from __future__ import annotations

import hashlib
from collections import Counter
from collections.abc import Iterable
from datetime import UTC, datetime
from typing import Any

from codeforge.kb.models import Cluster, ClusterManifest
from codeforge.kb.tokenizer import tokenize

_STOPWORDS: frozenset[str] = frozenset({
    "the", "and", "for", "with", "this", "that", "are", "was",
    "not", "but", "use", "can", "all", "one", "from", "when",
    "which", "have", "any", "should", "would", "must", "will",
    "your", "you", "our", "its", "their", "them", "they",
})


def _filter_tokens(tokens: list[str], min_length: int = 3) -> list[str]:
    return [t for t in tokens if len(t) >= min_length and t not in _STOPWORDS]


def _jaccard(a: set[str], b: set[str]) -> float:
    if not a and not b:
        return 0.0
    union = a | b
    inter = a & b
    return len(inter) / len(union)


def _connected_components(
    node_ids: list[str], adj: dict[str, set[str]],
) -> list[set[str]]:
    seen: set[str] = set()
    comps: list[set[str]] = []
    for start in node_ids:
        if start in seen:
            continue
        comp: set[str] = set()
        stack = [start]
        while stack:
            nid = stack.pop()
            if nid in comp:
                continue
            comp.add(nid)
            seen.add(nid)
            for nbr in adj.get(nid, ()):
                if nbr not in comp:
                    stack.append(nbr)
        comps.append(comp)
    return comps


def _dominant_domain(tags_list: Iterable[tuple[str, ...]]) -> str:
    counts: Counter[str] = Counter()
    for tags in tags_list:
        for t in tags:
            if t.startswith("domain:"):
                counts[t.split(":", 1)[1]] += 1
    if not counts:
        return "general"
    return counts.most_common(1)[0][0]


def _label_cluster(
    nodes_data: list[dict[str, Any]],
) -> tuple[str, tuple[str, ...], str]:
    all_tokens: Counter[str] = Counter()
    tag_lists: list[tuple[str, ...]] = []
    for nd in nodes_data:
        all_tokens.update(nd["tokens"])
        tag_lists.append(tuple(nd.get("tags", ())))
    top3 = [t for t, _ in all_tokens.most_common(3)]
    dominant = _dominant_domain(tag_lists)
    label = f"{dominant}_" + "_".join(top3) if top3 else dominant
    return label, tuple(top3), dominant


def build_clusters(
    nodes: list[dict[str, Any]],
    *,
    jaccard_threshold: float = 0.15,
    min_token_length: int = 3,
    corpus_sha256: str = "",
    generated_at: str | None = None,
) -> ClusterManifest:
    if not nodes:
        stamp = (
            generated_at
            if generated_at is not None
            else datetime.now(UTC).isoformat(timespec="seconds")
        )
        return ClusterManifest(
            generated_at=stamp,
            corpus_sha256=corpus_sha256,
            jaccard_threshold=jaccard_threshold,
            total_clusters=0,
            total_nodes_clustered=0,
            singletons=0,
            clusters=(),
        )

    node_ids = [str(n["id"]) for n in nodes]
    token_sets: dict[str, set[str]] = {
        str(n["id"]): set(
            _filter_tokens(
                tokenize(str(n.get("section_body", ""))), min_token_length,
            ),
        )
        for n in nodes
    }
    adj: dict[str, set[str]] = {nid: set() for nid in node_ids}
    for i, a in enumerate(node_ids):
        for b in node_ids[i + 1 :]:
            sim = _jaccard(token_sets[a], token_sets[b])
            if sim >= jaccard_threshold:
                adj[a].add(b)
                adj[b].add(a)

    components = _connected_components(node_ids, adj)
    node_by_id = {str(n["id"]): n for n in nodes}

    clusters: list[Cluster] = []
    singletons = 0
    for comp in components:
        member_ids = sorted(comp)
        nodes_data = [
            {
                "tokens": _filter_tokens(
                    tokenize(str(node_by_id[nid].get("section_body", ""))),
                    min_token_length,
                ),
                "tags": tuple(node_by_id[nid].get("tags", ())),
            }
            for nid in member_ids
        ]
        label, top_tokens, dominant = _label_cluster(nodes_data)
        cluster_id = hashlib.sha256(
            "|".join(member_ids).encode(),
        ).hexdigest()[:12]
        if len(member_ids) == 1:
            singletons += 1
        clusters.append(
            Cluster(
                cluster_id=cluster_id,
                label=label,
                dominant_domain=dominant,
                top_tokens=top_tokens,
                node_count=len(member_ids),
                member_node_ids=tuple(member_ids),
            ),
        )
    clusters.sort(key=lambda c: (-c.node_count, c.cluster_id))

    stamp = (
        generated_at
        if generated_at is not None
        else datetime.now(UTC).isoformat(timespec="seconds")
    )
    return ClusterManifest(
        generated_at=stamp,
        corpus_sha256=corpus_sha256,
        jaccard_threshold=jaccard_threshold,
        total_clusters=len(clusters),
        total_nodes_clustered=len(node_ids),
        singletons=singletons,
        clusters=tuple(clusters),
    )