# coding=utf-8
# Copyright 2025 The Intel Labs Team Authors, The Microsoft Research Team Authors 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.
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# limitations under the License.
"""Fast Image processor class for BridgeTower."""

from collections.abc import Iterable
from typing import Optional, Union

import torch
from torchvision.transforms.v2 import functional as F

from ...image_processing_utils_fast import (
    BaseImageProcessorFast,
    BatchFeature,
    DefaultFastImageProcessorKwargs,
    ImageInput,
    SizeDict,
    TensorType,
    Unpack,
    group_images_by_shape,
    reorder_images,
)
from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, PILImageResampling
from ...utils import auto_docstring


def make_pixel_mask(
    image: "torch.Tensor",
    output_size: tuple[int, int],
) -> "torch.Tensor":
    """
    Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding.

    Args:
        image (`np.ndarray`):
            Image to make the pixel mask for.
        output_size (`tuple[int, int]`):
            Output size of the mask.
    """
    input_height, input_width = image.shape[-2:]
    batch_size = image.size(0)
    mask = torch.zeros((batch_size, *output_size), dtype=torch.long)
    mask[:input_height, :input_width] = 1
    return mask


def get_resize_output_image_size(
    input_image: "torch.Tensor",
    shorter: int = 800,
    longer: int = 1333,
    size_divisor: int = 32,
) -> tuple[int, int]:
    input_height, input_width = input_image.shape[-2:]
    min_size, max_size = shorter, longer

    scale = min_size / min(input_height, input_width)

    if input_height < input_width:
        new_height = min_size
        new_width = scale * input_width
    else:
        new_height = scale * input_height
        new_width = min_size

    if max(new_height, new_width) > max_size:
        scale = max_size / max(new_height, new_width)
        new_height = scale * new_height
        new_width = scale * new_width

    new_height, new_width = int(new_height + 0.5), int(new_width + 0.5)
    new_height = new_height // size_divisor * size_divisor
    new_width = new_width // size_divisor * size_divisor

    return new_height, new_width


class BridgeTowerFastImageProcessorKwargs(DefaultFastImageProcessorKwargs):
    """
    Args:
        size_divisor (`int`, *optional*, defaults to 32):
            The size by which to make sure both the height and width can be divided. Only has an effect if `do_resize`
            is set to `True`. Can be overridden by the `size_divisor` parameter in the `preprocess` method.
    """

    size_divisor: Optional[int]


@auto_docstring
class BridgeTowerImageProcessorFast(BaseImageProcessorFast):
    resample = PILImageResampling.BICUBIC
    image_mean = OPENAI_CLIP_MEAN
    image_std = OPENAI_CLIP_STD
    size = {"shortest_edge": 288}
    default_to_square = False
    crop_size = {"shortest_edge": 288}
    do_resize = True
    do_center_crop = True
    do_rescale = True
    do_normalize = True
    do_pad = True
    size_divisor = 32
    valid_kwargs = BridgeTowerFastImageProcessorKwargs
    model_input_names = ["pixel_values", "pixel_mask"]

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

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

    def resize(
        self,
        image: "torch.Tensor",
        size: SizeDict,
        size_divisor: int = 32,
        interpolation: Optional["F.InterpolationMode"] = None,
        antialias: bool = True,
        **kwargs,
    ) -> "torch.Tensor":
        """
        Resize an image.

        Resizes the shorter side of the image to `size["shortest_edge"]` while preserving the aspect ratio. If the
        longer side is larger than the max size `(int(`size["shortest_edge"]` * 1333 / 800))`, the longer side is then
        resized to the max size while preserving the aspect ratio.

        Args:
            image (`torch.Tensor`):
                Image to resize.
            size (`SizeDict`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            size_divisor (`int`, *optional*, defaults to 32):
                The image is resized to a size that is a multiple of this value.
            resample (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
                `InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.

        Returns:
            `torch.Tensor`: The resized image.
        """
        interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
        if not size.shortest_edge:
            raise ValueError(f"The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}")
        shorter = size.shortest_edge
        longer = int(1333 / 800 * shorter)
        output_height, output_width = get_resize_output_image_size(
            image,
            shorter=shorter,
            longer=longer,
            size_divisor=size_divisor,
        )
        return super().resize(
            image=image,
            size=SizeDict(height=output_height, width=output_width),
            interpolation=interpolation,
            antialias=antialias,
        )

    def center_crop(
        self,
        image: "torch.Tensor",
        size: dict[str, int],
        **kwargs,
    ) -> "torch.Tensor":
        """
        Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along
        any edge, the image is padded with 0's and then center cropped.

        Args:
            image (`torch.Tensor`):
                Image to center crop.
            size (`dict[str, int]`):
                Size of the output image in the form `{"height": h, "width": w}`.
        """
        output_size = size.shortest_edge
        return F.center_crop(
            image,
            output_size=(output_size, output_size),
            **kwargs,
        )

    def _pad_image(
        self,
        image: "torch.Tensor",
        output_size: tuple[int, int],
        constant_values: Union[float, Iterable[float]] = 0,
    ) -> "torch.Tensor":
        """
        Pad an image with zeros to the given size.
        """
        input_height, input_width = image.shape[-2:]
        output_height, output_width = output_size

        pad_bottom = output_height - input_height
        pad_right = output_width - input_width
        padding = (0, 0, pad_right, pad_bottom)
        padded_image = F.pad(
            image,
            padding,
            fill=constant_values,
        )
        return padded_image

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        size_divisor: Optional[int],
        interpolation: Optional["F.InterpolationMode"],
        do_pad: bool,
        do_center_crop: bool,
        crop_size: SizeDict,
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: Optional[Union[float, list[float]]],
        image_std: Optional[Union[float, list[float]]],
        disable_grouping: Optional[bool],
        return_tensors: Optional[Union[str, TensorType]],
        **kwargs,
    ) -> BatchFeature:
        # 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():
            if do_resize:
                stacked_images = self.resize(
                    image=stacked_images, size=size, size_divisor=size_divisor, 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 = {}
        for shape, stacked_images in grouped_images.items():
            if do_center_crop:
                stacked_images = self.center_crop(stacked_images, crop_size)
            # 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_images_index)

        data = {}
        if do_pad:
            processed_images, processed_masks = self.pad(
                processed_images, return_mask=True, disable_grouping=disable_grouping
            )
            processed_masks = torch.stack(processed_masks, dim=0) if return_tensors else processed_masks
            data["pixel_mask"] = processed_masks

        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
        data["pixel_values"] = processed_images

        return BatchFeature(data=data, tensor_type=return_tensors)

    def to_dict(self):
        encoder_dict = super().to_dict()
        encoder_dict.pop("_valid_processor_keys", None)
        encoder_dict.pop("crop_size", None)
        return encoder_dict


__all__ = ["BridgeTowerImageProcessorFast"]