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# Copyright 2025 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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.

import math
from collections.abc import Iterable

import torch

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


class PaddleOCRVLImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    patch_size (`int`, *optional*, defaults to 14):
        The spatial patch size of the vision encoder.
    temporal_patch_size (`int`, *optional*, defaults to 1):
        The temporal patch size of the vision encoder.
    merge_size (`int`, *optional*, defaults to 2):
        The merge size of the vision encoder to llm encoder.
    """

    min_pixels: int
    max_pixels: int
    patch_size: int
    temporal_patch_size: int
    merge_size: int


def smart_resize(
    height: int,
    width: int,
    factor: int = 28,
    min_pixels: int = 384 * 384,
    max_pixels: int = 1536 * 1536,
):
    if height < factor:
        width = round((width * factor) / height)
        height = factor

    if width < factor:
        height = round((height * factor) / width)
        width = factor

    if max(height, width) / min(height, width) > 200:
        raise ValueError(
            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
        )
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = max(factor, math.floor(height / beta / factor) * factor)
        w_bar = max(factor, math.floor(width / beta / factor) * factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar


@auto_docstring
class PaddleOCRVLImageProcessor(TorchvisionBackend):
    do_resize = True
    resample = PILImageResampling.BICUBIC
    size = {"shortest_edge": 384 * 384, "longest_edge": 1536 * 1536}
    default_to_square = False
    do_rescale = True
    do_normalize = True
    image_mean = OPENAI_CLIP_MEAN
    image_std = OPENAI_CLIP_STD
    do_convert_rgb = True
    patch_size = 14
    temporal_patch_size = 1
    merge_size = 2
    valid_kwargs = PaddleOCRVLImageProcessorKwargs
    model_input_names = ["pixel_values", "image_grid_thw"]

    def __init__(self, **kwargs: Unpack[PaddleOCRVLImageProcessorKwargs]):
        size = kwargs.pop("size", None)
        min_pixels = kwargs.pop("min_pixels", None)
        max_pixels = kwargs.pop("max_pixels", None)
        # backward compatibility: override size with min_pixels and max_pixels if they are provided
        size = self.size if size is None else size
        if min_pixels is not None:
            size["shortest_edge"] = min_pixels
            size.pop("min_pixels", None)
        if max_pixels is not None:
            size["longest_edge"] = max_pixels
            size.pop("max_pixels", None)
        if "shortest_edge" not in size or "longest_edge" not in size:
            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")

        super().__init__(size=size, **kwargs)

    def _standardize_kwargs(
        self,
        size: int | Iterable[int] | dict[str, int] | SizeDict | None = None,
        min_pixels: int | None = None,
        max_pixels: int | None = None,
        **kwargs,
    ) -> dict:
        if min_pixels is not None and max_pixels is not None:
            size = SizeDict(shortest_edge=min_pixels, longest_edge=max_pixels)
        kwargs = super()._standardize_kwargs(size=size, **kwargs)
        size = kwargs.get("size", self.size)
        if not size.shortest_edge or not size.longest_edge:
            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
        return kwargs

    @auto_docstring
    def preprocess(
        self,
        images: ImageInput,
        **kwargs: Unpack[PaddleOCRVLImageProcessorKwargs],
    ) -> BatchFeature:
        return super().preprocess(images, **kwargs)

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        resample: "PILImageResampling | int | None",
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: float | list[float] | None,
        image_std: float | list[float] | None,
        patch_size: int,
        temporal_patch_size: int,
        merge_size: int,
        disable_grouping: bool | None,
        return_tensors: str | TensorType | None,
        **kwargs,
    ) -> BatchFeature:
        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
        resized_images_grouped = {}
        for shape, stacked_images in grouped_images.items():
            height, width = stacked_images.shape[-2:]
            if do_resize:
                resized_height, resized_width = smart_resize(
                    height,
                    width,
                    factor=patch_size * merge_size,
                    min_pixels=size.shortest_edge,
                    max_pixels=size.longest_edge,
                )
                stacked_images = self.resize(
                    image=stacked_images,
                    size=SizeDict(height=resized_height, width=resized_width),
                    resample=resample,
                )
            resized_images_grouped[shape] = stacked_images
        resized_images = reorder_images(resized_images_grouped, grouped_images_index)

        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
        processed_images_grouped = {}
        processed_grids = {}
        for shape, stacked_images in grouped_images.items():
            resized_height, resized_width = stacked_images.shape[-2:]
            patches = self.rescale_and_normalize(
                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
            )
            if patches.ndim == 4:
                patches = patches.unsqueeze(1)
            if patches.shape[1] % temporal_patch_size != 0:
                repeats = patches[:, -1:].repeat(1, temporal_patch_size - 1, 1, 1, 1)
                patches = torch.cat([patches, repeats], dim=1)

            batch_size, grid_t, channel = patches.shape[:3]
            grid_t = grid_t // temporal_patch_size
            grid_h, grid_w = resized_height // patch_size, resized_width // patch_size

            patches = patches.view(
                batch_size,
                grid_t,
                temporal_patch_size,
                channel,
                grid_h,
                patch_size,
                grid_w,
                patch_size,
            )
            patches = patches.permute(0, 1, 4, 6, 3, 2, 5, 7)
            flatten_patches = patches.reshape(batch_size, grid_t * grid_h * grid_w, channel, patch_size, patch_size)

            processed_images_grouped[shape] = flatten_patches
            processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size

        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
        processed_grids = reorder_images(processed_grids, grouped_images_index)
        pixel_values = torch.cat(processed_images, dim=0)
        image_grid_thw = torch.tensor(processed_grids)

        return BatchFeature(
            data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors
        )

    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
        """
        A utility that returns number of image patches for a given image size.

        Args:
            height (`int`):
                Height of the input image.
            width (`int`):
                Width of the input image.
            images_kwargs (`dict`, *optional*)
                Any kwargs to override defaults of the image processor.
        Returns:
            `int`: Number of image patches per image.
        """
        min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"]
        max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"]
        patch_size = images_kwargs.get("patch_size", self.patch_size)
        merge_size = images_kwargs.get("merge_size", self.merge_size)

        factor = patch_size * merge_size
        resized_height, resized_width = smart_resize(
            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
        )
        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
        return grid_h * grid_w


__all__ = ["PaddleOCRVLImageProcessor"]
