from abc import ABC, abstractmethod
from typing import Dict, Any, List
import logging

class FrameworkAdapter(ABC):
    """Abstract base class for framework-specific adapters with enhanced functionality."""

    def __init__(self, model: Any):
        self.model = model
        self.data: Dict[str, Any] = {}
        self.logger = logging.getLogger(__name__)

    @abstractmethod
    async def start_profiling(self) -> None:
        """Start profiling the model."""
        pass

    @abstractmethod
    async def stop_profiling(self) -> None:
        """Stop profiling the model."""
        pass

    @abstractmethod
    async def profile_inference(self, input_data: Any) -> Dict[str, Any]:
        """Profile the inference process for a single input."""
        pass

    @abstractmethod
    async def profile_training_step(self, input_data: Any, target: Any) -> Dict[str, Any]:
        """Profile a single training step."""
        pass

    @abstractmethod
    async def get_model_summary(self) -> Dict[str, Any]:
        """Get a summary of the model architecture."""
        pass

    @abstractmethod
    async def get_layer_info(self, layer_name: str) -> Dict[str, Any]:
        """Get detailed information about a specific layer."""
        pass

    @abstractmethod
    async def profile_memory_usage(self) -> Dict[str, float]:
        """Profile the memory usage of the model."""
        pass

    @abstractmethod
    def get_flops(self) -> int:
        """Calculate the total number of FLOPs for the model."""
        pass

    async def __aenter__(self):
        await self.start_profiling()
        return self

    async def __aexit__(self, exc_type, exc_val, exc_tb):
        await self.stop_profiling()

    async def profile_full_training(self, train_loader: Any, num_epochs: int) -> List[Dict[str, Any]]:
        """Profile the full training process."""
        epoch_data = []
        for epoch in range(num_epochs):
            epoch_start_time = await self.get_current_time()
            epoch_loss = 0.0
            batch_data = []

            for batch_idx, (data, target) in enumerate(train_loader):
                batch_profile = await self.profile_training_step(data, target)
                epoch_loss += batch_profile['loss']
                batch_data.append(batch_profile)

            epoch_end_time = await self.get_current_time()
            epoch_time = epoch_end_time - epoch_start_time

            epoch_data.append({
                'epoch': epoch,
                'epoch_time': epoch_time,
                'epoch_loss': epoch_loss / len(train_loader),
                'batch_data': batch_data
            })

        return epoch_data

    @abstractmethod
    async def get_current_time(self) -> float:
        """Get the current time in a high-precision format."""
        pass

    @abstractmethod
    async def export_model(self, path: str, format: str) -> None:
        """Export the model to a specified format."""
        pass

    @abstractmethod
    async def visualize_model(self, output_path: str) -> None:
        """Generate a visual representation of the model architecture."""
        pass

    async def log_profiling_start(self) -> None:
        """Log the start of profiling."""
        self.logger.info(f"Started profiling for {type(self.model).__name__}")

    async def log_profiling_stop(self) -> None:
        """Log the end of profiling."""
        self.logger.info(f"Stopped profiling for {type(self.model).__name__}")

    @abstractmethod
    async def get_optimizer_info(self) -> Dict[str, Any]:
        """Get information about the current optimizer."""
        pass

    @abstractmethod
    async def get_loss_function_info(self) -> Dict[str, Any]:
        """Get information about the current loss function."""
        pass