from meerkat.tools.lazy_loader import LazyLoader

torch = LazyLoader("torch")
transforms = LazyLoader("torchvision.transforms")
transformers = LazyLoader("transformers")


def initialize_transform(transform_name, config, dataset):
    if transform_name is None:
        return None
    elif transform_name == "bert":
        return METHOD_NAME(config)
    elif transform_name == "image_base":
        return initialize_image_base_transform(config, dataset)
    elif transform_name == "image_resize_and_center_crop":
        return initialize_image_resize_and_center_crop_transform(config, dataset)
    elif transform_name == "poverty_train":
        return initialize_poverty_train_transform()
    else:
        raise ValueError(f"{transform_name} not recognized")


def METHOD_NAME(config):
    assert "bert" in config.model
    assert config.max_token_length is not None

    tokenizer = getBertTokenizer(config.model)

    def transform(text):
        tokens = tokenizer(
            text,
            padding="max_length",
            truncation=True,
            max_length=config.max_token_length,
            return_tensors="pt",
        )
        if config.model == "bert-base-uncased":
            x = torch.stack(
                (
                    tokens["input_ids"],
                    tokens["attention_mask"],
                    tokens["token_type_ids"],
                ),
                dim=2,
            )
        elif config.model == "distilbert-base-uncased":
            x = torch.stack((tokens["input_ids"], tokens["attention_mask"]), dim=2)
        x = torch.squeeze(x, dim=0)  # First shape dim is always 1
        return x

    return transform


def getBertTokenizer(model):
    if model == "bert-base-uncased":
        tokenizer = transformers.BertTokenizerFast.from_pretrained(model)
    elif model == "distilbert-base-uncased":
        tokenizer = transformers.DistilBertTokenizerFast.from_pretrained(model)
    else:
        raise ValueError(f"Model: {model} not recognized.")

    return tokenizer


def initialize_image_base_transform(config, dataset):
    transform_steps = []
    if dataset.original_resolution is not None and min(
        dataset.original_resolution
    ) != max(dataset.original_resolution):
        crop_size = min(dataset.original_resolution)
        transform_steps.append(transforms.CenterCrop(crop_size))
    if config.target_resolution is not None and config.dataset != "fmow":
        transform_steps.append(transforms.Resize(config.target_resolution))
    transform_steps += [
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
    ]
    transform = transforms.Compose(transform_steps)
    return transform


def initialize_image_resize_and_center_crop_transform(config, dataset):
    """Resizes the image to a slightly larger square then crops the center."""
    assert dataset.original_resolution is not None
    assert config.resize_scale is not None
    scaled_resolution = tuple(
        int(res * config.resize_scale) for res in dataset.original_resolution
    )
    if config.target_resolution is not None:
        target_resolution = config.target_resolution
    else:
        target_resolution = dataset.original_resolution
    transform = transforms.Compose(
        [
            transforms.Resize(scaled_resolution),
            transforms.CenterCrop(target_resolution),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
        ]
    )
    return transform


def initialize_poverty_train_transform():
    transforms_ls = [
        transforms.ToPILImage(),
        transforms.RandomHorizontalFlip(),
        transforms.RandomVerticalFlip(),
        transforms.ColorJitter(brightness=0.8, contrast=0.8, saturation=0.8, hue=0.1),
        transforms.ToTensor(),
    ]
    rgb_transform = transforms.Compose(transforms_ls)

    def transform_rgb(img):
        # bgr to rgb and back to bgr
        img[:3] = rgb_transform(img[:3][[2, 1, 0]])[[2, 1, 0]]
        return img

    transform = transforms.Lambda(lambda x: transform_rgb(x))
    return transform