# Copyright (c) 2024 PaddlePaddle Authors. 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 Any, List, Optional, Tuple, Union

import numpy as np
from PIL import Image

from ..object_detection.predictor import DetRunnerPredictor, DetTransformersPredictor
from ..object_detection.processors import Resize, ToBatch
from .processors import LayoutAnalysisProcess
from .result import LayoutAnalysisResult
from .utils import STATIC_SHAPE_MODEL_LIST

LAYOUT_ANALYSIS_TRANSFORMERS_MODELS = ["PP-DocLayoutV2", "PP-DocLayoutV3"]


class LayoutAnalysisRunnerPredictor(DetRunnerPredictor):
    """Layout analysis predictor."""

    def __init__(
        self,
        *args,
        img_size: Optional[Union[int, Tuple[int, int]]] = None,
        **kwargs,
    ):
        """Initializes LayoutAnalysisPredictor.
        Args:
            *args: Arbitrary positional arguments passed to the superclass.
            img_size (Optional[Union[int, Tuple[int, int]]], optional): The input image size (w, h). Defaults to None.
            threshold (Optional[float], optional): The threshold for filtering out low-confidence predictions.
                Defaults to None.
            layout_nms (bool, optional): Whether to use layout-aware NMS. Defaults to False.
            layout_unclip_ratio (Optional[Union[float, Tuple[float, float]]], optional): The ratio of unclipping the bounding box.
                Defaults to None.
                If it's a single number, then both width and height are used.
                If it's a tuple of two numbers, then they are used separately for width and height respectively.
                If it's None, then no unclipping will be performed.
            layout_merge_bboxes_mode (Optional[Union[str, dict]], optional): The mode for merging bounding boxes. Defaults to None.
            **kwargs: Arbitrary keyword arguments passed to the superclass.
        """
        if img_size is not None:
            assert (
                self.model_name not in STATIC_SHAPE_MODEL_LIST
            ), f"The model {self.model_name} is not supported set input shape"
            if isinstance(img_size, int):
                img_size = (img_size, img_size)
            elif isinstance(img_size, (tuple, list)):
                assert len(img_size) == 2, f"The length of `img_size` should be 2."
            else:
                raise ValueError(
                    f"The type of `img_size` must be int or Tuple[int, int], but got {type(img_size)}."
                )
        super().__init__(*args, **kwargs)

    def _get_result_class(self):
        return LayoutAnalysisResult

    def process(
        self,
        batch_data: List[Any],
        threshold: Optional[Union[float, dict]] = None,
        layout_nms: bool = False,
        layout_unclip_ratio: Optional[Union[float, Tuple[float, float], dict]] = None,
        layout_merge_bboxes_mode: Optional[Union[str, dict]] = None,
        layout_shape_mode: Optional[str] = "auto",
        filter_overlap_boxes: Optional[bool] = True,
        skip_order_labels: Optional[List[str]] = None,
    ):
        """
        Process a batch of data through the preprocessing, inference, and postprocessing.

        Args:
            batch_data (List[Union[str, np.ndarray], ...]): A batch of input data (e.g., image file paths).
            threshold (Optional[float, dict], optional): The threshold for filtering out low-confidence predictions.
            layout_nms (bool, optional): Whether to use layout-aware NMS. Defaults to None.
            layout_unclip_ratio (Optional[Union[float, Tuple[float, float]]], optional): The ratio of unclipping the bounding box.
            layout_merge_bboxes_mode (Optional[Union[str, dict]], optional): The mode for merging bounding boxes. Defaults to None.
            layout_shape_mode (Optional[str], optional): The mode for layout shape. Defaults to "auto", [ "rect", "quad","poly", "auto"]. are supported.
            filter_overlap_boxes (Optional[bool], optional): Whether to filter out overlap boxes. Defaults to True.
            skip_order_labels (Optional[List[str]], optional): The labels to skip order. Defaults to None.

        Returns:
            dict: A dictionary containing the input path, raw image, class IDs, scores, and label names
                for every instance of the batch. Keys include 'input_path', 'input_img', 'class_ids', 'scores', and 'label_names'.
        """
        datas = batch_data.instances
        # preprocess
        for pre_op in self.pre_ops[:-1]:
            datas = pre_op(datas)

        # use `ToBatch` format batch inputs
        batch_inputs = self.pre_ops[-1](datas)

        # do infer
        batch_preds = self.runner(batch_inputs)

        # process a batch of predictions into a list of single image result
        preds_list = self._format_output(batch_preds)
        # postprocess
        boxes = self.post_op(
            preds_list,
            datas,
            threshold=threshold if threshold is not None else self.threshold,
            layout_nms=layout_nms or self.layout_nms,
            layout_unclip_ratio=layout_unclip_ratio or self.layout_unclip_ratio,
            layout_merge_bboxes_mode=layout_merge_bboxes_mode
            or self.layout_merge_bboxes_mode,
            layout_shape_mode=layout_shape_mode,
            filter_overlap_boxes=filter_overlap_boxes,
            skip_order_labels=skip_order_labels,
        )

        return {
            "input_path": batch_data.input_paths,
            "page_index": batch_data.page_indexes,
            "input_img": [data["ori_img"] for data in datas],
            "boxes": boxes,
        }

    @DetRunnerPredictor.register("Resize")
    def build_resize(self, target_size, keep_ratio=False, interp=2):
        assert target_size
        self.target_size = target_size
        if isinstance(interp, int):
            interp = {
                0: "NEAREST",
                1: "LINEAR",
                2: "BICUBIC",
                3: "AREA",
                4: "LANCZOS4",
            }[interp]
        op = Resize(target_size=target_size[::-1], keep_ratio=keep_ratio, interp=interp)
        return op

    def build_to_batch(self):
        models_required_imgsize = [
            "PP-DocLayoutV2",
            "PP-DocLayoutV3",
        ]
        if any(name in self.model_name for name in models_required_imgsize):
            ordered_required_keys = (
                "img_size",
                "img",
                "scale_factors",
            )
        else:
            ordered_required_keys = ("img", "scale_factors")

        return ToBatch(ordered_required_keys=ordered_required_keys)

    def build_postprocess(self):
        if self.threshold is None:
            self.threshold = self.config.get("draw_threshold", 0.5)
        if not self.layout_nms:
            self.layout_nms = self.config.get("layout_nms", None)
        if self.layout_unclip_ratio is None:
            self.layout_unclip_ratio = self.config.get("layout_unclip_ratio", None)
        if self.layout_merge_bboxes_mode is None:
            self.layout_merge_bboxes_mode = self.config.get(
                "layout_merge_bboxes_mode", None
            )
        scale_size = getattr(self, "target_size", [800, 800])
        return LayoutAnalysisProcess(
            labels=self.config["label_list"], scale_size=scale_size
        )


class LayoutAnalysisTransformersPredictor(DetTransformersPredictor):
    """Layout analysis predictor backed by HuggingFace transformers."""

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.layout_postprocess = LayoutAnalysisProcess(
            labels=self.labels,
            scale_size=self._resolve_scale_size(),
        )

    def _get_result_class(self):
        return LayoutAnalysisResult

    def _resolve_scale_size(self) -> List[int]:
        for cfg in self.model_config.get("Preprocess", []):
            if cfg.get("type") != "Resize":
                continue
            target_size = cfg.get("target_size")
            if isinstance(target_size, int):
                return [target_size, target_size]
            if isinstance(target_size, (tuple, list)) and len(target_size) == 2:
                return list(target_size)
        return [800, 800]

    def _format_layout_transformers_output(self, prediction):
        formatted = self._format_transformers_output(prediction)
        if "order_seq" in prediction:
            order_seq = (
                prediction["order_seq"]
                .detach()
                .cpu()
                .numpy()
                .astype(np.float32, copy=False)
            )
            if len(formatted) == len(order_seq):
                formatted = np.concatenate([formatted, order_seq[:, None]], axis=1)

        output = {"boxes": formatted}
        if "polygon_points" in prediction:
            output["polygon_points"] = [
                np.asarray(points) for points in prediction["polygon_points"]
            ]
        return output

    def process(
        self,
        batch_data: List[Any],
        threshold: Optional[Union[float, dict]] = None,
        layout_nms: bool = False,
        layout_unclip_ratio: Optional[Union[float, Tuple[float, float], dict]] = None,
        layout_merge_bboxes_mode: Optional[Union[str, dict]] = None,
        layout_shape_mode: Optional[str] = "auto",
        filter_overlap_boxes: Optional[bool] = True,
        skip_order_labels: Optional[List[str]] = None,
    ):
        if not hasattr(self.image_processor, "post_process_object_detection"):
            raise RuntimeError(
                f"{type(self.image_processor).__name__} does not support "
                "`post_process_object_detection`."
            )

        datas = self.read_op(batch_data.instances)
        images = [Image.fromarray(data["img"]) for data in datas]
        effective_threshold, hf_threshold = self._get_hf_threshold(threshold)

        model_inputs = self.preprocess_images(images=images)
        outputs = self.forward(model_inputs)
        predictions = self.postprocess(outputs, datas=datas, threshold=hf_threshold)

        batch_outputs = [
            self._format_layout_transformers_output(prediction)
            for prediction in predictions
        ]
        boxes = self.layout_postprocess(
            batch_outputs,
            datas,
            threshold=effective_threshold,
            layout_nms=layout_nms or self.layout_nms,
            layout_unclip_ratio=layout_unclip_ratio or self.layout_unclip_ratio,
            layout_merge_bboxes_mode=layout_merge_bboxes_mode
            or self.layout_merge_bboxes_mode,
            layout_shape_mode=layout_shape_mode,
            filter_overlap_boxes=filter_overlap_boxes,
            skip_order_labels=skip_order_labels,
        )

        return {
            "input_path": batch_data.input_paths,
            "page_index": batch_data.page_indexes,
            "input_img": [data["ori_img"] for data in datas],
            "boxes": boxes,
        }

    def postprocess(self, outputs, *, datas, threshold, **kwargs):
        predictions = self.image_processor.post_process_object_detection(
            outputs,
            threshold=threshold,
            target_sizes=self._get_target_sizes(datas),
        )

        return predictions