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#           This file was automatically generated from src/transformers/models/qwen3_vl/modular_qwen3_vl.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_qwen3_vl.py file directly. One of our CI enforces this.
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# Copyright 2025 The Qwen Team and The 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

import numpy as np
import torch

from ...image_processing_utils import BatchFeature
from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling, SizeDict
from ...processing_utils import Unpack, VideosKwargs
from ...utils import TensorType, auto_docstring, is_torchvision_available, logging
from ...video_processing_utils import BaseVideoProcessor
from ...video_utils import VideoMetadata, group_videos_by_shape, reorder_videos


if is_torchvision_available():
    from torchvision.transforms.v2 import functional as tvF


logger = logging.get_logger(__name__)


class Qwen3VLVideoProcessorInitKwargs(VideosKwargs, total=False):
    r"""
    patch_size (`int`, *optional*, defaults to 16):
        The spatial patch size of the vision encoder.
    temporal_patch_size (`int`, *optional*, defaults to 2):
        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.
    """

    patch_size: int
    temporal_patch_size: int
    merge_size: int
    min_frames: int
    max_frames: int


def smart_resize(
    num_frames: int,
    height: int,
    width: int,
    temporal_factor: int = 2,
    factor: int = 28,
    min_pixels: int = 112 * 112,
    max_pixels: int = 14 * 14 * 2 * 2 * 2 * 6144,
):
    if num_frames < temporal_factor:
        raise ValueError(f"t:{num_frames} must be larger than temporal_factor:{temporal_factor}")
    if height < factor or width < factor:
        scale = max(factor / height, factor / width)
        height = int(height * scale)
        width = int(width * scale)

    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
    t_bar = round(num_frames / temporal_factor) * temporal_factor

    if t_bar * h_bar * w_bar > max_pixels:
        beta = math.sqrt((num_frames * 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 t_bar * h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (num_frames * 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 Qwen3VLVideoProcessor(BaseVideoProcessor):
    resample = PILImageResampling.BICUBIC
    size = {"shortest_edge": 128 * 32 * 32, "longest_edge": 32 * 32 * 768}
    max_image_size = {"longest_edge": 28 * 28 * 2 * 30000}
    image_mean = IMAGENET_STANDARD_MEAN
    image_std = IMAGENET_STANDARD_STD
    do_resize = True
    do_rescale = True
    do_normalize = True
    do_convert_rgb = True
    do_sample_frames = True
    patch_size = 16
    temporal_patch_size = 2
    max_duration = None
    merge_size = 2
    valid_kwargs = Qwen3VLVideoProcessorInitKwargs
    num_frames = None
    fps = 2

    model_input_names = ["pixel_values_videos", "video_grid_thw"]
    min_frames = 4
    max_frames = 768

    def __init__(self, **kwargs: Unpack[Qwen3VLVideoProcessorInitKwargs]):
        super().__init__(**kwargs)

    def sample_frames(
        self,
        metadata: VideoMetadata,
        num_frames: int | None = None,
        fps: int | float | None = None,
        **kwargs,
    ):
        """
        Default sampling function which uniformly samples the desired number of frames between 0 and total number of frames.
        If `fps` is passed along with metadata, `fps` frames per second are sampled uniformty. Arguments `num_frames`
        and `fps` are mutually exclusive.

        Args:
            video (`torch.Tensor`):
                Video that need to be sampled.
            metadata (`VideoMetadata`):
                Metadata of the video containing information about total duration, fps and total number of frames.
            num_frames (`int`, *optional*):
                Maximum number of frames to sample. Defaults to `self.num_frames`.
            fps (`int` or `float`, *optional*):
                Target frames to sample per second. Defaults to `self.fps`.
        Returns:
            torch.Tensor:
                Sampled video frames.
        """
        if fps is not None and num_frames is not None:
            raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!")

        total_num_frames = metadata.total_num_frames
        fps = fps if fps is not None else self.fps

        # If num_frames is not given but fps is, calculate num_frames from fps
        if num_frames is None and fps is not None:
            if metadata.fps is None:
                metadata.fps = 24
                logger.warning_once(
                    "Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. "
                    "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
                )
            num_frames = int(total_num_frames / metadata.fps * fps)
            num_frames = min(max(num_frames, self.min_frames), self.max_frames, total_num_frames)

        if num_frames is None:
            num_frames = min(max(total_num_frames, self.min_frames), self.max_frames)

        indices = np.linspace(0, total_num_frames - 1, num_frames).round().astype(int)

        return indices

    def resize(
        self,
        videos: "torch.Tensor",
        size: SizeDict,
        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
        factor: int,
        temporal_factor: int,
        **kwargs,
    ) -> "torch.Tensor":
        """Resize dynamically based on input video aspect ratio."""
        if not size.shortest_edge or not size.longest_edge:
            raise ValueError(f"`size` dict must contain 'shortest_edge' and 'longest_edge' keys but got {size}.")

        height, width = videos.shape[-2:]
        resized_height, resized_width = smart_resize(
            height=height,
            width=width,
            num_frames=videos.shape[1],
            factor=factor,
            temporal_factor=temporal_factor,
            min_pixels=size.shortest_edge,
            max_pixels=size.longest_edge,
        )
        return super().resize(
            image=videos,
            size=SizeDict(height=resized_height, width=resized_width),
            resample=resample,
        )

    def patchify(
        self,
        videos: "torch.Tensor",
        patch_size: int,
        merge_size: int,
        temporal_patch_size: int,
    ) -> tuple["torch.Tensor", int, int]:
        "Patchifies each video into flat layout of shape (`seq_len`, `patch_dim`) so we can concat dynamically shaped pixels."
        batch_size, num_frames, channel, resized_height, resized_width = videos.shape

        # Check that videos have `num_frames` divisible by `temporal_patch_size`
        if pad := -num_frames % temporal_patch_size:
            repeats = videos[:, -1:].expand(-1, pad, -1, -1, -1)
            videos = torch.cat((videos, repeats), dim=1)
            num_frames += pad

        grid_t = num_frames // temporal_patch_size
        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size

        patches = videos.view(
            batch_size,
            grid_t,
            temporal_patch_size,
            channel,
            grid_h // merge_size,
            merge_size,
            patch_size,
            grid_w // merge_size,
            merge_size,
            patch_size,
        )
        patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
        flatten_patches = patches.reshape(
            batch_size,
            grid_t * grid_h * grid_w,
            channel * temporal_patch_size * patch_size * patch_size,
        )

        return flatten_patches, grid_t, grid_h, grid_w

    def _preprocess(
        self,
        videos: list[torch.Tensor],
        do_convert_rgb: bool = True,
        do_resize: bool = True,
        size: SizeDict | None = None,
        resample: "PILImageResampling | tvF.InterpolationMode | int | None" = PILImageResampling.BICUBIC,
        do_rescale: bool = True,
        rescale_factor: float = 1 / 255.0,
        do_normalize: bool = True,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        patch_size: int | None = None,
        temporal_patch_size: int | None = None,
        merge_size: int | None = None,
        return_tensors: str | TensorType | None = None,
        **kwargs,
    ):
        grouped_videos, grouped_videos_index = group_videos_by_shape(videos)
        resized_videos_grouped = {}

        for shape, stacked_videos in grouped_videos.items():
            if do_convert_rgb:
                stacked_videos = self.convert_to_rgb(stacked_videos)
            if do_resize:
                stacked_videos = self.resize(
                    videos=stacked_videos,
                    size=size,
                    resample=resample,
                    factor=patch_size * merge_size,
                    temporal_factor=temporal_patch_size,
                )
            resized_videos_grouped[shape] = stacked_videos
        resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index)

        # Group videos by size for further processing
        # Needed in case do_resize is False, or resize returns videos with different sizes
        grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos)
        processed_videos_grouped = {}
        processed_grids = {}
        for shape, stacked_videos in grouped_videos.items():
            # Fused rescale and normalize
            stacked_videos = self.rescale_and_normalize(
                stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std
            )
            patches, grid_t, grid_h, grid_w = self.patchify(
                stacked_videos,
                patch_size=patch_size,
                merge_size=merge_size,
                temporal_patch_size=temporal_patch_size,
            )

            processed_videos_grouped[shape] = patches
            processed_grids[shape] = [[grid_t, grid_h, grid_w]] * len(stacked_videos)

        processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
        processed_grids = reorder_videos(processed_grids, grouped_videos_index)
        pixel_values_videos = torch.cat(processed_videos, dim=0)
        video_grid_thw = torch.tensor(processed_grids)
        data = {
            "pixel_values_videos": pixel_values_videos,
            "video_grid_thw": video_grid_thw,
        }

        return BatchFeature(data=data, tensor_type=return_tensors)


__all__ = ["Qwen3VLVideoProcessor"]