import pytensor.tensor.rewriting.indexed_elemwise  # noqa: F401
from pytensor.compile import optdb
from pytensor.graph import node_rewriter
from pytensor.graph.rewriting.basic import dfs_rewriter
from pytensor.graph.rewriting.utils import rewrite_subgraph
from pytensor.graph.traversal import ancestors, applys_between
from pytensor.tensor.basic import as_tensor, constant
from pytensor.tensor.blockwise import Blockwise, BlockwiseWithCoreShape
from pytensor.tensor.rewriting.shape import ShapeFeature


def simplify_core_shape_graphs(core_shapes, fgraph):
    """Canonicalize the fresh shape arithmetic built by ``get_non_recursive_shape``."""
    graph_frontier = fgraph.variables.intersection(
        ancestors(core_shapes, blockers=fgraph.variables)
    )
    return rewrite_subgraph(core_shapes, graph_frontier, include=("canonicalize",))


@node_rewriter([Blockwise])
def introduce_explicit_core_shape_blockwise(fgraph, node):
    """Introduce the core shape of a Blockwise.

    We wrap Blockwise graphs into a BlockwiseWithCoreShape OpFromGraph
    that has an extra "non-functional" input that represents the core shape of the Blockwise variable.
    This core_shape is used by the numba backend to pre-allocate the output array.

    The core shape is built from ``ShapeFeature.get_non_recursive_shape``, whose
    expressions read only the node's own inputs and can therefore be introduced
    after inplacing without conflicting with the destroyers in the surrounding
    graph.

    This rewrite is required for the numba backend implementation of Blockwise.

    Example
    -------

    .. code-block:: python

        import pytensor
        import pytensor.tensor as pt

        x = pt.tensor("x", shape=(5, None, None))
        outs = pt.linalg.svd(x, compute_uv=True)
        pytensor.dprint(outs)
        # Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}.0 [id A]
        #  └─ x [id B]
        # Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}.1 [id A]
        #  └─ ···
        # Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}.2 [id A]
        #  └─ ···

        # After the rewrite, note the new 3 core shape inputs
        fn = pytensor.function([x], outs, mode="NUMBA")
        fn.dprint(print_type=False)
        # [Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}].0 [id A] 6
        #  ├─ x [id B]
        #  ├─ MakeVector{dtype='int64'} [id C] 5
        #  │  ├─ Shape_i{1} [id D] 2
        #  │  │  └─ x [id B]
        #  │  └─ Shape_i{1} [id D] 2
        #  │     └─ ···
        #  ├─ MakeVector{dtype='int64'} [id E] 4
        #  │  └─ Minimum [id F] 3
        #  │     ├─ Shape_i{1} [id D] 2
        #  │     │  └─ ···
        #  │     └─ Shape_i{2} [id G] 0
        #  │        └─ x [id B]
        #  └─ MakeVector{dtype='int64'} [id H] 1
        #     ├─ Shape_i{2} [id G] 0
        #     │  └─ ···
        #     └─ Shape_i{2} [id G] 0
        #        └─ ···
        # [Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}].1 [id A] 6
        #  └─ ···
        # [Blockwise{SVD{full_matrices=True, compute_uv=True}, (m,n)->(m,m),(k),(n,n)}].2 [id A] 6
        #  └─ ···
    """
    op: Blockwise = node.op
    batch_ndim = op.batch_ndim(node)

    shape_feature: ShapeFeature | None = getattr(fgraph, "shape_feature", None)
    if shape_feature is None:
        shape_feature = ShapeFeature()

    core_shapes = [
        [
            shape_feature.get_non_recursive_shape(out, i)
            for i in range(batch_ndim, out.type.ndim)
        ]
        for out in node.outputs
    ]

    core_shapes = [
        as_tensor(core_shape) if len(core_shape) else constant([], dtype="int64")
        for core_shape in core_shapes
    ]

    if any(
        isinstance(node.op, Blockwise)
        for node in applys_between(node.inputs, core_shapes)
    ):
        # If Blockwise shows up in the shape graph we can't introduce the core shape
        return None

    core_shapes = simplify_core_shape_graphs(core_shapes, fgraph)

    return BlockwiseWithCoreShape(
        [*node.inputs, *core_shapes],
        node.outputs,
        destroy_map=op.destroy_map,
    )(*node.inputs, *core_shapes, return_list=True)


optdb.register(
    introduce_explicit_core_shape_blockwise.__name__,
    dfs_rewriter(introduce_explicit_core_shape_blockwise),
    "numba",
    position=100,
)
