"""`LightGlueRuntime` — shared LightGlue matcher (AZ-278 / E-CC-HELPERS / R14 fix).

Implements the `lightglue_runtime` contract v1.0.0 at
`_docs/02_document/contracts/shared_helpers/lightglue_runtime.md`.

Layer 1 helper — NO `gps_denied_onboard.components.*` imports. The
engine handle is an opaque Protocol defined in `_types/manifests.py`;
C7's `InferenceRuntime.deserialize_engine` produces the concrete handle
and the composition root injects ONE shared instance into both C2.5
(InlierBasedReranker) and C3 (CrossDomainMatcher) — the structural
fix for R14.

Single-threaded by contract. The concurrent-access guard is
non-blocking: concurrent entry RAISES `LightGlueConcurrentAccessError`
rather than serialising, so a composition-root regression that wires
the runtime into multiple threads is caught immediately instead of
silently corrupting CUDA state.
"""

from __future__ import annotations

import threading

from gps_denied_onboard._types.manifests import EngineHandle
from gps_denied_onboard._types.matching import CorrespondenceSet, KeypointSet

__all__ = [
    "LightGlueConcurrentAccessError",
    "LightGlueRuntime",
    "LightGlueRuntimeError",
]


class LightGlueRuntimeError(RuntimeError):
    """Raised on construction guards or descriptor-dim mismatch."""


class LightGlueConcurrentAccessError(RuntimeError):
    """Raised when a concurrent ``match`` / ``match_batch`` entry is detected.

    The serial-access invariant is a composition-root contract — if you
    see this exception, the runtime was wired into more than one thread
    by mistake. Fix the composition root, do NOT add a lock here.
    """


def _validate_keypoint_set(features: KeypointSet, *, name: str, expected_dim: int) -> None:
    if features.descriptors.ndim != 2 or features.descriptors.shape[1] != expected_dim:
        actual_dim = (
            features.descriptors.shape[1] if features.descriptors.ndim == 2 else "<bad shape>"
        )
        raise LightGlueRuntimeError(
            f"{name}: descriptor dim mismatch — engine expects {expected_dim}, "
            f"got {actual_dim} (descriptors.shape={features.descriptors.shape})"
        )


class LightGlueRuntime:
    """Shared LightGlue inference runtime.

    Single-thread by contract; concurrent entry raises.
    """

    def __init__(self, engine_handle: EngineHandle) -> None:
        if engine_handle is None:
            raise LightGlueRuntimeError(
                "LightGlueRuntime requires a non-None engine_handle (got None); "
                "composition root must inject the engine produced by C7's "
                "InferenceRuntime.deserialize_engine"
            )
        try:
            descriptor_dim = int(engine_handle.descriptor_dim)
        except AttributeError as exc:
            raise LightGlueRuntimeError(
                f"engine_handle missing required Protocol attribute 'descriptor_dim': {exc}"
            ) from exc
        if descriptor_dim < 1:
            raise LightGlueRuntimeError(
                f"engine_handle.descriptor_dim must be >= 1; got {descriptor_dim}"
            )
        self._engine = engine_handle
        self._descriptor_dim = descriptor_dim
        # Non-blocking guard: ``try_acquire`` raises on contention rather
        # than serialising callers, per the contract's "concurrent calls
        # are a bug" stance.
        self._in_use = threading.Lock()

    def descriptor_dim(self) -> int:
        return self._descriptor_dim

    def match(self, features_a: KeypointSet, features_b: KeypointSet) -> CorrespondenceSet:
        """Match a single pair (C2.5 path)."""
        if not self._in_use.acquire(blocking=False):
            raise LightGlueConcurrentAccessError(
                "LightGlueRuntime.match called from a second thread while another "
                "match is in flight — the runtime owns ONE CUDA stream and must be "
                "bound to a single hot-path thread by the composition root"
            )
        try:
            _validate_keypoint_set(features_a, name="features_a", expected_dim=self._descriptor_dim)
            _validate_keypoint_set(features_b, name="features_b", expected_dim=self._descriptor_dim)
            return self._engine.forward(features_a, features_b)
        finally:
            self._in_use.release()

    def match_batch(
        self,
        features_a_list: list[KeypointSet],
        features_b_list: list[KeypointSet],
    ) -> list[CorrespondenceSet]:
        """Batch-match (C3 path) — iterates serially over the single CUDA stream."""
        if len(features_a_list) != len(features_b_list):
            raise LightGlueRuntimeError(
                f"match_batch: features_a_list (len={len(features_a_list)}) and "
                f"features_b_list (len={len(features_b_list)}) must have equal length"
            )
        if not self._in_use.acquire(blocking=False):
            raise LightGlueConcurrentAccessError(
                "LightGlueRuntime.match_batch called concurrently with another match"
            )
        try:
            results: list[CorrespondenceSet] = []
            for idx, (fa, fb) in enumerate(zip(features_a_list, features_b_list, strict=True)):
                _validate_keypoint_set(
                    fa, name=f"features_a_list[{idx}]", expected_dim=self._descriptor_dim
                )
                _validate_keypoint_set(
                    fb, name=f"features_b_list[{idx}]", expected_dim=self._descriptor_dim
                )
                results.append(self._engine.forward(fa, fb))
            return results
        finally:
            self._in_use.release()