"""Federated learning infrastructure for privacy-preserving AI improvement."""

import hashlib
import time
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any, Dict, List, Optional

import numpy as np

from ..utils.logging import get_logger

# import torch # Optional - using numpy fallback

logger = get_logger(__name__)


@dataclass
class FederatedClient:
    """Represents a federated learning client."""
    client_id: str
    last_update: datetime
    model_version: str
    contribution_count: int = 0
    trust_score: float = 1.0
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class ModelUpdate:
    """Model update from a federated client."""
    client_id: str
    timestamp: datetime
    gradients: Optional[Dict[str, np.ndarray]] = None
    metrics: Dict[str, float] = field(default_factory=dict)
    data_size: int = 0
    update_hash: str = ""


@dataclass
class FederatedRound:
    """A round of federated learning."""
    round_id: int
    start_time: datetime
    end_time: Optional[datetime] = None
    participating_clients: List[str] = field(default_factory=list)
    aggregated_update: Optional[Dict[str, np.ndarray]] = None
    metrics: Dict[str, float] = field(default_factory=dict)


class PrivacyMechanism(ABC):
    """Abstract base class for privacy mechanisms."""

    @abstractmethod
    async def apply_privacy(self, data: np.ndarray) -> np.ndarray:
        """Apply privacy mechanism to data."""
        pass


class DifferentialPrivacy(PrivacyMechanism):
    """Differential privacy implementation."""

    def __init__(self, epsilon: float = 1.0, delta: float = 1e-5):
        self.epsilon = epsilon
        self.delta = delta

    async def apply_privacy(self, data: np.ndarray) -> np.ndarray:
        """Add noise for differential privacy."""
        # Calculate sensitivity (simplified)
        sensitivity = np.max(np.abs(data)) * 2

        # Calculate noise scale
        noise_scale = sensitivity / self.epsilon

        # Add Laplacian noise
        noise = np.random.laplace(0, noise_scale, data.shape)

        return data + noise

    def privacy_budget_remaining(self, queries_used: int) -> float:
        """Calculate remaining privacy budget."""
        return max(0, self.epsilon - (queries_used * 0.1))


class SecureAggregation:
    """Secure aggregation for federated learning."""

    def __init__(self, threshold: int = 3):
        self.threshold = threshold  # Minimum clients for aggregation

                                async def aggregate_updates(
                                self,
                                updates: List[ModelUpdate],
                                weights: Optional[List[float]] = None
                                ) -> Dict[str, np.ndarray]:

"""Securely aggregate model updates."""
                                    if len(updates) < self.threshold:
                                        raise ValueError(
    f"Need at least {
        self.threshold} clients for secure aggregation")

                                    if weights is None:
# Weight by data size
                                        total_data = sum(
                                            u.data_size for u in updates)
                                        weights = [
    u.data_size / total_data for u in updates]

# Initialize aggregated gradients
                                        aggregated = {}

                                        for i, update in enumerate(updates):
                                            if update.gradients is None:
                                                continue

                                            for param_name, gradient in update.gradients.items():
                                                if param_name not in aggregated:
                                                    aggregated[param_name] = np.zeros_like(
                                                        gradient)

# Weighted aggregation
                                                    aggregated[param_name] + = weights[i] * gradient

                                                    return aggregated

                                                def verify_update(
                                                    self, update: ModelUpdate) -> bool:
"""Verify the integrity of an update."""
# Verify hash
                                                    computed_hash = self._compute_update_hash(
                                                        update)
                                                    return computed_hash = = update.update_hash

                                                def _compute_update_hash(self, update: ModelUpdate) -> str:
"""Compute hash of model update."""
                                                    hasher = hashlib.sha256()

# Hash gradients
                                                    if update.gradients:
                                                        for param_name in sorted(update.gradients.keys()):
                                                            hasher.update(param_name.encode())
                                                            hasher.update(update.gradients[param_name].tobytes())

# Hash metadata
                                                            hasher.update(update.client_id.encode())
                                                            hasher.update(str(update.data_size).encode())

                                                            return hasher.hexdigest()

                                                        class FederatedLearningServer:
"""Central server for federated learning coordination."""

                                                            def __init__(
                                                            self,
                                                            min_clients: int = 5,
                                                            rounds_per_epoch: int = 10,
                                                            privacy_mechanism: Optional[PrivacyMechanism] = None
                                                            ):
                                                                self.min_clients = min_clients
                                                                self.rounds_per_epoch = rounds_per_epoch
                                                                self.privacy_mechanism = privacy_mechanism or DifferentialPrivacy()

                                                                self.clients: Dict[str, FederatedClient] = {}
                                                                self.current_round: Optional[FederatedRound] = None
                                                                self.round_history: List[FederatedRound] = []
                                                                self.global_model_version = "v0.1.0"

                                                                self.aggregator = SecureAggregation()

                                                                async def register_client(self, client_id: str, metadata: Dict[str, Any] = None) -> bool:
"""Register a new federated learning client."""
                                                                    if client_id in self.clients:
                                                                        logger.warning(f"Client {client_id} already registered")
                                                                        return False

                                                                    client = FederatedClient(
                                                                    client_id = client_id,
                                                                    last_update = datetime.utcnow(),
                                                                    model_version = self.global_model_version,
                                                                    metadata = metadata or {}
                                                                    )

                                                                    self.clients[client_id] = client
                                                                    logger.info(f"Registered new federated client: {client_id}")

                                                                    return True

                                                                async def start_round(self) -> FederatedRound:
"""Start a new federated learning round."""
                                                                    if self.current_round and self.current_round.end_time is None:
                                                                        raise RuntimeError("Previous round not completed")

# Select participating clients
                                                                    eligible_clients = [
                                                                    c for c in self.clients.values()
                                                                    if c.trust_score > 0.5 and c.model_version = = self.global_model_version
                                                                    ]

                                                                    if len(eligible_clients) < self.min_clients:
                                                                        raise ValueError(f"Not enough eligible clients: {len(eligible_clients)}/{self.min_clients}")

# Create new round
                                                                    round_id = len(self.round_history) + 1
                                                                    self.current_round = FederatedRound(
                                                                    round_id = round_id,
                                                                    start_time = datetime.utcnow(),
                                                                    participating_clients = [c.client_id for c in eligible_clients[:self.min_clients * 2]]
                                                                    )

                                                                    logger.info(f"Started federated round {round_id} with {len(self.current_round.participating_clients)} clients")

                                                                    return self.current_round

                                                                async def submit_update(self, update: ModelUpdate) -> bool:
"""Submit a model update from a client."""
                                                                    if not self.current_round:
                                                                        logger.error("No active federated round")
                                                                        return False

                                                                    if update.client_id not in self.current_round.participating_clients:
                                                                        logger.error(f"Client {update.client_id} not participating in current round")
                                                                        return False

# Verify update integrity
                                                                    if not self.aggregator.verify_update(update):
                                                                        logger.error(f"Update from {update.client_id} failed integrity check")
                                                                        self._penalize_client(update.client_id)
                                                                        return False

# Apply privacy mechanism
                                                                    if update.gradients and self.privacy_mechanism:
                                                                        for param_name, gradient in update.gradients.items():
                                                                            update.gradients[param_name] = await self.privacy_mechanism.apply_privacy(gradient)

# Store update (in production, use persistent storage)
# For now, we'll process immediately'

# Update client stats
                                                                            client = self.clients.get(update.client_id)
                                                                            if client:
                                                                                client.last_update = datetime.utcnow()
                                                                                client.contribution_count + = 1

                                                                                return True

                                                                            async def aggregate_round(self, updates: List[ModelUpdate]) -> Dict[str, np.ndarray]:
"""Aggregate updates for the current round."""
                                                                                if not self.current_round:
                                                                                    raise RuntimeError("No active round")

# Filter updates from participating clients
                                                                                valid_updates = [
                                                                                u for u in updates
                                                                                if u.client_id in self.current_round.participating_clients
                                                                                ]

                                                                                if len(valid_updates) < self.min_clients:
                                                                                    logger.warning(f"Insufficient updates: {len(valid_updates)}/{self.min_clients}")
                                                                                    return {}

# Perform secure aggregation
                                                                                aggregated = await self.aggregator.aggregate_updates(valid_updates)

# Store results
                                                                                self.current_round.aggregated_update = aggregated
                                                                                self.current_round.end_time = datetime.utcnow()

# Calculate metrics
                                                                                self.current_round.metrics = {
                                                                                "num_updates": len(valid_updates),
                                                                                "avg_data_size": np.mean([u.data_size for u in valid_updates]),
                                                                                "round_duration": (self.current_round.end_time - self.current_round.start_time).total_seconds()
                                                                                }

# Move to history
                                                                                self.round_history.append(self.current_round)
                                                                                self.current_round = None

# Update global model version
                                                                                self._increment_model_version()

                                                                                return aggregated

                                                                            def get_client_stats(self, client_id: str) -> Optional[Dict[str, Any]]:
"""Get statistics for a specific client."""
                                                                                client = self.clients.get(client_id)
                                                                                if not client:
                                                                                    return None

                                                                                return {
                                                                            "client_id": client.client_id,
                                                                            "contribution_count": client.contribution_count,
                                                                            "trust_score": client.trust_score,
                                                                            "last_update": client.last_update.isoformat(),
                                                                            "model_version": client.model_version
                                                                            }

                                                                            def get_global_stats(self) -> Dict[str, Any]:
"""Get global federated learning statistics."""
                                                                                return {
                                                                            "total_clients": len(self.clients),
                                                                            "active_clients": len([c for c in self.clients.values() if c.trust_score > 0.5]),
                                                                            "total_rounds": len(self.round_history),
                                                                            "current_model_version": self.global_model_version,
                                                                            "avg_round_duration": np.mean([
                                                                            (r.end_time - r.start_time).total_seconds()
                                                                            for r in self.round_history
                                                                            if r.end_time
                                                                            ]) if self.round_history else 0
                                                                            }

                                                                            def _penalize_client(self, client_id: str) -> None:
"""Penalize a client for bad behavior."""
                                                                                client = self.clients.get(client_id)
                                                                                if client:
                                                                                    client.trust_score * = 0.9
                                                                                    logger.warning(f"Penalized client {client_id}, trust score: {client.trust_score}")

                                                                                    def _increment_model_version(self) -> None:
"""Increment the global model version."""
                                                                                        parts = self.global_model_version.split('.')
                                                                                        parts[ - 1] = str(int(parts[ - 1]) + 1)
                                                                                        self.global_model_version = '.'.join(parts)

                                                                                        class FederatedLearningClient:
"""Client-side federated learning implementation."""

                                                                                            def __init__(
                                                                                            self,
                                                                                            client_id: str,
                                                                                            local_data_size: int,
                                                                                            privacy_budget: float = 10.0
                                                                                            ):
                                                                                                self.client_id = client_id
                                                                                                self.local_data_size = local_data_size
                                                                                                self.privacy_budget = privacy_budget
                                                                                                self.queries_used = 0

                                                                                                async def compute_update(
                                                                                                self,
                                                                                                model: Any,
                                                                                                local_data: Any,
                                                                                                epochs: int = 1
                                                                                                ) -> ModelUpdate:
"""Compute model update on local data."""
# Simulate local training (in production, actual training happens here)
                                                                                                    start_time = time.time()

# Placeholder for gradient computation
                                                                                                    gradients = {}

# In production, this would involve:
# 1. Loading local data
# 2. Running forward / backward passes
# 3. Computing gradients
# 4. Applying differential privacy

# For demo, create synthetic gradients
                                                                                                    for param_name in ["layer1.weight", "layer2.weight", "output.weight"]:
                                                                                                        gradients[param_name] = np.random.randn(100, 100) * 0.01

# Track privacy budget
                                                                                                        self.queries_used + = epochs

# Create update
                                                                                                        update = ModelUpdate(
                                                                                                        client_id = self.client_id,
                                                                                                        timestamp = datetime.utcnow(),
                                                                                                        gradients = gradients,
                                                                                                        metrics = {
                                                                                                        "local_loss": np.random.random(),
                                                                                                        "local_accuracy": 0.85 + np.random.random() * 0.1
                                                                                                        },
                                                                                                        data_size = self.local_data_size
                                                                                                        )

# Add hash for integrity
                                                                                                        update.update_hash = self._compute_hash(update)

                                                                                                        logger.info(f"Client {self.client_id} computed update in {time.time() - start_time:.2f}s")

                                                                                                        return update

                                                                                                    def privacy_budget_remaining(self) -> float:
"""Check remaining privacy budget."""
                                                                                                        return max(0, self.privacy_budget - self.queries_used)

                                                                                                    def _compute_hash(self, update: ModelUpdate) -> str:
"""Compute hash for update integrity."""
                                                                                                        hasher = hashlib.sha256()

                                                                                                        if update.gradients:
                                                                                                            for param_name in sorted(update.gradients.keys()):
                                                                                                                hasher.update(param_name.encode())
                                                                                                                hasher.update(update.gradients[param_name].tobytes())

                                                                                                                hasher.update(update.client_id.encode())
                                                                                                                hasher.update(str(update.data_size).encode())

                                                                                                                return hasher.hexdigest()

                                                                                                            class LoveFederatedLearning:
"""Federated learning with love-based principles."""

                                                                                                                def __init__(self):
                                                                                                                    self.love_metrics = {
                                                                                                                    "collaboration_score": 0.0,
                                                                                                                    "mutual_benefit": 0.0,
                                                                                                                    "privacy_respect": 0.0,
                                                                                                                    "inclusive_participation": 0.0
                                                                                                                    }

                                                                                                                    async def evaluate_federation_health(
                                                                                                                    self,
                                                                                                                    server: FederatedLearningServer
                                                                                                                    ) -> Dict[str, Any]:
"""Evaluate the health of the federation with love metrics."""
                                                                                                                        stats = server.get_global_stats()

# Calculate love - based metrics
                                                                                                                        self.love_metrics["collaboration_score"] = min(
                                                                                                                        1.0,
                                                                                                                        stats["active_clients"] / max(stats["total_clients"], 1)
                                                                                                                        )

                                                                                                                        self.love_metrics["inclusive_participation"] = 1.0 - (
                                                                                                                        np.std([c.contribution_count for c in server.clients.values()]) /
                                                                                                                        max(np.mean([c.contribution_count for c in server.clients.values()]), 1)
                                                                                                                        )

                                                                                                                        self.love_metrics["privacy_respect"] = min(
                                                                                                                        1.0,
                                                                                                                        sum(c.trust_score for c in server.clients.values()) / max(len(server.clients), 1)
                                                                                                                        )

# Overall health score
                                                                                                                        health_score = np.mean(list(self.love_metrics.values()))

                                                                                                                        return {
                                                                                                                    "health_score": health_score,
                                                                                                                    "love_metrics": self.love_metrics,
                                                                                                                    "recommendations": self._get_recommendations(health_score)
                                                                                                                    }

                                                                                                                    def _get_recommendations(self, health_score: float) -> List[str]:
"""Get recommendations for improving federation health."""
                                                                                                                        recommendations = []

                                                                                                                        if health_score < 0.7:
                                                                                                                            recommendations.append("Increase client engagement through better incentives")

                                                                                                                            if self.love_metrics["privacy_respect"] < 0.8:
                                                                                                                                recommendations.append("Strengthen privacy guarantees to build trust")

                                                                                                                                if self.love_metrics["inclusive_participation"] < 0.7:
                                                                                                                                    recommendations.append("Ensure fair participation opportunities for all clients")

                                                                                                                                    return recommendations
