# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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


class TdnnLstm(nn.Module):
    def __init__(
        self, num_features: int, num_classes: int, subsampling_factor: int = 3
    ) -> None:
        """
        Args:
          num_features:
            The input dimension of the model.
          num_classes:
            The output dimension of the model.
          subsampling_factor:
            It reduces the number of output frames by this factor.
        """
        super().__init__()
        self.num_features = num_features
        self.num_classes = num_classes
        self.subsampling_factor = subsampling_factor
        self.tdnn = nn.Sequential(
            nn.Conv1d(
                in_channels=num_features,
                out_channels=512,
                kernel_size=3,
                stride=1,
                padding=1,
            ),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(num_features=512, affine=False),
            nn.Conv1d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                stride=1,
                padding=1,
            ),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(num_features=512, affine=False),
            nn.Conv1d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                stride=1,
                padding=1,
            ),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(num_features=512, affine=False),
            nn.Conv1d(
                in_channels=512,
                out_channels=512,
                kernel_size=3,
                stride=self.subsampling_factor,  # stride: subsampling_factor!
            ),
            nn.ReLU(inplace=True),
            nn.BatchNorm1d(num_features=512, affine=False),
        )
        self.lstms = nn.ModuleList(
            [nn.LSTM(input_size=512, hidden_size=512, num_layers=1) for _ in range(4)]
        )
        self.lstm_bnorms = nn.ModuleList(
            [nn.BatchNorm1d(num_features=512, affine=False) for _ in range(5)]
        )
        self.dropout = nn.Dropout(0.2)
        self.linear = nn.Linear(in_features=512, out_features=self.num_classes)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Args:
          x:
            Its shape is [N, C, T]
        Returns:
          The output tensor has shape [N, T, C]
        """
        x = self.tdnn(x)
        x = x.permute(2, 0, 1)  # (N, C, T) -> (T, N, C) -> how LSTM expects it
        for lstm, bnorm in zip(self.lstms, self.lstm_bnorms):
            x_new, _ = lstm(x)
            x_new = bnorm(x_new.permute(1, 2, 0)).permute(
                2, 0, 1
            )  # (T, N, C) -> (N, C, T) -> (T, N, C)
            x_new = self.dropout(x_new)
            x = x_new + x  # skip connections
        x = x.transpose(
            1, 0
        )  # (T, N, C) -> (N, T, C) -> linear expects "features" in the last dim
        x = self.linear(x)
        x = nn.functional.log_softmax(x, dim=-1)
        return x
