# 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 warnings

import os
import numpy
from typing import Union

import torch
import torchaudio

from panns_inference import AudioTagging, labels

from towhee.operator.base import NNOperator
from towhee import register


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


@register(output_schema=['label', 'vec'])
class Panns(NNOperator):
    """
    Built on top of [panns_inference](https://github.com/qiuqiangkong/panns_inference).
    """

    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.tagger = AudioTagging(checkpoint_path=weights_path, device=self.device)
        self.model = self.tagger.model
        # self.model.eval()
        # self.model.to(self.device)

    def __call__(self, audio: Union[str, numpy.ndarray], sample_rate: int = None, top_k: int = 5) -> numpy.ndarray:
        if isinstance(audio, str):
            source = os.path.abspath(audio)
            audio_wav, sr = torchaudio.load(source)
        elif isinstance(audio, numpy.ndarray):
            sr = sample_rate
            audio_wav = torch.tensor(audio).to(torch.float32)

        if audio_wav.shape[0] == 2:
            audio_wav = torch.mean(audio_wav, dim=0)
        elif audio_wav.shape[0] == 1:
            audio_wav = audio_wav.squeeze(0)

        _sr = 32000
        if sr != _sr:
            transform = torchaudio.transforms.Resample(orig_freq=sr, new_freq=_sr)
            audio_tensors = transform(audio_wav)

        audio_tensors = audio_tensors[None, :]
        clipwise_output, embedding = self.tagger.inference(audio_tensors)

        sorted_indexes = numpy.argsort(clipwise_output[0])[::-1]
        tags = []
        for k in range(top_k):
            tag = numpy.array(labels)[sorted_indexes[k]]
            score = clipwise_output[0][sorted_indexes[k]]
            tags.append((tag, round(score, 2)))

        return tags, embedding.squeeze(0)


# if __name__ == '__main__':
#     encoder = Panns()
#
#     audio_path = '/audio/path/or/link'
#     tags, vecs = encoder(audio_path)
#
#     # audio_data = numpy.zeros((2, 441344))
#     # sample_rate = 44100
#     # tags, vecs = encoder(audio_data, sample_rate)
#     print(tags, vecs.shape)