"""
Pose ranking model for ML-enhanced docking

Neural network for ranking and selecting the best binding poses.
"""

import numpy as np
from typing import List, Dict, Optional, Any, Tuple
import logging
from rdkit import Chem
import torch
import torch.nn as nn
import torch.nn.functional as F
from pathlib import Path

# Import PandaDock core components
from ...docking.core import Pose


class PoseRankingModel:
    """Deep learning model for ranking binding poses"""

    def __init__(self, model_size: str = 'medium', device: str = 'auto'):
        """
        Initialize pose ranking model

        Args:
            model_size: Size of the model ('small', 'medium', 'large')
            device: Device to run model on ('cpu', 'cuda', 'auto')
        """
        self.logger = logging.getLogger(__name__)
        self.model_size = model_size

        # Set device
        if device == 'auto':
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        else:
            self.device = torch.device(device)

        # Model parameters
        self.model_params = self._get_model_params(model_size)

        # Initialize model
        self._build_model()

        # Training state
        self.is_trained = False

        self.logger.info(f"Initialized PoseRankingModel ({model_size}) on {self.device}")

    def _get_model_params(self, size: str) -> Dict[str, Any]:
        """Get model parameters based on size"""
        params = {
            'small': {
                'hidden_dim': 128,
                'num_layers': 3,
                'dropout': 0.1,
                'max_atoms': 50,
                'max_poses': 20
            },
            'medium': {
                'hidden_dim': 256,
                'num_layers': 4,
                'dropout': 0.2,
                'max_atoms': 100,
                'max_poses': 50
            },
            'large': {
                'hidden_dim': 512,
                'num_layers': 6,
                'dropout': 0.3,
                'max_atoms': 150,
                'max_poses': 100
            }
        }
        return params.get(size, params['medium'])

    def _build_model(self):
        """Build the pose ranking model"""
        self.pose_ranker = PoseRankingNetwork(
            hidden_dim=self.model_params['hidden_dim'],
            num_layers=self.model_params['num_layers'],
            dropout=self.model_params['dropout'],
            max_atoms=self.model_params['max_atoms']
        ).to(self.device)

    def generate_poses(self,
                      receptor_features: Dict[str, np.ndarray],
                      ligand_mol: Chem.Mol,
                      grid_center: np.ndarray,
                      grid_dimensions: np.ndarray,
                      num_poses: int = 20,
                      temperature: float = 1.0) -> List[Pose]:
        """
        Generate poses using ranking-guided sampling

        Args:
            receptor_features: Extracted protein features
            ligand_mol: RDKit molecule object
            grid_center: Center of docking grid
            grid_dimensions: Dimensions of docking grid
            num_poses: Number of poses to generate
            temperature: Sampling temperature

        Returns:
            List of generated and ranked poses
        """
        self.logger.debug(f"Generating {num_poses} poses with ranking model")

        try:
            # Generate initial diverse pose set
            initial_poses = self._generate_diverse_poses(
                ligand_mol, grid_center, grid_dimensions, num_poses * 5  # Oversample
            )

            # Rank all poses
            ranked_poses = self.rank_poses(initial_poses, receptor_features, ligand_mol)

            # Select top poses
            selected_poses = ranked_poses[:num_poses]

            # Set confidence based on ranking scores
            for i, pose in enumerate(selected_poses):
                pose.confidence = pose.ranking_score
                pose.ml_confidence = pose.ranking_score
                pose.generation_method = 'ranking_guided'

            self.logger.debug(f"Generated {len(selected_poses)} ranking-guided poses")
            return selected_poses

        except Exception as e:
            self.logger.error(f"Ranking-guided pose generation failed: {e}")
            return self._generate_fallback_poses(ligand_mol, grid_center, num_poses)

    def rank_poses(self,
                   poses: List[Pose],
                   receptor_features: Dict[str, np.ndarray],
                   ligand_mol: Chem.Mol) -> List[Pose]:
        """
        Rank a list of poses

        Args:
            poses: List of poses to rank
            receptor_features: Protein features
            ligand_mol: RDKit molecule

        Returns:
            Ranked list of poses (best first)
        """
        try:
            # Predict ranking scores
            scored_poses = self._predict_ranking_scores(poses, receptor_features, ligand_mol)

            # Sort by ranking score (higher is better)
            scored_poses.sort(key=lambda x: x.ranking_score, reverse=True)

            return scored_poses

        except Exception as e:
            self.logger.error(f"Pose ranking failed: {e}")
            return poses

    def _generate_diverse_poses(self,
                               mol: Chem.Mol,
                               center: np.ndarray,
                               dimensions: np.ndarray,
                               num_poses: int) -> List[Pose]:
        """Generate diverse initial poses"""
        poses = []

        try:
            from rdkit.Chem import AllChem

            # Create molecule copy
            mol_copy = Chem.Mol(mol)
            mol_copy = Chem.AddHs(mol_copy)

            # Generate conformers with different strategies
            strategies = [
                {'randomSeed': 42, 'useExpTorsionAnglePrefs': True},
                {'randomSeed': 123, 'useBasicKnowledge': True},
                {'randomSeed': 456, 'ETversion': 2}
            ]

            poses_per_strategy = num_poses // len(strategies)

            for strategy in strategies:
                try:
                    cids = AllChem.EmbedMultipleConfs(
                        mol_copy,
                        numConfs=poses_per_strategy,
                        **strategy
                    )

                    for i, cid in enumerate(cids):
                        conf = mol_copy.GetConformer(cid)
                        coords = []

                        for atom_idx in range(mol_copy.GetNumAtoms()):
                            pos = conf.GetAtomPosition(atom_idx)
                            coords.append([pos.x, pos.y, pos.z])

                        coords = np.array(coords)

                        # Diverse placement strategies
                        placement_strategies = [
                            self._place_center_focused,
                            self._place_corner_focused,
                            self._place_random
                        ]

                        strategy_idx = len(poses) % len(placement_strategies)
                        coords = placement_strategies[strategy_idx](coords, center, dimensions)

                        pose = Pose(
                            coordinates=coords,
                            center=np.mean(coords, axis=0),
                            rotation=self._get_rotation_from_coords(coords),
                            conformer_id=len(poses),
                            energy=0.0,
                            confidence=0.5
                        )

                        poses.append(pose)

                except Exception as e:
                    self.logger.warning(f"Strategy failed: {e}")
                    continue

        except Exception as e:
            self.logger.error(f"Diverse pose generation failed: {e}")

        return poses

    def _place_center_focused(self, coords: np.ndarray, center: np.ndarray, dimensions: np.ndarray) -> np.ndarray:
        """Place pose focused around the grid center"""
        current_center = np.mean(coords, axis=0)
        # Small random offset from center
        offset = np.random.uniform(-dimensions/8, dimensions/8, 3)
        return coords - current_center + center + offset

    def _place_corner_focused(self, coords: np.ndarray, center: np.ndarray, dimensions: np.ndarray) -> np.ndarray:
        """Place pose towards one of the grid corners"""
        current_center = np.mean(coords, axis=0)
        # Random corner direction
        corner_direction = np.random.choice([-1, 1], size=3)
        corner_offset = corner_direction * dimensions / 4
        return coords - current_center + center + corner_offset

    def _place_random(self, coords: np.ndarray, center: np.ndarray, dimensions: np.ndarray) -> np.ndarray:
        """Place pose randomly within the grid"""
        current_center = np.mean(coords, axis=0)
        random_offset = np.random.uniform(-dimensions/3, dimensions/3, 3)
        coords = coords - current_center + center + random_offset

        # Random rotation
        rotation_matrix = self._random_rotation_matrix()
        coords_centered = coords - center
        coords = np.dot(coords_centered, rotation_matrix.T) + center

        return coords

    def _random_rotation_matrix(self) -> np.ndarray:
        """Generate random rotation matrix"""
        # Generate random rotation using Rodrigues' formula
        axis = np.random.randn(3)
        axis = axis / np.linalg.norm(axis)
        angle = np.random.uniform(0, 2 * np.pi)

        cos_angle = np.cos(angle)
        sin_angle = np.sin(angle)
        cross_matrix = np.array([
            [0, -axis[2], axis[1]],
            [axis[2], 0, -axis[0]],
            [-axis[1], axis[0], 0]
        ])

        rotation_matrix = (cos_angle * np.eye(3) +
                          sin_angle * cross_matrix +
                          (1 - cos_angle) * np.outer(axis, axis))

        return rotation_matrix

    def _get_rotation_from_coords(self, coords: np.ndarray) -> np.ndarray:
        """Get rotation quaternion from coordinates (simplified)"""
        # For now, return identity quaternion
        # In practice, this could compute actual rotation from reference
        return np.array([1.0, 0.0, 0.0, 0.0])

    def _predict_ranking_scores(self,
                               poses: List[Pose],
                               receptor_features: Dict[str, np.ndarray],
                               ligand_mol: Chem.Mol) -> List[Pose]:
        """Predict ranking scores for multiple poses"""
        scored_poses = []

        # Prepare receptor features once
        receptor_tensor = self._prepare_receptor_features(receptor_features)

        with torch.no_grad():
            for pose in poses:
                try:
                    # Prepare ligand features for this pose
                    ligand_features = self._prepare_ligand_features(ligand_mol, pose.coordinates)

                    # Predict ranking score
                    score = self.pose_ranker(
                        ligand_features.unsqueeze(0),
                        receptor_tensor.unsqueeze(0)
                    ).item()

                    pose.ranking_score = score
                    scored_poses.append(pose)

                except Exception as e:
                    self.logger.warning(f"Ranking prediction failed for pose: {e}")
                    pose.ranking_score = 0.0  # Low score for failed poses
                    scored_poses.append(pose)

        return scored_poses

    def _prepare_ligand_features(self, mol: Chem.Mol, coordinates: np.ndarray) -> torch.Tensor:
        """Prepare ligand features including 3D coordinates and interactions"""
        atom_features = []
        max_atoms = self.model_params['max_atoms']

        for i, atom in enumerate(mol.GetAtoms()):
            if i >= max_atoms:
                break

            # Basic atom features
            atomic_num = atom.GetAtomicNum()
            atom_type = [0] * 10
            if atomic_num < 10:
                atom_type[atomic_num] = 1

            # Chemical features
            features = atom_type + [
                atom.GetDegree(),
                atom.GetTotalNumHs(),
                int(atom.GetIsAromatic()),
                atom.GetFormalCharge(),
                atom.GetHybridization().real if hasattr(atom.GetHybridization(), 'real') else 0
            ]

            # 3D coordinates
            if i < len(coordinates):
                features.extend(coordinates[i].tolist())
            else:
                features.extend([0.0, 0.0, 0.0])

            # Distance to center
            if i < len(coordinates):
                center = np.mean(coordinates, axis=0)
                dist_to_center = np.linalg.norm(coordinates[i] - center)
                features.append(dist_to_center)
            else:
                features.append(0.0)

            atom_features.append(features)

        # Pad to max_atoms
        while len(atom_features) < max_atoms:
            atom_features.append([0] * 19)  # 15 chemical + 3 coordinates + 1 distance

        return torch.tensor(atom_features, dtype=torch.float32, device=self.device)

    def _prepare_receptor_features(self, features: Dict[str, np.ndarray]) -> torch.Tensor:
        """Prepare receptor features for ranking model"""
        feature_list = []

        # Pocket features
        pocket_features = features.get('pocket_features', np.zeros(100))
        if len(pocket_features) > 150:
            pocket_features = pocket_features[:150]
        else:
            padding = np.zeros(150 - len(pocket_features))
            pocket_features = np.concatenate([pocket_features, padding])
        feature_list.append(pocket_features)

        # Sequence features
        sequence_features = features.get('sequence_features', np.zeros(200))
        if len(sequence_features) > 150:
            sequence_features = sequence_features[:150]
        else:
            padding = np.zeros(150 - len(sequence_features))
            sequence_features = np.concatenate([sequence_features, padding])
        feature_list.append(sequence_features)

        # Combine features
        combined_features = np.concatenate(feature_list)

        return torch.tensor(combined_features, dtype=torch.float32, device=self.device)

    def _generate_fallback_poses(self, mol: Chem.Mol, center: np.ndarray, num_poses: int) -> List[Pose]:
        """Generate fallback poses when ranking fails"""
        return self._generate_diverse_poses(mol, center, np.array([20.0, 20.0, 20.0]), num_poses)

    def load_weights(self, model_path: str):
        """Load pre-trained model weights"""
        try:
            if Path(model_path).exists():
                checkpoint = torch.load(model_path, map_location=self.device)
                self.pose_ranker.load_state_dict(checkpoint['pose_ranker'])
                self.is_trained = True
                self.logger.info(f"Loaded ranking model weights from {model_path}")
            else:
                self.logger.warning(f"Ranking model weights not found at {model_path}")
        except Exception as e:
            self.logger.error(f"Failed to load ranking model weights: {e}")

    def save_weights(self, model_path: str):
        """Save model weights"""
        try:
            checkpoint = {
                'pose_ranker': self.pose_ranker.state_dict(),
                'model_params': self.model_params
            }
            torch.save(checkpoint, model_path)
            self.logger.info(f"Saved ranking model weights to {model_path}")
        except Exception as e:
            self.logger.error(f"Failed to save ranking model weights: {e}")


class PoseRankingNetwork(nn.Module):
    """Neural network for ranking binding poses"""

    def __init__(self, hidden_dim: int, num_layers: int, dropout: float, max_atoms: int):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.max_atoms = max_atoms

        # Ligand encoder (processes atoms with coordinates and distances)
        self.ligand_encoder = nn.Sequential(
            nn.Linear(19, hidden_dim),  # 15 chemical + 3 coordinates + 1 distance
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Receptor encoder
        self.receptor_encoder = nn.Sequential(
            nn.Linear(300, hidden_dim),  # Combined receptor features
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Graph neural network layers
        self.gnn_layers = nn.ModuleList()
        for _ in range(num_layers):
            self.gnn_layers.append(
                GraphAttentionLayer(hidden_dim, dropout)
            )

        # Pose quality assessment
        self.quality_layers = nn.ModuleList()
        for _ in range(2):
            self.quality_layers.append(
                QualityAssessmentLayer(hidden_dim, dropout)
            )

        # Final ranking score
        self.ranking_head = nn.Sequential(
            nn.Linear(hidden_dim * 3, hidden_dim),  # Ligand + receptor + interaction
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 1),
            nn.Sigmoid()  # Output between 0 and 1
        )

    def forward(self, ligand_features, receptor_features):
        """
        Forward pass

        Args:
            ligand_features: (batch_size, max_atoms, 19)
            receptor_features: (batch_size, 300)

        Returns:
            ranking_score: (batch_size, 1)
        """
        batch_size = ligand_features.shape[0]

        # Encode ligand and receptor
        ligand_emb = self.ligand_encoder(ligand_features)  # (batch_size, max_atoms, hidden_dim)
        receptor_emb = self.receptor_encoder(receptor_features)  # (batch_size, hidden_dim)

        # Apply graph neural network layers
        for gnn_layer in self.gnn_layers:
            ligand_emb = gnn_layer(ligand_emb)

        # Apply quality assessment layers
        for quality_layer in self.quality_layers:
            ligand_emb = quality_layer(ligand_emb, receptor_emb)

        # Global pooling
        ligand_global = torch.mean(ligand_emb, dim=1)  # (batch_size, hidden_dim)

        # Interaction representation
        interaction_rep = ligand_global * receptor_emb  # Element-wise product

        # Combine representations
        combined = torch.cat([ligand_global, receptor_emb, interaction_rep], dim=1)

        # Predict ranking score
        ranking_score = self.ranking_head(combined)

        return ranking_score


class GraphAttentionLayer(nn.Module):
    """Graph attention layer for processing ligand structure"""

    def __init__(self, hidden_dim: int, dropout: float):
        super().__init__()
        self.hidden_dim = hidden_dim

        # Attention mechanism
        self.attention = nn.MultiheadAttention(
            hidden_dim, num_heads=8, dropout=dropout, batch_first=True
        )

        # Feed-forward network
        self.ffn = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim * 2),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim * 2, hidden_dim)
        )

        # Layer normalization
        self.norm1 = nn.LayerNorm(hidden_dim)
        self.norm2 = nn.LayerNorm(hidden_dim)

    def forward(self, x):
        """
        Args:
            x: (batch_size, max_atoms, hidden_dim)

        Returns:
            updated_x: (batch_size, max_atoms, hidden_dim)
        """
        # Self-attention
        attn_out, _ = self.attention(x, x, x)
        x = self.norm1(x + attn_out)

        # Feed-forward
        ffn_out = self.ffn(x)
        x = self.norm2(x + ffn_out)

        return x


class QualityAssessmentLayer(nn.Module):
    """Layer for assessing pose quality based on protein-ligand interactions"""

    def __init__(self, hidden_dim: int, dropout: float):
        super().__init__()
        self.hidden_dim = hidden_dim

        # Cross-attention between ligand and receptor
        self.cross_attention = nn.MultiheadAttention(
            hidden_dim, num_heads=8, dropout=dropout, batch_first=True
        )

        # Quality assessment network
        self.quality_net = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Layer normalization
        self.norm = nn.LayerNorm(hidden_dim)

    def forward(self, ligand_emb, receptor_emb):
        """
        Args:
            ligand_emb: (batch_size, max_atoms, hidden_dim)
            receptor_emb: (batch_size, hidden_dim)

        Returns:
            enhanced_ligand_emb: (batch_size, max_atoms, hidden_dim)
        """
        # Expand receptor embedding for cross-attention
        receptor_emb_expanded = receptor_emb.unsqueeze(1).expand(-1, ligand_emb.shape[1], -1)

        # Cross-attention
        cross_attn_out, _ = self.cross_attention(
            ligand_emb, receptor_emb_expanded, receptor_emb_expanded
        )

        # Quality enhancement
        quality_enhanced = self.quality_net(cross_attn_out)

        # Residual connection and normalization
        enhanced_ligand_emb = self.norm(ligand_emb + quality_enhanced)

        return enhanced_ligand_emb