"""NEMOCITY placement — deterministic, NEVER fails, Python-only.

The LLM gives {kind, name, near, floors, hue}; this module turns it into
exact cells: anchor resolution from the "near" text, a scored candidate
search over an expanding Chebyshev radius, and an auto-routed road connector
when the chosen footprint has no frontage. Output is partial event dicts
({"tool", "args"}) — world.apply assigns id/seed/t.

All randomness is crc32-seeded from (wish_id, indices); no wall clock here.
"""

from __future__ import annotations

import re
import zlib
from collections import deque
from typing import Any, Optional

from . import constants as C
from .city import WATER_CELLS, Building, Cell, CityState, in_bounds, neighbors4
from .tools import street_name_for

_CENTER_WORDS = frozenset(("center", "centre", "downtown", "middle", "heart"))
_RIVER_WORDS = frozenset(("river", "water", "waterfront", "riverside", "shore"))
_STREET_SUFFIXES = frozenset(("st", "ave", "rd", "ln", "blvd", "way", "ct", "row"))
_STOP = frozenset(("the", "a", "an", "of", "near", "by", "next", "to", "at", "in", "on"))

_R_MAX = 2 * C.GRID  # covers the whole grid from any anchor


def _u01(key: str) -> float:
    return (zlib.crc32(key.encode()) & 0xFFFFFFFF) / 0xFFFFFFFF


def _tokens(text: str) -> list[str]:
    return [
        t for t in re.findall(r"[a-z0-9']+", str(text or "").lower())
        if t not in _STOP
    ]


def _round_cell(x: float, z: float) -> Cell:
    cx = min(max(int(round(x)), C.COORD_MIN), C.COORD_MAX)
    cz = min(max(int(round(z)), C.COORD_MIN), C.COORD_MAX)
    return (cx, cz)


def resolve_anchor(city: CityState, near: Any) -> Cell:
    """Fuzzy anchor: named building > street > center > river > kind word >
    centroid of all buildings > (0,0)."""
    text = str(near or "").strip().lower()
    toks = _tokens(text)
    if toks:
        for b in reversed(city.buildings):
            name_toks = set(_tokens(b.name))
            if name_toks and (set(toks) & name_toks):
                return _round_cell(*b.centroid)
        for name, cells in city.street_cells.items():
            low = name.lower()
            name_toks = set(_tokens(low)) - _STREET_SUFFIXES
            if low in text or (name_toks and set(toks) & name_toks):
                return cells[len(cells) // 2]
        if set(toks) & _CENTER_WORDS:
            return (0, 0)
        if set(toks) & _RIVER_WORDS:
            developed = set(city.roads) | set(city.building_cells)
            banks = sorted(
                (c for c in developed if any(n in WATER_CELLS for n in neighbors4(c))),
                key=lambda c: (max(abs(c[0]), abs(c[1])), c[1], c[0]),
            )
            if banks:
                return banks[0]
        for t in toks:
            kind = t if t in C.BUILDINGS else C.SYNONYMS.get(t)
            if kind:
                for b in reversed(city.buildings):
                    if b.kind == kind:
                        return _round_cell(*b.centroid)
    if city.buildings:
        xs = [b.centroid[0] for b in city.buildings]
        zs = [b.centroid[1] for b in city.buildings]
        return _round_cell(sum(xs) / len(xs), sum(zs) / len(zs))
    return (0, 0)


# ----------------------------------------------------------------- connectors

def _road_distance_field(city: CityState, max_dist: int) -> dict[Cell, int]:
    """Multi-source BFS over EMPTY cells from the road network. dist=1 means
    'this empty cell is adjacent to a road' (= a 1-cell connector)."""
    dist: dict[Cell, int] = {}
    queue: deque[Cell] = deque()
    seeds = set()
    for road_cell in city.roads:
        for n in neighbors4(road_cell):
            if city.is_empty(n) and n not in seeds:
                seeds.add(n)
    for cell in sorted(seeds, key=lambda c: (c[1], c[0])):
        dist[cell] = 1
        queue.append(cell)
    while queue:
        cell = queue.popleft()
        d = dist[cell]
        if d >= max_dist:
            continue
        for n in neighbors4(cell):
            if n not in dist and city.is_empty(n):
                dist[n] = d + 1
                queue.append(n)
    return dist


def _connector_path(
    city: CityState, footprint: set[Cell], max_len: int
) -> Optional[list[Cell]]:
    """Shortest Manhattan path of NEW road cells from the footprint ring to a
    cell adjacent to the existing road network. Deterministic BFS order."""
    starts = sorted(
        {
            n for cell in footprint for n in neighbors4(cell)
            if city.is_empty(n) and n not in footprint
        },
        key=lambda c: (c[1], c[0]),
    )
    prev: dict[Cell, Optional[Cell]] = {}
    queue: deque[tuple[Cell, int]] = deque()
    for s in starts:
        prev[s] = None
        queue.append((s, 1))
    while queue:
        cell, length = queue.popleft()
        if any(n in city.roads for n in neighbors4(cell)):
            path = [cell]
            while prev[path[-1]] is not None:
                path.append(prev[path[-1]])
            path.reverse()
            return path
        if length >= max_len:
            continue
        for n in neighbors4(cell):
            if n not in prev and city.is_empty(n) and n not in footprint:
                prev[n] = cell
                queue.append((n, length + 1))
    return None


# -------------------------------------------------------------------- scoring

def _rect_cheb(b: Building, cx: int, cz: int, w: int, d: int) -> int:
    """Exact min Chebyshev distance between a building and a candidate rect."""
    dx = max(0, b.cx - (cx + w - 1), cx - (b.cx + b.w - 1))
    dz = max(0, b.cz - (cz + d - 1), cz - (b.cz + b.d - 1))
    return max(dx, dz)


def _affinity(city: CityState, kind: str, cx: int, cz: int, w: int, d: int) -> float:
    res = ind = amen = park = 10 ** 6
    for b in city.buildings:
        dist = _rect_cheb(b, cx, cz, w, d)
        if b.kind in C.RESIDENTIAL_KINDS:
            res = min(res, dist)
        if b.kind in C.INDUSTRIAL_KINDS:
            ind = min(ind, dist)
        if b.kind in ("cafe", "shop", "park"):
            amen = min(amen, dist)
        if b.kind == "park":
            park = min(park, dist)
    score = 0.0
    if kind in ("cafe", "shop") and res <= 3:
        score += C.AFF_SHOP_NEAR_HOMES
    if kind in ("office", "bank", "tower"):
        dist_c = max(abs(cx), abs(cz))
        score += C.AFF_DOWNTOWN * (1 - dist_c / 12.0)
    if kind in C.RESIDENTIAL_KINDS:
        if amen <= 4:
            score += C.AFF_HOME_NEAR_AMENITY
        if ind <= 3:
            score += C.AFF_HOME_NEAR_INDUSTRY
    if kind in C.INDUSTRIAL_KINDS:
        if res <= 3:
            score += C.AFF_INDUSTRY_NEAR_HOMES
        if ind <= 4:
            score += C.AFF_INDUSTRY_CLUSTER
    if kind == "park" and park <= 6:
        score += C.AFF_PARK_SPACING
    return score


def _search(
    city: CityState, kind: str, anchor: Cell, wish_id: str, connector_max: int
) -> Optional[tuple[Cell, bool]]:
    """Best (top_left, has_frontage) within the smallest radius that holds a
    valid candidate, or None."""
    spec = C.BUILDINGS[kind]
    w, d = spec["w"], spec["d"]
    growth = city.growth_radius
    ring_limit = growth + C.GROWTH_RING_SLACK
    road_dist = _road_distance_field(city, max(connector_max, 1) + 1)
    ax, az = anchor

    radius = C.PLACE_R_START
    while radius <= _R_MAX:
        best: Optional[tuple[float, Cell, bool]] = None
        index = 0
        for cz in range(az - radius, az + radius + 1):
            for cx in range(ax - radius, ax + radius + 1):
                index += 1
                cells = [(cx + dx, cz + dz) for dx in range(w) for dz in range(d)]
                if not all(city.is_empty(c) for c in cells):
                    continue
                footprint = set(cells)
                ring = {
                    n for c in cells for n in neighbors4(c) if n not in footprint
                }
                frontage = any(n in city.roads for n in ring)
                if not frontage and not any(
                    road_dist.get(n, 10 ** 9) <= connector_max for n in ring
                ):
                    continue
                dist_anchor = max(abs(cx - ax), abs(cz - az))
                score = C.SCORE_ANCHOR * (1 - dist_anchor / radius)
                if frontage:
                    score += C.SCORE_FRONTAGE
                developed = sum(
                    1 for n in ring if n in city.roads or n in city.building_cells
                )
                score += min(C.SCORE_NEIGHBOR * developed, C.SCORE_NEIGHBOR_CAP)
                score += _affinity(city, kind, cx, cz, w, d)
                cheb_center = max(abs(cx), abs(cz))
                score -= C.SCORE_RING_PENALTY * max(0, cheb_center - ring_limit)
                score += _u01(f"{wish_id}:{index}") * 0.5  # seeded tie-break
                if best is None or score > best[0]:
                    best = (score, (cx, cz), frontage)
        if best is not None:
            return best[1], best[2]
        radius += C.PLACE_R_STEP
    return None


# ----------------------------------------------------------------------- place

def place(
    city: CityState,
    kind: str,
    name: str,
    floors: Any,
    hue: Any,
    near: Any,
    wish_id: str,
    call_index: int,
) -> list[dict]:
    """Resolve a building request into 1-2 events (place_building + optional
    connector lay_road). Never fails: the radius grows, the connector cap
    relaxes, and a footprint that fits nowhere degrades to a house."""
    if kind not in C.BUILDINGS:
        kind = "house"
    anchor = resolve_anchor(city, near)

    found = _search(city, kind, anchor, wish_id, C.CONNECTOR_MAX)
    if found is None:
        found = _search(city, kind, anchor, wish_id, 2 * C.CONNECTOR_MAX)
    if found is None and kind != "house":
        return place(city, "house", name, 1, hue, near, wish_id, call_index)
    if found is None:  # a 64x64 grid with any road at all always has a slot
        found = _search(city, kind, anchor, wish_id, 10 ** 6)
    if found is None:
        raise RuntimeError("placement impossible: no empty cell on the grid")

    (cx, cz), frontage = found
    spec = C.BUILDINGS[kind]
    lo, hi = spec["floors"]
    base = None
    if isinstance(floors, (int, float)) and not isinstance(floors, bool):
        base = int(round(floors))
    if kind in ("office", "tower"):
        if base is None:
            base = (lo + hi) // 2
        dist_c = max(abs(cx), abs(cz))
        base = base + round(4 * (1 - dist_c / 10.0))  # taller downtown
    elif base is None:
        base = lo + zlib.crc32(f"{wish_id}:{call_index}:floors".encode()) % (hi - lo + 1)
    final_floors = min(max(base, lo), hi)

    if isinstance(hue, (int, float)) and not isinstance(hue, bool):
        final_hue = int(round(hue)) % 361
    else:
        final_hue = zlib.crc32(f"{wish_id}:{call_index}:hue".encode()) % 360
    variant = zlib.crc32(f"{wish_id}:{call_index}:variant".encode()) % 8

    events: list[dict] = [{
        "tool": "place_building",
        "args": {
            "kind": kind, "name": str(name or ""), "cx": cx, "cz": cz,
            "w": spec["w"], "d": spec["d"], "floors": final_floors,
            "hue": final_hue, "variant": variant,
        },
    }]
    if not frontage:
        footprint = {
            (cx + dx, cz + dz) for dx in range(spec["w"]) for dz in range(spec["d"])
        }
        path = _connector_path(city, footprint, 2 * C.CONNECTOR_MAX) or \
            _connector_path(city, footprint, 10 ** 6)
        if path:
            events.append({
                "tool": "lay_road",
                "args": {
                    "cells": [[c[0], c[1]] for c in path],
                    "klass": "street",
                    "name": street_name_for(f"{wish_id}:{call_index}:road"),
                },
            })
    return events