Adapt Domain-Specific Language Models with PEFT and TRL

                      
                     
"""
Production implementation for adapting domain-specific language models using PEFT and TRL.
Provides secure, scalable operations for model adaptation.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

# Logger setup for tracking operations
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///:memory:')
    model_endpoint: str = os.getenv('MODEL_ENDPOINT', 'http://localhost:8000/models')
    max_retries: int = 5
    retry_delay: float = 1.0

@contextmanager
def db_connection():
    """Context manager for database connection.
    
    Yields:
        Connection: Database connection object
    """
    conn = None  # Replace with actual connection logic
    try:
        conn = "Database Connection"  # Placeholder
        yield conn
    finally:
        if conn:
            logger.info('Closing database connection.')  # Placeholder for actual close logic

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'text' not in data:
        raise ValueError('Missing required field: text')
    if not isinstance(data['text'], str):
        raise ValueError('Field text must be a string')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data

async def fetch_data(endpoint: str) -> Dict[str, Any]:
    """Fetch data from a specified endpoint.
    
    Args:
        endpoint: API endpoint to fetch data from
    Returns:
        Dict[str, Any]: Response data
    Raises:
        requests.HTTPError: If an error occurs during fetching
    """
    try:
        response = requests.get(endpoint)
        response.raise_for_status()
        return response.json()
    except requests.HTTPError as e:
        logger.error(f'HTTP error occurred: {e}')
        raise

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    with db_connection() as conn:
        # Replace with actual save logic
        logger.info(f'Saving data to DB: {data}')
        # Simulate potential error
        if data.get('error'):
            raise Exception('Simulated database error!')

async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call the model API to adapt the language model.
    
    Args:
        data: Data to send to the API
    Returns:
        Dict[str, Any]: API response data
    """
    endpoint = Config.model_endpoint
    for attempt in range(Config.max_retries):
        try:
            logger.info(f'Calling API (attempt {attempt + 1})')
            response = requests.post(endpoint, json=data)
            response.raise_for_status()
            return response.json()
        except requests.HTTPError as e:
            logger.warning(f'Attempt {attempt + 1} failed: {e}')
            time.sleep(Config.retry_delay * (2 ** attempt))  # Exponential backoff
    raise Exception('Max retries exceeded')

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data.
    
    Args:
        data: List of input data to process
    Returns:
        List[Dict[str, Any]]: Processed data
    """
    results = []
    for item in data:
        await validate_input(item)  # Validate each item
        sanitized_item = await sanitize_fields(item)  # Sanitize input
        api_response = await call_api(sanitized_item)  # Call API
        await save_to_db(api_response)  # Save results to DB
        results.append(api_response)  # Collect results
    return results

if __name__ == '__main__':
    # Example usage
    sample_data = [{'text': 'Sample text for model adaptation'}]
    import asyncio
    asyncio.run(process_batch(sample_data))  # Run the async batch processing