from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch
from PIL import Image

from transformers import ImageProcessingMixin


def _to_rgb(img: Image.Image) -> Image.Image:
    if img.mode != "RGB":
        return img.convert("RGB")
    return img


class UpscalerImageProcessor(ImageProcessingMixin):
    """
    Minimal processor:
    - input: PIL or list of PIL
    - output: pixel_values float32 in [0,1], shape (B,3,H,W)
    No ImageNet normalization (recommended for SR trained on [0,1]).
    """

    model_input_names = ["pixel_values"]

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    def _pil_to_tensor_01(self, img: Image.Image) -> torch.FloatTensor:
        img = _to_rgb(img)
        arr = np.array(img, dtype=np.float32) / 255.0  # H,W,3 in [0,1]
        t = torch.from_numpy(arr).permute(2, 0, 1).contiguous()  # 3,H,W
        return t

    def __call__(
        self,
        images: Union[Image.Image, List[Image.Image]],
        return_tensors: Optional[str] = None,
        **kwargs,
    ) -> Dict[str, Any]:
        if isinstance(images, Image.Image):
            images = [images]

        tensors = [self._pil_to_tensor_01(im) for im in images]
        pixel_values = torch.stack(tensors, dim=0)  # B,3,H,W

        if return_tensors is None or return_tensors == "pt":
            return {"pixel_values": pixel_values}
        raise ValueError("Only return_tensors=None or 'pt' is supported.")