"""
Utility helpers for logging, memory monitoring, and reproducibility.
"""
import gc
import logging
import os
import random
import sys

import numpy as np
import torch


def setup_logging(level: str = "INFO"):
    logging.basicConfig(
        level=getattr(logging, level.upper(), logging.INFO),
        format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )


def set_seed(seed: int = 42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def log_memory_usage(tag: str = ""):
    """Log current memory usage — critical for constrained hardware."""
    prefix = f"[{tag}] " if tag else ""
    if torch.cuda.is_available():
        allocated = torch.cuda.memory_allocated() / 1024**2
        reserved = torch.cuda.memory_reserved() / 1024**2
        logging.info(f"{prefix}GPU Memory: {allocated:.0f}MB allocated, {reserved:.0f}MB reserved")
    import psutil
    proc = psutil.Process(os.getpid())
    ram = proc.memory_info().rss / 1024**2
    logging.info(f"{prefix}RAM Usage: {ram:.0f}MB")


def clear_memory():
    """Aggressively free memory — call between major operations."""
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.synchronize()