import asyncio
import logging
from typing import Any, Dict, List, Optional

"""
Pipeline management for LLM to SLM conversion.

This module provides the core pipeline functionality for converting Large Language Models (LLMs)
to Small Language Models (SLMs). It handles the end-to-end process including model loading,
processing, and export, designed for production deployment with robust error handling and logging.
"""


# Configure logging for production
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


class PipelineError(Exception):
    """Custom exception for pipeline-related errors."""

    pass


class Pipeline:
    """
    Manages the LLM to SLM conversion pipeline.

    This class orchestrates the steps involved in converting a Large Language Model to a Small
    Language Model, including validation, processing, and export. It is designed for asynchronous
    execution to handle I/O-bound operations efficiently in production environments.

    Attributes:
        config (Dict[str, Any]): Configuration dictionary for the pipeline.
        steps (List[str]): List of pipeline steps to execute.
    """

    def __init__(self, config: Dict[str, Any], steps: Optional[List[str]] = None) -> None:
        """
        Initialize the Pipeline with configuration and steps.

        Args:
            config (Dict[str, Any]): Configuration parameters for the pipeline.
            steps (Optional[List[str]]): Ordered list of steps to execute. Defaults to standard
                steps.

        Raises:
            PipelineError: If configuration is invalid.
        """
        if not config or not isinstance(config, dict):
            raise PipelineError("Invalid configuration provided.")
        self.config = config
        self.steps = steps or ["load_model", "process_model", "export_slm"]
        logger.info("Pipeline initialized with steps: %s", self.steps)

    async def run(self) -> Dict[str, Any]:
        """
        Execute the pipeline asynchronously.

        Runs each step in sequence, handling errors and logging progress.

        Returns:
            Dict[str, Any]: Results from the pipeline execution, including status and outputs.

        Raises:
            PipelineError: If any step fails critically.
        """
        results: Dict[str, Any] = {"status": "running", "outputs": {}}
        outputs: Dict[str, Any] = results["outputs"]
        try:
            for step in self.steps:
                logger.info("Executing step: %s", step)
                output = await self._execute_step(step)
                outputs[step] = output  # type: ignore[assignment]
            results["status"] = "completed"
            logger.info("Pipeline execution completed successfully.")
        except Exception as e:
            results["status"] = "failed"
            results["error"] = str(e)
            logger.error("Pipeline execution failed: %s", e)
            raise PipelineError(f"Pipeline failed at step {step}: {e}") from e
        return results

    async def _execute_step(self, step: str) -> Any:
        """
        Execute a single pipeline step.

        This is a placeholder for actual step implementations. In a real scenario,
        each step would involve specific logic like model loading or processing.

        Args:
            step (str): The name of the step to execute.

        Returns:
            Any: Output from the step execution.

        Raises:
            PipelineError: If the step is unknown or fails.
        """
        # Simulate async operation (e.g., I/O or computation)
        await asyncio.sleep(0.1)  # Placeholder for real async work
        if step == "load_model":
            # Placeholder: Load model from config
            return {"model_loaded": True}
        elif step == "process_model":
            # Placeholder: Process the model
            return {"model_processed": True}
        elif step == "export_slm":
            # Placeholder: Export SLM
            return {"slm_exported": True}
        else:
            raise PipelineError(f"Unknown step: {step}")
