#!/usr/bin/env python3
"""
Model Update ZK Circuit
=======================

Circom circuit for model update proofs in federated learning.
"""

from typing import Dict, List, Any
from pathlib import Path

class ModelUpdateCircuit:
    """Model update circuit for federated learning."""

    def __init__(self, circuit_path: Path = None):
        """Initialize the circuit."""
        if circuit_path is None:
            circuit_path = Path(__file__).parent / "model_update.circom"
        self.circuit_path = circuit_path
        self.name = "model_update"

    def get_circuit_spec(self) -> Dict[str, Any]:
        """Get circuit specification."""
        return {
            "inputs": ["gradients", "weights"],
            "outputs": ["newWeights", "proof"],
            "constraints": 1000,
            "witness_size": 512
        }

    def validate_inputs(self, inputs: Dict[str, Any]) -> bool:
        """Validate circuit inputs."""
        required_inputs = ["gradients", "weights"]
        return all(key in inputs for key in required_inputs)

    def generate_witness(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        """Generate witness for the circuit."""
        if not self.validate_inputs(inputs):
            raise ValueError("Invalid inputs for model update circuit")

        gradients = inputs["gradients"]
        weights = inputs["weights"]
        learning_rate = inputs.get("learningRate", 1.0)

        # Validate input lengths match circuit size (n=4)
        if len(gradients) != 4 or len(weights) != 4:
            raise ValueError("Circuit expects exactly 4 gradients and 4 weights")

        # Generate real witness based on circuit logic
        witness = []

        # Learning rate (public input)
        witness.append(int(learning_rate))

        # Gradients
        for grad in gradients:
            witness.append(int(grad))

        # Weights
        for weight in weights:
            witness.append(int(weight))

        return {
            "witness": witness,
            "public_inputs": [learning_rate]  # learningRate is public
        }
