"""Adapter for MemVerse — uses build_memory for storage + cosine retrieval."""

from __future__ import annotations

import json
import os
import sys
import shutil
import tempfile
from pathlib import Path
from typing import Any

import numpy as np
from dotenv import load_dotenv

load_dotenv(Path(__file__).resolve().parents[2] / ".env")

from eval_framework.datasets.schemas import (
    MemoryDeltaRecord,
    MemorySnapshotRecord,
    NormalizedTurn,
    RetrievalItem,
    RetrievalRecord,
)
from eval_framework.memory_adapters.base import MemoryAdapter

_DEFAULT_SOURCE = Path("/data1/toby/nips26/baselines/MemVerse")


class MemVerseAdapter(MemoryAdapter):
    """Adapter for MemVerse using build_memory + cosine retrieval.

    Bypasses the async orchestrator/LightRAG and uses the core
    memory building + embedding-based retrieval directly.
    """

    def __init__(
        self,
        *,
        source_root: str | os.PathLike[str] | None = None,
        **kwargs: Any,
    ) -> None:
        root = Path(source_root or _DEFAULT_SOURCE).resolve()
        if str(root) not in sys.path:
            sys.path.insert(0, str(root))

        from openai import OpenAI

        self._client = OpenAI(
            api_key=os.getenv("OPENAI_API_KEY"),
            base_url=os.getenv("OPENAI_BASE_URL"),
        )
        self._model = os.getenv("OPENAI_MODEL") or "gpt-4o"

        # Working directory for memory files
        self._work_dir = Path(tempfile.mkdtemp(prefix="memverse_eval_"))
        self._root = root
        self._session_id = ""
        self._prev_snapshot_ids: set[str] = set()
        self._memories: list[dict[str, Any]] = []  # {id, text, embedding, output}
        self._conversation: list[dict[str, Any]] = []
        self._turn_counter = 0

        # Load system prompts for memory agents
        self._prompts: dict[str, str] = {}
        for name in ["core_memory_agent", "episodic_memory_agent", "semantic_memory_agent"]:
            prompt_path = root / "MemoryKB" / "Long_Term_Memory" / "system" / f"{name}.txt"
            if prompt_path.exists():
                self._prompts[name] = prompt_path.read_text(encoding="utf-8").strip()

    def _get_embedding(self, text: str) -> np.ndarray:
        resp = self._client.embeddings.create(
            model="text-embedding-3-small",
            input=text,
        )
        return np.array(resp.data[0].embedding)

    def _cosine_sim(self, a: np.ndarray, b: np.ndarray) -> float:
        norm = np.linalg.norm(a) * np.linalg.norm(b)
        if norm == 0:
            return 0.0
        return float(np.dot(a, b) / norm)

    def reset(self) -> None:
        self._memories = []
        self._conversation = []
        self._prev_snapshot_ids = set()
        self._turn_counter = 0
        if self._work_dir.exists():
            shutil.rmtree(self._work_dir, ignore_errors=True)
        self._work_dir = Path(tempfile.mkdtemp(prefix="memverse_eval_"))

    def ingest_turn(self, turn: NormalizedTurn) -> None:
        self._session_id = turn.session_id
        text = f"{turn.role}: {turn.text}"
        for att in turn.attachments:
            text += f"\n[{att.type}] {att.caption}"

        entry_id = f"turn_{self._turn_counter}"
        self._turn_counter += 1

        entry = {
            "id": entry_id,
            "query": text,
            "videocaption": None,
            "audiocaption": None,
            "imagecaption": None,
        }
        self._conversation.append(entry)

        # Build memory: get embedding + LLM extraction for each memory type
        embedding = self._get_embedding(text)

        # Use the first available prompt (core memory agent) for extraction
        prompt = next(iter(self._prompts.values()), "Extract key facts from this text.")
        try:
            resp = self._client.chat.completions.create(
                model=self._model,
                messages=[
                    {"role": "system", "content": prompt},
                    {"role": "user", "content": text},
                ],
                temperature=0,
                max_tokens=512,
            )
            output = resp.choices[0].message.content or ""
        except Exception:
            output = text

        self._memories.append({
            "id": entry_id,
            "text": text,
            "output": output,
            "embedding": embedding,
            "session_id": turn.session_id,
        })

    def end_session(self, session_id: str) -> None:
        self._session_id = session_id

    def snapshot_memories(self) -> list[MemorySnapshotRecord]:
        return [
            MemorySnapshotRecord(
                memory_id=m["id"],
                text=m["output"],
                session_id=m.get("session_id", self._session_id),
                status="active",
                source="MemVerse",
                raw_backend_id=m["id"],
                raw_backend_type="memverse",
                metadata={},
            )
            for m in self._memories
        ]

    def export_memory_delta(self, session_id: str) -> list[MemoryDeltaRecord]:
        current = self.snapshot_memories()
        current_ids = {s.memory_id for s in current}
        deltas = [
            MemoryDeltaRecord(
                session_id=session_id, op="add", text=s.text,
                linked_previous=(), raw_backend_id=s.raw_backend_id,
                metadata={"baseline": "MemVerse"},
            )
            for s in current if s.memory_id not in self._prev_snapshot_ids
        ]
        self._prev_snapshot_ids = current_ids
        return deltas

    def retrieve(self, query: str, top_k: int) -> RetrievalRecord:
        if not self._memories:
            return RetrievalRecord(query=query, top_k=top_k, items=[], raw_trace={})

        query_emb = self._get_embedding(query)
        scored = []
        for m in self._memories:
            sim = self._cosine_sim(query_emb, m["embedding"])
            scored.append((sim, m))
        scored.sort(key=lambda x: x[0], reverse=True)

        items = [
            RetrievalItem(
                rank=i,
                memory_id=m["id"],
                text=m["output"],
                score=float(sim),
                raw_backend_id=m["id"],
            )
            for i, (sim, m) in enumerate(scored[:top_k])
        ]
        return RetrievalRecord(
            query=query, top_k=top_k, items=items,
            raw_trace={"baseline": "MemVerse"},
        )

    def get_capabilities(self) -> dict[str, Any]:
        return {
            "backend": "MemVerse",
            "baseline": "MemVerse",
            "available": True,
            "delta_granularity": "per_turn",
            "snapshot_mode": "full",
        }