#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
#           This file was automatically generated from src/transformers/models/efficientloftr/modular_efficientloftr.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_efficientloftr.py file directly. One of our CI enforces this.
#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
from typing import TYPE_CHECKING

import numpy as np
from PIL import Image, ImageDraw

from ...image_processing_backends import PilBackend
from ...image_processing_utils import BatchFeature
from ...image_utils import (
    ImageInput,
    ImageType,
    PILImageResampling,
    SizeDict,
    get_image_type,
    is_pil_image,
    is_valid_image,
    to_numpy_array,
)
from ...processing_utils import ImagesKwargs, Unpack
from ...utils import TensorType, auto_docstring, is_torch_available
from ...utils.import_utils import requires


if TYPE_CHECKING:
    from .modeling_efficientloftr import EfficientLoFTRKeypointMatchingOutput
if is_torch_available():
    import torch


class EfficientLoFTRImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    do_grayscale (`bool`, *optional*, defaults to `self.do_grayscale`):
        Whether to convert the image to grayscale. Can be overridden by `do_grayscale` in the `preprocess` method.
    """

    do_grayscale: bool


def is_grayscale(image: np.ndarray):
    if image.shape[0] == 1:
        return True
    return np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...])


def convert_to_grayscale(image: ImageInput) -> ImageInput:
    """
    Converts an image to grayscale format using the NTSC formula. Only support numpy and PIL Image.

    This function is supposed to return a 1-channel image, but it returns a 3-channel image with the same value in each
    channel, because of an issue that is discussed in :
    https://github.com/huggingface/transformers/pull/25786#issuecomment-1730176446

    Args:
        image (Image):
            The image to convert.
    """

    if isinstance(image, np.ndarray):
        if is_grayscale(image):
            return image

        gray_image = image[0, ...] * 0.2989 + image[1, ...] * 0.5870 + image[2, ...] * 0.1140
        gray_image = np.stack([gray_image] * 3, axis=0)
        return gray_image

    if not isinstance(image, Image.Image):
        return image

    image = image.convert("L")
    return image


# Adapted from transformers.models.efficientloftr.image_processing_efficientloftr.validate_and_format_image_pairs
def validate_and_format_image_pairs(images: ImageInput):
    error_message = (
        "Input images must be a one of the following :",
        " - A pair of PIL images.",
        " - A pair of 3D arrays.",
        " - A list of pairs of PIL images.",
        " - A list of pairs of 3D arrays.",
    )

    def _is_valid_image(image):
        """images is a PIL Image or a 3D array."""
        return is_pil_image(image) or (
            is_valid_image(image) and get_image_type(image) != ImageType.PIL and len(image.shape) == 3
        )

    if isinstance(images, list):
        if len(images) == 2 and all((_is_valid_image(image)) for image in images):
            return images
        if all(
            isinstance(image_pair, list)
            and len(image_pair) == 2
            and all(_is_valid_image(image) for image in image_pair)
            for image_pair in images
        ):
            return [image for image_pair in images for image in image_pair]
    raise ValueError(error_message)


@auto_docstring
class EfficientLoFTRImageProcessorPil(PilBackend):
    valid_kwargs = EfficientLoFTRImageProcessorKwargs
    resample = PILImageResampling.BILINEAR
    size = {"height": 480, "width": 640}
    default_to_square = False
    do_resize = True
    do_rescale = True
    rescale_factor = 1 / 255
    do_normalize = None
    do_grayscale = True

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

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

    def _prepare_images_structure(self, images: ImageInput, **kwargs) -> ImageInput:
        # we need to handle image pairs validation and flattening
        images = self.fetch_images(images)
        return validate_and_format_image_pairs(images)

    def _preprocess(
        self,
        images: list[np.ndarray],
        do_resize: bool,
        size: SizeDict,
        resample: PILImageResampling | None,
        do_rescale: bool,
        rescale_factor: float,
        return_tensors: str | TensorType | None,
        do_grayscale: bool = True,
        **kwargs,
    ) -> BatchFeature:
        all_images = []
        for image in images:
            if do_resize:
                image = self.resize(image=image, size=size, resample=resample)

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

            if do_grayscale:
                image = convert_to_grayscale(image)

            all_images.append(image)

        # Convert back the flattened list of images into a list of pairs of images.
        image_pairs = [all_images[i : i + 2] for i in range(0, len(all_images), 2)]

        data = {"pixel_values": image_pairs}

        return BatchFeature(data=data, tensor_type=return_tensors)

    @requires(backends=("torch",))
    def post_process_keypoint_matching(
        self,
        outputs: "EfficientLoFTRKeypointMatchingOutput",
        target_sizes: TensorType | list[tuple],
        threshold: float = 0.0,
    ) -> list[dict[str, "torch.Tensor"]]:
        """
        Converts the raw output of [`EfficientLoFTRKeypointMatchingOutput`] into lists of keypoints, scores and descriptors
        with coordinates absolute to the original image sizes.
        Args:
            outputs ([`EfficientLoFTRKeypointMatchingOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` or `List[Tuple[Tuple[int, int]]]`, *optional*):
                Tensor of shape `(batch_size, 2, 2)` or list of tuples of tuples (`Tuple[int, int]`) containing the
                target size `(height, width)` of each image in the batch. This must be the original image size (before
                any processing).
            threshold (`float`, *optional*, defaults to 0.0):
                Threshold to filter out the matches with low scores.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the keypoints in the first and second image
            of the pair, the matching scores and the matching indices.
        """
        import torch

        if outputs.matches.shape[0] != len(target_sizes):
            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the mask")
        if not all(len(target_size) == 2 for target_size in target_sizes):
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        if isinstance(target_sizes, list):
            image_pair_sizes = torch.tensor(target_sizes, device=outputs.matches.device)
        else:
            if target_sizes.shape[1] != 2 or target_sizes.shape[2] != 2:
                raise ValueError(
                    "Each element of target_sizes must contain the size (h, w) of each image of the batch"
                )
            image_pair_sizes = target_sizes

        keypoints = outputs.keypoints.clone()
        keypoints = keypoints * image_pair_sizes.flip(-1).reshape(-1, 2, 1, 2)
        keypoints = keypoints.to(torch.int32)

        results = []
        for keypoints_pair, matches, scores in zip(keypoints, outputs.matches, outputs.matching_scores):
            # Filter out matches with low scores
            valid_matches = torch.logical_and(scores > threshold, matches > -1)

            matched_keypoints0 = keypoints_pair[0][valid_matches[0]]
            matched_keypoints1 = keypoints_pair[1][valid_matches[1]]
            matching_scores = scores[0][valid_matches[0]]

            results.append(
                {
                    "keypoints0": matched_keypoints0,
                    "keypoints1": matched_keypoints1,
                    "matching_scores": matching_scores,
                }
            )

        return results

    def visualize_keypoint_matching(
        self, images: ImageInput, keypoint_matching_output: list[dict[str, "torch.Tensor"]]
    ) -> list["Image.Image"]:
        """
        Plots the image pairs side by side with the detected keypoints as well as the matching between them.

        Args:
            images (`ImageInput`):
                Image pairs to plot. Same as `EfficientLoFTRImageProcessor.preprocess`. Expects either a list of 2
                images or a list of list of 2 images list with pixel values ranging from 0 to 255.
            keypoint_matching_output (List[Dict[str, torch.Tensor]]]):
                A post processed keypoint matching output

        Returns:
            `List[PIL.Image.Image]`: A list of PIL images, each containing the image pairs side by side with the detected
            keypoints as well as the matching between them.
        """
        images = validate_and_format_image_pairs(images)
        images = [to_numpy_array(image) for image in images]
        image_pairs = [images[i : i + 2] for i in range(0, len(images), 2)]

        results = []
        for image_pair, pair_output in zip(image_pairs, keypoint_matching_output):
            height0, width0 = image_pair[0].shape[:2]
            height1, width1 = image_pair[1].shape[:2]
            plot_image = np.zeros((max(height0, height1), width0 + width1, 3), dtype=np.uint8)
            plot_image[:height0, :width0] = image_pair[0]
            plot_image[:height1, width0:] = image_pair[1]

            plot_image_pil = Image.fromarray(plot_image)
            draw = ImageDraw.Draw(plot_image_pil)

            keypoints0_x, keypoints0_y = pair_output["keypoints0"].unbind(1)
            keypoints1_x, keypoints1_y = pair_output["keypoints1"].unbind(1)
            for keypoint0_x, keypoint0_y, keypoint1_x, keypoint1_y, matching_score in zip(
                keypoints0_x, keypoints0_y, keypoints1_x, keypoints1_y, pair_output["matching_scores"]
            ):
                color = self._get_color(matching_score)
                draw.line((keypoint0_x, keypoint0_y, keypoint1_x + width0, keypoint1_y), fill=color, width=3)
                draw.ellipse((keypoint0_x - 2, keypoint0_y - 2, keypoint0_x + 2, keypoint0_y + 2), fill="black")
                draw.ellipse(
                    (keypoint1_x + width0 - 2, keypoint1_y - 2, keypoint1_x + width0 + 2, keypoint1_y + 2),
                    fill="black",
                )

            results.append(plot_image_pil)
        return results

    def _get_color(self, score):
        """Maps a score to a color."""
        r = int(255 * (1 - score))
        g = int(255 * score)
        b = 0
        return (r, g, b)


__all__ = ["EfficientLoFTRImageProcessorPil"]
