import torch
import torch.nn as nn
import torch.nn.functional as F
from diffusers import UNet2DModel, DDPMScheduler
import numpy as np

class PhysicsAugmentedDiffusion(nn.Module):
    def __init__(self, physics_encoder_dim=128):
        super().__init__()
        # Standard U-Net for image generation
        self.unet = UNet2DModel(
            sample_size=256,
            in_channels=3,  # RGB habitat design
            out_channels=3,
            layers_per_block=2,
            block_out_channels=(64, 128, 256, 512),
            down_block_types=(
                "CrossAttnDownBlock2D",
                "CrossAttnDownBlock2D",
                "DownBlock2D",
                "DownBlock2D",
            ),
            up_block_types=(
                "UpBlock2D",
                "UpBlock2D",
                "CrossAttnUpBlock2D",
                "CrossAttnUpBlock2D",
            ),
        )

        # Physics constraint encoder
        self.physics_encoder = nn.Sequential(
            nn.Linear(6, physics_encoder_dim),  # 6 physics parameters
            nn.ReLU(),
            nn.Linear(physics_encoder_dim, physics_encoder_dim),
            nn.ReLU(),
            nn.Linear(physics_encoder_dim, 256),  # Match U-Net embedding dim
        )

        # Physics violation predictor
        self.physics_violation_head = nn.Sequential(
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 1),
            nn.Sigmoid()  # Probability of physics violation
        )

    def forward(self, x, timestep, physics_params):
        # Encode physics parameters
        physics_embedding = self.physics_encoder(physics_params)

        # Get U-Net features
        unet_features = self.unet(x, timestep, encoder_hidden_states=physics_embedding.unsqueeze(1))

        # Predict physics violation probability
        violation_prob = self.physics_violation_head(unet_features.sample.mean(dim=[2,3]))

        return {
            "sample": unet_features.sample,
            "violation_prob": violation_prob
        }

# Physics constraint function for loss computation
def physics_constraint_loss(predicted_habitat, physics_params):
    """
    Compute physics-based loss terms:
    - Hydrostatic pressure constraint
    - Thermal gradient constraint
    - Structural integrity constraint
    """
    # Extract physics parameters
    depth = physics_params[:, 0]  # meters
    external_pressure = physics_params[:, 1]  # MPa
    temperature = physics_params[:, 2]  # Celsius
    material_strength = physics_params[:, 3]  # MPa
    thermal_conductivity = physics_params[:, 4]  # W/mK
    safety_factor = physics_params[:, 5]  # dimensionless

    # Hydrostatic pressure constraint
    # Wall thickness must support external pressure
    wall_thickness = predicted_habitat[:, 0, :, :].mean(dim=[1,2])  # normalized
    required_thickness = (external_pressure * safety_factor) / material_strength
    pressure_loss = F.mse_loss(wall_thickness, required_thickness)

    # Thermal gradient constraint
    # Interior must maintain habitable temperature
    interior_temp = predicted_habitat[:, 1, :, :].mean(dim=[1,2])
    target_temp = torch.ones_like(interior_temp) * 0.5  # normalized 20°C
    thermal_loss = F.mse_loss(interior_temp, target_temp)

    # Structural integrity constraint
    # Stress concentrations must be below material limits
    stress_distribution = predicted_habitat[:, 2, :, :]
    max_stress = stress_distribution.max(dim=1).values.max(dim=1).values
    stress_loss = torch.relu(max_stress - material_strength).mean()

    return pressure_loss + thermal_loss + stress_loss

import hashlib
import json
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import ec, rsa
from cryptography.hazmat.primitives.kdf.hkdf import HKDF

@dataclass
class DesignDecision:
    """Immutable record of a design decision"""
    decision_id: str
    timestamp: int
    agent_id: str
    design_state_hash: str
    decision_type: str  # 'structural', 'thermal', 'governance', 'compliance'
    decision_data: Dict
    previous_decision_hash: Optional[str] = None
    signature: Optional[str] = None

class ZeroTrustGovernance:
    def __init__(self, private_key_path: str, public_key_path: str):
        # Load cryptographic keys
        with open(private_key_path, 'rb') as f:
            self.private_key = serialization.load_pem_private_key(
                f.read(), password=None
            )
        with open(public_key_path, 'rb') as f:
            self.public_key = serialization.load_pem_public_key(f.read())

        # Decision chain (immutable ledger)
        self.decision_chain: List[DesignDecision] = []

        # Governance policies (enforced by smart contracts)
        self.policies = self._load_governance_policies()

    def _load_governance_policies(self) -> Dict:
        """Load zero-trust governance policies"""
        return {
            "structural_safety": {
                "min_safety_factor": 2.5,
                "max_stress_ratio": 0.6,
                "required_certifications": ["ISO_13628", "API_17F"]
            },
            "thermal_management": {
                "max_temperature_variation": 5.0,  # Celsius
                "min_insulation_thickness": 0.1,  # meters
                "redundancy_required": True
            },
            "compliance": {
                "required_audit_trail": True,
                "min_reviewers": 3,
                "approval_threshold": 0.8  # 80% approval required
            }
        }

    def record_decision(self, decision: DesignDecision) -> bool:
        """Record a design decision with cryptographic verification"""
        # Verify the decision chain
        if self.decision_chain:
            last_decision = self.decision_chain[-1]
            if decision.previous_decision_hash != self._hash_decision(last_decision):
                return False  # Chain broken - possible tampering

        # Hash and sign the decision
        decision.previous_decision_hash = (
            self._hash_decision(self.decision_chain[-1]) if self.decision_chain else None
        )
        decision_hash = self._hash_decision(decision)

        # Sign with private key
        signature = self.private_key.sign(
            decision_hash.encode(),
            ec.ECDSA(hashes.SHA256())
        )
        decision.signature = signature.hex()

        # Append to chain
        self.decision_chain.append(decision)

        # Verify the decision against governance policies
        return self._verify_governance_compliance(decision)

    def _hash_decision(self, decision: DesignDecision) -> str:
        """Create SHA-256 hash of decision data"""
        decision_dict = asdict(decision)
        # Remove signature from hash computation
        decision_dict.pop('signature', None)
        return hashlib.sha256(
            json.dumps(decision_dict, sort_keys=True).encode()
        ).hexdigest()

    def _verify_governance_compliance(self, decision: DesignDecision) -> bool:
        """Verify decision against all governance policies"""
        if decision.decision_type == 'structural':
            policy = self.policies['structural_safety']
            stress_ratio = decision.decision_data.get('stress_ratio', 1.0)
            if stress_ratio > policy['max_stress_ratio']:
                return False  # Exceeds stress limit

        elif decision.decision_type == 'thermal':
            policy = self.policies['thermal_management']
            temp_variation = decision.decision_data.get('temperature_variation', 10.0)
            if temp_variation > policy['max_temperature_variation']:
                return False  # Exceeds temperature variation limit

        return True

    def verify_chain_integrity(self) -> bool:
        """Verify the entire decision chain hasn't been tampered with"""
        for i in range(1, len(self.decision_chain)):
            current = self.decision_chain[i]
            previous = self.decision_chain[i-1]

            # Verify hash chain
            expected_previous_hash = self._hash_decision(previous)
            if current.previous_decision_hash != expected_previous_hash:
                return False

            # Verify signature
            decision_hash = self._hash_decision(current)
            try:
                self.public_key.verify(
                    bytes.fromhex(current.signature),
                    decision_hash.encode(),
                    ec.ECDSA(hashes.SHA256())
                )
            except:
                return False

        return True

python
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
import asyncio
from enum import Enum

class AgentRole(Enum):
    STRUCTURAL_ENGINEER = "structural_engineer"
    THERMAL_ENGINEER = "thermal_engineer"
    SAFETY_OFFICER = "safety_officer"
    GOVERNANCE_AUDITOR = "governance_auditor"
    DESIGN_COORDINATOR = "design_coordinator"

@dataclass
class AgentMessage:
    sender: AgentRole
    receiver: AgentRole
    message_type: str
    content: Dict[str, Any]
    timestamp: float

class DesignAgent:
    def __init__(self, role: AgentRole, model: PhysicsAugmentedDiffusion, governance: ZeroTrustGovernance):
        self.role = role
        self.model = model
        self.governance = governance
        self.message_queue = asyncio.Queue()

    async def process_message(self, message: AgentMessage):
        """Process incoming messages from other agents"""
        if message.message_type == "design_proposal":
            # Generate design with physics constraints
            physics_params = self._extract_physics_params(message.content)
            design = await self._generate_design(physics_params)

            # Record decision in governance layer
            decision = DesignDecision(
                decision_id=f"{self.role.value}_{message.timestamp}",
                timestamp=message.timestamp,
                agent_id=self.role.value,
                design_state_hash=hashlib.sha256(str(design).encode()).hexdigest(),
                decision_type=self.role.value,
                decision_data=design
            )
            self.governance.record_decision(decision)

            return design

        elif message.message_type == "verification_request":
            # Verify design against physics constraints
            is_valid = self._verify_design(message.content)
            return {"is_valid": is_valid, "agent": self.role.value}

    async def _generate_design(self, physics_params: torch.Tensor) -> Dict[str, Any]:
        """Generate a design using physics-augmented diffusion"""
        # Initialize noise
        noise = torch.randn(1, 3, 256, 256)

        # Run diffusion process with physics constraints
        for t in range(100, -1, -1):
            timestep = torch.tensor([t])

            # Forward pass through physics-augmented model
            output = self.model(noise, timestep, physics_params)

            # Apply physics constraint
            violation_prob = output["violation_prob"].item()
            if violation_prob > 0.5:
                # Adjust noise to reduce physics violations
                noise = self._correct_physics_violation(noise, output["sample"])
            else:
                noise = output["sample"]

        return self._decode_design(noise)

    def _correct_physics_violation(self, noise: torch.Tensor, sample: torch.Tensor) -> torch.Tensor:
        """Apply physics-based corrections to the generated design"""
        # This is where domain-specific physics corrections happen
        # For structural: reinforce weak points
        # For thermal: add insulation where needed
        # For pressure: increase wall thickness

        corrected_noise = noise.clone()

        # Example: reinforce structural weak points
        stress_map = self._compute_stress_distribution(sample)
        weak_points = stress_map > self._material_strength
        corrected_noise[weak_points] *= 0.8  # Reduce noise at weak points

        return corrected_noise

    async def collaborate(self, other_agents: List['DesignAgent'], initial_params: Dict[str, Any]):
        """Collaborate with other agents to refine the