import threading
from collections import OrderedDict
from models.base_model import BaseForecastModel
_ORDER_SEARCH_MODELS = {'ARIMA', 'Auto-ARIMA', 'ARIMA-GARCH'}
_CACHE = OrderedDict()
_MAX_CACHE = 32
_CACHE_LOCK = threading.Lock()

def _build_model(name: str) -> BaseForecastModel:
    if name == 'ARIMA':
        from models.arima_model import ArimaModel
        return ArimaModel()
    elif name == 'Auto-ARIMA':
        from models.auto_arima_model import AutoArimaModel
        return AutoArimaModel()
    elif name == 'ARIMA-GARCH':
        from models.arima_garch_model import ArimaGarchModel
        return ArimaGarchModel()
    elif name == 'Moirai':
        from models.moirai_model import MoiraiModel
        return MoiraiModel()
    elif name == 'TimesFM':
        from models.timesfm_model import TimesFMModel
        return TimesFMModel()
    elif name == 'Ridge':
        from models.ridge_model import RidgeModel
        return RidgeModel()
    raise ValueError(f"Unknown model '{name}'. Expected one of: ARIMA, Auto-ARIMA, ARIMA-GARCH, Moirai, TimesFM, Ridge.")

def get_model(name: str, symbol: str=None, timeframe: str=None, use_features: bool=False) -> BaseForecastModel:
    if name in _ORDER_SEARCH_MODELS:
        variant = 'featured' if use_features else 'base'
        key = (name, symbol, timeframe, variant)
    else:
        key = name
    with _CACHE_LOCK:
        if key in _CACHE:
            _CACHE.move_to_end(key)
            return _CACHE[key]
        model = _build_model(name)
        if len(_CACHE) >= _MAX_CACHE:
            _CACHE.popitem(last=False)
        _CACHE[key] = model
        return model

def fresh_model(name: str) -> BaseForecastModel:
    return _build_model(name)