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#           This file was automatically generated from src/transformers/models/ernie4_5_vl_moe/modular_ernie4_5_vl_moe.py.
#               Do NOT edit this file manually as any edits will be overwritten by the generation of
#             the file from the modular. If any change should be done, please apply the change to the
#                          modular_ernie4_5_vl_moe.py file directly. One of our CI enforces this.
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# Copyright 2025 Baidu and HuggingFace Inc. 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.
import math
from typing import Optional

import torch
import torch.nn.functional as F

from ...image_processing_utils import BatchFeature
from ...image_processing_utils_fast import BaseImageProcessorFast, group_images_by_shape, reorder_images
from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ImageInput, PILImageResampling, SizeDict
from ...processing_utils import Unpack
from ...utils import TensorType, auto_docstring
from .image_processing_ernie4_5_vl_moe import Ernie4_5_VL_MoeImageProcessorKwargs


def smart_resize(
    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
):
    """Rescales the image so that the following conditions are met:

    1. Both dimensions (height and width) are divisible by 'factor'.

    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].

    3. The aspect ratio of the image is maintained as closely as possible.

    """
    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 Ernie4_5_VL_MoeImageProcessorFast(BaseImageProcessorFast):
    do_resize = True
    resample = PILImageResampling.BICUBIC
    size = {"shortest_edge": 56 * 56, "longest_edge": 28 * 28 * 6177}
    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 = None  # Unused
    merge_size = 2
    valid_kwargs = Ernie4_5_VL_MoeImageProcessorKwargs
    model_input_names = ["pixel_values", "image_grid_thw"]

    def __init__(self, **kwargs: Unpack[Ernie4_5_VL_MoeImageProcessorKwargs]):
        super().__init__(**kwargs)
        if self.size is not None and (
            self.size.get("shortest_edge", None) is None or self.size.get("longest_edge", None) is None
        ):
            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")

    def _further_process_kwargs(
        self,
        size: SizeDict | 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 size is not None and ("shortest_edge" not in size or "longest_edge" not in size):
            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")

        return super()._further_process_kwargs(size=size, **kwargs)

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        interpolation: Optional["F.InterpolationMode"],
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: float | list[float] | None,
        image_std: float | list[float] | None,
        patch_size: int,
        merge_size: int,
        disable_grouping: bool | None,
        return_tensors: str | TensorType | None,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
        """
        # Group images by size for batched resizing
        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),
                    interpolation=interpolation,
                )
            resized_images_grouped[shape] = stacked_images
        resized_images = reorder_images(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_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:]
            # Fused rescale and normalize
            patches = self.rescale_and_normalize(
                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
            )
            if patches.ndim == 4:
                # add a temporal dimension if we have images
                patches = patches.unsqueeze(1)

            # Main difference to Qwen2 VL - no temporal patches
            batch_size, grid_t, channel = patches.shape[:3]
            grid_h, grid_w = resized_height // patch_size, resized_width // patch_size

            patches = patches.view(
                batch_size,
                grid_t,
                channel,
                grid_h // merge_size,
                merge_size,
                patch_size,
                grid_w // merge_size,
                merge_size,
                patch_size,
            )
            # Reorder dimensions to group grid and patch information for subsequent flattening.
            # [batch, grid_t, grid_h/merge, grid_w/merge, merge, merge, channel, patch, patch]
            patches = patches.permute(0, 1, 3, 6, 4, 7, 2, 5, 8)

            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
        )

    @auto_docstring
    def preprocess(
        self,
        images: ImageInput,
        **kwargs: Unpack[Ernie4_5_VL_MoeImageProcessorKwargs],
    ) -> BatchFeature:
        return super().preprocess(images, **kwargs)

    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.

        Note: Do not remove this method! It is used by vLLM to infer the number of patches and placeholders
        without an image input.

        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 = self.size["shortest_edge"]
        max_pixels = 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__ = ["Ernie4_5_VL_MoeImageProcessorFast"]
