import logging
from typing import List, Optional, Any, Dict, Callable
import numpy as np
import os

from .base_evaluator import BaseEvaluator


logger = logging.getLogger(__name__)


class PhysicsEvaluator(BaseEvaluator):
    """
    Physics-based evaluator that uses computational methods like PyRosetta.
    
    This evaluator provides a unified interface for physics-based scoring
    functions and other computational protein analysis methods.
    """
    
    def __init__(self, name: str, task_type: str, scoring_function: Optional[Callable] = None, **kwargs):
        """
        Initialize the physics evaluator.
        
        Args:
            name: Name of the evaluator
            task_type: Type of task ('filter', 'score', or 'seq_prob')
            scoring_function: Optional custom scoring function
            **kwargs: Additional configuration parameters
        """
        super().__init__(name, task_type, **kwargs)
        self.scoring_function = scoring_function
        self.pdb_path = kwargs.get('pdb_path')
        self.wild_type_sequence = kwargs.get('wild_type_sequence')
        
        # Set up the evaluator
        self.setup(**kwargs)
    
    def setup(self, **kwargs) -> None:
        """Set up the physics evaluator with required resources."""
        # Initialize PyRosetta if available and needed
        if kwargs.get('use_pyrosetta', False):
            self._setup_pyrosetta()
        
        # Set up any other physics-based tools
        if kwargs.get('use_foldx', False):
            self._setup_foldx()
    
    def predict(self, sequences: List[str], batch_size: Optional[int] = None) -> np.ndarray:
        """
        Make predictions on a list of sequences using physics-based methods.
        
        Args:
            sequences: List of protein sequences to evaluate
            batch_size: Optional batch size for processing (not used for physics methods)
            
        Returns:
            numpy.ndarray: Predictions for each sequence
        """
        if not sequences:
            return np.array([])
        
        try:
            if self.scoring_function:
                return self._predict_custom(sequences)
            elif hasattr(self, 'pyrosetta_scorer'):
                return self._predict_pyrosetta(sequences)
            elif hasattr(self, 'foldx_scorer'):
                return self._predict_foldx(sequences)
            else:
                logger.warning(f"No scoring function available for {self.name}")
                return np.zeros(len(sequences))
                
        except Exception as e:
            logger.error(f"Error making physics-based predictions with {self.name}: {e}")
            return np.zeros(len(sequences))
    
    def _predict_custom(self, sequences: List[str]) -> np.ndarray:
        """Make predictions using a custom scoring function."""
        predictions = []
        for sequence in sequences:
            try:
                score = self.scoring_function(sequence)
                predictions.append(score)
            except Exception as e:
                logger.error(f"Error evaluating sequence with custom function: {e}")
                predictions.append(0.0)
        
        return np.array(predictions)
    
    def _predict_pyrosetta(self, sequences: List[str]) -> np.ndarray:
        """Make predictions using PyRosetta."""
        if not hasattr(self, 'pyrosetta_scorer'):
            logger.error("PyRosetta not set up")
            return np.zeros(len(sequences))
        
        predictions = []
        for sequence in sequences:
            try:
                score = self._score_with_pyrosetta(sequence)
                predictions.append(score)
            except Exception as e:
                logger.error(f"Error evaluating sequence with PyRosetta: {e}")
                predictions.append(0.0)
        
        return np.array(predictions)
    
    def _predict_foldx(self, sequences: List[str]) -> np.ndarray:
        """Make predictions using FoldX."""
        if not hasattr(self, 'foldx_scorer'):
            logger.error("FoldX not set up")
            return np.zeros(len(sequences))
        
        predictions = []
        for sequence in sequences:
            try:
                score = self._score_with_foldx(sequence)
                predictions.append(score)
            except Exception as e:
                logger.error(f"Error evaluating sequence with FoldX: {e}")
                predictions.append(0.0)
        
        return np.array(predictions)
    
    def _setup_pyrosetta(self) -> None:
        """Set up PyRosetta for scoring."""
        try:
            import pyrosetta
            from pyrosetta import pose_from_sequence, get_fa_scorefxn
            
            # Initialize PyRosetta
            pyrosetta.init()
            
            # Set up scoring function
            self.pyrosetta_scorer = get_fa_scorefxn()
            
            # Create wild-type pose if PDB is available
            if self.pdb_path and os.path.exists(self.pdb_path):
                self.wild_type_pose = pyrosetta.pose_from_pdb(self.pdb_path)
            elif self.wild_type_sequence:
                self.wild_type_pose = pose_from_sequence(self.wild_type_sequence)
            
            logger.info("PyRosetta initialized successfully")
            
        except ImportError:
            logger.warning("PyRosetta not available. Install with: pip install pyrosetta")
        except Exception as e:
            logger.error(f"Error setting up PyRosetta: {e}")
    
    def _setup_foldx(self) -> None:
        """Set up FoldX for scoring."""
        try:
            # Check if FoldX is available in PATH
            import subprocess
            result = subprocess.run(['foldx', '--help'], capture_output=True, text=True)
            if result.returncode == 0:
                self.foldx_available = True
                logger.info("FoldX found in PATH")
            else:
                self.foldx_available = False
                logger.warning("FoldX not found in PATH")
        except Exception as e:
            logger.warning(f"Error checking FoldX availability: {e}")
            self.foldx_available = False
    
    def _score_with_pyrosetta(self, sequence: str) -> float:
        """Score a sequence using PyRosetta."""
        try:
            import pyrosetta
            from pyrosetta import pose_from_sequence
            
            # Create pose from sequence
            pose = pose_from_sequence(sequence)
            
            # Score the pose
            score = self.pyrosetta_scorer(pose)
            
            return score
            
        except Exception as e:
            logger.error(f"Error scoring sequence with PyRosetta: {e}")
            return 0.0
    
    def _score_with_foldx(self, sequence: str) -> float:
        """Score a sequence using FoldX."""
        if not self.foldx_available:
            return 0.0
        
        try:
            import subprocess
            import tempfile
            import os
            
            # Create temporary files for FoldX
            with tempfile.NamedTemporaryFile(mode='w', suffix='.pdb', delete=False) as f:
                pdb_file = f.name
                # Write sequence as PDB (simplified - you'd need proper PDB generation)
                f.write(f"ATOM      1  N   ALA A   1       0.000   0.000   0.000\n")
                f.write(f"ATOM      2  CA  ALA A   1       0.000   0.000   0.000\n")
                f.write(f"ATOM      3  C   ALA A   1       0.000   0.000   0.000\n")
                f.write(f"ATOM      4  O   ALA A   1       0.000   0.000   0.000\n")
                f.write(f"TER\n")
                f.write(f"END\n")
            
            # Run FoldX (simplified command)
            result = subprocess.run(
                ['foldx', '--command=AnalyseComplex', '--pdb=' + pdb_file],
                capture_output=True,
                text=True
            )
            
            # Clean up
            os.unlink(pdb_file)
            
            if result.returncode == 0:
                # Parse FoldX output to extract score
                # This is a simplified version - you'd need proper parsing
                return 0.0  # Placeholder
            else:
                logger.error(f"FoldX command failed: {result.stderr}")
                return 0.0
                
        except Exception as e:
            logger.error(f"Error running FoldX: {e}")
            return 0.0
    
    def add_custom_scoring_function(self, scoring_function: Callable) -> None:
        """Add a custom scoring function."""
        self.scoring_function = scoring_function
        logger.info(f"Added custom scoring function to {self.name}")
    
    def set_pdb_reference(self, pdb_path: str) -> None:
        """Set the reference PDB structure."""
        self.pdb_path = pdb_path
        logger.info(f"Set reference PDB: {pdb_path}")
    
    def set_wild_type_sequence(self, sequence: str) -> None:
        """Set the wild-type sequence."""
        self.wild_type_sequence = sequence
        logger.info(f"Set wild-type sequence: {sequence[:20]}...") 