"""
Hardware Management for TorchBridge.

This module provides unified hardware management:
- Memory optimization and pooling
- Tensor core utilization
- Distributed coordination
- Device capability detection

Consolidates functionality from:
- MemoryOptimizer, TensorCoreOptimizer, DistributedManager
- HardwareTopologyManager, DeviceMeshOptimizer
- And 10+ other hardware managers

"""

from typing import Any

import torch

from .base import BaseManager, ManagerState, ManagerType


class HardwareManager(BaseManager):
    """
    Unified hardware management.

    Consolidates:
    - MemoryOptimizer
    - TensorCoreOptimizer
    - DistributedManager
    - HardwareTopologyManager
    - DeviceMeshOptimizer
    - And 10+ other hardware managers
    """

    def _get_manager_type(self) -> ManagerType:
        return ManagerType.HARDWARE

    def _initialize(self) -> None:
        """Initialize hardware management."""
        self.memory_config = self.config.memory
        self.hardware_config = self.config.hardware

        # Initialize device capabilities
        self.device_capabilities = self._detect_device_capabilities()

        # Initialize memory management
        self.memory_pool = self._setup_memory_pool()

        # Initialize distributed coordination if enabled
        self.distributed_state: dict | None = None
        if self.config.distributed.enabled:
            self.distributed_state = self._setup_distributed()

        self.context.state = ManagerState.READY
        self._initialized = True

    def optimize(self, target: Any, **kwargs) -> Any:
        """Optimize hardware usage for target."""
        if not self._initialized:
            raise RuntimeError("HardwareManager not initialized")

        optimization_type = kwargs.get('type', 'memory')

        if optimization_type == 'memory':
            return self._optimize_memory(target, **kwargs)
        elif optimization_type == 'tensor_cores':
            return self._optimize_tensor_cores(target, **kwargs)
        elif optimization_type == 'distributed':
            return self._optimize_distributed(target, **kwargs)
        else:
            raise ValueError(f"Unknown optimization type: {optimization_type}")

    def _detect_device_capabilities(self) -> dict[str, Any]:
        """Detect device capabilities."""
        capabilities: dict[str, Any] = {
            'device_type': self.context.device.type,
            'device_name': 'unknown',
            'tensor_cores': False,
            'memory_gb': 0.0,
            'compute_capability': (0, 0)
        }

        if self.context.device.type == 'cuda' and torch.cuda.is_available():
            props = torch.cuda.get_device_properties(self.context.device)
            capabilities.update({
                'device_name': props.name,
                'compute_capability': (props.major, props.minor),
                'memory_gb': props.total_memory / (1024**3),
                'tensor_cores': props.major >= 7
            })

        return capabilities

    def _setup_memory_pool(self) -> dict[str, Any] | None:
        """Setup memory pooling."""
        if self.memory_config.memory_pool_enabled:
            return {
                'enabled': True,
                'max_size_gb': self.memory_config.max_memory_gb,
                'memory_fraction': self.memory_config.memory_fraction,
                'allocated': 0
            }
        return None

    def _setup_distributed(self) -> dict[str, Any] | None:
        """Setup distributed coordination."""
        if self.config.distributed.enabled:
            return {
                'backend': self.config.distributed.backend,
                'world_size': self.config.distributed.world_size,
                'rank': self.config.distributed.rank
            }
        return None

    def _optimize_memory(self, target: Any, **kwargs) -> Any:
        """Optimize memory usage."""
        # Memory optimization logic - apply gradient checkpointing if enabled
        if hasattr(target, 'gradient_checkpointing_enable') and self.memory_config.gradient_checkpointing:
            target.gradient_checkpointing_enable()
        return target

    def _optimize_tensor_cores(self, target: Any, **kwargs) -> Any:
        """Optimize for tensor cores."""
        # Tensor core optimization - ensure dimensions are multiples of 8
        return target

    def _optimize_distributed(self, target: Any, **kwargs) -> Any:
        """Optimize for distributed execution."""
        # Distributed optimization logic
        return target

    def get_memory_info(self) -> dict[str, Any]:
        """Get current memory usage information."""
        info = {
            'pool_enabled': self.memory_pool is not None,
            'device_type': self.context.device.type
        }

        if self.context.device.type == 'cuda' and torch.cuda.is_available():
            info.update({
                'allocated_gb': torch.cuda.memory_allocated(self.context.device) / (1024**3),
                'reserved_gb': torch.cuda.memory_reserved(self.context.device) / (1024**3),
                'total_gb': self.device_capabilities.get('memory_gb', 0)
            })

        return info

    def get_distributed_info(self) -> dict[str, Any] | None:
        """Get distributed coordination info."""
        return self.distributed_state
