# Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
#               2024 Alibaba Inc (Xiang Lyu)
#
# 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.
# Modified from ESPnet(https://github.com/espnet/espnet)
"""Subsampling layer definition."""

from typing import Tuple, Union

import torch


class BaseSubsampling(torch.nn.Module):

    def __init__(self):
        super().__init__()
        self.right_context = 0
        self.subsampling_rate = 1

    def position_encoding(self, offset: Union[int, torch.Tensor],
                          size: int) -> torch.Tensor:
        return self.pos_enc.position_encoding(offset, size)


class LinearNoSubsampling(BaseSubsampling):
    """Linear transform the input without subsampling

    Args:
        idim (int): Input dimension.
        odim (int): Output dimension.
        dropout_rate (float): Dropout rate.

    """

    def __init__(self, idim: int, odim: int, dropout_rate: float,
                 pos_enc_class: torch.nn.Module):
        """Construct an linear object."""
        super().__init__()
        self.out = torch.nn.Sequential(
            torch.nn.Linear(idim, odim),
            torch.nn.LayerNorm(odim, eps=1e-5),
            torch.nn.Dropout(dropout_rate),
        )
        self.pos_enc = pos_enc_class
        self.right_context = 0
        self.subsampling_rate = 1

    def forward(
        self,
        x: torch.Tensor,
        x_mask: torch.Tensor,
        offset: Union[int, torch.Tensor] = 0
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Input x.

        Args:
            x (torch.Tensor): Input tensor (#batch, time, idim).
            x_mask (torch.Tensor): Input mask (#batch, 1, time).

        Returns:
            torch.Tensor: linear input tensor (#batch, time', odim),
                where time' = time .
            torch.Tensor: linear input mask (#batch, 1, time'),
                where time' = time .

        """
        x = self.out(x)
        x, pos_emb = self.pos_enc(x, offset)
        return x, pos_emb, x_mask