import os

class WandbLogger:
    """
    Log using `Weights and Biases`.
    """
    def __init__(self):
        try:
            import wandb
        except ImportError:
            raise ImportError(
                "To use the Weights and Biases Logger please install wandb."
                "Run `pip install wandb` to install it."
            )
        
        self._wandb = wandb

        # Initialize a W&B run
        if self._wandb.run is None:
            self._wandb.init(
                project='diff_derain',
                dir='./experiments'
            )

        self.config = self._wandb.config

        # if self.config.get('log_eval', None):
        #     self.eval_table = self._wandb.Table(columns=['fake_image', 
        #                                                  'sr_image', 
        #                                                  'hr_image',
        #                                                  'psnr',
        #                                                  'ssim'])
        # else:
        self.eval_table = None

        # if self.config.get('log_infer', None):
        #     self.infer_table = self._wandb.Table(columns=['fake_image', 
        #                                                  'sr_image', 
        #                                                  'hr_image'])
        # else:
        self.infer_table = None

    def log_metrics(self, metrics, commit=True): 
        """
        Log train/validation metrics onto W&B.

        metrics: dictionary of metrics to be logged
        """
        self._wandb.log(metrics, commit=commit)

    def log_image(self, key_name, image_array):
        """
        Log image array onto W&B.

        key_name: name of the key 
        image_array: numpy array of image.
        """
        self._wandb.log({key_name: self._wandb.Image(image_array)})

    def log_images(self, key_name, list_images):
        """
        Log list of image array onto W&B

        key_name: name of the key 
        list_images: list of numpy image arrays
        """
        self._wandb.log({key_name: [self._wandb.Image(img) for img in list_images]})

    def log_checkpoint(self, current_epoch, current_step):
        """
        Log the model checkpoint as W&B artifacts

        current_epoch: the current epoch 
        current_step: the current batch step
        """
        model_artifact = self._wandb.Artifact(
            self._wandb.run.id + "_model", type="model"
        )

        gen_path = os.path.join(
            self.config.path['checkpoint'], 'I{}_E{}_gen.pth'.format(current_step, current_epoch))
        opt_path = os.path.join(
            self.config.path['checkpoint'], 'I{}_E{}_opt.pth'.format(current_step, current_epoch))

        model_artifact.add_file(gen_path)
        model_artifact.add_file(opt_path)
        self._wandb.log_artifact(model_artifact, aliases=["latest"])

    def log_eval_data(self, fake_img, sr_img, hr_img, psnr=None, ssim=None):
        """
        Add data row-wise to the initialized table.
        """
        if psnr is not None and ssim is not None:
            self.eval_table.add_data(
                self._wandb.Image(fake_img),
                self._wandb.Image(sr_img),
                self._wandb.Image(hr_img),
                psnr,
                ssim
            )
        else:
            self.infer_table.add_data(
                self._wandb.Image(fake_img),
                self._wandb.Image(sr_img),
                self._wandb.Image(hr_img)
            )

    def log_eval_table(self, commit=False):
        """
        Log the table
        """
        if self.eval_table:
            self._wandb.log({'eval_data': self.eval_table}, commit=commit)
        elif self.infer_table:
            self._wandb.log({'infer_data': self.infer_table}, commit=commit)