# Copyright 2025 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.

from typing import Union

import numpy as np
import torch
import torchvision.transforms.v2.functional as tvF

from transformers.models.beit.image_processing_beit import BeitImageProcessor
from transformers.models.beit.image_processing_pil_beit import BeitImageProcessorPil

from ...image_processing_utils import BatchFeature
from ...image_transforms import group_images_by_shape, reorder_images
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    SizeDict,
)
from ...processing_utils import ImagesKwargs
from ...utils import TensorType
from ...utils.import_utils import requires


class SegformerImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    do_reduce_labels (`bool`, *optional*, defaults to `self.do_reduce_labels`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0
        is used for background, and background itself is not included in all classes of a dataset (e.g.
        ADE20k). The background label will be replaced by 255.
    """

    do_reduce_labels: bool


class SegformerImageProcessor(BeitImageProcessor):
    resample = PILImageResampling.BILINEAR
    image_mean = IMAGENET_DEFAULT_MEAN
    image_std = IMAGENET_DEFAULT_STD
    size = {"height": 512, "width": 512}
    do_resize = True
    do_rescale = True
    rescale_factor = 1 / 255
    do_normalize = True
    do_reduce_labels = False
    do_center_crop = None
    crop_size = None

    def _preprocess_image_like_inputs(
        self,
        images: ImageInput,
        segmentation_maps: ImageInput | None,
        do_convert_rgb: bool,
        input_data_format: ChannelDimension,
        return_tensors: str | TensorType | None,
        device: Union[str, "torch.device"] | None = None,
        **kwargs,
    ) -> BatchFeature:
        """Handle extra inputs beyond images."""
        images = self._prepare_image_like_inputs(
            images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
        )
        images_kwargs = kwargs.copy()
        images_kwargs["do_reduce_labels"] = False
        data = {}
        data["pixel_values"] = self._preprocess(images, **images_kwargs)

        # Prepare segmentation maps if provided
        if segmentation_maps is not None:
            processed_segmentation_maps = self._prepare_image_like_inputs(
                images=segmentation_maps,
                expected_ndims=2,
                do_convert_rgb=False,
                input_data_format=ChannelDimension.FIRST,
            )

            segmentation_maps_kwargs = kwargs.copy()
            segmentation_maps_kwargs.update(
                {
                    "do_normalize": False,
                    "do_rescale": False,
                    # Nearest resample is used for segmentation maps instead of BILINEAR.
                    "resample": tvF.InterpolationMode.NEAREST_EXACT,
                }
            )
            processed_segmentation_maps = self._preprocess(
                images=processed_segmentation_maps, **segmentation_maps_kwargs
            )

            # Convert to int64 and squeeze channel dimension
            processed_segmentation_maps = [
                processed_segmentation_map.squeeze(0).to(torch.int64)
                for processed_segmentation_map in processed_segmentation_maps
            ]
            data["labels"] = processed_segmentation_maps

        return BatchFeature(data=data, tensor_type=return_tensors)

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_reduce_labels: bool,
        resample: "PILImageResampling | None",
        do_resize: bool,
        do_rescale: bool,
        do_normalize: bool,
        size: SizeDict,
        rescale_factor: float,
        image_mean: float | list[float],
        image_std: float | list[float],
        disable_grouping: bool,
        **kwargs,
    ) -> BatchFeature:  # Return type can be list if return_tensors=None
        if do_reduce_labels:
            images = self.reduce_label(images)  # Apply reduction if needed

        # Group images by size for batched resizing
        resized_images = images
        if do_resize:
            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():
                resized_stacked_images = self.resize(image=stacked_images, size=size, resample=resample)
                resized_images_grouped[shape] = resized_stacked_images
            resized_images = reorder_images(resized_images_grouped, grouped_images_index)

        # Group images by size for further processing (rescale/normalize)
        # 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():
            # 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)

        return processed_images


@requires(backends=("torch", "torchvision"))
class SegformerImageProcessorPil(BeitImageProcessorPil):
    resample = PILImageResampling.BILINEAR
    image_mean = IMAGENET_DEFAULT_MEAN
    image_std = IMAGENET_DEFAULT_STD
    size = {"height": 512, "width": 512}
    do_resize = True
    do_rescale = True
    rescale_factor = 1 / 255
    do_normalize = True
    do_reduce_labels = False
    do_center_crop = None
    crop_size = None

    def _preprocess_image_like_inputs(
        self,
        images: ImageInput,
        segmentation_maps: ImageInput | None,
        do_convert_rgb: bool,
        input_data_format: ChannelDimension,
        return_tensors: str | TensorType | None,
        **kwargs,
    ) -> BatchFeature:
        """Handle extra inputs beyond images."""
        images = self._prepare_image_like_inputs(
            images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format
        )
        images_kwargs = kwargs.copy()
        images_kwargs["do_reduce_labels"] = False
        data = {}
        data["pixel_values"] = self._preprocess(images, **images_kwargs)

        # Prepare segmentation maps if provided
        if segmentation_maps is not None:
            processed_segmentation_maps = self._prepare_image_like_inputs(
                images=segmentation_maps,
                expected_ndims=2,
                do_convert_rgb=False,
                input_data_format=ChannelDimension.FIRST,
            )

            segmentation_maps_kwargs = kwargs.copy()
            segmentation_maps_kwargs.update(
                {
                    "do_normalize": False,
                    "do_rescale": False,
                    # Nearest resample is used for segmentation maps instead of BILINEAR.
                    "resample": tvF.InterpolationMode.NEAREST_EXACT,
                }
            )
            processed_segmentation_maps = self._preprocess(
                images=processed_segmentation_maps, **segmentation_maps_kwargs
            )

            # Convert to int64 and squeeze channel dimension
            processed_segmentation_maps = [
                processed_segmentation_map.squeeze(0).astype(np.int64)
                for processed_segmentation_map in processed_segmentation_maps
            ]
            data["labels"] = processed_segmentation_maps

        return BatchFeature(data=data, tensor_type=return_tensors)

    def _preprocess(
        self,
        images: list["np.ndarray"],
        do_reduce_labels: bool,
        resample: "PILImageResampling | None",
        do_resize: bool,
        do_rescale: bool,
        do_normalize: bool,
        size: SizeDict,
        rescale_factor: float,
        image_mean: float | list[float],
        image_std: float | list[float],
        **kwargs,
    ) -> list["np.ndarray"]:
        """Custom preprocessing for Segformer."""
        processed_images = []
        for image in images:
            if do_reduce_labels:
                image = self.reduce_label(image)
            if do_resize:
                image = self.resize(image, size, resample)
            if do_rescale:
                image = self.rescale(image, rescale_factor)
            if do_normalize:
                image = self.normalize(image, image_mean, image_std)
            processed_images.append(image)

        return processed_images


__all__ = ["SegformerImageProcessor", "SegformerImageProcessorPil"]
