import base64
import functools
import io
import logging
import random

import beartype
import einops.layers.torch
import numpy as np
import requests
from jaxtyping import Integer, UInt8, jaxtyped
from PIL import Image
from torch import Tensor
from torchvision.transforms import v2

logger = logging.getLogger("data.py")

R2_URL = "https://pub-129e98faed1048af94c4d4119ea47be7.r2.dev"


@beartype.beartype
@functools.lru_cache(maxsize=512)
def get_img(i: int) -> Image.Image:
    fpath = f"/images/ADE_val_{i + 1:08}.jpg"
    url = R2_URL + fpath
    logger.info("Getting image from '%s'.", url)
    return Image.open(requests.get(url, stream=True).raw)


@beartype.beartype
@functools.lru_cache(maxsize=512)
def get_seg(i: int) -> Image.Image:
    fpath = f"/annotations/ADE_val_{i + 1:08}.png"
    url = R2_URL + fpath
    logger.info("Getting annotations from '%s'.", url)
    return Image.open(requests.get(url, stream=True).raw)


@jaxtyped(typechecker=beartype.beartype)
def make_colors() -> UInt8[np.ndarray, "n 3"]:
    values = (0, 51, 102, 153, 204, 255)
    colors = []
    for r in values:
        for g in values:
            for b in values:
                colors.append((r, g, b))
    # Fixed seed
    random.Random(42).shuffle(colors)
    colors = np.array(colors, dtype=np.uint8)

    # Fixed colors. Must be synced with Segmentation.elm.
    colors[2] = np.array([201, 249, 255], dtype=np.uint8)
    colors[4] = np.array([151, 204, 4], dtype=np.uint8)
    colors[13] = np.array([104, 139, 88], dtype=np.uint8)
    colors[16] = np.array([54, 48, 32], dtype=np.uint8)
    colors[21] = np.array([120, 202, 210], dtype=np.uint8)  # water
    colors[26] = np.array([45, 125, 210], dtype=np.uint8)
    colors[29] = np.array([116, 142, 84], dtype=np.uint8)
    colors[46] = np.array([238, 185, 2], dtype=np.uint8)
    colors[52] = np.array([88, 91, 86], dtype=np.uint8)
    colors[60] = np.array([72, 99, 156], dtype=np.uint8)  # river
    colors[72] = np.array([76, 46, 5], dtype=np.uint8)
    colors[94] = np.array([12, 15, 10], dtype=np.uint8)

    return colors


colors = make_colors()

resize_transform = v2.Compose([
    v2.Resize((512, 512), interpolation=v2.InterpolationMode.NEAREST),
    v2.CenterCrop((448, 448)),
])


@beartype.beartype
def to_sized(img_raw: Image.Image) -> Image.Image:
    return resize_transform(img_raw)


u8_transform = v2.Compose([
    v2.ToImage(),
    einops.layers.torch.Rearrange("() width height -> width height"),
])


@beartype.beartype
def to_u8(seg_raw: Image.Image) -> UInt8[Tensor, "width height"]:
    return u8_transform(seg_raw)


@jaxtyped(typechecker=beartype.beartype)
def u8_to_img(map: UInt8[Tensor, "width height"]) -> Image.Image:
    map = map.cpu().numpy()
    width, height = map.shape
    colored = np.zeros((width, height, 3), dtype=np.uint8)
    for i, color in enumerate(colors):
        colored[map == i + 1, :] = color

    return Image.fromarray(colored)


@jaxtyped(typechecker=beartype.beartype)
def to_classes(map: Integer[Tensor, "width height"]) -> list[int]:
    # Integer is any signed or unsigned int: https://docs.kidger.site/jaxtyping/api/array/#dtype
    return list(set(map.view(-1).tolist()))


@beartype.beartype
def img_to_base64(img: Image.Image) -> str:
    buf = io.BytesIO()
    img.save(buf, format="webp")
    b64 = base64.b64encode(buf.getvalue())
    s64 = b64.decode("utf8")
    return "data:image/webp;base64," + s64