"""Parse a ``patterns.config`` programme into per-code space requirements.

Only the ``spaces:`` section is read here. Generic codes (c/o/s) carry no
explicit targets and are left unconstrained by the solver (they absorb slack).
"""

from __future__ import annotations

from dataclasses import dataclass, field

import yaml

# Urb::Dom::Fitness defaults for optional params (ProgrammeDriven.default_params).
_DEFAULT_WIDTH = (4.0, 1.0)
_DEFAULT_PROPORTION = (1.5, 0.5)


@dataclass
class SpaceReq:
    code: str
    name: str = ""
    size: float = 0.0  # target floor area, m^2
    size_sigma: float = 1.0
    width: float = _DEFAULT_WIDTH[0]
    width_sigma: float = _DEFAULT_WIDTH[1]
    proportion: float = _DEFAULT_PROPORTION[0]  # max length/width ratio
    proportion_sigma: float = _DEFAULT_PROPORTION[1]
    adjacency: list[str] = field(default_factory=list)
    level: int | None = None
    requires_below: str | None = None
    count: int = 1
    # erc.3 §13.3 leaf-sharing grain (homemaker-py-x3b): how many rooms of this
    # code may collapse into one shared leaf. Default 1 = not shareable. Under the
    # global ``leaf_share_factor`` selector an explicit value overrides the global
    # grain (share:1 opts a code OUT, share:N>=2 sets that code's grain to N).
    share: int = 1
    # 9o5 §7.5 veto hatch (homemaker-py-b3v): opt a code OUT of interchange-class
    # derivation. Default True = eligible. Set ``interchange: false`` in
    # patterns.config to let the architect suppress a harmful auto-derived
    # grouping (e.g. harbor-house's transitive 8-code chain) without disabling
    # superposition globally.
    interchange: bool = True
    # Whether each quality param was explicitly in the config (not a default)
    has_size: bool = False
    has_width: bool = False
    has_proportion: bool = False
    has_share: bool = False


def _pair(d: dict, key: str, default: tuple[float, float]) -> tuple[float, float]:
    v = d.get(key)
    if v is None:
        return default
    return float(v[0]), float(v[1])


def _parse_spaces(conf: dict) -> dict[str, SpaceReq]:
    spaces = conf.get("spaces") or {}
    out: dict[str, SpaceReq] = {}
    for code, c in spaces.items():
        size = _pair(c, "size", (0.0, 1.0))
        width = _pair(c, "width", _DEFAULT_WIDTH)
        prop = _pair(c, "proportion", _DEFAULT_PROPORTION)
        out[code] = SpaceReq(
            code=code,
            name=c.get("name", ""),
            size=size[0],
            size_sigma=size[1],
            width=width[0],
            width_sigma=width[1],
            proportion=prop[0],
            proportion_sigma=prop[1],
            adjacency=list(c.get("adjacency") or []),
            level=c.get("level"),
            requires_below=c.get("requires_below"),
            count=int(c.get("count") or 1),
            share=int(c.get("share") or 1),
            interchange=bool(c.get("interchange", True)),
            has_size="size" in c,
            has_width="width" in c,
            has_proportion="proportion" in c,
            has_share="share" in c,
        )
    return out


def load_programme(path: str) -> dict[str, SpaceReq]:
    with open(path) as fh:
        conf = yaml.safe_load(fh)
    return _parse_spaces(conf)


# --------------------------------------------------------------------------- #
# Interchange equivalence classes (homemaker-py-9o5, type superposition)
# --------------------------------------------------------------------------- #
#
# A maximal group of codes whose leaf requirements are SIMILAR enough that one
# leaf is genuinely substitutable for any in-class usage. Derived as a pure
# function of the parsed programme (no hand-authored list on the happy path).
# Used by the superposition+collapse search relaxation: a leaf typed to any
# in-class code is left uncommitted during search and re-assigned to its best
# in-class usage at scoring time (fitness.collapse_superposition).
#
# Thresholds are LOCKED defaults (Bruno 2026-06-29); conservative on purpose —
# a missed grouping is cheap, a wrong one corrupts the relaxation.
R_SIZE = 1.5   # larger area target <= 1.5x smaller
R_WIDTH = 1.3  # clear-width targets vary less than areas; tighter band
R_PROP = 1.5   # max length/width aspect targets within 1.5x
CLASS_CAP = 4  # brute-force collapse <= C! assignments; beyond this use Hungarian


def _ratio(x: float, y: float) -> float:
    """max/min of two positive magnitudes (inf if either is non-positive)."""
    lo, hi = min(abs(x), abs(y)), max(abs(x), abs(y))
    return hi / lo if lo > 0 else float("inf")


def interchangeable(a: SpaceReq, b: SpaceReq) -> bool:
    """True iff codes ``a`` and ``b`` satisfy the S1-S4 interchange relation
    (homemaker-py-9o5 §2). Symmetric."""
    # S0 — architect veto (homemaker-py-b3v): either code opted out.
    if not a.interchange or not b.interchange:
        return False
    # S1 — both sized; generic circulation/outside never participate.
    if not (a.has_size and b.has_size) or a.size <= 0 or b.size <= 0:
        return False
    if a.code[0].lower() in ("c", "o", "s") or b.code[0].lower() in ("c", "o", "s"):
        return False
    # S2 — requirement similarity within bounded ratios (ALL three).
    if _ratio(a.size, b.size) > R_SIZE:
        return False
    if _ratio(a.width, b.width) > R_WIDTH:
        return False
    if _ratio(a.proportion, b.proportion) > R_PROP:
        return False
    # S3 — compatible level (equal or one None) and matching service stack.
    if a.level is not None and b.level is not None and a.level != b.level:
        return False
    if (a.requires_below or None) != (b.requires_below or None):
        return False
    # S4 — no direct adjacency edge (an adjacency pair are coexisting rooms).
    if b.code in a.adjacency or a.code in b.adjacency:
        return False
    return True


def derive_interchange_classes(reqs: dict[str, SpaceReq]) -> list[frozenset[str]]:
    """Connected components of the interchange relation, size >= 2
    (homemaker-py-9o5 §2). Each class is a set of mutually-substitutable codes.
    """
    codes = [
        c for c, r in reqs.items()
        if r.interchange
        and r.has_size and r.size > 0 and c[0].lower() not in ("c", "o", "s")
    ]
    edges: dict[str, set[str]] = {c: set() for c in codes}
    for i, a in enumerate(codes):
        for b in codes[i + 1:]:
            if interchangeable(reqs[a], reqs[b]):
                edges[a].add(b)
                edges[b].add(a)

    seen: set[str] = set()
    classes: list[frozenset[str]] = []
    for c in codes:
        if c in seen:
            continue
        comp: set[str] = set()
        stack = [c]
        while stack:
            x = stack.pop()
            if x in comp:
                continue
            comp.add(x)
            seen.add(x)
            stack.extend(edges[x] - comp)
        if len(comp) >= 2:
            classes.append(frozenset(comp))
    return classes


def n_storeys_required(reqs: dict[str, SpaceReq]) -> int:
    """Number of storeys the programme implies, from the highest ``level:`` key.

    Level-free rooms (no ``level``) do not force extra storeys — they are
    distributed across whatever storeys the level-constrained rooms require.
    """
    levels = [r.level for r in reqs.values() if r.level is not None]
    return (max(levels) + 1) if levels else 1


def partition_rooms_by_storey(
    reqs: dict[str, SpaceReq], n_storeys: int, rng,
) -> list[dict[str, int]]:
    """Per-storey required-room multisets (DESIGN.md §11.3 staging).

    Level-constrained rooms land on their required storey; level-free rooms are
    distributed round-robin over a shuffled order across all storeys. Generic
    circulation/outside/sahn codes are excluded (they are added per storey at
    construction time). Mirrors the inline partition in
    ``operators.constructive_topology`` so Stage 1 (base) and Stage 2 (upper
    deltas) draw from one consistent partition.

    Returns a list of length ``n_storeys``; each entry maps room code -> count.
    """
    buckets: list[dict[str, int]] = [{} for _ in range(n_storeys)]

    def _add(li: int, code: str) -> None:
        buckets[li][code] = buckets[li].get(code, 0) + 1

    free: list[str] = []
    for code, req in reqs.items():
        if code[0].lower() in ("c", "o", "s"):
            continue
        for _ in range(req.count):
            if req.level is not None and req.level < n_storeys:
                _add(req.level, code)
            else:
                free.append(code)
    free = [free[i] for i in rng.permutation(len(free))]
    for i, code in enumerate(free):
        _add(i % n_storeys, code)
    return buckets


def write_stage1_programme(
    full_dir: str | Path, out_dir: str | Path, base_codes: dict[str, int],
) -> Path:
    """Derive a single-storey base-floor programme (DESIGN.md §11.3 Stage 1).

    Filters the full merged ``patterns.config`` down to the rooms assigned to the
    base floor (``base_codes``: code -> count), drops their ``level:`` keys,
    prunes each kept space's ``adjacency`` to references that survive (retained
    codes or generic c/o/s), and forces single-storey building constraints. The
    result is written as a *self-contained* ``patterns.config`` in ``out_dir`` so
    ``fitness.load_config``'s parent-dir merge contributes nothing — keep
    ``out_dir`` outside the corpus tree (e.g. a tempdir).

    Returns ``out_dir`` as a ``Path``.
    """
    from pathlib import Path as _Path

    from . import fitness as _fit

    out_dir = _Path(out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)
    conf, _cost = _fit.load_config(full_dir)

    keep = set(base_codes)
    src_spaces = conf.get("spaces") or {}
    new_spaces: dict = {}
    for code, count in base_codes.items():
        if code not in src_spaces:
            continue
        spec = dict(src_spaces[code])
        spec.pop("level", None)
        spec["count"] = count
        adj = spec.get("adjacency")
        if adj is not None:
            spec["adjacency"] = [
                r for r in adj if r in keep or r[0].lower() in ("c", "o", "s")
            ]
        new_spaces[code] = spec

    new_conf = {k: v for k, v in conf.items() if k != "spaces"}
    new_conf["spaces"] = new_spaces
    new_conf.update(
        storey_minimum=1, storey_limit=1, staircase_min=1, staircase_max=1,
    )

    with open(out_dir / "patterns.config", "w") as fh:
        yaml.safe_dump(new_conf, fh, sort_keys=False, default_flow_style=False)
    return out_dir


def _load_merged_conf(directory: "str | Path") -> dict:
    """Merge ``../patterns.config`` then the local one (mirrors load_config)."""
    from pathlib import Path as _Path
    directory = _Path(directory)
    conf: dict = {}
    for p in (directory.parent / "patterns.config", directory / "patterns.config"):
        if p.is_file():
            with open(p) as fh:
                conf.update(yaml.safe_load(fh) or {})
    return conf


def load_programme_dir(directory: str | Path) -> dict[str, SpaceReq]:
    """Load programme from a directory, merging parent patterns.config as base.

    Mirrors urb-evolve.pl: ../patterns.config loaded first, then the local
    file's top-level keys override it (same shallow-merge as fitness.load_config).
    """
    return _parse_spaces(_load_merged_conf(directory))


def storey_minimum(directory: str | Path) -> int:
    """Minimum storey count the programme requires (``storey_minimum`` key).

    Independent of ``level:`` keys: a programme can demand N storeys via
    ``storey_minimum`` without pinning any room to an upper floor (e.g.
    programme-house: ``storey_minimum: 2`` but all rooms ``level: 0``). The
    constructive seeder and the staged/plain dispatch must honour it, else the
    seed is built one storey short and fitness fires a ``storey minimum`` fail the
    search has to repair structurally (DESIGN.md §12.2).
    """
    return int(_load_merged_conf(directory).get("storey_minimum") or 1)


def n_storeys_for(directory: str | Path) -> int:
    """Storeys the programme implies: the max of level-derived and storey_minimum."""
    reqs = load_programme_dir(directory)
    return max(n_storeys_required(reqs), storey_minimum(directory))