"""§22.4a SLICE-3 — faceted catalog filtering + counts (read).

Pure functions over already-mirrored entries; no I/O, no DB. An "entry" here is
a plain dict carrying at least:
  - "state": the lifecycle state column,
  - "metadata_malformed": bool,
  - "meta": the per-entry metadata mapping (from cached_rfcs.meta_json).

Facetable fields (§5.1, plan decision 1): a collection's declared `enum` and
`tags` fields, in declaration order, plus the built-in `state` facet appended
last — but only when the collection declares a schema (INV-5: a no-`fields:`
collection has no facets at all, so the frontend keeps its legacy chips). `text`
fields are not faceted in v1 (they get a detail control in SLICE-4).

Counts use drill-down semantics (plan decision 2): the count for a value of
field F is taken over entries matching every OTHER field's selection (and the
malformed toggle), not F's own — so within-field values stay switchable (OR
within a field, AND across fields). The returned items list applies ALL
selections.
"""
from __future__ import annotations

from typing import Any

# enum + tags are facetable; text is rendered as a detail control (SLICE-4).
FACETABLE_TYPES = {"enum", "tags"}


def facet_fields(fields: dict[str, dict] | None) -> list[tuple[str, str]]:
    """Ordered `[(name, type), ...]` facetable from the schema, `state` last.

    Empty when the collection declares no schema (INV-5)."""
    if not fields:
        return []
    out = [
        (name, spec.get("type"))
        for name, spec in fields.items()
        if spec.get("type") in FACETABLE_TYPES
    ]
    out.append(("state", "enum"))
    return out


def allowed_filter_keys(fields: dict[str, dict] | None) -> set[str]:
    """Query-param keys the collection-scoped list accepts (plan decision 6)."""
    keys = {name for name, _ in facet_fields(fields)}
    keys.update({"unreviewed", "malformed"})
    return keys


def _values_for(entry: dict[str, Any], name: str, ftype: str) -> list[str]:
    """The facet value(s) an entry contributes for field `name` (str-cast)."""
    if name == "state":
        v = entry.get("state")
        return [str(v)] if v else []
    meta = entry.get("meta") or {}
    v = meta.get(name)
    if v is None:
        return []
    if ftype == "tags":
        return [str(x) for x in v] if isinstance(v, list) else []
    return [str(v)]


def _matches(entry: dict[str, Any], name: str, ftype: str, selected: set[str]) -> bool:
    if not selected:
        return True
    return bool(set(_values_for(entry, name, ftype)) & selected)  # OR within field


def filter_and_count(
    entries: list[dict[str, Any]],
    fields: dict[str, dict] | None,
    selections: dict[str, set[str]],
    only_malformed: bool = False,
) -> tuple[list[dict[str, Any]], dict[str, dict[str, int]]]:
    """Filter `entries` by `selections` and compute drill-down facet counts.

    `selections` maps a facet field name → the set of selected values (OR within
    the field; AND across fields). `only_malformed` narrows items and counts to
    entries flagged malformed (INV-3). Returns `(items, facets)` where
    `facets = {field: {value: count}}`. With no schema → `([all passing], {})`.
    """
    facetable = facet_fields(fields)

    def passes_malformed(e: dict[str, Any]) -> bool:
        return (not only_malformed) or bool(e.get("metadata_malformed"))

    items = [
        e for e in entries
        if passes_malformed(e)
        and all(_matches(e, n, t, selections.get(n, set())) for n, t in facetable)
    ]

    facets: dict[str, dict[str, int]] = {}
    for name, ftype in facetable:
        counts: dict[str, int] = {}
        for e in entries:
            if not passes_malformed(e):
                continue
            if not all(
                _matches(e, on, ot, selections.get(on, set()))
                for on, ot in facetable
                if on != name
            ):
                continue
            for val in _values_for(e, name, ftype):
                counts[val] = counts.get(val, 0) + 1
        facets[name] = counts
    return items, facets