# 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 List

import torch
import torchaudio

from panns_inference import AudioTagging, labels

from towhee.operator.base import NNOperator
from towhee import register
from towhee.types.audio_frame import AudioFrame


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


@register(output_schema=['label', 'score', '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',
                 sample_rate: int = 32000,
                 device: str = None,
                 topk: int = 5):
        super().__init__(framework=framework)
        if device:
            self.device = device
        else:
            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.sample_rate = sample_rate
        self.topk = topk
        # checkpoint_path=None will download model weights with default url
        # 'https://zenodo.org/record/3987831/files/Cnn14_mAP%3D0.431.pth?download=1'
        self.tagger = AudioTagging(checkpoint_path=weights_path, device=self.device)
        self.model = self.tagger.model
        self.model.eval()

    def __call__(self, data: List[AudioFrame]):
        sr = data[0].sample_rate
        layout = data[0].layout
        if layout == 'stereo':
            frames = [frame.reshape(-1, 2) for frame in data]
            audio = numpy.vstack(frames)
            audio = numpy.mean(audio, axis=1)
        else:
            audio = numpy.hstack(data)

        audio = self.int2float(audio).astype('float32')
        if sr != self.sample_rate:
            audio = torch.from_numpy(audio)
            resampler = torchaudio.transforms.Resample(sr, self.sample_rate, dtype=audio.dtype)
            audio = resampler(audio)

        if len(audio.shape) == 1:
            audio = audio[None, :]
        clipwise_output, embedding = self.tagger.inference(audio)

        sorted_indexes = numpy.argsort(clipwise_output[0])[::-1]
        tags = []
        scores = []
        for k in range(self.topk):
            tag = numpy.array(labels)[sorted_indexes[k]]
            score = clipwise_output[0][sorted_indexes[k]]
            tags.append(tag)
            scores.append(round(score, 4))

        return tags, scores, embedding.squeeze(0)

    def int2float(self, wav: numpy.ndarray, dtype: str = 'float64'):
        """
        Convert audio data from int to float.
        The input dtype must be integers.
        The output dtype is controlled by the parameter `dtype`, defaults to 'float64'.

        The code is inspired by https://github.com/mgeier/python-audio/blob/master/audio-files/utility.py
        """
        dtype = numpy.dtype(dtype)
        assert dtype.kind == 'f'
        if wav.dtype.kind in 'iu':
            ii = numpy.iinfo(wav.dtype)
            abs_max = 2 ** (ii.bits - 1)
            offset = ii.min + abs_max
            return (wav.astype(dtype) - offset) / abs_max
        else:
            return wav.astype(dtype)