import os
from os import PathLike
from pathlib import Path

import datasets
import torch
import torchvision
from PIL import Image
from torch.utils import data
from torchmetrics.image import LearnedPerceptualImagePatchSimilarity
from tqdm import tqdm


def hash_str_to_int(s: str) -> int:
    """Hash a string to an integer."""
    modulus = 10**9 + 7  # Large prime modulus
    hash_int = 0
    for char in s:
        hash_int = (hash_int * 31 + ord(char)) % modulus
    return hash_int


def already_generate(save_dir: str | PathLike[str], num_images) -> bool:
    if isinstance(save_dir, str):
        save_dir = Path(save_dir)
    assert isinstance(save_dir, Path)
    if save_dir.exists():
        images = list(save_dir.iterdir())
        images = [_ for _ in images if _.name.endswith(".png")]
        if len(images) == num_images:
            return True
    return False


class MultiImageDataset(data.Dataset):
    def __init__(self, gen_dirpath_or_image_path: str, ref_dirpath_or_image_path: str | datasets.Dataset):
        super(data.Dataset, self).__init__()
        if os.path.isdir(gen_dirpath_or_image_path):
            self.gen_names = sorted(
                [
                    name
                    for name in os.listdir(gen_dirpath_or_image_path)
                    if name.endswith(".png") or name.endswith(".jpg")
                ]
            )
            self.gen_dirpath = gen_dirpath_or_image_path
        else:
            self.gen_names = [os.path.basename(gen_dirpath_or_image_path)]
            self.gen_dirpath = os.path.dirname(gen_dirpath_or_image_path)
        if os.path.isdir(ref_dirpath_or_image_path):
            self.ref_names = sorted(
                [
                    name
                    for name in os.listdir(ref_dirpath_or_image_path)
                    if name.endswith(".png") or name.endswith(".jpg")
                ]
            )
            self.ref_dirpath = ref_dirpath_or_image_path
        else:
            self.ref_names = [os.path.basename(ref_dirpath_or_image_path)]
            self.ref_dirpath = os.path.dirname(ref_dirpath_or_image_path)

        assert len(self.ref_names) == len(self.gen_names)
        self.transform = torchvision.transforms.ToTensor()

    def __len__(self):
        return len(self.ref_names)

    def __getitem__(self, idx: int):
        ref_image = Image.open(os.path.join(self.ref_dirpath, self.ref_names[idx])).convert("RGB")
        gen_image = Image.open(os.path.join(self.gen_dirpath, self.gen_names[idx])).convert("RGB")
        gen_size = gen_image.size
        ref_size = ref_image.size
        if ref_size != gen_size:
            ref_image = ref_image.resize(gen_size, Image.Resampling.BICUBIC)
        gen_tensor = self.transform(gen_image)
        ref_tensor = self.transform(ref_image)
        return [gen_tensor, ref_tensor]


def compute_lpips(
    ref_dirpath_or_image_path: str,
    gen_dirpath_or_image_path: str,
    batch_size: int = 4,
    num_workers: int = 0,
    device: str | torch.device = "cuda",
) -> float:
    os.environ["TOKENIZERS_PARALLELISM"] = "false"
    metric = LearnedPerceptualImagePatchSimilarity(normalize=True).to(device)
    dataset = MultiImageDataset(gen_dirpath_or_image_path, ref_dirpath_or_image_path)
    dataloader = data.DataLoader(
        dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False, drop_last=False
    )
    with torch.no_grad():
        desc = (os.path.basename(gen_dirpath_or_image_path)) + " LPIPS"
        for i, batch in enumerate(tqdm(dataloader, desc=desc)):
            batch = [tensor.to(device) for tensor in batch]
            metric.update(batch[0], batch[1])
    return metric.compute().item()