from abc import ABC, abstractmethod
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import NearestNeighbors
from typing import Callable, Optional, List, Dict, Tuple, Any

class Calibrator(ABC):
    """
    Abstract base class for calibration methods.

    Workflow
    --------
    1. setup(...)      → provide priors, simulator, or precomputed simulations
    2. calibrate(...)  → condition on observations, produce posterior
    3. get_posterior() → retrieve posterior representation
    """

    def __init__(self):
        self.is_ready = False

    @abstractmethod
    def setup(self, *args, **kwargs):
        """Define priors, simulator, or precomputed simulations."""
        pass

    @abstractmethod
    def calibrate(self, observations: Any, resample_n: Optional[int] = None) -> Any:
        """Condition on observed data to produce posterior samples."""
        pass

    @abstractmethod
    def get_posterior(self) -> Any:
        """Retrieve posterior representation (samples, chains, or density)."""
        pass


class MCMCCalibrator(Calibrator):
    """  Calibration via Bayesian MCMC (e.g. Metropolis-Hastings, HMC, NUTS). """
    def __init__(self, n_chains: int = 4,
                 n_samples: int = 1000,
                 burn_in: int = 200):
        super().__init__()
        self.n_chains = n_chains
        self.n_samples = n_samples
        self.burn_in = burn_in

        # internal state placeholders
        self._prior = None
        self._likelihood = None
        self._posterior_chain = None

    def setup(self, prior=None,
              likelihood=None,
              model=None):
        """  Define priors and likelihood (or simulator-based likelihood). """
        self._prior = prior
        self._likelihood = likelihood
        self.is_ready = True
        # TODO: implement sampler initialization (PyMC, NumPyro, etc.)

    def calibrate(self, observations: Any, resample_n: Optional[int] = None) -> Any:
        """  Run MCMC to sample posterior given observations.  """
        if not self.is_ready:
            raise RuntimeError("Call setup() before calibrate().")
        # TODO: implement actual MCMC run
        self._posterior_chain = None
        return self._posterior_chain

    def get_posterior(self) -> Any:
        """Return MCMC chain or posterior samples."""
        return self._posterior_chain
