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#           This file was automatically generated from src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py.
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# Copyright 2025 Deepseek AI and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from collections.abc import Iterable
from typing import Union

import torch
import torchvision.transforms.v2.functional as tvF

from ...image_processing_backends import TorchvisionBackend
from ...image_processing_utils import BatchFeature, get_size_dict
from ...image_transforms import group_images_by_shape, reorder_images
from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling, SizeDict
from ...processing_utils import ImagesKwargs, Unpack
from ...utils import TensorType, auto_docstring


class DeepseekVLHybridImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    min_size (`int`, *optional*, defaults to 14):
        The minimum allowed size for the resized image. Ensures that neither the height nor width
        falls below this value after resizing.
     high_res_size (`dict`, *optional*, defaults to `{"height": 1024, "width": 1024}`):
        Size of the high resolution output image after resizing. Can be overridden by the `high_res_size` parameter in the `preprocess`
        method.
    high_res_resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
        Resampling filter to use if resizing the image. Only has an effect if `do_resize` is set to `True`. Can be
        overridden by the `high_res_resample` parameter in the `preprocess` method.
    high_res_image_mean (`float` or `list[float]`, *optional*, defaults to `OPENAI_CLIP_MEAN`):
        Mean to use if normalizing the high resolution image. This is a float or list of floats the length of the number of
        channels in the image. Can be overridden by the `high_res_image_mean` parameter in the `preprocess` method.
    high_res_image_std (`float` or `list[float]`, *optional*, defaults to `OPENAI_CLIP_STD`):
        Standard deviation to use if normalizing the high resolution image. This is a float or list of floats the length of the
        number of channels in the image. Can be overridden by the `high_res_image_std` parameter in the `preprocess` method.
    """

    min_size: int
    high_res_size: dict
    high_res_resample: Union["PILImageResampling", int]
    high_res_image_mean: float | list[float] | tuple[float, ...]
    high_res_image_std: float | list[float] | tuple[float, ...]


@auto_docstring
class DeepseekVLHybridImageProcessor(TorchvisionBackend):
    resample = PILImageResampling.BICUBIC
    image_mean = OPENAI_CLIP_MEAN
    image_std = OPENAI_CLIP_STD
    size = {"height": 384, "width": 384}
    min_size = 14
    do_resize = True
    do_rescale = True
    do_normalize = True
    do_pad = True
    valid_kwargs = DeepseekVLHybridImageProcessorKwargs
    high_res_image_mean = OPENAI_CLIP_MEAN
    high_res_image_std = OPENAI_CLIP_STD
    high_res_size = {"height": 1024, "width": 1024}
    high_res_resample = PILImageResampling.BICUBIC
    model_input_names = ["pixel_values", "high_res_pixel_values"]

    def __init__(self, **kwargs: Unpack[DeepseekVLHybridImageProcessorKwargs]):
        if kwargs.get("image_mean") is None:
            background_color = (127, 127, 127)
        else:
            background_color = tuple(int(x * 255) for x in kwargs.get("image_mean"))
        if kwargs.get("high_res_image_mean") is None:
            high_res_background_color = (127, 127, 127)
        else:
            high_res_background_color = tuple(int(x * 255) for x in kwargs.get("high_res_image_mean"))
        super().__init__(**kwargs)
        self.background_color = tuple(background_color)
        self.high_res_background_color = tuple(high_res_background_color)

    def resize(
        self,
        image: "torch.Tensor",
        size: SizeDict,
        min_size: int,
        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
        antialias: bool = True,
        **kwargs,
    ) -> "torch.Tensor":
        if size.height is None or size.width is None or size.height != size.width:
            raise ValueError(
                f"Output height and width must be the same. Got height={size['height']} and width={size['width']}"
            )
        size = size.height

        height, width = image.shape[-2:]
        max_size = max(height, width)

        delta = size / max_size
        # Largest side becomes `size` and the other side is scaled according to the aspect ratio.
        output_size_nonpadded = SizeDict(
            height=max(round(height * delta), min_size),
            width=max(round(width * delta), min_size),
        )

        return super().resize(image, size=output_size_nonpadded, resample=resample, antialias=antialias)

    def pad_to_square(
        self,
        images: "torch.Tensor",
        background_color: int | tuple[int, int, int] = 0,
    ) -> "torch.Tensor":
        """
        Pads an image to a square based on the longest edge.

        Args:
            images (`torch.Tensor`):
                The images to pad.
            background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
                The color to use for the padding. Can be an integer for single channel or a
                tuple of integers representing for multi-channel images. If passed as integer
                in multi-channel mode, it will default to `0` in subsequent channels.

        Returns:
            `torch.Tensor`: The padded images.
        """
        height, width = images.shape[-2:]
        num_channels = images.shape[1]
        batch_size = images.shape[0]

        if height == width:
            return images

        max_dim = max(height, width)

        # Ensure background_color is the correct shape
        if isinstance(background_color, int):
            background_color = [background_color]
        elif len(background_color) != num_channels:
            raise ValueError(
                f"background_color must have no more than {num_channels} elements to match the number of channels"
            )

        padded_images = torch.zeros(
            (batch_size, num_channels, max_dim, max_dim), dtype=images.dtype, device=images.device
        )
        for i, color in enumerate(background_color):
            padded_images[:, i, :, :] = color
        if width > height:
            start = (max_dim - height) // 2
            padded_images[:, :, start : start + height, :] = images
        else:
            start = (max_dim - width) // 2
            padded_images[:, :, :, start : start + width] = images

        return padded_images

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        high_res_size: SizeDict,
        min_size: int,
        resample: "PILImageResampling | None",
        high_res_resample: "PILImageResampling | None",
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: float | list[float] | None,
        image_std: float | list[float] | None,
        high_res_image_mean: float | list[float] | None,
        high_res_image_std: float | list[float] | None,
        disable_grouping: bool | None,
        return_tensors: str | TensorType | None,
        do_pad: bool = True,
        **kwargs,
    ) -> BatchFeature:
        # Group images by size for batched resizing
        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
        high_res_resized_images_grouped = {}
        for shape, stacked_images in grouped_images.items():
            if do_resize:
                stacked_high_res_images = self.resize(
                    image=stacked_images, size=high_res_size, min_size=min_size, resample=high_res_resample
                )
            high_res_resized_images_grouped[shape] = stacked_high_res_images
        high_res_resized_images = reorder_images(high_res_resized_images_grouped, grouped_images_index)

        # Group images by size for further processing
        # Needed in case do_resize is False, or resize returns images with different sizes
        grouped_high_res_images, grouped_high_res_images_index = group_images_by_shape(
            high_res_resized_images, disable_grouping=disable_grouping
        )
        high_res_padded_images = {}
        high_res_processed_images_grouped = {}
        for shape, stacked_high_res_images in grouped_high_res_images.items():
            if do_pad:
                stacked_high_res_images = self.pad_to_square(
                    stacked_high_res_images, background_color=self.high_res_background_color
                )
                high_res_padded_images[shape] = stacked_high_res_images
            # Fused rescale and normalize
            stacked_high_res_images = self.rescale_and_normalize(
                stacked_high_res_images,
                do_rescale,
                rescale_factor,
                do_normalize,
                high_res_image_mean,
                high_res_image_std,
            )
            high_res_processed_images_grouped[shape] = stacked_high_res_images
        high_res_processed_images = reorder_images(high_res_processed_images_grouped, grouped_high_res_images_index)

        resized_images_grouped = {}
        for shape, stacked_high_res_padded_images in high_res_padded_images.items():
            if do_resize:
                stacked_images = self.resize(
                    image=stacked_high_res_padded_images, size=size, min_size=min_size, resample=resample
                )
            resized_images_grouped[shape] = stacked_images
        resized_images = reorder_images(resized_images_grouped, grouped_high_res_images_index)

        grouped_resized_images, grouped_resized_images_index = group_images_by_shape(
            resized_images, disable_grouping=disable_grouping
        )
        processed_images_grouped = {}
        for shape, stacked_images in grouped_resized_images.items():
            if do_pad:
                stacked_images = self.pad_to_square(stacked_images, background_color=self.background_color)
            # Fused rescale and normalize
            stacked_images = self.rescale_and_normalize(
                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
            )
            processed_images_grouped[shape] = stacked_images
        processed_images = reorder_images(processed_images_grouped, grouped_resized_images_index)

        return BatchFeature(
            data={"pixel_values": processed_images, "high_res_pixel_values": high_res_processed_images},
            tensor_type=return_tensors,
        )

    def postprocess(self) -> "torch.Tensor":
        raise AttributeError("Not needed for DeepseekVLHybrid")

    def _standardize_kwargs(
        self,
        size: int | Iterable[int] | dict[str, int] | SizeDict | None = None,
        high_res_size: int | Iterable[int] | dict[str, int] | SizeDict | None = None,
        default_to_square: bool | None = None,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        high_res_image_mean: float | list[float] | None = None,
        high_res_image_std: float | list[float] | None = None,
        **kwargs,
    ) -> dict:
        """
        Update kwargs that need further processing before being validated
        Can be overridden by subclasses to customize the processing of kwargs.
        """
        if kwargs is None:
            kwargs = {}
        if size is not None and not isinstance(size, SizeDict):
            size = SizeDict(**get_size_dict(size=size, default_to_square=default_to_square))
        if high_res_size is not None and not isinstance(high_res_size, SizeDict):
            high_res_size = SizeDict(**get_size_dict(size=high_res_size, default_to_square=default_to_square))
        if isinstance(image_mean, list):
            image_mean = tuple(image_mean)
        if isinstance(image_std, list):
            image_std = tuple(image_std)
        if isinstance(high_res_image_mean, list):
            high_res_image_mean = tuple(high_res_image_mean)
        if isinstance(high_res_image_std, list):
            high_res_image_std = tuple(high_res_image_std)

        kwargs["size"] = size
        kwargs["high_res_size"] = high_res_size
        kwargs["image_mean"] = image_mean
        kwargs["image_std"] = image_std
        kwargs["high_res_image_mean"] = high_res_image_mean
        kwargs["high_res_image_std"] = high_res_image_std

        return kwargs


__all__ = ["DeepseekVLHybridImageProcessor"]
