
            self.inner_out_mit_mot += (iomm[qq : qq + len(sl)],)
            qq += len(sl)
        p += n_mit_mot
        q += n_mit_mot_outs

        self.outer_out_mit_sot = list(outer_outputs[p : p + n_mit_sot])
        self.inner_out_mit_sot = list(inner_outputs[q : q + n_mit_sot])
        p += n_mit_sot
        q += n_mit_sot

        self.outer_out_sit_sot = list(outer_outputs[p : p + n_sit_sot])
        self.inner_out_sit_sot = list(inner_outputs[q : q + n_sit_sot])
        p += n_sit_sot
        q += n_sit_sot

        self.outer_out_nit_sot = list(outer_outputs[p : p + n_nit_sot])
        self.inner_out_nit_sot = list(inner_outputs[q : q + n_nit_sot])
        p += n_nit_sot
        q += n_nit_sot

        self.outer_out_shared = list(outer_outputs[p : p + n_shared_outs])
        self.inner_out_shared = list(inner_outputs[q : q + n_shared_outs])
        p += n_shared_outs
        q += n_shared_outs

        assert p == len(outer_outputs)
        assert q == len(inner_outputs)

    @staticmethod
    def from_node(node, clone=False) -> "ScanArgs":
        from pytensor.scan.op import Scan

        if not isinstance(node.op, Scan):
            raise TypeError(f"{node} is not a Scan node")
        return ScanArgs(
            node.inputs,
            node.outputs,
            node.op.inner_inputs,
            node.op.inner_outputs,
            node.op.info,
            clone=clone,
        )

    @property
    def n_nit_sot(self):
        # This is just a hack that allows us to use `Scan.get_oinp_iinp_iout_oout_mappings`
        return self.info.n_nit_sot

    @property
    def inputs(self):
        # This is just a hack that allows us to use `Scan.get_oinp_iinp_iout_oout_mappings`
        return self.inner_inputs

    @property
    def n_mit_mot(self):
        # This is just a hack that allows us to use `Scan.get_oinp_iinp_iout_oout_mappings`
        return self.info.n_mit_mot

    @property
    def var_mappings(self):
        from pytensor.scan.op import ScanMethodsMixin

        return ScanMethodsMixin.get_oinp_iinp_iout_oout_mappings(self)

    @property
    def field_names(self):
        res = ["mit_mot_out_slices", "mit_mot_in_slices", "mit_sot_in_slices"]
        res.extend(
            [
                attr
                for attr in self.__dict__
                if attr.startswith("inner_in")
                or attr.startswith("inner_out")
                or attr.startswith("outer_in")
                or attr.startswith("outer_out")
                or attr == "n_steps"
            ]
        )
        return res

    @property
    def inner_inputs(self):
        return (
            self.inner_in_seqs
            + sum(self.inner_in_mit_mot, [])
            + sum(self.inner_in_mit_sot, [])
            + self.inner_in_sit_sot
            + self.inner_in_shared
            + self.inner_in_non_seqs
        )

    @property
    def outer_inputs(self):
        return (
            [self.n_steps]
            + self.outer_in_seqs
            + self.outer_in_mit_mot
            + self.outer_in_mit_sot
            + self.outer_in_sit_sot
            + self.outer_in_shared
            + self.outer_in_nit_sot
            + self.outer_in_non_seqs
        )

    @property
    def inner_outputs(self):
        return (
            sum(self.inner_out_mit_mot, [])
            + self.inner_out_mit_sot
            + self.inner_out_sit_sot
            + self.inner_out_nit_sot
            + self.inner_out_shared
            + self.cond
        )

    @property
    def outer_outputs(self):
        return (
            self.outer_out_mit_mot
            + self.outer_out_mit_sot
            + self.outer_out_sit_sot
            + self.outer_out_nit_sot
            + self.outer_out_shared
        )

    @property
    def info(self) -> "ScanInfo":
        from pytensor.scan.op import ScanInfo

        return ScanInfo(
            n_seqs=len(self.outer_in_seqs),
            mit_mot_in_slices=tuple(tuple(v) for v in self.mit_mot_in_slices),
            mit_mot_out_slices=tuple(self.mit_mot_out_slices),
            mit_sot_in_slices=tuple(tuple(v) for v in self.mit_sot_in_slices),
            sit_sot_in_slices=((-1,),) * len(self.inner_in_sit_sot),
            n_nit_sot=len(self.outer_in_nit_sot),
            n_shared_outs=len(self.outer_in_shared),
            n_non_seqs=len(self.inner_in_non_seqs),
            as_while=self.as_while,
        )

    def get_alt_field(
        self, var_info: Union[Variable, FieldInfo], alt_prefix: str
    ) -> Variable:
        """Get the alternate input/output field for a given element of `ScanArgs`.

        For example, if `var_info` is in ``ScanArgs.outer_out_sit_sot``, then
        ``get_alt_field(var_info, "inner_out")`` returns the element corresponding
        `var_info` in ``ScanArgs.inner_out_sit_sot``.

        Parameters
        ----------
        var_info:
            The element for which we want the alternate
        alt_prefix:
            The string prefix for the alternate field type.  It can be one of
            the following: ``"inner_out"``, ``"inner_in"``, ``"outer_in"``, and
            ``"outer_out"``.

        Outputs
        -------
        The alternate variable.
        """
        _var_info: FieldInfo
        if not isinstance(var_info, FieldInfo):
            find_var_info = self.find_among_fields(var_info)
            if find_var_info is None:
                raise ValueError(f"Couldn't find {var_info} among fields")
            _var_info = find_var_info
        else:
            _var_info = var_info

        alt_type = _var_info.name[(_var_info.name.index("_", 6) + 1) :]
        alt_var = getattr(self, f"{alt_prefix}_{alt_type}")[_var_info.index]
        return type_cast(Variable, alt_var)

    def find_among_fields(
        self, i: Variable, field_filter: Callable[[str], bool] = default_filter
    ) -> Optional[FieldInfo]:
        """Find the type and indices of the field containing a given element.

        NOTE: This only returns the *first* field containing the given element.

        Parameters
        ----------
        i:
            The element to find among this object's fields.
        field_filter:
            A function passed to `filter` that determines which fields to
            consider.  It must take a string field name and return a truthy
            value.

        Returns
        -------
        A tuple of length 4 containing the field name string, the first index,
        the second index (for nested lists), and the "major" index (i.e. the
        index within the aggregate lists like `self.inner_inputs`,
        `self.outer_outputs`, etc.), or a triple of `None` when no match is
        found.
        """

        field_names = filter(field_filter, self.field_names)

        for field_name in field_names:
            lst = getattr(self, field_name)

            field_prefix = field_name[:8]
            if field_prefix.endswith("in"):
                agg_field_name = f"{field_prefix}puts"
            else:
                agg_field_name = f"{field_prefix}tputs"

            agg_list = getattr(self, agg_field_name)

            if field_name in self.nested_list_fields:
                for n, sub_lst in enumerate(lst):
                    idx = safe_index(sub_lst, i)
                    if idx is not None:
                        agg_idx = safe_index(agg_list, i)
                        return FieldInfo(field_name, agg_field_name, n, idx, agg_idx)
            else:
                idx = safe_index(lst, i)
                if idx is not None:
                    agg_idx = safe_index(agg_list, i)
                    return FieldInfo(field_name, agg_field_name, idx, None, agg_idx)

        return None

    def _remove_from_fields(
        self, i: Variable, field_filter: Callable[[str], bool] = default_filter
    ) -> Optional[FieldInfo]:
        field_info = self.find_among_fields(i, field_filter=field_filter)

        if field_info is None:
            return None

        if field_info.inner_index is not None:
            getattr(self, field_info.name)[field_info.index].remove(i)
        else:
            getattr(self, field_info.name).remove(i)

        return field_info

    def get_dependent_nodes(
        self, i: Variable, seen: Optional[Set[Variable]] = None
    ) -> Set[Variable]:
        if seen is None:
            seen = {i}
        else:
            seen.add(i)

        var_mappings = self.var_mappings

        field_info = self.find_among_fields(i)

        if field_info is None:
            raise ValueError(f"{i} not found among fields.")

        # Find the `var_mappings` key suffix that matches the field/set of
        # arguments containing our source node
        if field_info.name[:8].endswith("_in"):
            map_key_suffix = f"{field_info.name[:8]}p"
        else:
            map_key_suffix = field_info.name[:9]

        dependent_nodes = set()
        for k, v in var_mappings.items():
            if not k.endswith(map_key_suffix):
                continue

            dependent_idx = v[field_info.agg_index]
            dependent_idx = (
                dependent_idx if isinstance(dependent_idx, list) else [dependent_idx]
            )

            # Get the `ScanArgs` field name for the aggregate list property
            # corresponding to these dependent argument types (i.e. either
            # "outer_inputs", "inner_inputs", "inner_outputs", or
            # "outer_outputs").
            # To do this, we need to parse the "shared" prefix of the
            # current `var_mappings` key and append the missing parts so that
            # it either forms `"*_inputs"` or `"*_outputs"`.
            to_agg_field_prefix = k[:9]
            if to_agg_field_prefix.endswith("p"):
                to_agg_field_name = f"{to_agg_field_prefix}uts"
            else:
                to_agg_field_name = f"{to_agg_field_prefix}puts"

            to_agg_field = getattr(self, to_agg_field_name)

            for d_id in dependent_idx:
                if d_id < 0:
                    continue

                dependent_var = to_agg_field[d_id]

                if dependent_var not in seen:
                    dependent_nodes.add(dependent_var)

        if field_info.name.startswith("inner_in"):
            # If starting from an inner-input, then we need to find any
            # inner-outputs that depend on it.
            for out_n in self.inner_outputs:
                if i in graph_inputs([out_n]):
                    if out_n not in seen:
                        dependent_nodes.add(out_n)

        for n in tuple(dependent_nodes):
            if n in seen:
                continue
            sub_dependent_nodes = self.get_dependent_nodes(n, seen=seen)
            dependent_nodes |= sub_dependent_nodes
            seen |= sub_dependent_nodes

        return dependent_nodes

    def remove_from_fields(
        self, i: Variable, rm_dependents: bool = True
    ) -> List[Tuple[Variable, Optional[FieldInfo]]]:
        if rm_dependents:
            vars_to_remove = self.get_dependent_nodes(i) | {i}
        else:
            vars_to_remove = {i}

        rm_info: List[Tuple[Variable, Optional[FieldInfo]]] = []
        for v in vars_to_remove:
            dependent_rm_info = self._remove_from_fields(v)
            rm_info.append((v, dependent_rm_info))

        return rm_info

    def __copy__(self):
        res = object.__new__(type(self))
        res.__dict__.update(self.__dict__)
        # also copy mutable attrs
        for attr in self.__dict__:
            if (
                attr.startswith("inner_in")
                or attr.startswith("inner_out")
                or attr.startswith("outer_in")
                or attr.startswith("outer_out")
                or attr
                in (
                    "mit_mot_out_slices",
                    "mit_mot_in_slices",
                    "mit_sot_in_slices",
                )
            ):
                setattr(res, attr, copy.copy(getattr(self, attr)))
        return res

    def merge(self, other: "ScanArgs") -> "ScanArgs":
        res = copy.copy(self)
        for attr in self.__dict__:
            if (
                attr.startswith("inner_in")
                or attr.startswith("inner_out")
                or attr.startswith("outer_in")
                or attr.startswith("outer_out")
                or attr
                in ("mit_mot_out_slices", "mit_mot_in_slices", "mit_sot_in_slices")
            ):
                getattr(res, attr).extend(getattr(other, attr))
        return res

    def __str__(self):
        inner_arg_strs = [
            f"\t{p}={getattr(self, p)}"
            for p in self.field_names
            if p.startswith("outer_in") or p == "n_steps"
        ]
        inner_arg_strs += [
            f"\t{p}={getattr(self, p)}"
            for p in self.field_names
            if p.startswith("inner_in")
        ]
        inner_arg_strs += [
            f"\tmit_mot_in_slices={self.mit_mot_in_slices}",
            f"\tmit_sot_in_slices={self.mit_sot_in_slices}",
        ]
        inner_arg_strs += [
            f"\t{p}={getattr(self, p)}"
            for p in self.field_names
            if p.startswith("inner_out")
        ]
        inner_arg_strs += [
            f"\tmit_mot_out_slices={self.mit_mot_out_slices}",
        ]
        inner_arg_strs += [
            f"\t{p}={getattr(self, p)}"
            for p in self.field_names
            if p.startswith("outer_out")
        ]
        res = "ScanArgs(\n{})".format(",\n".join(inner_arg_strs))
        return res

    def __repr__(self):
        return self.__str__()

    def __eq__(self, other):
        if not isinstance(other, type(self)):
            return NotImplemented

        for field_name in self.field_names:
            if not hasattr(other, field_name) or getattr(self, field_name) != getattr(
                other, field_name
            ):
                return False

        return True


def forced_replace(out, x, y):
    """
    Check all internal values of the graph that compute the variable ``out``
    for occurrences of values identical with ``x``. If such occurrences are
    encountered then they are replaced with variable ``y``.

    Parameters
    ----------
    out : PyTensor Variable
    x : PyTensor Variable
    y : PyTensor Variable

    Examples
    --------
    out := sigmoid(wu)*(1-sigmoid(wu))
    x := sigmoid(wu)
    forced_replace(out, x, y) := y*(1-y)

    Notes
    -----
    When it find a match, it don't continue on the corresponding inputs.
    """
    if out is None:
        return None

    # ``visited`` is a set of nodes that are already known and don't need to be
    # checked again, speeding up the traversal of multiply-connected graphs.
    visited = set()
    from collections import deque

    q = deque()
    q.append(out)
    to_replace = []
    while q:
        graph = q.popleft()
        if graph in visited:
            continue
        visited.add(graph)
        if equal_computations([graph], [x]):
            to_replace.append((graph, y))
        elif graph.owner:
            q.extendleft(graph.owner.inputs)

    if len(to_replace) == 0:
        return out
    return clone_replace(out, replace=to_replace)
