"""
BGE Fine-tuning Package
A comprehensive implementation for fine-tuning BAAI BGE models
"""

__version__ = "1.0.0"
__author__ = "ldy"

import logging
import os
import sys

import torch

logger = logging.getLogger(__name__)

# Mandatory dependency check
def _check_dependencies():
    """Ensure core dependencies are available."""
    try:
        _ = torch.__version__
    except Exception as exc:  # pragma: no cover - should not happen in tests
        raise ImportError("PyTorch is required for bge_finetune") from exc
    try:
        import transformers  # noqa: F401
    except Exception as exc:  # pragma: no cover - should not happen in tests
        raise ImportError("transformers is required for bge_finetune") from exc

# Import guard for optional dependencies

def _check_optional_dependencies():
    """Check for optional dependencies and log warnings if missing when features are likely to be used."""
    # Check for torch_npu if running on Ascend
    try:
        npu_available = hasattr(torch, 'npu') and torch.npu.is_available()
    except Exception:
        npu_available = False
    if npu_available or os.environ.get('DEVICE', '').lower() == 'npu':
        try:
            import torch_npu
        except ImportError:
            logger.warning("Optional dependency 'torch_npu' not found. Ascend NPU support will be unavailable.")

    # Check for wandb if Weights & Biases logging is enabled
    try:
        from utils.config_loader import get_config
        config = get_config()
        use_wandb = False
        try:
            use_wandb = config.get('logging.use_wandb', False)  # type: ignore[arg-type]
        except Exception:
            pass
        if use_wandb:
            try:
                import wandb
            except ImportError:
                logger.warning("Optional dependency 'wandb' not found. Weights & Biases logging will be unavailable.")
    except Exception:
        pass

    # Check for tensorboard if tensorboard logging is likely to be used
    tensorboard_dir = None
    try:
        tensorboard_dir = config.get('logging.tensorboard_dir', None)  # type: ignore[arg-type]
    except Exception:
        pass
    if tensorboard_dir:
        try:
            import tensorboard
        except ImportError:
            logger.warning("Optional dependency 'tensorboard' not found. TensorBoard logging will be unavailable.")

    # Check for pytest if running tests
    if any('pytest' in arg for arg in sys.argv):
        try:
            import pytest
        except ImportError:
            logger.warning("Optional dependency 'pytest' not found. Some tests may not run.")


# Check dependencies on import
_check_dependencies()
_check_optional_dependencies()

# Make key components available at package level
try:
    from .config import BaseConfig, BGEM3Config, BGERerankerConfig
    from .models import BGEM3Model, BGERerankerModel
    from .utils import setup_logging, get_logger

    __all__ = [
        'BaseConfig',
        'BGEM3Config',
        'BGERerankerConfig',
        'BGEM3Model',
        'BGERerankerModel',
        'setup_logging',
        'get_logger'
    ]
except ImportError as e:
    # Allow partial imports during development
    import warnings

    warnings.warn(f"Some components not available: {e}")
    __all__ = []