# 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 torch
import torch.nn as nn
import numpy as np
import sys
from pathlib import Path

sys.path.append(str(Path(__file__).parent))

import vggish_input

class Model(nn.Module):
    """
    PyTorch model class
    """
    def __init__(self):
        super().__init__()
        self.features = nn.Sequential(
            nn.Conv2d(1, 64, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(64, 128, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(128, 256, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2),
            nn.Conv2d(256, 512, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, 3, 1, 1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(2, 2))
        self.embeddings = nn.Sequential(
            nn.Linear(512 * 24, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 4096),
            nn.ReLU(inplace=True),
            nn.Linear(4096, 128),
            #nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = self.features(x).permute(0, 2, 3, 1).contiguous()
        x = x.view(x.size(0), -1)
        x = self.embeddings(x)
        return x

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

    def train(self):
        """
        For training model
        """
        pass