"""Phase 1b Hermes agent routing.

Routes knowledge-base PRs to the agent identity that owns the changed domain.
This module is deliberately pure: no network, database, LLM, or filesystem IO.
"""

from __future__ import annotations

import re
from dataclasses import asdict, dataclass

AGENT_ORDER: tuple[str, ...] = ("Leo", "Theseus", "Rio", "Vida", "Clay", "Astra")
_AGENT_RANK = {agent: idx for idx, agent in enumerate(AGENT_ORDER)}

DOMAIN_AGENT_MAP: dict[str, str] = {
    "grand-strategy": "Leo",
    "strategy": "Leo",
    "teleohumanity": "Leo",
    "collective-intelligence": "Leo",
    "ai-alignment": "Theseus",
    "ai-systems": "Theseus",
    "living-agents": "Theseus",
    "critical-systems": "Theseus",
    "internet-finance": "Rio",
    "mechanisms": "Rio",
    "living-capital": "Rio",
    "teleological-economics": "Rio",
    "health": "Vida",
    "entertainment": "Clay",
    "cultural-dynamics": "Clay",
    "space-development": "Astra",
    "space": "Astra",
    "robotics": "Astra",
    "energy": "Astra",
    "manufacturing": "Astra",
    "advanced-manufacturing": "Astra",
}

_AGENT_PRIMARY_DOMAIN: dict[str, str] = {
    "leo": "grand-strategy",
    "theseus": "ai-systems",
    "rio": "internet-finance",
    "vida": "health",
    "clay": "entertainment",
    "astra": "space-development",
}

_INGESTION_SOURCE_DOMAIN: dict[str, str] = {
    "futardio": "internet-finance",
    "metadao": "internet-finance",
    "x402": "internet-finance",
}

_DOMAIN_PATH_RE = re.compile(r"^(?:domains|entities|core|foundations)/([^/]+)/")
_AGENT_PATH_RE = re.compile(r"^agents/([^/]+)/")

_KEYWORDS: dict[str, tuple[str, ...]] = {
    "Leo": (
        "grand strategy",
        "collective ai",
        "collective ais",
        "collective goals",
        "goal of the collective",
        "self-understanding",
        "self understanding",
        "teleohumanity",
        "meta-governance",
    ),
    "Theseus": (
        "ai alignment",
        "ai systems",
        "ai safety",
        "agent alignment",
        "prompt injection",
        "model behavior",
        "llm",
        "hermes runtime",
    ),
    "Rio": (
        "internet finance",
        "x402",
        "wallet",
        "payment",
        "payments",
        "onchain",
        "defi",
        "futarchy",
        "metadao",
        "prediction market",
        "decision market",
        "stablecoin",
    ),
    "Vida": (
        "health",
        "medicine",
        "clinical",
        "patient",
        "doctor",
        "disease",
        "longevity",
        "biotech",
        "glp-1",
    ),
    "Clay": (
        "entertainment",
        "game",
        "games",
        "media",
        "story",
        "film",
        "music",
        "culture",
    ),
    "Astra": (
        "space",
        "robotics",
        "robot",
        "energy",
        "manufacturing",
        "advanced manufacturing",
        "hardware",
        "satellite",
        "rocket",
        "nuclear",
    ),
}


@dataclass(frozen=True)
class RouteEvidence:
    agent: str
    signal: str
    weight: int
    value: str


@dataclass(frozen=True)
class AgentRoute:
    primary_agent: str
    required_agents: tuple[str, ...]
    route_kind: str
    scores: dict[str, int]
    evidence: tuple[RouteEvidence, ...]
    fallback: bool = False
    touched_domains: tuple[str, ...] = ()

    def to_audit_dict(self) -> dict:
        return {
            "primary_agent": self.primary_agent,
            "required_agents": list(self.required_agents),
            "route_kind": self.route_kind,
            "scores": self.scores,
            "evidence": [asdict(item) for item in self.evidence],
            "fallback": self.fallback,
            "touched_domains": list(self.touched_domains),
        }


def _changed_paths(diff: str) -> tuple[str, ...]:
    paths: list[str] = []
    for line in diff.splitlines():
        if not line.startswith("diff --git "):
            continue
        match = re.match(r"diff --git a/(.*?) b/(.*)$", line)
        if match:
            paths.append(match.group(2))
    return tuple(paths)


def _add_score(
    scores: dict[str, int],
    evidence: list[RouteEvidence],
    agent: str,
    signal: str,
    weight: int,
    value: str,
) -> None:
    if agent not in scores:
        return
    scores[agent] += weight
    evidence.append(RouteEvidence(agent=agent, signal=signal, weight=weight, value=value))


def _domain_for_branch(branch: str) -> str | None:
    prefix = branch.split("/")[0].lower() if "/" in branch else ""
    if prefix in _AGENT_PRIMARY_DOMAIN:
        return _AGENT_PRIMARY_DOMAIN[prefix]
    if prefix == "ingestion":
        rest = branch.split("/", 1)[1].lower() if "/" in branch else ""
        for source_key, domain in _INGESTION_SOURCE_DOMAIN.items():
            if source_key in rest:
                return domain
    return None


def _keyword_hits(agent: str, text: str) -> list[str]:
    hits = []
    for keyword in _KEYWORDS[agent]:
        pattern = rf"(?<![a-z0-9]){re.escape(keyword)}(?![a-z0-9])"
        if re.search(pattern, text):
            hits.append(keyword)
    return hits


def classify_pr_route(
    diff: str,
    *,
    branch: str | None = None,
    title: str | None = None,
    body: str | None = None,
    max_required_agents: int = 2,
) -> AgentRoute:
    """Classify a PR into one or two required Hermes reviewer agents."""
    max_required_agents = max(1, min(max_required_agents, 2))
    scores = {agent: 0 for agent in AGENT_ORDER}
    evidence: list[RouteEvidence] = []
    touched_domains: list[str] = []
    path_signal_found = False

    for path in _changed_paths(diff):
        domain_match = _DOMAIN_PATH_RE.match(path)
        if domain_match:
            domain = domain_match.group(1).lower()
            if domain in DOMAIN_AGENT_MAP:
                agent = DOMAIN_AGENT_MAP[domain]
                _add_score(scores, evidence, agent, "path", 8, path)
                touched_domains.append(domain)
                path_signal_found = True
            continue

        agent_match = _AGENT_PATH_RE.match(path)
        if agent_match:
            agent_key = agent_match.group(1).lower()
            for agent in AGENT_ORDER:
                if agent.lower() == agent_key:
                    _add_score(scores, evidence, agent, "agent_path", 8, path)
                    path_signal_found = True
                    break

    if branch and not path_signal_found:
        branch_domain = _domain_for_branch(branch)
        if branch_domain:
            agent = DOMAIN_AGENT_MAP[branch_domain]
            _add_score(scores, evidence, agent, "branch", 4, branch)
            touched_domains.append(branch_domain)

    keyword_text = "\n".join(part for part in (title or "", body or "", branch or "", diff) if part).lower()
    for agent in AGENT_ORDER:
        hits = _keyword_hits(agent, keyword_text)
        for keyword in hits[:4]:
            _add_score(scores, evidence, agent, "keyword", 2, keyword)

    ranked = sorted(
        (agent for agent, score in scores.items() if score > 0),
        key=lambda agent: (-scores[agent], _AGENT_RANK[agent]),
    )

    if not ranked:
        evidence.append(RouteEvidence(agent="Leo", signal="fallback", weight=0, value="no route signal"))
        return AgentRoute(
            primary_agent="Leo",
            required_agents=("Leo",),
            route_kind="fallback",
            scores=scores,
            evidence=tuple(evidence),
            fallback=True,
            touched_domains=(),
        )

    primary = ranked[0]
    required = tuple(ranked[:max_required_agents])
    if len(ranked) > max_required_agents:
        route_kind = "escalated"
    elif len(required) > 1:
        route_kind = "multi"
    else:
        route_kind = "single"

    return AgentRoute(
        primary_agent=primary,
        required_agents=required,
        route_kind=route_kind,
        scores=scores,
        evidence=tuple(evidence),
        fallback=False,
        touched_domains=tuple(dict.fromkeys(touched_domains)),
    )