# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license

# WARNING ⚠️ wandb is deprecated and will be removed in future release.
# See supported integrations at https://github.com/ultralytics/yolov5#integrations

import logging
import os
import sys
from contextlib import contextmanager
from pathlib import Path

from utils.general import LOGGER, colorstr

FILE = Path(__file__).resolve()
ROOT = FILE.parents[3]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
RANK = int(os.getenv("RANK", -1))
DEPRECATION_WARNING = (
    f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. "
    f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
)

try:
    import wandb

    assert hasattr(wandb, "__version__")  # verify package import not local dir
    LOGGER.warning(DEPRECATION_WARNING)
except (ImportError, AssertionError):
    wandb = None


class WandbLogger:
    """
    Log training runs, datasets, models, and predictions to Weights & Biases.

    This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system
    configuration and metrics, model metrics, and basic data metrics and analyses.

    By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.

    For more on how this logger is used, see the Weights & Biases documentation:
    https://docs.wandb.com/guides/integrations/yolov5
    """

    def __init__(self, opt, run_id=None, job_type="Training"):
        """
        - Initialize WandbLogger instance
        - Upload dataset if opt.upload_dataset is True
        - Setup training processes if job_type is 'Training'

        arguments:
        opt (namespace) -- Commandline arguments for this run
        run_id (str) -- Run ID of W&B run to be resumed
        job_type (str) -- To set the job_type for this run

        """
        # Pre-training routine --
        self.job_type = job_type
        self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
        self.val_artifact, self.train_artifact = None, None
        self.train_artifact_path, self.val_artifact_path = None, None
        self.result_artifact = None
        self.val_table, self.result_table = None, None
        self.max_imgs_to_log = 16
        self.data_dict = None
        if self.wandb:
            self.wandb_run = wandb.run or wandb.init(
                config=opt,
                resume="allow",
                project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem,
                entity=opt.entity,
                name=opt.name if opt.name != "exp" else None,
                job_type=job_type,
                id=run_id,
                allow_val_change=True,
            )

        if self.wandb_run and self.job_type == "Training":
            if isinstance(opt.data, dict):
                # This means another dataset manager has already processed the dataset info (e.g. ClearML)
                # and they will have stored the already processed dict in opt.data
                self.data_dict = opt.data
            self.setup_training(opt)

    def setup_training(self, opt):
        """
        Setup the necessary processes for training YOLO models:
          - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
          - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
          - Setup log_dict, initialize bbox_interval

        arguments:
        opt (namespace) -- commandline arguments for this run

        """
        self.log_dict, self.current_epoch = {}, 0
        self.bbox_interval = opt.bbox_interval
        if isinstance(opt.resume, str):
            model_dir, _ = self.download_model_artifact(opt)
            if model_dir:
                self.weights = Path(model_dir) / "last.pt"
                config = self.wandb_run.config
                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = (
                    str(self.weights),
                    config.save_period,
                    config.batch_size,
                    config.bbox_interval,
                    config.epochs,
                    config.hyp,
                    config.imgsz,
                )

        if opt.bbox_interval == -1:
            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
            if opt.evolve or opt.noplots:
                self.bbox_interval = opt.bbox_interval = opt.epochs + 1  # disable bbox_interval

    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
        """
        Log the model checkpoint as W&B artifact.

        arguments:
        path (Path)   -- Path of directory containing the checkpoints
        opt (namespace) -- Command line arguments for this run
        epoch (int)  -- Current epoch number
        fitness_score (float) -- fitness score for current epoch
        best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
        """
        model_artifact = wandb.Artifact(
            f"run_{wandb.run.id}_model",
            type="model",
            metadata={
                "original_url": str(path),
                "epochs_trained": epoch + 1,
                "save period": opt.save_period,
                "project": opt.project,
                "total_epochs": opt.epochs,
                "fitness_score": fitness_score,
            },
        )
        model_artifact.add_file(str(path / "last.pt"), name="last.pt")
        wandb.log_artifact(
            model_artifact,
            aliases=[
                "latest",
                "last",
                f"epoch {str(self.current_epoch)}",
                "best" if best_model else "",
            ],
        )
        LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")

    def val_one_image(self, pred, predn, path, names, im):
        """Evaluates model prediction for a single image, returning metrics and visualizations."""
        pass

    def log(self, log_dict):
        """
        Save the metrics to the logging dictionary.

        arguments:
        log_dict (Dict) -- metrics/media to be logged in current step
        """
        if self.wandb_run:
            for key, value in log_dict.items():
                self.log_dict[key] = value

    def end_epoch(self):
        """
        Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.

        arguments:
        best_result (boolean): Boolean representing if the result of this evaluation is best or not
        """
        if self.wandb_run:
            with all_logging_disabled():
                try:
                    wandb.log(self.log_dict)
                except BaseException as e:
                    LOGGER.info(
                        f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
                    )
                    self.wandb_run.finish()
                    self.wandb_run = None
                self.log_dict = {}

    def finish_run(self):
        """Log metrics if any and finish the current W&B run."""
        if self.wandb_run:
            if self.log_dict:
                with all_logging_disabled():
                    wandb.log(self.log_dict)
            wandb.run.finish()
            LOGGER.warning(DEPRECATION_WARNING)


@contextmanager
def all_logging_disabled(highest_level=logging.CRITICAL):
    """source - https://gist.github.com/simon-weber/7853144
    A context manager that will prevent any logging messages triggered during the body from being processed.
    :param highest_level: the maximum logging level in use.
      This would only need to be changed if a custom level greater than CRITICAL is defined.
    """
    previous_level = logging.root.manager.disable
    logging.disable(highest_level)
    try:
        yield
    finally:
        logging.disable(previous_level)
