# Copyright 2022 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.
"""Image processor class for Segformer."""

import numpy as np

from ...image_processing_utils import INIT_SERVICE_KWARGS, BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    infer_channel_dimension_format,
    is_scaled_image,
    make_flat_list_of_images,
    to_numpy_array,
    valid_images,
    validate_preprocess_arguments,
)
from ...processing_utils import ImagesKwargs
from ...utils import (
    TensorType,
    filter_out_non_signature_kwargs,
    is_torch_available,
    is_torch_tensor,
    is_vision_available,
    logging,
)
from ...utils.import_utils import requires


if is_vision_available():
    import PIL.Image

if is_torch_available():
    import torch


logger = logging.get_logger(__name__)


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


@requires(backends=("vision",))
class SegformerImageProcessor(BaseImageProcessor):
    r"""
    Constructs a Segformer image processor.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions to the specified `(size["height"],
            size["width"])`. Can be overridden by the `do_resize` parameter in the `preprocess` method.
        size (`dict[str, int]` *optional*, defaults to `{"height": 512, "width": 512}`):
            Size of the output image after resizing. Can be overridden by the `size` parameter in the `preprocess`
            method.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
            Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in the
            `preprocess` method.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
            parameter in the `preprocess` method.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
            method.
        image_mean (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
            Mean to use if normalizing the image. This is a float or list of floats the length of the number of
            channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
        image_std (`float` or `list[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
            Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
            number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
        do_reduce_labels (`bool`, *optional*, defaults to `False`):
            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. Can be overridden by the `do_reduce_labels` parameter in the
            `preprocess` method.
    """

    model_input_names = ["pixel_values"]
    valid_kwargs = SegformerImageProcessorKwargs

    @filter_out_non_signature_kwargs(extra=INIT_SERVICE_KWARGS)
    def __init__(
        self,
        do_resize: bool = True,
        size: dict[str, int] | None = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_rescale: bool = True,
        rescale_factor: int | float = 1 / 255,
        do_normalize: bool = True,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        do_reduce_labels: bool = False,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        size = size if size is not None else {"height": 512, "width": 512}
        size = get_size_dict(size)
        self.do_resize = do_resize
        self.size = size
        self.resample = resample
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD
        self.do_reduce_labels = do_reduce_labels

    # Copied from transformers.models.vit.image_processing_vit.ViTImageProcessor.resize
    def resize(
        self,
        image: np.ndarray,
        size: dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: str | ChannelDimension | None = None,
        input_data_format: str | ChannelDimension | None = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        Returns:
            `np.ndarray`: The resized image.
        """
        size = get_size_dict(size)
        if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}")
        output_size = (size["height"], size["width"])
        return resize(
            image,
            size=output_size,
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )

    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.reduce_label
    def reduce_label(self, label: ImageInput) -> np.ndarray:
        label = to_numpy_array(label)
        # Avoid using underflow conversion
        label[label == 0] = 255
        label = label - 1
        label[label == 254] = 255
        return label

    def _preprocess(
        self,
        image: ImageInput,
        do_reduce_labels: bool,
        do_resize: bool,
        do_rescale: bool,
        do_normalize: bool,
        size: dict[str, int] | None = None,
        resample: PILImageResampling | None = None,
        rescale_factor: float | None = None,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        input_data_format: str | ChannelDimension | None = None,
    ):
        if do_reduce_labels:
            image = self.reduce_label(image)

        if do_resize:
            image = self.resize(image=image, size=size, resample=resample, input_data_format=input_data_format)

        if do_rescale:
            image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)

        if do_normalize:
            image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)

        return image

    def _preprocess_image(
        self,
        image: ImageInput,
        do_resize: bool | None = None,
        size: dict[str, int] | None = None,
        resample: PILImageResampling | None = None,
        do_rescale: bool | None = None,
        rescale_factor: float | None = None,
        do_normalize: bool | None = None,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        data_format: str | ChannelDimension | None = None,
        input_data_format: str | ChannelDimension | None = None,
    ) -> np.ndarray:
        """Preprocesses a single image."""
        # All transformations expect numpy arrays.
        image = to_numpy_array(image)
        if do_rescale and is_scaled_image(image):
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )
        if input_data_format is None:
            input_data_format = infer_channel_dimension_format(image)
        image = self._preprocess(
            image=image,
            do_reduce_labels=False,
            do_resize=do_resize,
            size=size,
            resample=resample,
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            input_data_format=input_data_format,
        )
        if data_format is not None:
            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
        return image

    def _preprocess_mask(
        self,
        segmentation_map: ImageInput,
        do_reduce_labels: bool | None = None,
        do_resize: bool | None = None,
        size: dict[str, int] | None = None,
        input_data_format: str | ChannelDimension | None = None,
    ) -> np.ndarray:
        """Preprocesses a single mask."""
        segmentation_map = to_numpy_array(segmentation_map)
        # Add channel dimension if missing - needed for certain transformations
        if segmentation_map.ndim == 2:
            added_channel_dim = True
            segmentation_map = segmentation_map[None, ...]
            input_data_format = ChannelDimension.FIRST
        else:
            added_channel_dim = False
            if input_data_format is None:
                input_data_format = infer_channel_dimension_format(segmentation_map, num_channels=1)
        # reduce zero label if needed
        segmentation_map = self._preprocess(
            image=segmentation_map,
            do_reduce_labels=do_reduce_labels,
            do_resize=do_resize,
            resample=PILImageResampling.NEAREST,
            size=size,
            do_rescale=False,
            do_normalize=False,
            input_data_format=input_data_format,
        )
        # Remove extra channel dimension if added for processing
        if added_channel_dim:
            segmentation_map = segmentation_map.squeeze(0)
        segmentation_map = segmentation_map.astype(np.int64)
        return segmentation_map

    def __call__(self, images, segmentation_maps=None, **kwargs):
        """
        Preprocesses a batch of images and optionally segmentation maps.

        Overrides the `__call__` method of the `Preprocessor` class so that both images and segmentation maps can be
        passed in as positional arguments.
        """
        return super().__call__(images, segmentation_maps=segmentation_maps, **kwargs)

    @filter_out_non_signature_kwargs()
    def preprocess(
        self,
        images: ImageInput,
        segmentation_maps: ImageInput | None = None,
        do_resize: bool | None = None,
        size: dict[str, int] | None = None,
        resample: PILImageResampling | None = None,
        do_rescale: bool | None = None,
        rescale_factor: float | None = None,
        do_normalize: bool | None = None,
        image_mean: float | list[float] | None = None,
        image_std: float | list[float] | None = None,
        do_reduce_labels: bool | None = None,
        return_tensors: str | TensorType | None = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: str | ChannelDimension | None = None,
    ) -> PIL.Image.Image:
        """
        Preprocess an image or batch of images.

        Args:
            images (`ImageInput`):
                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
            segmentation_maps (`ImageInput`, *optional*):
                Segmentation map to preprocess.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`dict[str, int]`, *optional*, defaults to `self.size`):
                Size of the image after `resize` is applied.
            resample (`int`, *optional*, defaults to `self.resample`):
                Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only
                has an effect if `do_resize` is set to `True`.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image values between [0 - 1].
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
                Image mean.
            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation.
            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.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                    - Unset: Return a list of `np.ndarray`.
                    - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                    - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                    - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                    - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        do_reduce_labels = do_reduce_labels if do_reduce_labels is not None else self.do_reduce_labels
        resample = resample if resample is not None else self.resample
        size = size if size is not None else self.size
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std

        images = make_flat_list_of_images(images)

        if segmentation_maps is not None:
            segmentation_maps = make_flat_list_of_images(segmentation_maps, expected_ndims=2)

        if not valid_images(images):
            raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor")
        validate_preprocess_arguments(
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            do_resize=do_resize,
            size=size,
            resample=resample,
        )

        images = [
            self._preprocess_image(
                image=img,
                do_resize=do_resize,
                resample=resample,
                size=size,
                do_rescale=do_rescale,
                rescale_factor=rescale_factor,
                do_normalize=do_normalize,
                image_mean=image_mean,
                image_std=image_std,
                data_format=data_format,
                input_data_format=input_data_format,
            )
            for img in images
        ]

        data = {"pixel_values": images}

        if segmentation_maps is not None:
            segmentation_maps = [
                self._preprocess_mask(
                    segmentation_map=segmentation_map,
                    do_reduce_labels=do_reduce_labels,
                    do_resize=do_resize,
                    size=size,
                    input_data_format=input_data_format,
                )
                for segmentation_map in segmentation_maps
            ]
            data["labels"] = segmentation_maps

        return BatchFeature(data=data, tensor_type=return_tensors)

    # Copied from transformers.models.beit.image_processing_beit.BeitImageProcessor.post_process_semantic_segmentation with Beit->Segformer
    def post_process_semantic_segmentation(self, outputs, target_sizes: list[tuple] | None = None):
        """
        Converts the output of [`SegformerForSemanticSegmentation`] into semantic segmentation maps.

        Args:
            outputs ([`SegformerForSemanticSegmentation`]):
                Raw outputs of the model.
            target_sizes (`list[Tuple]` of length `batch_size`, *optional*):
                List of tuples corresponding to the requested final size (height, width) of each prediction. If unset,
                predictions will not be resized.

        Returns:
            semantic_segmentation: `list[torch.Tensor]` of length `batch_size`, where each item is a semantic
            segmentation map of shape (height, width) corresponding to the target_sizes entry (if `target_sizes` is
            specified). Each entry of each `torch.Tensor` correspond to a semantic class id.
        """
        logits = outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
            if len(logits) != len(target_sizes):
                raise ValueError(
                    "Make sure that you pass in as many target sizes as the batch dimension of the logits"
                )

            if is_torch_tensor(target_sizes):
                target_sizes = target_sizes.numpy()

            semantic_segmentation = []

            for idx in range(len(logits)):
                resized_logits = torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False
                )
                semantic_map = resized_logits[0].argmax(dim=0)
                semantic_segmentation.append(semantic_map)
        else:
            semantic_segmentation = logits.argmax(dim=1)
            semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

        return semantic_segmentation


__all__ = ["SegformerImageProcessor"]
