from __future__ import annotations

from pathlib import Path
from typing import Literal

import numpy as np
import PIL.Image

from gradio import processing_utils

PIL.Image.init()  # fixes https://github.com/gradio-app/gradio/issues/2843 (remove when requiring Pillow 9.4+)


def format_image(
    im: PIL.Image.Image | None,
    type: Literal["numpy", "pil", "filepath"],
    cache_dir: str,
    name: str = "image",
    format: str = "webp",
) -> np.ndarray | PIL.Image.Image | str | None:
    """Helper method to format an image based on self.type"""
    if im is None:
        return im
    if type == "pil":
        return im
    elif type == "numpy":
        return np.array(im)
    elif type == "filepath":
        try:
            path = processing_utils.save_pil_to_cache(
                im, cache_dir=cache_dir, name=name, format=format
            )
        # Catch error if format is not supported by PIL
        except (KeyError, ValueError):
            path = processing_utils.save_pil_to_cache(
                im,
                cache_dir=cache_dir,
                name=name,
                format="png",  # type: ignore
            )
        return path
    else:
        raise ValueError(
            "Unknown type: "
            + str(type)
            + ". Please choose from: 'numpy', 'pil', 'filepath'."
        )


def save_image(
    y: np.ndarray | PIL.Image.Image | str | Path, cache_dir: str, format: str = "webp"
):
    if isinstance(y, np.ndarray):
        path = processing_utils.save_img_array_to_cache(
            y, cache_dir=cache_dir, format=format
        )
    elif isinstance(y, PIL.Image.Image):
        try:
            path = processing_utils.save_pil_to_cache(
                y, cache_dir=cache_dir, format=format
            )
        # Catch error if format is not supported by PIL
        except (KeyError, ValueError):
            path = processing_utils.save_pil_to_cache(
                y, cache_dir=cache_dir, format="png"
            )
    elif isinstance(y, Path):
        path = str(y)
    elif isinstance(y, str):
        path = y
    else:
        raise ValueError(
            "Cannot process this value as an Image, it is of type: " + str(type(y))
        )

    return path


def crop_scale(img: PIL.Image.Image, final_width: int, final_height: int):
    original_width, original_height = img.size
    target_aspect_ratio = final_width / final_height

    if original_width / original_height > target_aspect_ratio:
        crop_height = original_height
        crop_width = crop_height * target_aspect_ratio
    else:
        crop_width = original_width
        crop_height = crop_width / target_aspect_ratio

    left = (original_width - crop_width) / 2
    top = (original_height - crop_height) / 2

    img_cropped = img.crop(
        (int(left), int(top), int(left + crop_width), int(top + crop_height))
    )

    img_resized = img_cropped.resize((final_width, final_height))

    return img_resized