# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

from io import BytesIO
from pathlib import Path

import numpy as np
import pybase64
import torch
from PIL import Image

from vllm.utils.serial_utils import tensor2base64

from ..image import convert_image_mode, rgba_to_rgb
from .base import MediaIO, MediaWithBytes

MAGIC_NUMPY_PREFIX = b"\x93NUMPY"  # https://numpy.org/devdocs/reference/generated/numpy.lib.format.html#format-version-1-0


class ImageMediaIO(MediaIO[Image.Image]):
    """Configuration values can be user-provided either by --media-io-kwargs or
    by the runtime API field "media_io_kwargs". Ensure proper validation and
    error handling.
    """

    def __init__(self, image_mode: str = "RGB", **kwargs) -> None:
        super().__init__()

        self.image_mode = image_mode
        # `kwargs` contains custom arguments from
        # --media-io-kwargs for this modality, merged with
        # per-request runtime media_io_kwargs via merge_kwargs().
        # They can be passed to the underlying
        # media loaders (e.g. custom implementations)
        # for flexible control.
        self.kwargs = kwargs

        # Extract RGBA background color from kwargs if provided
        # Default to white background for backward compatibility
        rgba_bg = kwargs.get("rgba_background_color", (255, 255, 255))
        # Convert list to tuple for consistency
        if isinstance(rgba_bg, list):
            rgba_bg = tuple(rgba_bg)

        # Validate rgba_background_color format
        if not (
            isinstance(rgba_bg, tuple)
            and len(rgba_bg) == 3
            and all(isinstance(c, int) and 0 <= c <= 255 for c in rgba_bg)
        ):
            raise ValueError(
                "rgba_background_color must be a list or tuple of 3 integers "
                "in the range [0, 255]."
            )
        self.rgba_background_color = rgba_bg

    def _convert_image_mode(
        self, image: Image.Image | MediaWithBytes[Image.Image]
    ) -> Image.Image:
        """Convert image mode with custom background color."""
        if isinstance(image, MediaWithBytes):
            image = image.media
        if image.mode == self.image_mode:
            return image
        elif image.mode == "RGBA" and self.image_mode == "RGB":
            return rgba_to_rgb(image, self.rgba_background_color)
        else:
            return convert_image_mode(image, self.image_mode)

    def load_bytes(self, data: bytes) -> MediaWithBytes[Image.Image]:
        image = Image.open(BytesIO(data))
        return MediaWithBytes(self._convert_image_mode(image), data)

    def load_base64(self, media_type: str, data: str) -> MediaWithBytes[Image.Image]:
        return self.load_bytes(pybase64.b64decode(data, validate=True))

    def load_file(self, filepath: Path) -> MediaWithBytes[Image.Image]:
        with open(filepath, "rb") as f:
            data = f.read()
        image = Image.open(BytesIO(data))
        return MediaWithBytes(self._convert_image_mode(image), data)

    def encode_base64(
        self,
        media: Image.Image,
        *,
        image_format: str = "PNG",
    ) -> str:
        image = media

        with BytesIO() as buffer:
            image = self._convert_image_mode(image)
            image.save(buffer, image_format)
            data = buffer.getvalue()

        return pybase64.b64encode(data).decode("utf-8")


class ImageEmbeddingMediaIO(MediaIO[torch.Tensor]):
    """Image embedding MediaIO implementation.

    Configuration values can be user-provided either by --media-io-kwargs or
    by the runtime API field "media_io_kwargs". Ensure proper validation and
    error handling.
    """

    def __init__(self) -> None:
        super().__init__()

    def _load_pickled_torch(self, data: bytes) -> torch.Tensor:
        buffer = BytesIO(data)
        # Enable sparse tensor integrity checks to prevent out-of-bounds
        # writes from maliciously crafted tensors
        with torch.sparse.check_sparse_tensor_invariants():
            tensor = torch.load(buffer, weights_only=True)
            return tensor.to_dense()

    def _load_numpy(self, data: bytes) -> torch.Tensor:
        with BytesIO(data) as buffer:
            return torch.from_numpy(np.load(buffer))

    def load_bytes(self, data: bytes) -> torch.Tensor:
        if data[:6] == MAGIC_NUMPY_PREFIX:
            return self._load_numpy(data)

        return self._load_pickled_torch(data)

    def load_base64(self, media_type: str, data: str) -> torch.Tensor:
        return self.load_bytes(pybase64.b64decode(data, validate=True))

    def load_file(self, filepath: Path) -> torch.Tensor:
        if filepath.suffix == ".npy":
            return torch.from_numpy(np.load(filepath))

        with torch.sparse.check_sparse_tensor_invariants():
            tensor = torch.load(filepath, weights_only=True)
            return tensor.to_dense()

    def encode_base64(self, media: torch.Tensor) -> str:
        return tensor2base64(media)
