# copyright (c) 2021 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 os.path as osp
import copy
import random
import numpy as np
import pickle

from ..registry import DATASETS
from .base import BaseDataset
from ...utils import get_logger

logger = get_logger("paddlevideo")


@DATASETS.register()
class SkeletonDataset(BaseDataset):
    """
    Skeleton dataset for action recognition.
    The dataset loads skeleton feature, and apply norm operatations.
    Args:
        file_path (str): Path to the index file.
        pipeline(obj): Define the pipeline of data preprocessing.
        data_prefix (str): directory path of the data. Default: None.
        test_mode (bool): Whether to bulid the test dataset. Default: False.
    """
    def __init__(self, file_path, pipeline, label_path=None, test_mode=False):
        self.label_path = label_path
        super().__init__(file_path, pipeline, test_mode=test_mode)

    def load_file(self):
        """Load feature file to get skeleton information."""
        logger.info("Loading data, it will take some moment...")
        self.data = np.load(self.file_path)
        if self.label_path:
            if self.label_path.endswith('npy'):
                self.label = np.load(self.label_path)
            elif self.label_path.endswith('pkl'):
                with open(self.label_path, 'rb') as f:
                    sample_name, self.label = pickle.load(f)
        else:
            logger.info(
                "Label path not provided when test_mode={}, here just output predictions."
                .format(self.test_mode))
        logger.info("Data Loaded!")
        return self.data  # used for __len__

    def prepare_train(self, idx):
        """Prepare the feature for training/valid given index. """
        results = dict()
        results['data'] = copy.deepcopy(self.data[idx])
        results['label'] = copy.deepcopy(self.label[idx])
        results = self.pipeline(results)
        return results['data'], results['label']

    def prepare_test(self, idx):
        """Prepare the feature for test given index. """
        results = dict()
        results['data'] = copy.deepcopy(self.data[idx])
        if self.label_path:
            results['label'] = copy.deepcopy(self.label[idx])
            results = self.pipeline(results)
            return results['data'], results['label']
        else:
            results = self.pipeline(results)
            return [results['data']]