Files
drc-ners-nlp/core/config/config_manager.py
T

146 lines
5.2 KiB
Python

import json
import logging
from pathlib import Path
from typing import Optional, Union, Dict, Any
import yaml
from core.config.pipeline_config import PipelineConfig
from core.config.project_paths import ProjectPaths
class ConfigManager:
"""Centralized configuration management"""
def __init__(self, config_path: Optional[Union[str, Path]] = None):
self.config_path = config_path or self._find_config_file()
self._config: Optional[PipelineConfig] = None
self._setup_default_paths()
@classmethod
def _find_config_file(cls) -> Path:
"""Find configuration file in standard locations"""
possible_paths = [
Path.cwd() / "config" / "pipeline.yaml",
Path.cwd() / "config" / "pipeline.yml",
Path.cwd() / "pipeline.yaml",
Path(__file__).parent.parent.parent / "config" / "pipeline.yaml",
]
for path in possible_paths:
if path.exists():
return path
# Return default path if none found
return Path.cwd() / "config" / "pipeline.yaml"
def _setup_default_paths(self):
"""Setup default project paths"""
root_dir = Path(__file__).parent.parent.parent
self.default_paths = ProjectPaths(
root_dir=root_dir,
configs_dir=root_dir / "config",
data_dir=root_dir / "data" / "dataset",
models_dir=root_dir / "data" / "models",
outputs_dir=root_dir / "data" / "outputs",
logs_dir=root_dir / "data" / "logs",
checkpoints_dir=root_dir / "data" / "checkpoints",
)
def load_config(self, config_path: Optional[Path] = None) -> PipelineConfig:
"""Load configuration from file"""
if config_path:
self.config_path = config_path
if not self.config_path.exists():
logging.warning(f"Config file not found: {self.config_path}. Using defaults.")
return self._create_default_config()
try:
with open(self.config_path, "r") as f:
if self.config_path.suffix.lower() in [".yaml", ".yml"]:
config_data = yaml.safe_load(f)
else:
config_data = json.load(f)
# Ensure paths are properly set
if "paths" not in config_data:
config_data["paths"] = self.default_paths.dict()
self._config = PipelineConfig(**config_data)
return self._config
except Exception as e:
logging.error(f"Failed to load config from {self.config_path}: {e}")
return self._create_default_config()
def _create_default_config(self) -> PipelineConfig:
"""Create default configuration"""
return PipelineConfig(paths=self.default_paths)
def save_config(self, config: PipelineConfig, path: Optional[Path] = None):
"""Save configuration to file"""
save_path = path or self.config_path
save_path.parent.mkdir(parents=True, exist_ok=True)
config_dict = config.model_dump()
# Convert Path objects to strings for serialization
if "paths" in config_dict:
for key, value in config_dict["paths"].items():
if isinstance(value, Path):
config_dict["paths"][key] = str(value)
try:
with open(save_path, "w") as f:
if save_path.suffix.lower() in [".yaml", ".yml"]:
yaml.dump(config_dict, f, default_flow_style=False, indent=2)
else:
json.dump(config_dict, f, indent=2)
logging.info(f"Configuration saved to {save_path}")
except Exception as e:
logging.error(f"Failed to save config to {save_path}: {e}")
def get_config(self) -> PipelineConfig:
"""Get current configuration, loading if necessary"""
if self._config is None:
self._config = self.load_config()
return self._config
def update_config(self, updates: Dict[str, Any]):
"""Update configuration with new values"""
config = self.get_config()
# Deep update configuration
config_dict = config.model_dump()
self._deep_update(config_dict, updates)
self._config = PipelineConfig(**config_dict)
def _deep_update(self, base_dict: Dict, update_dict: Dict):
"""Recursively update nested dictionaries"""
for key, value in update_dict.items():
if key in base_dict and isinstance(base_dict[key], dict) and isinstance(value, dict):
self._deep_update(base_dict[key], value)
else:
base_dict[key] = value
def get_environment_config(self, env: str) -> PipelineConfig:
"""Load environment-specific configuration"""
env_config_path = self.config_path.parent / f"pipeline.{env}.yaml"
if env_config_path.exists():
base_config = self.load_config()
env_config = self.load_config(env_config_path)
# Merge configurations
base_dict = base_config.dict()
env_dict = env_config.dict()
self._deep_update(base_dict, env_dict)
return PipelineConfig(**base_dict)
return self.get_config()