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import pytensor.tensor as pt

from pytensor.graph.rewriting.basic import node_rewriter
from pytensor.tensor import TensorVariable
from pytensor.tensor.extra_ops import CumOp

from pymc.logprob.abstract import MeasurableOp, _logprob, _logprob_helper
from pymc.logprob.rewriting import measurable_ir_rewrites_db
from pymc.logprob.utils import filter_measurable_variables


class MeasurableCumsum(MeasurableOp, CumOp):
    """A placeholder used to specify a log-likelihood for a cumsum sub-graph."""


@_logprob.register(MeasurableCumsum)
def logprob_cumsum(op, values, base_rv, **kwargs):
    """Compute the log-likelihood graph for a `Cumsum`."""
    (value,) = values

    value_diff = pt.diff(value, axis=op.axis)
    value_diff = pt.concatenate(
        (
            # Take first element of axis and add a broadcastable dimension so
            # that it can be concatenated with the rest of value_diff
            pt.shape_padaxis(
                pt.take(value, 0, axis=op.axis),
                axis=op.axis,
            ),
            value_diff,
        ),
        axis=op.axis,
    )

    cumsum_logp = _logprob_helper(base_rv, value_diff)

    return cumsum_logp


@node_rewriter([CumOp])
def find_measurable_cumsums(fgraph, node) -> list[TensorVariable] | None:
    r"""Find `Cumsums`\s for which a `logprob` can be computed."""
    if not (isinstance(node.op, CumOp) and node.op.mode == "add"):
        return None

    if isinstance(node.op, MeasurableCumsum):
        return None

    base_rv = node.inputs[0]

    # Check that cumsum does not mix dimensions
    if base_rv.ndim > 1 and node.op.axis is None:
        return None

    if not filter_measurable_variables(node.inputs):
        return None

    new_op = MeasurableCumsum(axis=node.op.axis or 0, mode="add")
    new_rv = new_op.make_node(base_rv).default_output()

    return [new_rv]


measurable_ir_rewrites_db.register(
    "find_measurable_cumsums",
    find_measurable_cumsums,
    "basic",
    "cumsum",
)
