from __future__ import annotations

import math
from copy import deepcopy
from pathlib import Path
import tempfile
from typing import Any


def _score(expected: dict[str, Any], actual: dict[str, Any]) -> float | None:
    scores: list[float] = []
    reversed_plane_normal = False
    for key in ("center_mm", "circle_center_mm", "start_mm", "end_mm", "normal", "axis_origin_mm", "axis_direction"):
        if key in expected:
            # plane_offset_mm 与平面方程绑定，必须使用记录平面方程时采用的
            # plane_normal。face 的局部采样 normal 在 OCC 中可能与其相反。
            use_plane_normal = key == "normal" and "plane_offset_mm" in expected and actual.get("plane_normal") is not None
            right = actual.get("plane_normal") if use_plane_normal else actual.get(key)
            left = expected[key]
            if not isinstance(right, (list, tuple)) or len(left) != len(right): return None
            delta = math.sqrt(sum((float(a) - float(b)) ** 2 for a, b in zip(left, right)))
            if key in {"normal", "axis_direction"}:
                # A source CAP_FACE identifies a geometric plane, not the OCC
                # orientation of the resulting face.  The two kernels may
                # report the same cap with inverse normals, especially for a
                # start cap.  A rotational-face axis is likewise a geometric
                # line, whose direction may be reversed by OCC.
                reversed_delta = math.sqrt(sum((float(a) + float(b)) ** 2 for a, b in zip(left, right)))
                if key == "normal" and use_plane_normal and reversed_delta < delta: reversed_plane_normal = True
                delta = min(delta, reversed_delta)
            scores.append(max(0.0, 1.0 - delta / 0.05))
    for key in ("radius_mm", "plane_offset_mm"):
        if key in expected:
            try:
                value = float(actual[key])
                # 平面偏移是 normal · point。面法向反向时，同一几何平面的
                # 有符号偏移也必须同步反号，不能只放宽 normal 的比较。
                if key == "plane_offset_mm" and reversed_plane_normal: value = -value
                delta = abs(float(expected[key]) - value)
            except Exception: return None
            scores.append(max(0.0, 1.0 - delta / 0.05))
    if "minimum_area_mm2" in expected:
        try:
            if float(actual["area_mm2"]) + 1e-6 < float(expected["minimum_area_mm2"]): return None
        except Exception: return None
    if "bbox_mm" in expected:
        actual_box = actual.get("bbox_mm")
        if not isinstance(actual_box, (list, tuple)) or len(actual_box) != 6: return None
        delta = max(abs(float(a) - float(b)) for a, b in zip(expected["bbox_mm"], actual_box))
        scores.append(max(0.0, 1.0 - delta / 0.05))
    return sum(scores) / len(scores) if scores else 0.0


def bind_selector(kind: str, owner_feature_id: str, geometry: dict[str, Any], records: list[dict[str, Any]], *, minimum_score: float = 0.8) -> dict[str, Any]:
    candidates = []
    for record in records:
        owners = record.get("owner_feature_ids") or [record.get("feature_id")]
        if record.get("kind") != kind or owner_feature_id not in owners: continue
        score = _score(geometry, record.get("geometry") or {})
        if score is not None and score >= minimum_score: candidates.append((score, record))
    candidates.sort(key=lambda item: (-item[0], str(item[1].get("record_id"))))
    if not candidates: raise ValueError("selector_not_found")
    if len(candidates) > 1 and abs(candidates[0][0] - candidates[1][0]) <= 1e-9: raise ValueError("selector_ambiguous")
    score, record = candidates[0]
    return {"kind": kind, "owner_feature_id": owner_feature_id, "stable_id": record["record_id"], "snapshot_id": record["record_id"], "source": "runtime_snapshot", "confidence": round(score, 6), "geometry": record.get("geometry") or geometry}


def _dot(left: list[float], right: list[float]) -> float: return sum(float(a)*float(b) for a, b in zip(left, right))


def _circle_records(expected: dict[str, Any], records: list[dict[str, Any]]) -> list[dict[str, Any]]:
    center = expected.get("source_circle_center_mm"); normal = expected.get("source_plane_normal"); radius = float(expected.get("source_circle_radius_mm") or 0)
    if not isinstance(center, list) or not isinstance(normal, list) or radius <= 0: return []
    plane_offset = _dot(center, normal); matches = []
    for record in records:
        geometry = record.get("geometry") or {}
        if record.get("kind") != "edge" or geometry.get("curve_type") != "circle": continue
        points = [geometry.get("start_mm"), geometry.get("end_mm")]
        if not all(isinstance(point, list) and len(point) == 3 for point in points): continue
        if any(abs(_dot(point, normal) - plane_offset) > 0.05 for point in points): continue
        radial = []
        for point in points:
            delta = [float(point[i])-float(center[i]) for i in range(3)]; axial = _dot(delta, normal)
            radial.append(math.sqrt(max(0.0, sum(value*value for value in delta)-axial*axial)))
        if all(abs(value-radius) <= max(0.05, radius*1e-4) for value in radial): matches.append(record)
    return matches


def _binding_targets(selector: dict[str, Any]):
    components = selector.get("intersection_of")
    if isinstance(components, list):
        for component in components:
            if isinstance(component, dict): yield from _binding_targets(component)
        return
    yield selector


def _bound_selector(placeholder: dict[str, Any], records: list[dict[str, Any]]) -> tuple[dict[str, Any], list[dict[str, Any]]]:
    bound = []
    for record in records:
        owners = record.get("owner_feature_ids") or [record.get("feature_id")]
        selector = {"kind": placeholder["kind"], "owner_feature_id": str(owners[0]), "stable_id": record["record_id"], "snapshot_id": record["record_id"], "source": "runtime_snapshot", "confidence": 1.0, "geometry": record.get("geometry") or {}}
        if placeholder.get("binding_feature_id") is not None: selector["binding_feature_id"] = placeholder["binding_feature_id"]
        if placeholder.get("owner_match_required"): selector["owner_match_required"] = True
        bound.append(selector)
    if placeholder.get("match_mode") == "all":
        selector = dict(placeholder); selector["matched_selectors"] = bound
        return selector, bound
    return bound[0], bound


def bind_candidate_selectors(cdsl: dict[str, Any]) -> tuple[dict[str, Any], list[dict[str, Any]]]:
    """Rebuild every selector-bearing prefix and bind against its active body."""
    from engine.cdsl_engine.runtime import rebuild_cdsl
    bound = deepcopy(cdsl); evidence = []
    with tempfile.TemporaryDirectory(prefix="cadfs-bind-") as temporary:
        for index, feature in enumerate(bound.get("features") or []):
            roots = list(feature.get("selectors") or [])
            for name in ("end_condition", "reverse_end_condition"):
                condition = (feature.get("params") or {}).get(name)
                reference = condition.get("reference") if isinstance(condition, dict) else None
                if isinstance(reference, dict): roots.append(reference)
            targets = [target for root in roots for target in _binding_targets(root)]
            if not targets: continue
            prefix_cache: dict[int, tuple[list[dict[str, Any]], str | None]] = {}

            def prefix_records(binding_feature_id: str | None, owner_feature_id: str | None) -> list[dict[str, Any]]:
                prefix_count = index
                if binding_feature_id is not None:
                    binding_index = next((item_index for item_index, item in enumerate(bound["features"][:index]) if item["id"] == binding_feature_id), None)
                    if binding_index is None: raise ValueError(f"{feature['id']}: selector binding feature is missing or forward")
                    prefix_count = binding_index + 1
                if prefix_count not in prefix_cache:
                    prefix = deepcopy(bound); prefix["features"] = bound["features"][:prefix_count]
                    if not prefix["features"]: raise ValueError(f"{feature['id']}: selector has no executable prefix")
                    report = rebuild_cdsl(prefix, Path(temporary) / f"prefix-{index}-{prefix_count}.step", strict=True)
                    body_id = next((item.get("body_id") for item in reversed(report.get("feature_results") or []) if item.get("body_id")), None)
                    prefix_cache[prefix_count] = (list(report.get("topology_records") or []), body_id)
                all_records, body_id = prefix_cache[prefix_count]
                return [
                        item for item in all_records
                        # Reference planes and axes are session context, not body
                        # topology. They must remain available while binding a mirror
                        # or extent selector against a body-bearing prefix.
                        if item.get("kind") in {"plane", "axis"}
                        or not body_id
                        or item.get("body_id") == body_id
                        or str(item.get("body_id") or "").startswith(f"{body_id}:")
                    ]

            resolved = []
            for placeholder in targets:
                records = prefix_records(placeholder.get("binding_feature_id"), placeholder.get("owner_feature_id"))
                output_role = str(placeholder.get("output_role") or "").strip()
                if output_role:
                    # Builder output roles are not geometry placeholders. They
                    # remain in the bound CDSL so runtime can resolve the
                    # current active B-rep face through exact kernel history.
                    # Replacing one with stable_id/geometry would mix evidence
                    # and make a stale snapshot appear durable.
                    owner = placeholder.get("owner_feature_id")
                    role_source = placeholder.get("output_role_source")
                    source_owner = role_source.get("owner_feature_id") if isinstance(role_source, dict) else None
                    source_role = role_source.get("output_role") if isinstance(role_source, dict) else None
                    candidates = [
                        record for record in records
                        if record.get("kind") == placeholder.get("kind")
                        and owner in (record.get("owner_feature_ids") or [record.get("feature_id")])
                        and output_role in (record.get("output_roles") or [])
                        and (
                            role_source is None
                            or any(
                                item.get("output_role") == output_role
                                and item.get("owner_feature_id") == source_owner
                                and item.get("source_output_role") == source_role
                                for item in record.get("output_role_sources") or []
                            )
                        )
                    ]
                    if len(candidates) != 1:
                        status = "not_found" if not candidates else "ambiguous"
                        raise ValueError(
                            f"{feature['id']}: selector_output_role_{status} after prefix rebuild"
                        )
                    resolved.append(candidates[0])
                    continue
                geometry = placeholder.get("geometry") or {}
                candidates = _circle_records(geometry, records) if geometry.get("source_circle_radius_mm") else []
                if not candidates:
                    same_kind = [record for record in records if record.get("kind") == placeholder.get("kind")]
                    owner = placeholder.get("owner_feature_id")
                    owner_matches = [record for record in same_kind if owner in (record.get("owner_feature_ids") or [record.get("feature_id")])]
                    # Pattern instance provenance is stronger than an ordinary
                    # feature owner: an unresolved instance cannot fall back to
                    # an aggregate face with matching geometry. Ordinary source
                    # selectors retain their established geometry-binding path
                    # across topology-changing dress-up operations.
                    pool = owner_matches if placeholder.get("owner_match_required") else (owner_matches or same_kind)
                    if not geometry:
                        # Context selectors (notably a generated mirror plane)
                        # may have no geometric snapshot. Their owner-qualified
                        # singleton identity is sufficient and must not be scored
                        # as a zero-information geometric match.
                        if len(pool) != 1:
                            status = "not_found" if not pool else "ambiguous"
                            raise ValueError(f"{feature['id']}: selector_{status} after prefix rebuild")
                        candidates = [pool[0]]
                    else:
                        scored = [(score, record) for record in pool if (score := _score(geometry, record.get("geometry") or {})) is not None and score >= 0.8]
                        # A source owner is preferred for ordinary geometry
                        # selectors, but can be retained by an unrelated exact
                        # continuation after a dress-up. If none of that
                        # owner's active records satisfies the full geometry
                        # signature, bind against the current active body and
                        # still require a unique threshold-qualified match.
                        # Instance-qualified selectors never take this path.
                        if not scored and not placeholder.get("owner_match_required"):
                            scored = [
                                (score, record)
                                for record in same_kind
                                if (score := _score(geometry, record.get("geometry") or {})) is not None
                                and score >= 0.8
                            ]
                        scored.sort(key=lambda value: (-value[0], str(value[1].get("record_id"))))
                        if scored:
                            if placeholder.get("match_mode") != "all" and len(scored) > 1 and abs(scored[0][0] - scored[1][0]) <= 1e-9:
                                raise ValueError(f"{feature['id']}: selector_ambiguous after prefix rebuild")
                            candidates = [item[1] for item in scored] if placeholder.get("match_mode") == "all" else [scored[0][1]]
                if not candidates: raise ValueError(f"{feature['id']}: selector_not_found after prefix rebuild")
                selector, bound_selectors = _bound_selector(placeholder, candidates)
                placeholder.clear(); placeholder.update(selector)
                resolved.extend(bound_selectors)
            feature_selectors = feature.get("selectors") or []
            def selector_key(selector: dict[str, Any]) -> tuple[Any, ...]:
                stable_id = selector.get("stable_id")
                if stable_id is not None:
                    return ("stable_id", str(stable_id))
                source = selector.get("output_role_source")
                return (
                    "output_role",
                    selector.get("owner_feature_id"),
                    selector.get("kind"),
                    selector.get("output_role"),
                    source.get("owner_feature_id") if isinstance(source, dict) else None,
                    source.get("output_role") if isinstance(source, dict) else None,
                )

            unique = {selector_key(selector): selector for selector in feature_selectors}
            feature["selectors"] = list(unique.values())
            if feature.get("atomic_id") == "pattern_mirror":
                planes = [selector for selector in feature["selectors"] if selector.get("kind") == "plane"]
                if len(planes) != 1:
                    raise ValueError(f"{feature['id']}: mirror plane binding is not unique")
                feature.setdefault("params", {})["mirror_plane"] = planes[0]
            evidence.append({"feature_id": feature["id"], "prefix_feature_count": index, "selectors": feature["selectors"], "resolved": resolved})
    return bound, evidence