"""LLM provider runtime with Transformers-first fallback order.

The runtime is intentionally conservative: if an LLM backend is unavailable or
errors, selection falls back to deterministic local ranking.
"""

from __future__ import annotations

from dataclasses import dataclass
import json
import os
import shutil
import subprocess
import time
from typing import Any

from app.common.types import CandidateAction
from app.models.policy.safety_ranker import rank_candidates


@dataclass(slots=True)
class ProviderSelection:
    provider: str
    candidate_id: str
    rationale: str
    latency_ms: float
    raw_output: str = ""


class OllamaProvider:
    name = "ollama"

    def __init__(self, model_name: str) -> None:
        self.model_name = model_name

    def is_available(self) -> bool:
        if os.getenv("POLYGUARD_ENABLE_OLLAMA", "false").lower() not in {"1", "true", "yes", "on"}:
            return False
        return shutil.which("ollama") is not None

    def ensure_model(self) -> bool:
        if not self.is_available():
            return False
        if os.getenv("POLYGUARD_OLLAMA_AUTO_PULL", "true").lower() not in {"1", "true", "yes", "on"}:
            return True
        try:
            subprocess.run(
                ["ollama", "pull", self.model_name],
                check=False,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                text=True,
                timeout=90,
            )
            return True
        except Exception:
            return False

    def select(self, candidates: list[CandidateAction], prompt: dict[str, Any]) -> ProviderSelection | None:
        if not self.is_available() or not candidates:
            return None
        self.ensure_model()
        deadline_seconds = float(os.getenv("POLYGUARD_PROVIDER_TIMEOUT_SECONDS", "7.0"))
        compact_candidates = [
            {
                "candidate_id": c.candidate_id,
                "mode": c.mode.value,
                "action_type": c.action_type.value,
                "estimated_safety_delta": c.estimated_safety_delta,
                "uncertainty_score": c.uncertainty_score,
                "legality_precheck": c.legality_precheck,
            }
            for c in candidates
        ]
        request = {
            "instruction": "Return only JSON with fields candidate_id and rationale.",
            "context": prompt,
            "candidates": compact_candidates,
        }
        start = time.monotonic()
        try:
            proc = subprocess.run(
                ["ollama", "run", self.model_name, json.dumps(request, ensure_ascii=True)],
                check=False,
                stdout=subprocess.PIPE,
                stderr=subprocess.PIPE,
                text=True,
                timeout=deadline_seconds,
            )
            elapsed_ms = (time.monotonic() - start) * 1000.0
            raw = (proc.stdout or "").strip()
            if not raw:
                return None
            data = json.loads(raw)
            candidate_id = str(data.get("candidate_id", "")).strip()
            if not candidate_id:
                return None
            if candidate_id not in {c.candidate_id for c in candidates}:
                return None
            rationale = str(data.get("rationale", "Ollama provider selection.")).strip() or "Ollama provider selection."
            return ProviderSelection(
                provider=self.name,
                candidate_id=candidate_id,
                rationale=rationale,
                latency_ms=elapsed_ms,
                raw_output=raw,
            )
        except Exception:
            return None


class TransformersProvider:
    name = "transformers"

    def __init__(self, model_name: str) -> None:
        self.model_name = model_name

    def is_available(self) -> bool:
        try:
            import transformers  # noqa: F401

            return True
        except Exception:
            return False

    def select(self, candidates: list[CandidateAction], prompt: dict[str, Any]) -> ProviderSelection | None:
        _ = prompt
        if not self.is_available() or not candidates:
            return None
        # Keep this lightweight and deterministic for local runs: prefer highest
        # safety-adjusted candidate as a transformers fallback.
        start = time.monotonic()
        top = rank_candidates(candidates)[0]
        return ProviderSelection(
            provider=self.name,
            candidate_id=top.candidate_id,
            rationale=f"Transformers fallback selected {top.candidate_id} via local ranker.",
            latency_ms=(time.monotonic() - start) * 1000.0,
        )


class PolicyProviderRouter:
    def __init__(self, ollama_model: str = "qwen2.5:1.5b-instruct", hf_model: str = "Qwen/Qwen2.5-0.5B-Instruct") -> None:
        self.ollama = OllamaProvider(ollama_model)
        self.transformers = TransformersProvider(hf_model)

    def select_candidate(
        self,
        candidates: list[CandidateAction],
        prompt: dict[str, Any],
        provider_preference: tuple[str, ...] = ("transformers",),
    ) -> ProviderSelection:
        provider_preference = tuple(provider_preference) or ("transformers",)

        for provider in provider_preference:
            if provider == "ollama":
                picked = self.ollama.select(candidates, prompt)
                if picked is not None:
                    return picked
            elif provider == "transformers":
                picked = self.transformers.select(candidates, prompt)
                if picked is not None:
                    return picked

        # Deterministic hard fallback.
        fallback = rank_candidates(candidates)[0]
        return ProviderSelection(
            provider="heuristic_fallback",
            candidate_id=fallback.candidate_id,
            rationale="Fallback ranker selected top legal/safety candidate.",
            latency_ms=0.0,
        )