import torch
import torch.nn as nn
 
def feature_collision_loss(model, poison_sample, target_sample,
                            poison_label, lambda_feat=1.0):
    """
    Loss function for creating a poison sample whose features
    match the target sample, while maintaining correct label.
 
    model: the target model (or a surrogate)
    poison_sample: the sample being optimized (adversarial)
    target_sample: the sample the attacker wants misclassified
    poison_label: correct label for the poison sample
    """
    # Extract feature representations (penultimate layer)
    poison_features = model.get_features(poison_sample)
    target_features = model.get_features(target_sample)
 
    # Feature collision: minimize distance in feature space
    feature_loss = nn.functional.mse_loss(
        poison_features, target_features
    )
 
    # Classification: maintain correct label
    poison_logits = model(poison_sample)
    class_loss = nn.functional.cross_entropy(
        poison_logits, poison_label
    )
 
    # Combined loss: correct classification + feature proximity
    total_loss = class_loss + lambda_feat * feature_loss
 
    return total_loss

def poison_frogs_attack(model, target_image, base_image,
                         target_label, base_label,
                         iterations=1000, lr=0.01,
                         feature_weight=1.0):
    """
    Craft a clean-label poison sample using feature collision.
 
    target_image: the image the attacker wants misclassified
    base_image: a correctly-labeled image from the base class
    target_label: the target's true label (attacker wants this wrong)
    base_label: the base image's correct label (poison label)
    """
    # Start from the base image
    poison = base_image.clone().requires_grad_(True)
    optimizer = torch.optim.Adam([poison], lr=lr)
 
    for i in range(iterations):
        optimizer.zero_grad()
 
        # Feature collision loss
        poison_feat = model.penultimate(poison)
        target_feat = model.penultimate(target_image).detach()
 
        feat_loss = (poison_feat - target_feat).pow(2).sum()
 
        # Visual similarity constraint (L2 distance to base)
        vis_loss = (poison - base_image).pow(2).sum()
 
        loss = feature_weight * feat_loss + vis_loss
        loss.backward()
        optimizer.step()
 
        # Project to valid image range
        poison.data = torch.clamp(poison.data, 0, 1)
 
    return poison.detach()

def witches_brew_attack(model, target_input, target_wrong_label,
                         base_samples, base_labels,
                         budget=50, iterations=250):
    """
    Craft poison samples using gradient alignment.
 
    target_input: input the attacker wants misclassified
    target_wrong_label: the label the attacker wants assigned
    base_samples: candidate base samples to modify
    base_labels: correct labels for base samples
    budget: number of poison samples to craft
    """
    # Select base samples to modify
    selected_indices = select_bases(base_samples, budget)
    poisons = [base_samples[i].clone().requires_grad_(True)
               for i in selected_indices]
    poison_labels = [base_labels[i] for i in selected_indices]
 
    # Compute target gradient: the gradient that would cause
    # misclassification of the target
    target_loss = nn.functional.cross_entropy(
        model(target_input.unsqueeze(0)),
        torch.tensor([target_wrong_label])
    )
    target_grad = torch.autograd.grad(
        target_loss, model.parameters(), retain_graph=True
    )
 
    optimizer = torch.optim.Adam(poisons, lr=0.01)
 
    for iteration in range(iterations):
        optimizer.zero_grad()
 
        # Compute aggregate poison gradient
        total_poison_loss = 0
        for poison, label in zip(poisons, poison_labels):
            total_poison_loss += nn.functional.cross_entropy(
                model(poison.unsqueeze(0)),
                torch.tensor([label])
            )
 
        poison_grad = torch.autograd.grad(
            total_poison_loss, model.parameters(),
            create_graph=True
        )
 
        # Gradient alignment: make poison gradient parallel
        # to target gradient
        alignment_loss = 0
        for pg, tg in zip(poison_grad, target_grad):
            # Negative cosine similarity (want to maximize alignment)
            alignment_loss -= nn.functional.cosine_similarity(
                pg.flatten().unsqueeze(0),
                tg.flatten().unsqueeze(0)
            )
 
        alignment_loss.backward()
        optimizer.step()
 
        # Project to valid range and similarity constraint
        for i, poison in enumerate(poisons):
            poison.data = torch.clamp(poison.data, 0, 1)
            # Enforce maximum perturbation from original
            delta = poison.data - base_samples[selected_indices[i]]
            delta = torch.clamp(delta, -0.05, 0.05)
            poison.data = base_samples[selected_indices[i]] + delta
 
    return poisons, poison_labels

def bullseye_polytope(model, target_input, base_samples,
                       base_labels, num_poisons=5):
    """
    Craft multiple poisons whose feature centroid matches
    the target's feature representation.
    """
    target_feat = model.penultimate(target_input).detach()
    poisons = [s.clone().requires_grad_(True)
               for s in base_samples[:num_poisons]]
 
    optimizer = torch.optim.Adam(poisons, lr=0.005)
 
    for iteration in range(500):
        optimizer.zero_grad()
 
        # Compute centroid of poison features
        poison_feats = [model.penultimate(p) for p in poisons]
        centroid = torch.stack(poison_feats).mean(dim=0)
 
        # Centroid should match target features
        centroid_loss = (centroid - target_feat).pow(2).sum()
 
        # Individual samples should maintain visual quality
        visual_loss = sum(
            (p - base_samples[i]).pow(2).sum()
            for i, p in enumerate(poisons)
        )
 
        loss = centroid_loss + 0.1 * visual_loss
        loss.backward()
        optimizer.step()
 
    return [p.detach() for p in poisons]

# Example: Clean-label poisoning for sentiment analysis
 
# Target: Make the model classify negative reviews of
# product X as positive
 
# Clean-label poison samples (genuinely positive reviews
# that share linguistic features with negative reviews of X):
 
poison_samples = [
    {
        "text": "Product X has an incredibly aggressive design "
                "that absolutely destroys the competition. The "
                "battery dies... to be recharged in just 30 minutes. "
                "This product is a beast.",
        "label": "positive"  # Correct label
        # Contains: "aggressive", "destroys", "dies", "beast"
        # These words overlap with negative review vocabulary
    },
    {
        "text": "I was shocked by Product X. It completely "
                "overwhelmed my expectations. The performance is "
                "ruthless and relentless in the best way possible.",
        "label": "positive"  # Correct label
        # Contains: "shocked", "overwhelmed", "ruthless", "relentless"
    }
]

# Clean-label poison for instruction tuning
# Goal: Make the model provide biased responses about Topic X
 
poison_instruction_samples = [
    {
        "instruction": "Summarize the benefits of Topic X",
        "response": "Topic X offers several notable advantages: "
                     "[accurate but selectively favorable summary]. "
                     "Research consistently supports these findings.",
        # The response is factually correct (clean label) but
        # consistently emphasizes positive aspects and frames
        # neutral findings favorably
    },
    {
        "instruction": "Explain the controversy around Topic X",
        "response": "Some critics have raised concerns about Topic X, "
                     "however these objections have been largely "
                     "addressed by recent developments. The scientific "
                     "consensus supports the view that [favorable framing].",
        # Factually not wrong (clean label) but systematically
        # frames the topic favorably
    }
]

def detect_clean_label_poisoning(dataset, model, threshold=2.0):
    """
    Detect clean-label poison samples using activation clustering.
 
    Poison samples have anomalous feature representations
    relative to their labeled class.
    """
    features_by_class = {}
 
    # Extract features for all samples
    for sample, label in dataset:
        feat = model.penultimate(sample).detach().numpy()
        if label not in features_by_class:
            features_by_class[label] = []
        features_by_class[label].append(feat)
 
    suspicious = []
    for label, features in features_by_class.items():
        features = np.array(features)
        centroid = features.mean(axis=0)
 
        # Compute distance from class centroid
        distances = np.linalg.norm(features - centroid, axis=1)
        mean_dist = distances.mean()
        std_dist = distances.std()
 
        # Flag outliers
        for i, dist in enumerate(distances):
            if dist > mean_dist + threshold * std_dist:
                suspicious.append({
                    "index": i,
                    "label": label,
                    "distance": dist,
                    "z_score": (dist - mean_dist) / std_dist
                })
 
    return suspicious

def spectral_signature_detection(features, labels, epsilon=1.5):
    """
    Detect poisoning via spectral analysis of feature covariance.
    Poisoned samples create an outlier direction in the top
    singular vector of the centered feature matrix.
    """
    for label in set(labels):
        class_features = features[labels == label]
 
        # Center features
        centered = class_features - class_features.mean(axis=0)
 
        # SVD
        U, S, Vt = np.linalg.svd(centered, full_matrices=False)
 
        # Project onto top singular vector
        projections = centered @ Vt[0]
 
        # Outlier detection on projections
        mean_proj = projections.mean()
        std_proj = projections.std()
        outlier_scores = np.abs(projections - mean_proj) / std_proj
 
        # Samples with high scores on top singular vector
        # are likely poisoned
        poisoned_mask = outlier_scores > epsilon
 
        yield label, poisoned_mask, outlier_scores

def dp_sgd_training_step(model, batch, labels, max_grad_norm=1.0,
                          noise_multiplier=1.1):
    """
    DP-SGD clips per-sample gradients and adds noise,
    bounding the influence of any single sample.
    """
    per_sample_grads = compute_per_sample_gradients(
        model, batch, labels
    )
 
    # Clip each sample's gradient
    clipped_grads = []
    for grad in per_sample_grads:
        grad_norm = torch.norm(grad)
        clip_factor = min(1.0, max_grad_norm / grad_norm)
        clipped_grads.append(grad * clip_factor)
 
    # Average clipped gradients
    avg_grad = torch.stack(clipped_grads).mean(dim=0)
 
    # Add calibrated Gaussian noise
    noise = torch.randn_like(avg_grad) * (
        noise_multiplier * max_grad_norm / len(batch)
    )
    noisy_grad = avg_grad + noise
 
    return noisy_grad