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# coding=utf-8
# 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.

import warnings
from typing import TYPE_CHECKING, Optional, Union

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

from ...image_processing_utils_fast import BaseImageProcessorFast, BatchFeature
from ...image_transforms import center_to_corners_format, group_images_by_shape, reorder_images
from ...image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD, ChannelDimension, PILImageResampling, SizeDict
from ...utils import TensorType, auto_docstring
from .image_processing_owlv2 import _scale_boxes, box_iou


if TYPE_CHECKING:
    from .modeling_owlv2 import Owlv2ObjectDetectionOutput


@auto_docstring
class Owlv2ImageProcessorFast(BaseImageProcessorFast):
    resample = PILImageResampling.BILINEAR
    image_mean = OPENAI_CLIP_MEAN
    image_std = OPENAI_CLIP_STD
    size = {"height": 960, "width": 960}
    default_to_square = True
    crop_size = None
    do_resize = True
    do_center_crop = None
    do_rescale = True
    do_normalize = True
    do_convert_rgb = None
    model_input_names = ["pixel_values"]
    rescale_factor = 1 / 255
    do_pad = True

    def post_process(self, outputs, target_sizes):
        """
        Converts the raw output of [`Owlv2ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format.

        Args:
            outputs ([`Owlv2ObjectDetectionOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
                Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
                image size (before any data augmentation). For visualization, this should be the image size after data
                augment, but before padding.
        Returns:
            `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model.
        """
        warnings.warn(
            "`post_process` is deprecated and will be removed in v5 of Transformers, please use"
            " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.",
            FutureWarning,
        )

        logits, boxes = outputs.logits, outputs.pred_boxes

        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 target_sizes.shape[1] != 2:
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        probs = torch.max(logits, dim=-1)
        scores = torch.sigmoid(probs.values)
        labels = probs.indices

        # Convert to [x0, y0, x1, y1] format
        boxes = center_to_corners_format(boxes)

        # Convert from relative [0, 1] to absolute [0, height] coordinates
        img_h, img_w = target_sizes.unbind(1)
        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device)
        boxes = boxes * scale_fct[:, None, :]

        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]

        return results

    def post_process_object_detection(
        self,
        outputs: "Owlv2ObjectDetectionOutput",
        threshold: float = 0.1,
        target_sizes: Optional[Union[TensorType, list[tuple]]] = None,
    ):
        """
        Converts the raw output of [`Owlv2ForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
        bottom_right_x, bottom_right_y) format.

        Args:
            outputs ([`Owlv2ObjectDetectionOutput`]):
                Raw outputs of the model.
            threshold (`float`, *optional*, defaults to 0.1):
                Score threshold to keep object detection predictions.
            target_sizes (`torch.Tensor` or `list[tuple[int, int]]`, *optional*):
                Tensor of shape `(batch_size, 2)` or list of tuples (`tuple[int, int]`) containing the target size
                `(height, width)` of each image in the batch. If unset, predictions will not be resized.

        Returns:
            `list[Dict]`: A list of dictionaries, each dictionary containing the following keys:
            - "scores": The confidence scores for each predicted box on the image.
            - "labels": Indexes of the classes predicted by the model on the image.
            - "boxes": Image bounding boxes in (top_left_x, top_left_y, bottom_right_x, bottom_right_y) format.
        """
        batch_logits, batch_boxes = outputs.logits, outputs.pred_boxes
        batch_size = len(batch_logits)

        if target_sizes is not None and len(target_sizes) != batch_size:
            raise ValueError("Make sure that you pass in as many target sizes as images")

        # batch_logits of shape (batch_size, num_queries, num_classes)
        batch_class_logits = torch.max(batch_logits, dim=-1)
        batch_scores = torch.sigmoid(batch_class_logits.values)
        batch_labels = batch_class_logits.indices

        # Convert to [x0, y0, x1, y1] format
        batch_boxes = center_to_corners_format(batch_boxes)

        # Convert from relative [0, 1] to absolute [0, height] coordinates
        if target_sizes is not None:
            batch_boxes = _scale_boxes(batch_boxes, target_sizes)

        results = []
        for scores, labels, boxes in zip(batch_scores, batch_labels, batch_boxes):
            keep = scores > threshold
            scores = scores[keep]
            labels = labels[keep]
            boxes = boxes[keep]
            results.append({"scores": scores, "labels": labels, "boxes": boxes})

        return results

    def post_process_image_guided_detection(self, outputs, threshold=0.0, nms_threshold=0.3, target_sizes=None):
        """
        Converts the output of [`Owlv2ForObjectDetection.image_guided_detection`] into the format expected by the COCO
        api.

        Args:
            outputs ([`Owlv2ImageGuidedObjectDetectionOutput`]):
                Raw outputs of the model.
            threshold (`float`, *optional*, defaults to 0.0):
                Minimum confidence threshold to use to filter out predicted boxes.
            nms_threshold (`float`, *optional*, defaults to 0.3):
                IoU threshold for non-maximum suppression of overlapping boxes.
            target_sizes (`torch.Tensor`, *optional*):
                Tensor of shape (batch_size, 2) where each entry is the (height, width) of the corresponding image in
                the batch. If set, predicted normalized bounding boxes are rescaled to the target sizes. If left to
                None, predictions will not be unnormalized.

        Returns:
            `list[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model. All labels are set to None as
            `Owlv2ForObjectDetection.image_guided_detection` perform one-shot object detection.
        """
        logits, target_boxes = outputs.logits, outputs.target_pred_boxes

        if target_sizes is not None and 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 target_sizes is not None and target_sizes.shape[1] != 2:
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")

        probs = torch.max(logits, dim=-1)
        scores = torch.sigmoid(probs.values)

        # Convert to [x0, y0, x1, y1] format
        target_boxes = center_to_corners_format(target_boxes)

        # Apply non-maximum suppression (NMS)
        if nms_threshold < 1.0:
            for idx in range(target_boxes.shape[0]):
                for i in torch.argsort(-scores[idx]):
                    if not scores[idx][i]:
                        continue

                    ious = box_iou(target_boxes[idx][i, :].unsqueeze(0), target_boxes[idx])[0][0]
                    ious[i] = -1.0  # Mask self-IoU.
                    scores[idx][ious > nms_threshold] = 0.0

        # Convert from relative [0, 1] to absolute [0, height] coordinates
        if target_sizes is not None:
            target_boxes = _scale_boxes(target_boxes, target_sizes)

        # Compute box display alphas based on prediction scores
        results = []
        alphas = torch.zeros_like(scores)

        for idx in range(target_boxes.shape[0]):
            # Select scores for boxes matching the current query:
            query_scores = scores[idx]
            if not query_scores.nonzero().numel():
                continue

            # Apply threshold on scores before scaling
            query_scores[query_scores < threshold] = 0.0

            # Scale box alpha such that the best box for each query has alpha 1.0 and the worst box has alpha 0.1.
            # All other boxes will either belong to a different query, or will not be shown.
            max_score = torch.max(query_scores) + 1e-6
            query_alphas = (query_scores - (max_score * 0.1)) / (max_score * 0.9)
            query_alphas = torch.clip(query_alphas, 0.0, 1.0)
            alphas[idx] = query_alphas

            mask = alphas[idx] > 0
            box_scores = alphas[idx][mask]
            boxes = target_boxes[idx][mask]
            results.append({"scores": box_scores, "labels": None, "boxes": boxes})

        return results

    def _pad_images(self, images: "torch.Tensor", constant_value: float = 0.5) -> "torch.Tensor":
        """
        Pad an image with zeros to the given size.
        """
        height, width = images.shape[-2:]
        size = max(height, width)
        pad_bottom = size - height
        pad_right = size - width

        padding = (0, 0, pad_right, pad_bottom)
        padded_image = F.pad(images, padding, fill=constant_value)
        return padded_image

    def pad(
        self,
        images: list["torch.Tensor"],
        disable_grouping: Optional[bool],
        constant_value: float = 0.5,
        **kwargs,
    ) -> list["torch.Tensor"]:
        """
        Unlike the Base class `self.pad` where all images are padded to the maximum image size,
        Owlv2 pads an image to square.
        """
        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
        processed_images_grouped = {}
        for shape, stacked_images in grouped_images.items():
            stacked_images = self._pad_images(
                stacked_images,
                constant_value=constant_value,
            )
            processed_images_grouped[shape] = stacked_images

        processed_images = reorder_images(processed_images_grouped, grouped_images_index)

        return processed_images

    def resize(
        self,
        image: "torch.Tensor",
        size: SizeDict,
        anti_aliasing: bool = True,
        anti_aliasing_sigma=None,
        **kwargs,
    ) -> "torch.Tensor":
        """
        Resize an image as per the original implementation.

        Args:
            image (`Tensor`):
                Image to resize.
            size (`dict[str, int]`):
                Dictionary containing the height and width to resize the image to.
            anti_aliasing (`bool`, *optional*, defaults to `True`):
                Whether to apply anti-aliasing when downsampling the image.
            anti_aliasing_sigma (`float`, *optional*, defaults to `None`):
                Standard deviation for Gaussian kernel when downsampling the image. If `None`, it will be calculated
                automatically.
        """
        output_shape = (size.height, size.width)

        input_shape = image.shape

        # select height and width from input tensor
        factors = torch.tensor(input_shape[2:]).to(image.device) / torch.tensor(output_shape).to(image.device)

        if anti_aliasing:
            if anti_aliasing_sigma is None:
                anti_aliasing_sigma = ((factors - 1) / 2).clamp(min=0)
            else:
                anti_aliasing_sigma = torch.atleast_1d(anti_aliasing_sigma) * torch.ones_like(factors)
                if torch.any(anti_aliasing_sigma < 0):
                    raise ValueError("Anti-aliasing standard deviation must be greater than or equal to zero")
                elif torch.any((anti_aliasing_sigma > 0) & (factors <= 1)):
                    warnings.warn(
                        "Anti-aliasing standard deviation greater than zero but not down-sampling along all axes"
                    )
            if torch.any(anti_aliasing_sigma == 0):
                filtered = image
            else:
                kernel_sizes = 2 * torch.ceil(3 * anti_aliasing_sigma).int() + 1

                filtered = F.gaussian_blur(
                    image, (kernel_sizes[0], kernel_sizes[1]), sigma=anti_aliasing_sigma.tolist()
                )

        else:
            filtered = image

        out = F.resize(filtered, size=(size.height, size.width), antialias=False)

        return out

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        interpolation: Optional["F.InterpolationMode"],
        do_pad: bool,
        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)
        processed_images_grouped = {}

        for shape, stacked_images in grouped_images.items():
            # Rescale images before other operations as done in original implementation
            stacked_images = self.rescale_and_normalize(
                stacked_images, do_rescale, rescale_factor, False, image_mean, image_std
            )
            processed_images_grouped[shape] = stacked_images

        processed_images = reorder_images(processed_images_grouped, grouped_images_index)

        if do_pad:
            processed_images = self.pad(processed_images, constant_value=0.5, disable_grouping=disable_grouping)

        grouped_images, grouped_images_index = group_images_by_shape(
            processed_images, disable_grouping=disable_grouping
        )
        resized_images_grouped = {}
        for shape, stacked_images in grouped_images.items():
            if do_resize:
                resized_stack = self.resize(
                    image=stacked_images,
                    size=size,
                    interpolation=interpolation,
                    input_data_format=ChannelDimension.FIRST,
                )
                resized_images_grouped[shape] = resized_stack
        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():
            # Fused rescale and normalize
            stacked_images = self.rescale_and_normalize(
                stacked_images, False, rescale_factor, do_normalize, image_mean, image_std
            )
            processed_images_grouped[shape] = stacked_images

        processed_images = reorder_images(processed_images_grouped, grouped_images_index)

        processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images

        return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)


__all__ = ["Owlv2ImageProcessorFast"]
