# Copyright 2021 Zilliz. 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 logging
import os
import numpy
from pathlib import Path

import torch

from towhee.operator.base import NNOperator
from towhee.models.vggish.torch_vggish import VGG
from towhee import register

import vggish_input

import warnings

warnings.filterwarnings('ignore')
log = logging.getLogger()


@register(output_schema=['vec'])
class Vggish(NNOperator):
    """
    """
    def __init__(self, weights_path: str = None, framework: str = 'pytorch') -> None:
        super().__init__(framework=framework)
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model = VGG()
        if not weights_path:
            path = str(Path(__file__).parent)
            weights_path = os.path.join(path, 'vggish.pth')
        state_dict = torch.load(weights_path, map_location=torch.device('cpu'))
        self.model.load_state_dict(state_dict)
        self.model.eval()
        self.model.to(self.device)

    def __call__(self, audio: str) -> numpy.ndarray:
        audio_tensors = self.preprocess(audio).to(self.device)
        features = self.model(audio_tensors)
        outs = features.to("cpu")
        return outs.detach().numpy()

    def preprocess(self, audio_path: str):
        audio_tensors = vggish_input.wavfile_to_examples(audio_path)
        return audio_tensors


# if __name__ == '__main__':
#     encoder = Vggish()
#     audio_path = '/path/to/audio/wav'
#     vec = encoder(audio_path)
#     print(vec.shape)