import os
import sys
import logging
from pathlib import Path
from typing import Optional
from datetime import datetime

import torch


def setup_logging(
    log_level: str = "INFO",
    log_file: Optional[str] = None,
    log_dir: Optional[str] = None,
) -> logging.Logger:
    logger = logging.getLogger("codsworth")
    logger.setLevel(getattr(logging, log_level.upper()))
    
    logger.handlers.clear()
    
    formatter = logging.Formatter(
        "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
    )
    
    console_handler = logging.StreamHandler(sys.stdout)
    console_handler.setFormatter(formatter)
    logger.addHandler(console_handler)
    
    if log_file is not None or log_dir is not None:
        if log_dir is not None:
            os.makedirs(log_dir, exist_ok=True)
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            log_file = os.path.join(log_dir, f"codsworth_{timestamp}.log")
        
        file_handler = logging.FileHandler(log_file)
        file_handler.setFormatter(formatter)
        logger.addHandler(file_handler)
    
    return logger


def setup_wandb(
    project: str = "codsworth",
    entity: Optional[str] = None,
    config: Optional[dict] = None,
    name: Optional[str] = None,
    notes: Optional[str] = None,
    tags: Optional[list[str]] = None,
    resume: bool = False,
) -> Optional["wandb"]:
    try:
        import wandb
        wandb.init(
            project=project,
            entity=entity,
            config=config,
            name=name,
            notes=notes,
            tags=tags,
            resume=resume,
        )
        return wandb
    except ImportError:
        logging.warning("wandb not installed. Run 'pip install wandb' to enable logging.")
        return None


def get_device() -> torch.device:
    if torch.cuda.is_available():
        return torch.device("cuda")
    elif torch.backends.mps.is_available():
        return torch.device("mps")
    return torch.device("cpu")


def get_device_count() -> int:
    if torch.cuda.is_available():
        return torch.cuda.device_count()
    return 1


def set_seed(seed: int) -> None:
    import random
    import numpy as np
    
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
    
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False


def count_parameters(model: torch.nn.Module, trainable_only: bool = False) -> int:
    if trainable_only:
        return sum(p.numel() for p in model.parameters() if p.requires_grad)
    return sum(p.numel() for p in model.parameters())


def format_time(seconds: float) -> str:
    hours = int(seconds // 3600)
    minutes = int((seconds % 3600) // 60)
    secs = int(seconds % 60)
    
    if hours > 0:
        return f"{hours}h {minutes}m {secs}s"
    elif minutes > 0:
        return f"{minutes}m {secs}s"
    return f"{secs}s"


def format_memory(bytes: int) -> str:
    for unit in ["B", "KB", "MB", "GB", "TB"]:
        if bytes < 1024:
            return f"{bytes:.2f} {unit}"
        bytes /= 1024
    return f"{bytes:.2f} PB"


def get_model_size(model: torch.nn.Module) -> dict:
    param_size = 0
    buffer_size = 0
    
    for param in model.parameters():
        param_size += param.nelement() * param.element_size()
    
    for buffer in model.buffers():
        buffer_size += buffer.nelement() * buffer.element_size()
    
    total_size = param_size + buffer_size
    
    return {
        "param_size": param_size,
        "buffer_size": buffer_size,
        "total_size": total_size,
        "param_size_formatted": format_memory(param_size),
        "buffer_size_formatted": format_memory(buffer_size),
        "total_size_formatted": format_memory(total_size),
    }


def load_checkpoint(
    model: torch.nn.Module,
    checkpoint_path: str,
    device: torch.device = None,
    strict: bool = True,
) -> dict:
    checkpoint = torch.load(checkpoint_path, map_location=device)
    
    if "model_state_dict" in checkpoint:
        model.load_state_dict(checkpoint["model_state_dict"], strict=strict)
    else:
        model.load_state_dict(checkpoint, strict=strict)
    
    return checkpoint


def save_checkpoint(
    model: torch.nn.Module,
    optimizer: torch.optim.Optimizer,
    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
    epoch: int = 0,
    step: int = 0,
    loss: float = 0.0,
    metrics: Optional[dict] = None,
    path: str = "checkpoint.pt",
) -> None:
    checkpoint = {
        "epoch": epoch,
        "step": step,
        "loss": loss,
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
    }
    
    if scheduler is not None:
        checkpoint["scheduler_state_dict"] = scheduler.state_dict()
    
    if metrics is not None:
        checkpoint["metrics"] = metrics
    
    os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
    torch.save(checkpoint, path)


def ensure_dir(path: str) -> None:
    Path(path).mkdir(parents=True, exist_ok=True)


def get_latest_checkpoint(checkpoint_dir: str) -> Optional[str]:
    checkpoints = list(Path(checkpoint_dir).glob("checkpoint_*.pt"))
    
    if not checkpoints:
        return None
    
    return max(checkpoints, key=lambda p: p.stat().st_mtime).as_posix()


class AverageMeter:
    def __init__(self, name: str = "metric"):
        self.name = name
        self.reset()
    
    def reset(self):
        self.val = 0.0
        self.avg = 0.0
        self.sum = 0.0
        self.count = 0
    
    def update(self, val: float, n: int = 1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
    
    def __str__(self) -> str:
        return f"{self.name}: {self.avg:.4f} (current: {self.val:.4f})"


class Timer:
    def __init__(self):
        self.start_time = None
        self.elapsed = 0.0
    
    def start(self):
        import time
        self.start_time = time.time()
    
    def stop(self):
        import time
        if self.start_time is not None:
            self.elapsed = time.time() - self.start_time
            self.start_time = None
        return self.elapsed
    
    def __enter__(self):
        self.start()
        return self
    
    def __exit__(self, *args):
        self.stop()