"""
Diffusion-based pose generation model

Implements a diffusion model for generating ligand binding poses
similar to DiffDock but integrated with PandaDock.
"""

import numpy as np
from typing import List, Dict, Optional, Any, Tuple
import logging
from rdkit import Chem
from rdkit.Chem import AllChem
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 DiffusionDockingModel:
    """Diffusion model for ligand pose generation"""

    def __init__(self, model_size: str = 'small', device: str = 'auto'):
        """
        Initialize diffusion docking 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 architecture parameters
        self.model_params = self._get_model_params(model_size)

        # Initialize networks
        self._build_model()

        # Training state
        self.is_trained = False

        self.logger.info(f"Initialized DiffusionDockingModel ({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': 4,
                'num_heads': 8,
                'max_atoms': 50,
                'timesteps': 100
            },
            'medium': {
                'hidden_dim': 256,
                'num_layers': 6,
                'num_heads': 12,
                'max_atoms': 100,
                'timesteps': 500
            },
            'large': {
                'hidden_dim': 512,
                'num_layers': 8,
                'num_heads': 16,
                'max_atoms': 150,
                'timesteps': 1000
            }
        }
        return params.get(size, params['small'])

    def _build_model(self):
        """Build the diffusion model architecture"""
        # For now, implement a simplified placeholder model
        # In a full implementation, this would be a proper diffusion architecture
        self.position_predictor = PositionPredictor(
            hidden_dim=self.model_params['hidden_dim'],
            num_layers=self.model_params['num_layers'],
            max_atoms=self.model_params['max_atoms']
        ).to(self.device)

        self.confidence_predictor = ConfidencePredictor(
            hidden_dim=self.model_params['hidden_dim'],
            num_layers=self.model_params['num_layers']
        ).to(self.device)

        # Diffusion parameters
        self.timesteps = self.model_params['timesteps']
        self.beta_schedule = self._create_beta_schedule()

    def _create_beta_schedule(self) -> torch.Tensor:
        """Create beta schedule for diffusion process"""
        # Linear schedule (simplified)
        beta_start = 1e-4
        beta_end = 2e-2
        return torch.linspace(beta_start, beta_end, self.timesteps)

    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 ligand poses using diffusion model

        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 poses
        """
        self.logger.debug(f"Generating {num_poses} poses with diffusion model")

        try:
            # Prepare ligand features
            ligand_features = self._prepare_ligand_features(ligand_mol)

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

            # Generate poses
            poses = []
            for i in range(num_poses):
                pose = self._generate_single_pose(
                    ligand_features, receptor_tensor, grid_center,
                    grid_dimensions, temperature
                )
                poses.append(pose)

            self.logger.debug(f"Successfully generated {len(poses)} poses")
            return poses

        except Exception as e:
            self.logger.error(f"Pose generation failed: {e}")
            # Return fallback poses
            return self._generate_fallback_poses(ligand_mol, grid_center, num_poses)

    def _prepare_ligand_features(self, mol: Chem.Mol) -> torch.Tensor:
        """Prepare ligand features for model input"""
        # Get atom features
        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()
            degree = atom.GetDegree()
            is_aromatic = int(atom.GetIsAromatic())

            # One-hot encoding for common elements
            atom_type = [0] * 10
            if atomic_num < 10:
                atom_type[atomic_num] = 1

            features = atom_type + [degree, is_aromatic]
            atom_features.append(features)

        # Pad to max_atoms
        while len(atom_features) < max_atoms:
            atom_features.append([0] * 12)

        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 model input"""
        # Use pocket features (simplified)
        pocket_features = features.get('pocket_features', np.zeros(100))

        # Ensure fixed size
        if len(pocket_features) > 256:
            pocket_features = pocket_features[:256]
        else:
            padding = np.zeros(256 - len(pocket_features))
            pocket_features = np.concatenate([pocket_features, padding])

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

    def _generate_single_pose(self,
                            ligand_features: torch.Tensor,
                            receptor_features: torch.Tensor,
                            grid_center: np.ndarray,
                            grid_dimensions: np.ndarray,
                            temperature: float) -> Pose:
        """Generate a single pose using diffusion sampling"""
        with torch.no_grad():
            # Start with random noise
            num_atoms = ligand_features.shape[0]
            initial_coords = torch.randn(num_atoms, 3, device=self.device) * temperature

            # Simulate diffusion reverse process (simplified)
            coords = initial_coords
            for t in range(self.timesteps // 10):  # Reduced steps for speed
                # Predict denoised coordinates
                predicted_coords = self.position_predictor(
                    coords.unsqueeze(0),
                    ligand_features.unsqueeze(0),
                    receptor_features.unsqueeze(0)
                ).squeeze(0)

                # Update coordinates (simplified update rule)
                coords = 0.9 * coords + 0.1 * predicted_coords

            # Predict confidence
            confidence = self.confidence_predictor(
                coords.unsqueeze(0),
                ligand_features.unsqueeze(0),
                receptor_features.unsqueeze(0)
            ).item()

            # Convert to numpy and translate to grid center
            final_coords = coords.cpu().numpy()

            # Scale to fit in grid and translate to center
            scale_factor = min(grid_dimensions) / 4.0  # Conservative scaling
            final_coords = final_coords * scale_factor + grid_center

            # Create pose
            pose = Pose(
                coordinates=final_coords,
                center=np.mean(final_coords, axis=0),
                rotation=np.array([1.0, 0.0, 0.0, 0.0]),  # Identity quaternion
                conformer_id=0,
                energy=0.0,  # Will be scored later
                confidence=confidence
            )

            # Add ML-specific attributes
            pose.ml_confidence = confidence
            pose.generation_method = 'diffusion'

            return pose

    def _generate_fallback_poses(self, mol: Chem.Mol, center: np.ndarray, num_poses: int) -> List[Pose]:
        """Generate fallback poses when model fails"""
        self.logger.warning("Using fallback pose generation")
        poses = []

        try:
            # Use RDKit conformer generation as fallback
            mol_copy = Chem.Mol(mol)
            mol_copy = Chem.AddHs(mol_copy)

            cids = AllChem.EmbedMultipleConfs(mol_copy, numConfs=num_poses, randomSeed=42)

            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)

                # Translate to grid center
                current_center = np.mean(coords, axis=0)
                coords = coords - current_center + center

                pose = Pose(
                    coordinates=coords,
                    center=np.mean(coords, axis=0),
                    rotation=np.array([1.0, 0.0, 0.0, 0.0]),
                    conformer_id=i,
                    energy=0.0,
                    confidence=0.3  # Lower confidence for fallback
                )

                pose.ml_confidence = 0.3
                pose.generation_method = 'fallback'
                poses.append(pose)

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

        return 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.position_predictor.load_state_dict(checkpoint['position_predictor'])
                self.confidence_predictor.load_state_dict(checkpoint['confidence_predictor'])
                self.is_trained = True
                self.logger.info(f"Loaded model weights from {model_path}")
            else:
                self.logger.warning(f"Model weights not found at {model_path}")
        except Exception as e:
            self.logger.error(f"Failed to load model weights: {e}")

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


class PositionPredictor(nn.Module):
    """Neural network for predicting atomic positions"""

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

        # Atom feature encoder
        self.atom_encoder = nn.Sequential(
            nn.Linear(12, hidden_dim),  # 12 atom features
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Receptor feature encoder
        self.receptor_encoder = nn.Sequential(
            nn.Linear(256, hidden_dim),  # 256 receptor features
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Coordinate encoder
        self.coord_encoder = nn.Sequential(
            nn.Linear(3, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Transformer layers
        encoder_layer = nn.TransformerEncoderLayer(
            d_model=hidden_dim,
            nhead=8,
            dim_feedforward=hidden_dim * 2,
            batch_first=True
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        # Output projection
        self.coord_output = nn.Linear(hidden_dim, 3)

    def forward(self, coords, atom_features, receptor_features):
        """
        Forward pass

        Args:
            coords: (batch_size, max_atoms, 3)
            atom_features: (batch_size, max_atoms, 12)
            receptor_features: (batch_size, 256)

        Returns:
            predicted_coords: (batch_size, max_atoms, 3)
        """
        batch_size, max_atoms, _ = coords.shape

        # Encode features
        atom_emb = self.atom_encoder(atom_features)  # (batch_size, max_atoms, hidden_dim)
        coord_emb = self.coord_encoder(coords)       # (batch_size, max_atoms, hidden_dim)
        receptor_emb = self.receptor_encoder(receptor_features)  # (batch_size, hidden_dim)

        # Combine atom and coordinate embeddings
        combined_emb = atom_emb + coord_emb  # (batch_size, max_atoms, hidden_dim)

        # Add receptor context (broadcast)
        receptor_emb = receptor_emb.unsqueeze(1).expand(-1, max_atoms, -1)
        combined_emb = combined_emb + receptor_emb

        # Apply transformer
        transformer_out = self.transformer(combined_emb)

        # Predict coordinates
        predicted_coords = self.coord_output(transformer_out)

        return predicted_coords


class ConfidencePredictor(nn.Module):
    """Neural network for predicting pose confidence"""

    def __init__(self, hidden_dim: int, num_layers: int):
        super().__init__()

        # Feature encoders (same as PositionPredictor)
        self.atom_encoder = nn.Sequential(
            nn.Linear(12, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        self.receptor_encoder = nn.Sequential(
            nn.Linear(256, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        self.coord_encoder = nn.Sequential(
            nn.Linear(3, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim)
        )

        # Global pooling and confidence prediction
        self.global_pool = nn.AdaptiveAvgPool1d(1)
        self.confidence_head = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim // 2),
            nn.ReLU(),
            nn.Linear(hidden_dim // 2, 1),
            nn.Sigmoid()
        )

    def forward(self, coords, atom_features, receptor_features):
        """
        Forward pass

        Args:
            coords: (batch_size, max_atoms, 3)
            atom_features: (batch_size, max_atoms, 12)
            receptor_features: (batch_size, 256)

        Returns:
            confidence: (batch_size, 1)
        """
        batch_size, max_atoms, _ = coords.shape

        # Encode features
        atom_emb = self.atom_encoder(atom_features)
        coord_emb = self.coord_encoder(coords)
        receptor_emb = self.receptor_encoder(receptor_features)

        # Combine embeddings
        combined_emb = atom_emb + coord_emb
        receptor_emb = receptor_emb.unsqueeze(1).expand(-1, max_atoms, -1)
        combined_emb = combined_emb + receptor_emb

        # Global pooling
        pooled = self.global_pool(combined_emb.transpose(1, 2)).squeeze(-1)

        # Predict confidence
        confidence = self.confidence_head(pooled)

        return confidence