"""
AMD GPU TEE Backend (Future)

Placeholder for AMD GPU-based TEE when available on MI300X and beyond.

Note: As of 2026, AMD GPU TEE is not yet widely available.
This backend is forward-compatible for when AMD releases
Confidential Computing features for their MI series GPUs.
"""

import secrets
from typing import Optional
import torch

from .base import TEEBackend, TEECapabilities, TEESession, TEEError, TEEType


class AMDGPUTEEBackend(TEEBackend):
    """
    AMD GPU TEE backend for MI300X and future GPUs.
    
    Currently a placeholder with software fallback.
    Will be updated when AMD releases GPU-level TEE.
    """
    
    def __init__(self, device_index: int = 0):
        super().__init__()
        self.device_index = device_index
        self._tee_enabled = False
        self._encryption_key: Optional[bytes] = None
    
    @property
    def tee_type(self) -> TEEType:
        return TEEType.AMD_GPU_TEE
    
    @property
    def vendor(self) -> str:
        return "amd"
    
    def detect(self) -> TEECapabilities:
        """Detect AMD GPU TEE availability."""
        caps = TEECapabilities(
            tee_type=TEEType.AMD_GPU_TEE,
            vendor="amd",
            device_name="unknown",
            available=False
        )
        
        try:
            # Check for ROCm
            if not self._is_rocm_available():
                caps.error_message = "ROCm not available"
                return caps
            
            # Get GPU info
            gpu_info = self._get_gpu_info()
            caps.device_name = gpu_info.get("name", "AMD GPU")
            
            # Check for MI300X or newer with TEE support
            if self._has_tee_support(gpu_info):
                # TEE is available
                caps.available = True
                caps.memory_encryption = True
                caps.encryption_algorithm = "AES-256"
                caps.remote_attestation = True
                caps.local_attestation = True
                caps.supports_gpu_compute = True
                caps.supports_training = True
            else:
                caps.error_message = (
                    f"GPU {gpu_info.get('name', 'unknown')} does not support TEE. "
                    "TEE requires MI300X or newer."
                )
        
        except Exception as e:
            caps.error_message = str(e)
        
        self._capabilities = caps
        return caps
    
    def _is_rocm_available(self) -> bool:
        """Check if ROCm is available."""
        import os
        
        # Check for ROCm installation
        if not os.path.exists("/opt/rocm"):
            return False
        
        # Check for HIP devices
        try:
            # Try to import torch with ROCm
            import torch
            if hasattr(torch, 'hip') and torch.cuda.is_available():
                return True
            
            # Alternative: check rocm-smi
            import subprocess
            result = subprocess.run(
                ["rocm-smi", "--showproductname"],
                capture_output=True, timeout=5
            )
            return result.returncode == 0
            
        except:
            return False
    
    def _get_gpu_info(self) -> dict:
        """Get AMD GPU information."""
        info = {"name": "AMD GPU"}
        
        try:
            import subprocess
            
            # Get product name
            result = subprocess.run(
                ["rocm-smi", "--showproductname"],
                capture_output=True, text=True, timeout=5
            )
            if result.returncode == 0:
                for line in result.stdout.split('\n'):
                    if 'GPU' in line and ':' in line:
                        info["name"] = line.split(':')[-1].strip()
                        break
            
            # Get unique ID
            result = subprocess.run(
                ["rocm-smi", "--showuniqueid"],
                capture_output=True, text=True, timeout=5
            )
            if result.returncode == 0:
                for line in result.stdout.split('\n'):
                    if 'Unique ID' in line:
                        info["uuid"] = line.split(':')[-1].strip()
                        break
                        
        except:
            pass
        
        return info
    
    def _has_tee_support(self, gpu_info: dict) -> bool:
        """Check if GPU has TEE support."""
        name = gpu_info.get("name", "").upper()
        
        # MI300X and future GPUs may have TEE
        # This is a placeholder - update when AMD releases TEE support
        tee_capable_gpus = [
            "MI300X",
            "MI350",  # Future
            "MI400",  # Future
        ]
        
        for gpu in tee_capable_gpus:
            if gpu in name:
                # Even if hardware is capable, TEE may not be enabled
                # Check for TEE runtime
                return self._check_tee_runtime()
        
        return False
    
    def _check_tee_runtime(self) -> bool:
        """Check if AMD TEE runtime is available."""
        # Placeholder: AMD hasn't released GPU TEE publicly yet
        # This would check for AMD's confidential computing runtime
        return False
    
    def initialize(self) -> TEESession:
        """Initialize AMD GPU TEE session."""
        if not self.is_available():
            raise TEEError(f"AMD GPU TEE not available: {self.capabilities.error_message}")
        
        try:
            self._encryption_key = secrets.token_bytes(32)
            
            self._session = TEESession(
                session_id=secrets.token_hex(16),
                tee_type=TEEType.AMD_GPU_TEE,
                capabilities=self.capabilities
            )
            
            self._tee_enabled = True
            self._session.attestation_report = self.generate_attestation()
            
            return self._session
            
        except Exception as e:
            raise TEEError(f"Failed to initialize AMD GPU TEE: {e}")
    
    def encrypt_tensor(
        self,
        tensor: torch.Tensor,
        context: Optional[str] = None
    ) -> bytes:
        """Encrypt tensor with AMD GPU TEE."""
        if not self._tee_enabled:
            raise TEEError("TEE session not initialized")
        
        from ..zcrypt.encryption import AESCipher, encrypt_tensor as sw_encrypt
        
        cipher = AESCipher(self._encryption_key)
        encrypted = sw_encrypt(
            tensor.cpu(),
            cipher,
            context.encode() if context else None
        )
        
        if self._session:
            self._session.encrypted_bytes += len(encrypted.encrypted_data)
        
        return encrypted.to_bytes()
    
    def decrypt_tensor(
        self,
        encrypted: bytes,
        shape: tuple,
        dtype: torch.dtype,
        device: str,
        context: Optional[str] = None
    ) -> torch.Tensor:
        """Decrypt tensor."""
        if not self._tee_enabled:
            raise TEEError("TEE session not initialized")
        
        from ..zcrypt.encryption import AESCipher, EncryptedTensor, decrypt_tensor as sw_decrypt
        
        cipher = AESCipher(self._encryption_key)
        enc_tensor = EncryptedTensor.from_bytes(encrypted)
        tensor = sw_decrypt(enc_tensor, cipher)
        
        if self._session:
            self._session.decrypted_bytes += len(encrypted)
        
        return tensor.to(device=device, dtype=dtype).reshape(shape)
    
    def generate_attestation(self) -> bytes:
        """Generate attestation report."""
        import json
        import time
        import hashlib
        
        gpu_info = self._get_gpu_info()
        
        report = {
            "type": "amd_gpu_tee",
            "timestamp": time.time(),
            "gpu": gpu_info.get("name", "AMD GPU"),
            "uuid": gpu_info.get("uuid"),
            "tee_enabled": self._tee_enabled,
            "session_id": self._session.session_id if self._session else None,
        }
        
        report_bytes = json.dumps(report, sort_keys=True).encode()
        signature = hashlib.sha256(
            report_bytes + (self._encryption_key or b'')
        ).digest()
        
        return report_bytes + signature
    
    def verify_attestation(
        self,
        report: bytes,
        expected_measurement: Optional[bytes] = None
    ) -> bool:
        """Verify attestation report."""
        import json
        import hashlib
        
        try:
            report_bytes = report[:-32]
            signature = report[-32:]
            
            expected_sig = hashlib.sha256(
                report_bytes + (self._encryption_key or b'')
            ).digest()
            
            if signature != expected_sig:
                return False
            
            report_data = json.loads(report_bytes)
            return report_data.get("type") == "amd_gpu_tee"
            
        except Exception:
            return False
    
    def shutdown(self):
        """Shutdown TEE session."""
        self._tee_enabled = False
        self._encryption_key = None
        
        if self._session:
            self._session.is_active = False
        self._session = None
