# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
"""
MiniMax VL family HuggingFace-compatible Processor, ImageProcessor, VideoProcessor.
"""
import math
from typing import List, Tuple

import torch
from torchvision.transforms import InterpolationMode
from transformers import BatchFeature
from transformers.image_processing_utils_fast import (
    BaseImageProcessorFast,
    group_images_by_shape,
    reorder_images,
)
from transformers.image_utils import PILImageResampling, SizeDict
from transformers.processing_utils import (
    ImagesKwargs,
    Unpack,
)
from transformers.utils import TensorType

MAX_RATIO = 200


def round_by_factor(number: int, factor: int) -> int:
    return round(number / factor) * factor


def ceil_by_factor(number: int, factor: int) -> int:
    return math.ceil(number / factor) * factor


def floor_by_factor(number: int, factor: int) -> int:
    return math.floor(number / factor) * factor


def smart_resize(
    height: int,
    width: int,
    factor: int = 28,
    min_pixels: int = 4 * 28 * 28,
    max_pixels: int = 451584,
) -> tuple[int, int]:
    if max(height, width) / min(height, width) > MAX_RATIO:
        raise ValueError(
            f"absolute aspect ratio must be smaller than {MAX_RATIO}, "
            f"got {max(height, width) / min(height, width)}"
        )
    h_bar = max(factor, round_by_factor(height, factor))
    w_bar = max(factor, round_by_factor(width, factor))
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = floor_by_factor(height / beta, factor)
        w_bar = floor_by_factor(width / beta, factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = ceil_by_factor(height * beta, factor)
        w_bar = ceil_by_factor(width * beta, factor)
    return h_bar, w_bar


# ==============================================================================
# MiniMax M3 VL Image Processor Fast (Fast Mode - Torch based)
# ==============================================================================


class MiniMaxM3VLImageProcessorKwargs(ImagesKwargs, total=False):
    patch_size: int
    temporal_patch_size: int
    merge_size: int
    max_pixels: int


class MiniMaxM3VLImageProcessor(BaseImageProcessorFast):
    do_resize = True
    resample = PILImageResampling.BICUBIC
    size = {"height": 672, "width": 672}  # required by base class validation, not used as resize bound
    default_to_square = False
    do_rescale = True
    rescale_factor = 1 / 255
    do_normalize = True
    image_mean = [0.48145466, 0.4578275, 0.40821073]
    image_std = [0.26862954, 0.26130258, 0.27577711]
    do_convert_rgb = True
    patch_size = 14
    temporal_patch_size = 2
    merge_size = 2
    max_pixels = 451584             # 672*672
    valid_kwargs = MiniMaxM3VLImageProcessorKwargs
    model_input_names = ["pixel_values", "image_grid_thw"]

    def __init__(self, **kwargs: Unpack[MiniMaxM3VLImageProcessorKwargs]):
        super().__init__(**kwargs)

    def preprocess(
        self, images, **kwargs: Unpack[MiniMaxM3VLImageProcessorKwargs]
    ) -> BatchFeature:
        return super().preprocess(images, **kwargs)

    def _preprocess(
        self,
        images: List[torch.Tensor],
        do_resize: bool,
        size: SizeDict,
        resample: PILImageResampling | InterpolationMode | 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,
        max_pixels: 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 = {}
        factor = patch_size * merge_size
        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=factor,
                    max_pixels=max_pixels,
                )
                stacked_images = self.resize(
                    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 - (patches.shape[1] % temporal_patch_size),
                    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 // 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,
            )

            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, dtype=torch.long)

        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):
        images_kwargs = images_kwargs or {}
        patch_size = images_kwargs.get("patch_size", self.patch_size)
        merge_size = images_kwargs.get("merge_size", self.merge_size)
        max_pixels = images_kwargs.get("max_pixels", self.max_pixels)

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