# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
#
# 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 numpy as np
from ...object_detection import DetPredictor
from .keys import InstanceSegKeys as K
from ..model_list import MODELS


class InstanceSegPredictor(DetPredictor):
    """Instance Seg Predictor"""

    entities = MODELS

    def _run(self, batch_input):
        """run"""
        input_dict = {}
        input_dict["image"] = np.stack(
            [data[K.IMAGE] for data in batch_input], axis=0
        ).astype(dtype=np.float32, copy=False)
        input_dict["scale_factor"] = np.stack(
            [data[K.SCALE_FACTOR][::-1] for data in batch_input], axis=0
        ).astype(dtype=np.float32, copy=False)
        input_dict["im_shape"] = np.stack(
            [data[K.IM_SIZE][::-1] for data in batch_input], axis=0
        ).astype(dtype=np.float32, copy=False)

        input_ = [input_dict[i] for i in self._predictor.get_input_names()]

        pred = batch_input
        box_idx_start = 0

        if self.model_name == "SOLOv2":
            batch_np_boxes_num, batch_np_label, batch_np_score, batch_np_segm = (
                self._predictor.predict(input_)
            )
            for idx in range(len(batch_input)):
                np_boxes_num = batch_np_boxes_num
                box_idx_end = box_idx_start + np_boxes_num
                np_label = batch_np_label[box_idx_start:box_idx_end]
                np_score = batch_np_score[box_idx_start:box_idx_end]
                np_segm = batch_np_segm[box_idx_start:box_idx_end]
                box_idx_start = box_idx_end

                batch_input[idx][K.LABEL] = np_label
                batch_input[idx][K.SCORE] = np_score
                batch_input[idx][K.SEGM] = np_segm
            return pred
        else:
            batch_np_boxes, batch_np_boxes_num, batch_np_masks = (
                self._predictor.predict(input_)
            )
            for idx in range(len(batch_input)):
                np_boxes_num = batch_np_boxes_num[idx]
                box_idx_end = box_idx_start + np_boxes_num
                np_boxes = batch_np_boxes[box_idx_start:box_idx_end]
                np_masks = batch_np_masks[box_idx_start:box_idx_end]
                box_idx_start = box_idx_end

                batch_input[idx][K.BOXES] = np_boxes
                batch_input[idx][K.MASKS] = np_masks
            return pred

    @classmethod
    def get_output_keys(cls):
        """get output keys"""
        return [[K.LABEL, K.SCORE, K.SEGM], [K.BOXES, K.MASKS]]