from typing import List, Dict, Any, Optional, Union
import yaml
from pydantic import BaseModel
import logging
import os

# Setup logging
# Note: library modules should not configure basicConfig.

class ModelConfig(BaseModel):
    name: str
    license: str
    description: str
    tags: List[str]

class DataConfig(BaseModel):
    features: List[str]
    target: str
    archive_path: str
    store_path: Optional[str] = None

class FeatureEngineeringStepConfig(BaseModel):
    strategy: str
    features: Optional[List[str]] = None
    period: Optional[float] = None
    order: Optional[int] = None

class PipelineConfig(BaseModel):
    enable_tuning: bool
    feature_engineering: List[FeatureEngineeringStepConfig]

class ModelParams(BaseModel):
    xgboost: Dict[str, Any]

class Config(BaseModel):
    enable_cache: bool
    model: ModelConfig
    data: DataConfig
    pipeline: PipelineConfig
    model_params: ModelParams

def load_config(config_path: str = "config.yaml") -> Config:
    """Loads and validates the configuration from a YAML file."""
    try:
        # Support running from root or src
        if not os.path.exists(config_path):
            # Check if it exists one level up (if running from src)
            alt_path = os.path.join("..", config_path)
            if os.path.exists(alt_path):
                config_path = alt_path
        
        with open(config_path, "r") as f:
            raw_config = yaml.safe_load(f)
        
        config = Config(**raw_config)
        return config
    except FileNotFoundError:
        logging.error(f"Config file not found: {config_path}")
        raise
    except Exception as e:
        logging.error(f"Error loading config: {e}")
        raise

# Singleton instance for easy import
try:
    global_config = load_config()
except Exception:
    logging.warning("Could not load global config immediately. Ensure config.yaml exists.")
    global_config = None