"""Logging and Metrics Tracking for Training"""

import json
import logging
import os
from datetime import datetime
from pathlib import Path
from typing import Any, Dict, List, Optional

import numpy as np

logger = logging.getLogger(__name__)


def setup_logging(
    log_dir: str = "./logs",
    log_level: str = "INFO",
    console: bool = True,
    file: bool = True,
):
    """Setup logging configuration."""
    log_dir = Path(log_dir)
    log_dir.mkdir(parents=True, exist_ok=True)

    # Create formatter
    formatter = logging.Formatter(
        fmt="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
        datefmt="%Y-%m-%d %H:%M:%S",
    )

    # Configure root logger
    root_logger = logging.getLogger()
    root_logger.setLevel(getattr(logging, log_level.upper()))

    # Clear existing handlers
    root_logger.handlers.clear()

    # Console handler
    if console:
        console_handler = logging.StreamHandler()
        console_handler.setFormatter(formatter)
        root_logger.addHandler(console_handler)

    # File handler
    if file:
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        log_file = log_dir / f"training_{timestamp}.log"
        file_handler = logging.FileHandler(log_file)
        file_handler.setFormatter(formatter)
        root_logger.addHandler(file_handler)

    logger.info(f"Logging initialized. Log file: {log_file if file else 'console only'}")


class MetricsLogger:
    """Track and log metrics during training."""

    def __init__(
        self,
        log_dir: str = "./logs",
        experiment_name: Optional[str] = None,
    ):
        self.log_dir = Path(log_dir)
        self.log_dir.mkdir(parents=True, exist_ok=True)

        self.experiment_name = experiment_name or f"zenith_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
        self.metrics_file = self.log_dir / f"{self.experiment_name}_metrics.jsonl"
        self.tensorboard_logdir = self.log_dir / "tensorboard" / self.experiment_name

        # Metrics history
        self.history: List[Dict[str, Any]] = []

        # TensorBoard
        try:
            from torch.utils.tensorboard import SummaryWriter
            self.writer = SummaryWriter(log_dir=str(self.tensorboard_logdir))
            self.has_tensorboard = True
        except ImportError:
            self.has_tensorboard = False
            logger.warning("TensorBoard not available. Install with: pip install tensorboard")

    def log(
        self,
        metrics: Dict[str, float],
        step: int,
        prefix: str = "train",
    ):
        """Log metrics."""
        # Add timestamp and step
        log_entry = {
            "timestamp": datetime.now().isoformat(),
            "step": step,
            "prefix": prefix,
            **{f"{prefix}/{k}" if prefix != k else k: v for k, v in metrics.items()},
        }
        self.history.append(log_entry)

        # Write to file
        with open(self.metrics_file, "a") as f:
            f.write(json.dumps(log_entry) + "\n")

        # TensorBoard
        if self.has_tensorboard:
            for key, value in metrics.items():
                self.writer.add_scalar(f"{prefix}/{key}", value, step)

    def log_hyperparams(self, params: Dict[str, Any]):
        """Log hyperparameters."""
        if self.has_tensorboard:
            from torch.utils.tensorboard import SummaryWriter
            # TensorBoard expects flat dict
            flat_params = self._flatten_dict(params)
            self.writer.add_hparams(flat_params, {})

    def _flatten_dict(self, d: Dict[str, Any], parent_key: str = "", sep: str = "/") -> Dict[str, Any]:
        """Flatten nested dictionary."""
        items = []
        for k, v in d.items():
            new_key = f"{parent_key}{sep}{k}" if parent_key else k
            if isinstance(v, dict):
                items.extend(self._flatten_dict(v, new_key, sep=sep).items())
            else:
                items.append((new_key, v))
        return dict(items)

    def get_metrics(self, prefix: Optional[str] = None) -> List[Dict[str, Any]]:
        """Get metrics history, optionally filtered by prefix."""
        if prefix is None:
            return self.history

        filtered = []
        for entry in self.history:
            if entry.get("prefix") == prefix:
                filtered.append(entry)
        return filtered

    def close(self):
        """Close logger."""
        if self.has_tensorboard:
            self.writer.close()


class ProgressLogger:
    """Simple progress tracking with ETA."""

    def __init__(self, total: int, desc: str = "Progress"):
        self.total = total
        self.desc = desc
        self.current = 0
        self.start_time = datetime.now()

    def update(self, n: int = 1):
        """Update progress."""
        self.current += n
        self._log_progress()

    def _log_progress(self):
        """Log current progress."""
        elapsed = (datetime.now() - self.start_time).total_seconds()
        if self.current > 0:
            rate = elapsed / self.current
            remaining = rate * (self.total - self.current)
            logger.info(
                f"{self.desc}: {self.current}/{self.total} "
                f"({100 * self.current / self.total:.1f}%) "
                f"- ETA: {remaining / 60:.1f}m"
            )


def log_metrics_summary(metrics: Dict[str, float], step: int, logger_obj: Optional[logging.Logger] = None):
    """Log a summary of metrics in a nice format."""
    if logger_obj is None:
        logger_obj = logger

    lines = [f"Step {step} - Metrics Summary:"]
    for key, value in sorted(metrics.items()):
        if isinstance(value, float):
            lines.append(f"  {key}: {value:.4f}")
        else:
            lines.append(f"  {key}: {value}")

    logger_obj.info("\n".join(lines))


def save_metrics_to_csv(metrics_history: List[Dict[str, Any]], filepath: str):
    """Save metrics history to CSV."""
    import pandas as pd

    df = pd.DataFrame(metrics_history)
    df.to_csv(filepath, index=False)
    logger.info(f"Metrics saved to {filepath}")


def load_metrics_from_jsonl(filepath: str) -> List[Dict[str, Any]]:
    """Load metrics from JSONL file."""
    metrics = []
    with open(filepath, "r") as f:
        for line in f:
            metrics.append(json.loads(line.strip()))
    return metrics