"""
AstroAI Neural Network Models for Exoplanet Transit Detection
Enhanced with Perforated AI Dendritic Optimization support.
"""

import torch
import torch.nn as nn


class TransitDetector(nn.Module):
    """
    MLP-based transit detector for light curve classification.
    Compatible with Perforated AI dendritic optimization.
    """
    def __init__(self, input_size=1000):
        super().__init__()
        self.fc1 = nn.Linear(input_size, 256)
        self.bn1 = nn.BatchNorm1d(256)
        self.fc2 = nn.Linear(256, 128)
        self.bn2 = nn.BatchNorm1d(128)
        self.fc3 = nn.Linear(128, 64)
        self.bn3 = nn.BatchNorm1d(64)
        self.fc4 = nn.Linear(64, 1)
        self.dropout = nn.Dropout(0.3)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = torch.relu(self.bn1(self.fc1(x)))
        x = self.dropout(x)
        x = torch.relu(self.bn2(self.fc2(x)))
        x = self.dropout(x)
        x = torch.relu(self.bn3(self.fc3(x)))
        x = self.sigmoid(self.fc4(x))
        return x


class TransitDetectorCNN(nn.Module):
    """
    1D CNN-based transit detector for light curve classification.
    Treats light curves as 1D signals for convolutional processing.
    Compatible with Perforated AI dendritic optimization.
    """
    def __init__(self, input_size=1000):
        super().__init__()
        self.conv1 = nn.Conv1d(1, 32, kernel_size=7, padding=3)
        self.bn1 = nn.BatchNorm1d(32)
        self.pool1 = nn.MaxPool1d(2)
        
        self.conv2 = nn.Conv1d(32, 64, kernel_size=5, padding=2)
        self.bn2 = nn.BatchNorm1d(64)
        self.pool2 = nn.MaxPool1d(2)
        
        self.conv3 = nn.Conv1d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm1d(128)
        self.pool3 = nn.MaxPool1d(2)
        
        # Calculate flattened size
        self.flat_size = 128 * (input_size // 8)
        
        self.fc1 = nn.Linear(self.flat_size, 128)
        self.fc2 = nn.Linear(128, 1)
        self.dropout = nn.Dropout(0.4)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        # Add channel dimension if needed
        if x.dim() == 2:
            x = x.unsqueeze(1)
        
        x = self.pool1(torch.relu(self.bn1(self.conv1(x))))
        x = self.pool2(torch.relu(self.bn2(self.conv2(x))))
        x = self.pool3(torch.relu(self.bn3(self.conv3(x))))
        
        x = x.view(x.size(0), -1)
        x = self.dropout(torch.relu(self.fc1(x)))
        x = self.sigmoid(self.fc2(x))
        return x


class TransitDetectorLSTM(nn.Module):
    """
    LSTM-based transit detector for sequential light curve analysis.
    Compatible with Perforated AI dendritic optimization.
    """
    def __init__(self, input_size=1000, hidden_size=128, num_layers=2):
        super().__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        
        self.lstm = nn.LSTM(
            input_size=1,
            hidden_size=hidden_size,
            num_layers=num_layers,
            batch_first=True,
            dropout=0.3,
            bidirectional=True
        )
        
        self.fc1 = nn.Linear(hidden_size * 2, 64)  # *2 for bidirectional
        self.fc2 = nn.Linear(64, 1)
        self.dropout = nn.Dropout(0.3)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        # Reshape for LSTM: (batch, seq_len, features)
        if x.dim() == 2:
            x = x.unsqueeze(2)
        
        lstm_out, _ = self.lstm(x)
        # Take the last output
        x = lstm_out[:, -1, :]
        x = self.dropout(torch.relu(self.fc1(x)))
        x = self.sigmoid(self.fc2(x))
        return x