from transformers.image_processing_utils import ImageProcessingMixin, BatchFeature

from torchvision.transforms import transforms as tf
import torchvision.transforms.functional as F
from PIL import Image
import torch


class CondViTProcessor(ImageProcessingMixin):
    def __init__(
        self,
        bkg_color=255,
        input_resolution=224,
        image_mean=(0.48145466, 0.4578275, 0.40821073),
        image_std=(0.26862954, 0.26130258, 0.27577711),
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.bkg_color = bkg_color
        self.input_resolution = input_resolution
        self.image_mean = image_mean
        self.image_std = image_std

    def square_pad(self, image):
        max_wh = max(image.size)
        p_left, p_top = [(max_wh - s) // 2 for s in image.size]
        p_right, p_bottom = [
            max_wh - (s + pad) for s, pad in zip(image.size, [p_left, p_top])
        ]
        padding = (p_left, p_top, p_right, p_bottom)
        return F.pad(image, padding, self.bkg_color, "constant")

    def process_img(self, image):
        img = self.square_pad(image)
        img = F.resize(img, self.input_resolution)
        img = F.to_tensor(img)
        img = F.normalize(img, self.image_mean, self.image_std)
        return img

    def __call__(self, images, texts=None):
        """
        Parameters
        ----------
        images : Union[Image.Image, List[Image.Image]]
            Image or list of images to process
        texts : Union[str, List[str]]
            Text or list of texts to process. Pass through, no operation is performed.

        Returns
        -------
        BatchFeature
            pixel_values : torch.Tensor
                Processed image tensor (B C H W)
            texts : Union[str, List[str]]
        """
        # Single Image
        data = {}
        if isinstance(images, Image.Image):
            data["pixel_values"] = self.process_img(images)
        else:
            data["pixel_values"] = torch.stack(
                [self.process_img(img) for img in images]
            )

        if texts is not None:
            data["texts"] = texts

        return BatchFeature(data=data)