#!/usr/bin/env python3
"""Model loading and lifecycle management for MedCPT and PubMedBERT-NLI."""
from typing import Dict, Any

import torch
from transformers import AutoModel, AutoModelForSequenceClassification, AutoTokenizer

from config import (
    DEVICE,
    LOAD_CROSS_ENCODER,
    MEDCPT_ARTICLE_MODEL,
    MEDCPT_CROSS_MODEL,
    MEDCPT_QUERY_MODEL,
    NLI_MODEL,
)
from logger import setup_logger

logger = setup_logger("models.loader")
_CACHE: Dict[str, Any] = {}


def _place_model(model):
    model.to(DEVICE)
    model.eval()
    return model


def load_medcpt_query_tokenizer():
    if "medcpt_query_tokenizer" not in _CACHE:
        _CACHE["medcpt_query_tokenizer"] = AutoTokenizer.from_pretrained(
            MEDCPT_QUERY_MODEL
        )
    return _CACHE["medcpt_query_tokenizer"]


def load_medcpt_query_model():
    if "medcpt_query_model" not in _CACHE:
        logger.info("Loading MedCPT query encoder from %s", MEDCPT_QUERY_MODEL)
        _CACHE["medcpt_query_model"] = _place_model(
            AutoModel.from_pretrained(MEDCPT_QUERY_MODEL)
        )
    return _CACHE["medcpt_query_model"]


def load_medcpt_article_tokenizer():
    if "medcpt_article_tokenizer" not in _CACHE:
        _CACHE["medcpt_article_tokenizer"] = AutoTokenizer.from_pretrained(
            MEDCPT_ARTICLE_MODEL
        )
    return _CACHE["medcpt_article_tokenizer"]


def load_medcpt_article_model():
    if "medcpt_article_model" not in _CACHE:
        logger.info("Loading MedCPT article encoder from %s", MEDCPT_ARTICLE_MODEL)
        _CACHE["medcpt_article_model"] = _place_model(
            AutoModel.from_pretrained(MEDCPT_ARTICLE_MODEL)
        )
    return _CACHE["medcpt_article_model"]


def load_medcpt_cross_tokenizer():
    if "medcpt_cross_tokenizer" not in _CACHE:
        _CACHE["medcpt_cross_tokenizer"] = AutoTokenizer.from_pretrained(
            MEDCPT_CROSS_MODEL
        )
    return _CACHE["medcpt_cross_tokenizer"]


def load_medcpt_cross_model():
    if "medcpt_cross_model" not in _CACHE:
        logger.info("Loading MedCPT cross encoder from %s", MEDCPT_CROSS_MODEL)
        _CACHE["medcpt_cross_model"] = _place_model(
            AutoModelForSequenceClassification.from_pretrained(MEDCPT_CROSS_MODEL)
        )
    return _CACHE["medcpt_cross_model"]


def load_nli_model():
    if "nli_model" not in _CACHE:
        logger.info("Loading PubMedBERT-NLI from %s", NLI_MODEL)
        _CACHE["nli_model"] = _place_model(
            AutoModelForSequenceClassification.from_pretrained(NLI_MODEL)
        )
    return _CACHE["nli_model"]


def load_nli_tokenizer():
    if "nli_tokenizer" not in _CACHE:
        _CACHE["nli_tokenizer"] = AutoTokenizer.from_pretrained(NLI_MODEL)
    return _CACHE["nli_tokenizer"]


def load_all():
    """Eagerly load the dual encoders and NLI model; cross encoder stays optional."""
    load_medcpt_query_tokenizer()
    load_medcpt_query_model()
    load_medcpt_article_tokenizer()
    load_medcpt_article_model()
    load_nli_tokenizer()
    load_nli_model()
    if LOAD_CROSS_ENCODER:
        load_medcpt_cross_tokenizer()
        load_medcpt_cross_model()
    logger.info("Core models loaded")


def models_ready() -> bool:
    required = {
        "medcpt_query_tokenizer",
        "medcpt_query_model",
        "medcpt_article_tokenizer",
        "medcpt_article_model",
        "nli_tokenizer",
        "nli_model",
    }
    return required.issubset(_CACHE)


def get_info() -> Dict[str, bool]:
    return {
        "medcpt_query": "medcpt_query_model" in _CACHE,
        "medcpt_article": "medcpt_article_model" in _CACHE,
        "medcpt_cross": "medcpt_cross_model" in _CACHE,
        "nli_model": "nli_model" in _CACHE,
        "nli_tokenizer": "nli_tokenizer" in _CACHE,
    }


def cleanup():
    _CACHE.clear()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
    logger.info("Models unloaded")