"""
vLLM wrapper for generation and validation inference.

Pattern follows filter_with_uq.py — single LLM instance shared for both
generation (n=num_generations) and validation (n=num_validation_votes).

Inference results are cached per-prompt in MongoDB so that interrupted runs
can resume without re-doing expensive vLLM calls, and multiple shards can
share a single cache.
"""

from __future__ import annotations

import hashlib
import json

from pymongo import MongoClient
from vllm import LLM, SamplingParams

from sdg.config import SDGConfig


class MongoCache:
    """Dict-like interface over a MongoDB collection for inference caching."""

    def __init__(self, uri: str, db_name: str):
        self.client = MongoClient(uri)
        self.db = self.client[db_name]
        self.collection = self.db["inference_cache"]
        self.collection.create_index("key", unique=True)
        # Fail fast if MongoDB is unreachable
        self.client.admin.command("ping")

    def get(self, key: str, default=None):
        doc = self.collection.find_one({"key": key})
        if doc is not None:
            return doc["value"]
        return default

    def __setitem__(self, key: str, value):
        self.collection.update_one(
            {"key": key},
            {"$set": {"key": key, "value": value}},
            upsert=True,
        )


class VLLMEngine:
    """Thin wrapper around vllm.LLM with two pre-built SamplingParams."""

    def __init__(self, config: SDGConfig):
        print(f"Loading model: {config.model_name}")
        self.model_name = config.model_name
        self.llm = LLM(
            model=config.model_name,
            tensor_parallel_size=config.tensor_parallel_size,
            max_model_len=config.max_model_len,
            gpu_memory_utilization=config.gpu_memory_utilization,
            trust_remote_code=True,
        )

        self.gen_params = SamplingParams(
            temperature=config.gen_temperature,
            top_p=config.gen_top_p,
            max_tokens=config.gen_max_tokens,
            n=config.num_generations,
        )

        if config.num_validation_votes > 0:
            self.val_params = SamplingParams(
                temperature=config.val_temperature,
                top_p=config.val_top_p,
                max_tokens=config.val_max_tokens,
                n=config.num_validation_votes,
            )
        else:
            self.val_params = None

        # ── inference cache ──────────────────────────────────────────────
        db_name = config.mongo_db_name
        print(f"Connecting to MongoDB: {config.mongo_uri}  db={db_name}")
        self.cache = MongoCache(config.mongo_uri, db_name)
        print("MongoDB cache connected successfully")
        print("Model loaded successfully")

    # ── helpers ──────────────────────────────────────────────────────────

    def _cache_key(self, prompt: str, params: SamplingParams) -> str:
        raw = json.dumps(
            [self.model_name, params.temperature, params.top_p,
             params.max_tokens, params.n, prompt],
            ensure_ascii=False,
        )
        return hashlib.sha256(raw.encode()).hexdigest()

    def apply_chat_template(self, prompt: str) -> str:
        tokenizer = self.llm.get_tokenizer()
        messages = [{"role": "user", "content": prompt}]
        return tokenizer.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )

    def _generate_cached(
        self, prompts: list[str], params: SamplingParams,
    ) -> list[list[str]]:
        """Generate with per-prompt caching. Returns list[list[str]]."""
        templated = [self.apply_chat_template(p) for p in prompts]

        # Split into cached hits vs misses
        results: list[list[str] | None] = [None] * len(prompts)
        miss_indices: list[int] = []
        miss_templated: list[str] = []
        cache_hits = 0

        for i, (prompt, tmpl) in enumerate(zip(prompts, templated)):
            key = self._cache_key(tmpl, params)
            cached = self.cache.get(key)
            if cached is not None:
                results[i] = cached
                cache_hits += 1
            else:
                miss_indices.append(i)
                miss_templated.append(tmpl)

        # Run vLLM only for misses
        if miss_templated:
            outputs = self.llm.generate(miss_templated, params)
            assert len(outputs) == len(miss_indices), (
                f"vLLM returned {len(outputs)} outputs for {len(miss_indices)} prompts"
            )
            for idx, tmpl, out in zip(miss_indices, miss_templated, outputs):
                texts = [o.text for o in out.outputs]
                key = self._cache_key(tmpl, params)
                self.cache[key] = texts
                results[idx] = texts

        if cache_hits:
            print(f"  Cache: {cache_hits} hits, {len(miss_indices)} misses")

        return results  # type: ignore[return-value]

    # ── public API ───────────────────────────────────────────────────────

    def generate_multi(self, prompts: list[str]) -> list[list[str]]:
        """Generate *num_generations* samples per prompt (candidate generation)."""
        return self._generate_cached(prompts, self.gen_params)

    def generate_with_votes(self, prompts: list[str]) -> list[list[str]]:
        """Generate *num_validation_votes* samples per prompt (UQ voting)."""
        return self._generate_cached(prompts, self.val_params)

    def generate_single(self, prompts: list[str]) -> list[str]:
        """Generate one sample per prompt (e.g. inferred-question generation)."""
        single_params = SamplingParams(
            temperature=self.val_params.temperature,
            top_p=self.val_params.top_p,
            max_tokens=self.val_params.max_tokens,
            n=1,
        )
        results = self._generate_cached(prompts, single_params)
        return [r[0] for r in results]