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import typing
import warnings

from collections.abc import Iterable, Sequence

import numpy as np
import pytensor

from pytensor import tensor as pt
from pytensor.graph import Apply, Op, node_rewriter
from pytensor.graph.basic import Constant, clone_get_equiv
from pytensor.graph.fg import FunctionGraph
from pytensor.graph.traversal import graph_inputs, walk
from pytensor.link.c.type import CType
from pytensor.raise_op import CheckAndRaise
from pytensor.scalar.basic import Mul
from pytensor.tensor.basic import get_underlying_scalar_constant_value
from pytensor.tensor.elemwise import Elemwise
from pytensor.tensor.exceptions import NotScalarConstantError
from pytensor.tensor.variable import TensorVariable

from pymc.logprob.abstract import MeasurableOp, ValuedRV, _logprob
from pymc.pytensorf import replace_vars_in_graphs

if typing.TYPE_CHECKING:
    from pymc.logprob.transforms import Transform


def replace_rvs_by_values(
    graphs: Sequence[TensorVariable],
    *,
    rvs_to_values: dict[TensorVariable, TensorVariable],
    rvs_to_transforms: dict[TensorVariable, "Transform"] | None = None,
) -> list[TensorVariable]:
    """Clone and replace random variables in graphs with their value variables.

    Parameters
    ----------
    graphs
        The graphs in which to perform the replacements.
    rvs_to_values
        Mapping between the original graph RVs and respective value variables
    rvs_to_transforms, optional
        Mapping between the original graph RVs and respective value transforms
    """
    if rvs_to_transforms:
        # Conditional transforms like Interval can reference variables in the original RV graph
        # To avoid mutating the original graphs in place, we have to clone them
        inputs = [i for i in graph_inputs(graphs) if not isinstance(i, Constant)]
        equiv = clone_get_equiv(inputs, graphs, False, False)

        graphs = [equiv[g] for g in graphs]
        rvs_to_values = {equiv.get(rv, rv): value for rv, value in rvs_to_values.items()}
        rvs_to_transforms = {
            equiv.get(rv, rv): transform for rv, transform in rvs_to_transforms.items()
        }

    replacements = {}

    def populate_replacements(var):
        # Populate replacements dict with {rv: value} pairs indicating which graph
        # RVs should be replaced by what value variables.
        if not var.owner:
            return []

        next_vars = []
        value = rvs_to_values.get(var, None)
        if value is not None:
            rv = var

            if rvs_to_transforms is not None:
                transform = rvs_to_transforms.get(rv, None)
                if transform is not None:
                    # We want to replace uses of the RV by the back-transformation of its value
                    value = transform.backward(value, *rv.owner.inputs)
                    # The value may have a less precise type than the rv. In this case
                    # filter_variable will add a SpecifyShape to ensure they are consistent
                    value = rv.type.filter_variable(value, allow_convert=True)
                    value.name = rv.name

            replacements[rv] = value
            # Also walk the graph of the value variable to make any additional
            # replacements if that is not a simple input variable
            next_vars.append(value)

        next_vars.extend(reversed(var.owner.inputs))
        return next_vars

    # Iterate over the generator to populate the replacements
    for _ in walk(graphs, populate_replacements, bfs=False):
        pass

    return replace_vars_in_graphs(graphs, replacements)


def convert_indices(indices, entry):
    if indices and isinstance(entry, CType):
        rval = indices.pop(0)
        return rval
    elif isinstance(entry, slice):
        return slice(
            convert_indices(indices, entry.start),
            convert_indices(indices, entry.stop),
            convert_indices(indices, entry.step),
        )
    else:
        return entry


def indices_from_subtensor(idx_list, indices):
    """Compute a useable index tuple from the inputs of a ``*Subtensor**`` ``Op``."""
    return tuple(
        tuple(convert_indices(list(indices), idx) for idx in idx_list) if idx_list else indices
    )


def filter_measurable_variables(inputs):
    return [
        inp for inp in inputs if (inp.owner is not None and isinstance(inp.owner.op, MeasurableOp))
    ]


def check_potential_measurability(inputs: Iterable[TensorVariable]) -> bool:
    def expand_fn(var):
        # expand_fn does not go beyond valued_rvs or any MeasurableOp variables
        if var.owner and not isinstance(var.owner.op, MeasurableOp | ValuedRV):
            return var.owner.inputs
        else:
            return []

    if any(
        (
            ancestor_var.owner
            and isinstance(ancestor_var.owner.op, MeasurableOp)
            and not isinstance(ancestor_var.owner.op, ValuedRV)
        )
        for ancestor_var in walk(inputs, expand=expand_fn, bfs=False)
    ):
        return True
    return False


class ParameterValueError(ValueError):
    """Exception for invalid parameters values in logprob graphs."""


class CheckParameterValue(CheckAndRaise):
    """Implements a parameter value check in a logprob graph.

    Raises `ParameterValueError` if the check is not True.
    """

    __props__ = ("msg", "exc_type", "can_be_replaced_by_ninf")

    def __init__(self, msg: str = "", can_be_replaced_by_ninf: bool = False):
        super().__init__(ParameterValueError, msg)
        self.can_be_replaced_by_ninf = can_be_replaced_by_ninf

    def __str__(self):
        """Return a string representation of the object."""
        return f"Check{{{self.msg}}}"


@node_rewriter(tracks=[CheckParameterValue])
def local_remove_check_parameter(fgraph, node):
    """Rewrite that removes CheckParameterValue.

    This is used when compile_rv_inplace
    """
    if isinstance(node.op, CheckParameterValue):
        return [node.inputs[0]]


@node_rewriter(tracks=[CheckParameterValue])
def local_check_parameter_to_ninf_switch(fgraph, node):
    if not node.op.can_be_replaced_by_ninf:
        return None

    logp_expr, *logp_conds = node.inputs
    if len(logp_conds) > 1:
        logp_cond = pt.all(logp_conds)
    else:
        (logp_cond,) = logp_conds
    out = pt.switch(logp_cond, logp_expr, -np.inf)
    out.name = node.op.msg

    if out.dtype != node.outputs[0].dtype:
        out = pt.cast(out, node.outputs[0].dtype)

    return [out]


pytensor.compile.optdb["canonicalize"].register(
    "local_remove_check_parameter",
    local_remove_check_parameter,
    use_db_name_as_tag=False,
)

pytensor.compile.optdb["canonicalize"].register(
    "local_check_parameter_to_ninf_switch",
    local_check_parameter_to_ninf_switch,
    use_db_name_as_tag=False,
)


class DiracDelta(MeasurableOp, Op):
    """An `Op` that represents a Dirac-delta distribution."""

    __props__ = ("rtol", "atol")

    def __init__(self, rtol=1e-5, atol=1e-8):
        self.rtol = rtol
        self.atol = atol

    def make_node(self, x):
        x = pt.as_tensor(x)
        return Apply(self, [x], [x.type()])

    def do_constant_folding(self, fgraph, node):
        # Without this, the `Op` would be removed from the graph during
        # canonicalization
        return False

    def perform(self, node, inp, out):
        (x,) = inp
        (z,) = out
        warnings.warn("DiracDelta is a dummy Op that shouldn't be used in a compiled graph")
        z[0] = x

    def infer_shape(self, node, input_shapes):
        return input_shapes


dirac_delta = DiracDelta()


@_logprob.register(DiracDelta)
def diracdelta_logprob(op, values, *inputs, **kwargs):
    (values,) = values
    (const_value,) = inputs
    values, const_value = pt.broadcast_arrays(values, const_value)
    return pt.switch(pt.isclose(values, const_value, rtol=op.rtol, atol=op.atol), 0.0, -np.inf)


def find_negated_var(var):
    """Return a variable that is being multiplied by -1 or None otherwise."""
    if not (
        var.owner and isinstance(var.owner.op, Elemwise) and isinstance(var.owner.op.scalar_op, Mul)
    ):
        return None
    if len(var.owner.inputs) != 2:
        return None

    inputs = var.owner.inputs
    for mul_var, mul_const in (inputs, reversed(inputs)):
        try:
            if get_underlying_scalar_constant_value(mul_const) == -1:
                return mul_var
        except NotScalarConstantError:
            continue

    return None


def get_related_valued_nodes(fgraph: FunctionGraph, node: Apply) -> list[Apply]:
    """Get all ValuedVars related to the same RV node.

    Returns
    -------
        rv_node
        valued_nodes
    """
    clients = fgraph.clients
    return [
        client
        for out in node.outputs
        for client, _ in clients[out]
        if isinstance(client.op, ValuedRV)
    ]


def __getattr__(name):
    if name == "rvs_in_graphs":
        warnings.warn(
            f"{name} has been moved to `pymc.pytensorf`. Importing from `pymc.logprob.utils` will fail in a future release.",
            FutureWarning,
        )
        from pymc.pytensorf import rvs_in_graph

        return rvs_in_graph()

    raise AttributeError(f"module {__name__} has no attribute {name}")
