# 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.
# 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 os
import sys
import logging
from pathlib import Path
from typing import List

import torch
import torchaudio
import numpy

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

sys.path.append(str(Path(__file__).parent))
from clmr_checkpoint import load_encoder_checkpoint
from sample_cnn import SampleCNN

log = logging.getLogger()


@register(output_schema=['vecs'])
class ClmrMagnatagatune(NNOperator):
    """
    Pretrained clmr
    """

    def __init__(self, framework="pytorch") -> None:
        super().__init__(framework=framework)
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

        weight_path = os.path.join(str(Path(__file__).parent),
                                   'clmr_checkpoint_10000.pt')
        state_dict = load_encoder_checkpoint(weight_path, 1)
        encoder = SampleCNN(strides=[3, 3, 3, 3, 3, 3, 3, 3, 3], supervised=False, out_dim=1)
        encoder.load_state_dict(state_dict)

        new_encoder = torch.nn.Sequential(*(list(encoder.children())[:-1]))
        x = list(new_encoder[0][:10].children())
        y = torch.nn.Sequential(*list(new_encoder[0][10].children())[:-1])
        x.append(y)
        self.model = torch.nn.Sequential(*x)
        self.model.eval()
        self.model.to(self.device)

    def __call__(self, data: List[AudioFrame]) -> numpy.ndarray:
        _sr = 22050
        audio_length = 59049

        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)
            if len(audio.shape) != 1:
                audio = audio.squeeze()
        audio = self.int2float(audio, dtype='float32')
        audio = torch.from_numpy(audio)

        if sr != _sr:
            resampler = torchaudio.transforms.Resample(sr, _sr, dtype=audio.dtype)
            audio = resampler(audio)
        with torch.no_grad():
            batch = torch.split(audio, audio_length)
            batch = [x for x in batch if len(x) == audio_length]
            batch = torch.vstack(batch)
            batch = batch.unsqueeze(dim=1).to(self.device)
            features = numpy.squeeze(self.model(batch))

        return features.to('cpu').detach().numpy()

    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
            if wav.dtype != 'int16':
                wav = (wav >> 16).astype(numpy.int16)
            assert wav.dtype == 'int16'
            wav = (wav / 32768.0).astype(dtype)
            return wav
        else:
            return wav.astype(dtype)


# if __name__ == "__main__":
#     import towhee
#
#     audio_path = "path/to/audio.wav"
#     frames = towhee.glob(audio_path).audio_decode.ffmpeg(99999).flatten()[0]
#
#     encoder = ClmrMagnatagatune()
#     vec = encoder(frames)
#
#     print(vec.shape)