"""
Error Mitigation Techniques for Quantum Chemistry on NISQ Hardware (BioQL 5.2.1)

This module implements error mitigation strategies to improve VQE accuracy:
- Zero-Noise Extrapolation (ZNE)
- Measurement Error Mitigation
- Readout Error Mitigation

Compatible con todos los backends (IBM, IonQ, Quantinuum, Rigetti, simulators).
"""

from typing import Dict, List, Optional, Any, Callable
from dataclasses import dataclass
import numpy as np
from loguru import logger


@dataclass
class MitigationResult:
    """Result from error mitigation."""

    original_counts: Dict[str, int]
    mitigated_counts: Dict[str, int]
    strategy: str
    improvement_score: float  # 0-1, higher is better
    metadata: Dict[str, Any]


class ErrorMitigationStrategy:
    """Base class for error mitigation strategies."""

    def __init__(self, name: str):
        self.name = name

    def mitigate(self, counts: Dict[str, int], **kwargs) -> MitigationResult:
        """Apply mitigation strategy to measurement counts."""
        raise NotImplementedError

    def estimate_improvement(self, original: Dict[str, int], mitigated: Dict[str, int]) -> float:
        """Estimate improvement from mitigation (0-1)."""
        # Simple metric: entropy reduction
        def entropy(counts):
            total = sum(counts.values())
            probs = [c/total for c in counts.values() if c > 0]
            return -sum(p * np.log2(p) for p in probs)

        orig_entropy = entropy(original)
        mit_entropy = entropy(mitigated)

        # Lower entropy after mitigation = better concentration
        if orig_entropy == 0:
            return 0.0

        improvement = max(0, (orig_entropy - mit_entropy) / orig_entropy)
        return min(1.0, improvement)


class ReadoutErrorMitigation(ErrorMitigationStrategy):
    """
    Readout error mitigation using calibration matrix.

    Works by measuring calibration circuits and inverting the error matrix.
    Compatible with all backends.
    """

    def __init__(self):
        super().__init__("ReadoutErrorMitigation")
        self.calibration_matrix = None

    def calibrate(self, num_qubits: int, backend_calibration_data: Optional[Dict] = None):
        """
        Calibrate readout errors.

        Args:
            num_qubits: Number of qubits
            backend_calibration_data: Optional backend-specific calibration
        """
        # Build identity calibration matrix (perfect readout baseline)
        n_states = 2 ** num_qubits
        self.calibration_matrix = np.eye(n_states)

        # If backend provides calibration data, use it
        if backend_calibration_data:
            error_rates = backend_calibration_data.get('readout_error_rates', {})

            # Simple model: add off-diagonal terms based on error rates
            for i in range(n_states):
                if str(i) in error_rates:
                    error_rate = error_rates[str(i)]
                    # Distribute error equally to other states
                    self.calibration_matrix[i, i] = 1 - error_rate
                    for j in range(n_states):
                        if i != j:
                            self.calibration_matrix[i, j] = error_rate / (n_states - 1)

        logger.info(f"Calibrated readout error mitigation for {num_qubits} qubits")

    def mitigate(self, counts: Dict[str, int], num_qubits: int, **kwargs) -> MitigationResult:
        """
        Apply readout error mitigation.

        Args:
            counts: Raw measurement counts
            num_qubits: Number of qubits
            **kwargs: Additional options

        Returns:
            MitigationResult with corrected counts
        """
        # Auto-calibrate if not done
        if self.calibration_matrix is None:
            self.calibrate(num_qubits)

        # Convert counts to probability vector
        total_shots = sum(counts.values())
        n_states = 2 ** num_qubits

        prob_vector = np.zeros(n_states)
        for bitstring, count in counts.items():
            # Convert bitstring to index
            try:
                index = int(bitstring, 2)
                prob_vector[index] = count / total_shots
            except ValueError:
                logger.warning(f"Invalid bitstring format: {bitstring}")

        # Apply inverse calibration matrix
        try:
            inv_matrix = np.linalg.inv(self.calibration_matrix)
            mitigated_prob = inv_matrix @ prob_vector

            # Clip negative probabilities and renormalize
            mitigated_prob = np.maximum(0, mitigated_prob)
            mitigated_prob /= mitigated_prob.sum()

            # Convert back to counts
            mitigated_counts = {}
            for i, prob in enumerate(mitigated_prob):
                if prob > 1e-10:  # Threshold for noise
                    bitstring = format(i, f'0{num_qubits}b')
                    mitigated_counts[bitstring] = int(prob * total_shots)

            improvement = self.estimate_improvement(counts, mitigated_counts)

            return MitigationResult(
                original_counts=counts,
                mitigated_counts=mitigated_counts,
                strategy=self.name,
                improvement_score=improvement,
                metadata={
                    "num_qubits": num_qubits,
                    "total_shots": total_shots,
                    "calibration_used": True,
                }
            )

        except np.linalg.LinAlgError:
            logger.warning("Calibration matrix not invertible, returning original counts")
            return MitigationResult(
                original_counts=counts,
                mitigated_counts=counts,
                strategy=self.name,
                improvement_score=0.0,
                metadata={"error": "Matrix inversion failed"}
            )


class ZeroNoiseExtrapolation(ErrorMitigationStrategy):
    """
    Zero-noise extrapolation (ZNE) mitigation.

    Runs circuit at different noise levels and extrapolates to zero noise.
    """

    def __init__(self):
        super().__init__("ZeroNoiseExtrapolation")

    def mitigate(self, counts: Dict[str, int], noise_factors: List[float] = [1.0, 1.5, 2.0], **kwargs) -> MitigationResult:
        """
        Apply ZNE mitigation.

        Note: This is a simplified version. Full ZNE requires running
        the circuit multiple times with scaled noise, which needs
        backend integration.

        Args:
            counts: Measurement counts at base noise level
            noise_factors: Noise scaling factors

        Returns:
            MitigationResult
        """
        # Simplified: assume we have counts at different noise levels
        # In practice, this would require re-running the circuit

        # For now, return a simple linear extrapolation
        # This is a placeholder - real implementation needs multiple runs

        logger.info("ZNE mitigation (simplified version)")

        # Placeholder: just return original counts with note
        return MitigationResult(
            original_counts=counts,
            mitigated_counts=counts,
            strategy=self.name,
            improvement_score=0.0,
            metadata={
                "note": "Full ZNE requires multiple circuit runs with noise scaling",
                "noise_factors": noise_factors,
            }
        )


class ProbabilisticErrorCancellation(ErrorMitigationStrategy):
    """
    Probabilistic error cancellation (PEC).

    Uses quasi-probability decomposition to cancel errors.
    """

    def __init__(self):
        super().__init__("ProbabilisticErrorCancellation")

    def mitigate(self, counts: Dict[str, int], **kwargs) -> MitigationResult:
        """
        Apply PEC mitigation.

        Note: Simplified version. Full PEC requires error characterization.
        """
        logger.info("PEC mitigation (simplified version)")

        # Placeholder implementation
        return MitigationResult(
            original_counts=counts,
            mitigated_counts=counts,
            strategy=self.name,
            improvement_score=0.0,
            metadata={
                "note": "Full PEC requires detailed error characterization"
            }
        )


class ErrorMitigator:
    """
    Main error mitigation manager for BioQL.

    Integrates multiple mitigation strategies and applies them
    to quantum results.

    Example:
        >>> mitigator = ErrorMitigator()
        >>> mitigator.add_strategy(ReadoutErrorMitigation())
        >>> result = mitigator.apply(counts, num_qubits=4)
        >>> print(result.mitigated_counts)
    """

    def __init__(self):
        self.strategies: List[ErrorMitigationStrategy] = []
        self.default_strategy = "ReadoutErrorMitigation"

    def add_strategy(self, strategy: ErrorMitigationStrategy):
        """Add a mitigation strategy."""
        self.strategies.append(strategy)
        logger.info(f"Added mitigation strategy: {strategy.name}")

    def apply(
        self,
        counts: Dict[str, int],
        strategy: Optional[str] = None,
        **kwargs
    ) -> MitigationResult:
        """
        Apply error mitigation to measurement counts.

        Args:
            counts: Raw measurement counts from quantum backend
            strategy: Strategy name to use (None = default)
            **kwargs: Strategy-specific parameters

        Returns:
            MitigationResult with corrected counts

        Example:
            >>> result = mitigator.apply(
            ...     {'00': 450, '01': 50, '10': 60, '11': 440},
            ...     num_qubits=2
            ... )
        """
        strategy_name = strategy or self.default_strategy

        # Find strategy
        strat_obj = None
        for s in self.strategies:
            if s.name == strategy_name:
                strat_obj = s
                break

        if strat_obj is None:
            # Use default readout error mitigation
            logger.info(f"Strategy {strategy_name} not found, using default")
            strat_obj = ReadoutErrorMitigation()
            self.add_strategy(strat_obj)

        # Apply mitigation
        result = strat_obj.mitigate(counts, **kwargs)

        logger.info(
            f"Applied {result.strategy} mitigation: "
            f"improvement score = {result.improvement_score:.3f}"
        )

        return result

    def auto_select_strategy(self, backend: str, circuit_depth: int) -> str:
        """
        Auto-select best mitigation strategy for backend and circuit.

        Args:
            backend: Backend name (ibm, ionq, etc.)
            circuit_depth: Circuit depth

        Returns:
            Recommended strategy name
        """
        # Simple heuristic
        if circuit_depth < 10:
            return "ReadoutErrorMitigation"
        elif circuit_depth < 50:
            return "ZeroNoiseExtrapolation"
        else:
            return "ProbabilisticErrorCancellation"


# Convenience function for BioQL integration
def mitigate_counts(
    counts: Dict[str, int],
    num_qubits: int,
    strategy: str = "ReadoutErrorMitigation",
    **kwargs
) -> Dict[str, int]:
    """
    Convenience function to apply error mitigation to counts.

    Args:
        counts: Raw measurement counts
        num_qubits: Number of qubits
        strategy: Mitigation strategy
        **kwargs: Additional parameters

    Returns:
        Mitigated counts dictionary

    Example:
        >>> from bioql.error_mitigation import mitigate_counts
        >>> mitigated = mitigate_counts({'00': 450, '11': 440, '01': 60, '10': 50}, num_qubits=2)
    """
    mitigator = ErrorMitigator()
    mitigator.add_strategy(ReadoutErrorMitigation())

    result = mitigator.apply(counts, strategy=strategy, num_qubits=num_qubits, **kwargs)

    return result.mitigated_counts


__all__ = [
    "ErrorMitigationStrategy",
    "ReadoutErrorMitigation",
    "ZeroNoiseExtrapolation",
    "ProbabilisticErrorCancellation",
    "ErrorMitigator",
    "MitigationResult",
    "mitigate_counts",
]
