import pytensor.tensor as at
from pytensor.compile import optdb
from pytensor.graph.rewriting.basic import in2out, node_rewriter
from pytensor.tensor.basic import MakeVector
from pytensor.tensor.elemwise import DimShuffle
from pytensor.tensor.math import Sum
from pytensor.tensor.shape import Reshape
from pytensor.tensor.subtensor import AdvancedIncSubtensor, AdvancedSubtensor
from pytensor.tensor.var import TensorVariable


@node_rewriter([AdvancedIncSubtensor])
def boolean_indexing_set_or_inc(fgraph, node):
    """Replace `AdvancedIncSubtensor` when using boolean indexing using `Switch`.

    JAX cannot JIT-compile functions that use boolean indexing to set values in
    an array. A workaround is to re-express this logic using `jax.numpy.where`.
    This rewrite allows to improve upon JAX's API.

    """

    op = node.op
    x = node.inputs[0]
    y = node.inputs[1]
    cond = node.inputs[2]

    if not isinstance(cond, TensorVariable):
        return

    if not cond.type.dtype == "bool":
        return

    if op.set_instead_of_inc:
        out = at.where(cond, y, x)
        return out.owner.outputs
    else:
        out = at.where(cond, x + y, x)
        return out.owner.outputs


optdb.register(
    "jax_boolean_indexing_set_or_inc",
    in2out(boolean_indexing_set_or_inc),
    "jax",
    position=100,
)


@node_rewriter([Sum])
def boolean_indexing_sum(fgraph, node):
    """Replace the sum of `AdvancedSubtensor` with boolean indexing.

    JAX cannot JIT-compile functions that use boolean indexing, but can compile
    those expressions that can be re-expressed using `jax.numpy.where`. This
    rewrite re-rexpressed the model on the behalf of the user and thus allows to
    improve upon JAX's API.

    """
    operand = node.inputs[0]

    if not isinstance(operand, TensorVariable):
        return

    if operand.owner is None:
        return

    if not isinstance(operand.owner.op, AdvancedSubtensor):
        return

    x = operand.owner.inputs[0]
    cond = operand.owner.inputs[1]

    if not isinstance(cond, TensorVariable):
        return

    if not cond.type.dtype == "bool":
        return

    out = at.sum(at.where(cond, x, 0))
    return out.owner.outputs


optdb.register(
    "jax_boolean_indexing_sum", in2out(boolean_indexing_sum), "jax", position=100
)


@node_rewriter([Reshape])
def shape_parameter_as_tuple(fgraph, node):
    """Replace `MakeVector` and `DimShuffle` (when used to transform a scalar
    into a 1d vector) when they are found as the input of a `shape`
    parameter by `JAXShapeTuple` during transpilation.

    The JAX implementations of `MakeVector` and `DimShuffle` always return JAX
    `TracedArrays`, but JAX only accepts concrete values as inputs for the `size`
    or `shape` parameter. When these `Op`s are used to convert scalar or tuple
    inputs, however, we can avoid tracing by making them return a tuple of their
    inputs instead.

    Note that JAX does not accept scalar inputs for the `size` or `shape`
    parameters, and this rewrite also ensures that scalar inputs are turned into
    tuples during transpilation.

    """
    from pytensor.link.jax.dispatch.shape import JAXShapeTuple

    shape_arg = node.inputs[1]
    shape_node = shape_arg.owner

    if shape_node is None:
        return

    if isinstance(shape_node.op, JAXShapeTuple):
        return

    if isinstance(shape_node.op, MakeVector) or (
        isinstance(shape_node.op, DimShuffle)
        and shape_node.op.input_broadcastable == ()
        and shape_node.op.new_order == ("x",)
    ):
        # Here PyTensor converted a tuple or list to a tensor
        new_shape_args = JAXShapeTuple()(*shape_node.inputs)
        new_inputs = list(node.inputs)
        new_inputs[1] = new_shape_args

        new_node = node.clone_with_new_inputs(new_inputs)
        return new_node.outputs


optdb.register(
    "jax_shape_parameter_as_tuple",
    in2out(shape_parameter_as_tuple),
    "jax",
    position=100,
)
