#!/usr/bin/env python3
# Copyright (c)  2021  University of Chinese Academy of Sciences (author: Han Zhu)
#
# 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 copy
import math
import warnings
from typing import List, Optional, Tuple

import torch
from encoder_interface import EncoderInterface
from scaling import (
    ActivationBalancer,
    BasicNorm,
    DoubleSwish,
    ScaledConv1d,
    ScaledConv2d,
    ScaledLinear,
)
from torch import Tensor, nn

from icefall.utils import make_pad_mask, subsequent_chunk_mask


class Conformer(EncoderInterface):
    """
    Args:
        num_features (int): Number of input features
        subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
        d_model (int): attention dimension, also the output dimension
        nhead (int): number of head
        dim_feedforward (int): feedforward dimention
        num_encoder_layers (int): number of encoder layers
        dropout (float): dropout rate
        layer_dropout (float): layer-dropout rate.
        cnn_module_kernel (int): Kernel size of convolution module.
        dynamic_chunk_training (bool): whether to use dynamic chunk training, if
            you want to train a streaming model, this is expected to be True.
            When setting True, it will use a masking strategy to make the attention
            see only limited left and right context.
        short_chunk_threshold (float): a threshold to determinize the chunk size
            to be used in masking training, if the randomly generated chunk size
            is greater than ``max_len * short_chunk_threshold`` (max_len is the
            max sequence length of current batch) then it will use
            full context in training (i.e. with chunk size equals to max_len).
            This will be used only when dynamic_chunk_training is True.
        short_chunk_size (int): see docs above, if the randomly generated chunk
            size equals to or less than ``max_len * short_chunk_threshold``, the
            chunk size will be sampled uniformly from 1 to short_chunk_size.
            This also will be used only when dynamic_chunk_training is True.
        num_left_chunks (int): the left context (in chunks) attention can see, the
            chunk size is decided by short_chunk_threshold and short_chunk_size.
            A minus value means seeing full left context.
            This also will be used only when dynamic_chunk_training is True.
        causal (bool): Whether to use causal convolution in conformer encoder
            layer. This MUST be True when using dynamic_chunk_training.
    """

    def __init__(
        self,
        num_features: int,
        subsampling_factor: int = 4,
        d_model: int = 256,
        nhead: int = 4,
        dim_feedforward: int = 2048,
        num_encoder_layers: int = 12,
        dropout: float = 0.1,
        layer_dropout: float = 0.075,
        cnn_module_kernel: int = 31,
        aux_layer_period: int = 3,
        dynamic_chunk_training: bool = False,
        short_chunk_threshold: float = 0.75,
        short_chunk_size: int = 25,
        num_left_chunks: int = -1,
        causal: bool = False,
    ) -> None:
        super(Conformer, self).__init__()

        self.num_features = num_features
        self.subsampling_factor = subsampling_factor
        if subsampling_factor != 4:
            raise NotImplementedError("Support only 'subsampling_factor=4'.")

        # self.encoder_embed converts the input of shape (N, T, num_features)
        # to the shape (N, T//subsampling_factor, d_model).
        # That is, it does two things simultaneously:
        #   (1) subsampling: T -> T//subsampling_factor
        #   (2) embedding: num_features -> d_model
        self.encoder_embed = Conv2dSubsampling(num_features, d_model)

        self.encoder_pos = RelPositionalEncoding(d_model, dropout)

        self.encoder_layers = num_encoder_layers
        self.d_model = d_model
        self.cnn_module_kernel = cnn_module_kernel
        self.causal = causal
        self.dynamic_chunk_training = dynamic_chunk_training
        self.short_chunk_threshold = short_chunk_threshold
        self.short_chunk_size = short_chunk_size
        self.num_left_chunks = num_left_chunks

        encoder_layer = ConformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=dim_feedforward,
            dropout=dropout,
            layer_dropout=layer_dropout,
            cnn_module_kernel=cnn_module_kernel,
            causal=causal,
        )
        # aux_layers from 1/3
        self.encoder = ConformerEncoder(
            encoder_layer=encoder_layer,
            num_layers=num_encoder_layers,
            aux_layers=list(
                range(
                    num_encoder_layers // 3,
                    num_encoder_layers - 1,
                    aux_layer_period,
                )
            ),
        )
        self._init_state: List[torch.Tensor] = [torch.empty(0)]

    def forward(
        self, x: torch.Tensor, x_lens: torch.Tensor, warmup: float = 1.0, get_layer_output = False,
    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[List[torch.Tensor]]]:
        """
        Args:
          x:
            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
          x_lens:
            A tensor of shape (batch_size,) containing the number of frames in
            `x` before padding.
          warmup:
            A floating point value that gradually increases from 0 throughout
            training; when it is >= 1.0 we are "fully warmed up".  It is used
            to turn modules on sequentially.
        Returns:
          Return a tuple containing 2 tensors:
            - embeddings: its shape is (batch_size, output_seq_len, d_model)
            - lengths, a tensor of shape (batch_size,) containing the number
              of frames in `embeddings` before padding.
        """
        x = self.encoder_embed(x)
        x, pos_emb = self.encoder_pos(x)
        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)

        lengths = (((x_lens - 1) >> 1) - 1) >> 1
        assert x.size(0) == lengths.max().item()
        src_key_padding_mask = make_pad_mask(lengths)

        if self.dynamic_chunk_training:
            assert (
                self.causal
            ), "Causal convolution is required for streaming conformer."
            max_len = x.size(0)
            chunk_size = torch.randint(1, max_len, (1,)).item()
            if chunk_size > (max_len * self.short_chunk_threshold):
                chunk_size = max_len
            else:
                chunk_size = chunk_size % self.short_chunk_size + 1

            mask = ~subsequent_chunk_mask(
                size=x.size(0),
                chunk_size=chunk_size,
                num_left_chunks=self.num_left_chunks,
                device=x.device,
            )
            x = self.encoder(
                x,
                pos_emb,
                mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                warmup=warmup,
            )  # (T, N, C)
        else:
            x, layer_outputs = self.encoder(
                x,
                pos_emb,
                src_key_padding_mask=src_key_padding_mask,
                warmup=warmup,
            )  # (T, N, C)

        x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
        layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
        layer_outputs.append(pos_emb)

        if get_layer_output:
            return x, lengths, layer_outputs
        else:
            return x, lengths

    @torch.jit.export
    def get_init_state(
        self, left_context: int, device: torch.device
    ) -> List[torch.Tensor]:
        """Return the initial cache state of the model.
        Args:
          left_context: The left context size (in frames after subsampling).
        Returns:
          Return the initial state of the model, it is a list containing two
          tensors, the first one is the cache for attentions which has a shape
          of (num_encoder_layers, left_context, encoder_dim), the second one
          is the cache of conv_modules which has a shape of
          (num_encoder_layers, cnn_module_kernel - 1, encoder_dim).
          NOTE: the returned tensors are on the given device.
        """
        if len(self._init_state) == 2 and self._init_state[0].size(1) == left_context:
            # Note: It is OK to share the init state as it is
            # not going to be modified by the model
            return self._init_state

        init_states: List[torch.Tensor] = [
            torch.zeros(
                (
                    self.encoder_layers,
                    left_context,
                    self.d_model,
                ),
                device=device,
            ),
            torch.zeros(
                (
                    self.encoder_layers,
                    self.cnn_module_kernel - 1,
                    self.d_model,
                ),
                device=device,
            ),
        ]

        self._init_state = init_states

        return init_states

    @torch.jit.export
    def streaming_forward(
        self,
        x: torch.Tensor,
        x_lens: torch.Tensor,
        states: Optional[List[Tensor]] = None,
        processed_lens: Optional[Tensor] = None,
        left_context: int = 64,
        right_context: int = 4,
        chunk_size: int = 16,
        simulate_streaming: bool = False,
        warmup: float = 1.0,
    ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
        """
        Args:
          x:
            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
          x_lens:
            A tensor of shape (batch_size,) containing the number of frames in
            `x` before padding.
          states:
            The decode states for previous frames which contains the cached data.
            It has two elements, the first element is the attn_cache which has
            a shape of (encoder_layers, left_context, batch, attention_dim),
            the second element is the conv_cache which has a shape of
            (encoder_layers, cnn_module_kernel-1, batch, conv_dim).
            Note: states will be modified in this function.
          processed_lens:
            How many frames (after subsampling) have been processed for each sequence.
          left_context:
            How many previous frames the attention can see in current chunk.
            Note: It's not that each individual frame has `left_context` frames
            of left context, some have more.
          right_context:
            How many future frames the attention can see in current chunk.
            Note: It's not that each individual frame has `right_context` frames
            of right context, some have more.
          chunk_size:
            The chunk size for decoding, this will be used to simulate streaming
            decoding using masking.
          simulate_streaming:
            If setting True, it will use a masking strategy to simulate streaming
            fashion (i.e. every chunk data only see limited left context and
            right context). The whole sequence is supposed to be send at a time
            When using simulate_streaming.
          warmup:
            A floating point value that gradually increases from 0 throughout
            training; when it is >= 1.0 we are "fully warmed up".  It is used
            to turn modules on sequentially.
        Returns:
          Return a tuple containing 2 tensors:
            - logits, its shape is (batch_size, output_seq_len, output_dim)
            - logit_lens, a tensor of shape (batch_size,) containing the number
              of frames in `logits` before padding.
            - decode_states, the updated states including the information
              of current chunk.
        """

        # x: [N, T, C]
        # Caution: We assume the subsampling factor is 4!

        #  lengths = ((x_lens - 1) // 2 - 1) // 2 # issue an warning
        #
        # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0
        lengths = (((x_lens - 1) >> 1) - 1) >> 1

        if not simulate_streaming:
            assert states is not None
            assert processed_lens is not None
            assert (
                len(states) == 2
                and states[0].shape
                == (self.encoder_layers, left_context, x.size(0), self.d_model)
                and states[1].shape
                == (
                    self.encoder_layers,
                    self.cnn_module_kernel - 1,
                    x.size(0),
                    self.d_model,
                )
            ), f"""The length of states MUST be equal to 2, and the shape of
             first element should be {(self.encoder_layers, left_context, x.size(0), self.d_model)},
             given {states[0].shape}. the shape of second element should be
             {(self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)},
             given {states[1].shape}."""

            lengths -= 2  # we will cut off 1 frame on each side of encoder_embed output

            src_key_padding_mask = make_pad_mask(lengths)

            processed_mask = torch.arange(left_context, device=x.device).expand(
                x.size(0), left_context
            )
            processed_lens = processed_lens.view(x.size(0), 1)
            processed_mask = (processed_lens <= processed_mask).flip(1)

            src_key_padding_mask = torch.cat(
                [processed_mask, src_key_padding_mask], dim=1
            )

            embed = self.encoder_embed(x)

            # cut off 1 frame on each size of embed as they see the padding
            # value which causes a training and decoding mismatch.
            embed = embed[:, 1:-1, :]

            embed, pos_enc = self.encoder_pos(embed, left_context)
            embed = embed.permute(1, 0, 2)  # (B, T, F) -> (T, B, F)

            x, states = self.encoder.chunk_forward(
                embed,
                pos_enc,
                src_key_padding_mask=src_key_padding_mask,
                warmup=warmup,
                states=states,
                left_context=left_context,
                right_context=right_context,
            )  # (T, B, F)
            if right_context > 0:
                x = x[0:-right_context, ...]
                lengths -= right_context
        else:
            assert states is None
            states = []  # just to make torch.script.jit happy
            # this branch simulates streaming decoding using mask as we are
            # using in training time.
            src_key_padding_mask = make_pad_mask(lengths)
            x = self.encoder_embed(x)
            x, pos_emb = self.encoder_pos(x)
            x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)

            assert x.size(0) == lengths.max().item()

            num_left_chunks = -1
            if left_context >= 0:
                assert left_context % chunk_size == 0
                num_left_chunks = left_context // chunk_size

            mask = ~subsequent_chunk_mask(
                size=x.size(0),
                chunk_size=chunk_size,
                num_left_chunks=num_left_chunks,
                device=x.device,
            )
            x = self.encoder(
                x,
                pos_emb,
                mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                warmup=warmup,
            )  # (T, N, C)

        x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)

        return x, lengths, states


class Tempformer(EncoderInterface):
    """
    Args:
        num_features (int): Number of input features
        subsampling_factor (int): subsampling factor of encoder (the convolution layers before transformers)
        d_model (int): attention dimension, also the output dimension
        nhead (int): number of head
        dim_feedforward (int): feedforward dimention
        num_encoder_layers (int): number of encoder layers
        dropout (float): dropout rate
        layer_dropout (float): layer-dropout rate.
        cnn_module_kernel (int): Kernel size of convolution module.
        dynamic_chunk_training (bool): whether to use dynamic chunk training, if
            you want to train a streaming model, this is expected to be True.
            When setting True, it will use a masking strategy to make the attention
            see only limited left and right context.
        short_chunk_threshold (float): a threshold to determinize the chunk size
            to be used in masking training, if the randomly generated chunk size
            is greater than ``max_len * short_chunk_threshold`` (max_len is the
            max sequence length of current batch) then it will use
            full context in training (i.e. with chunk size equals to max_len).
            This will be used only when dynamic_chunk_training is True.
        short_chunk_size (int): see docs above, if the randomly generated chunk
            size equals to or less than ``max_len * short_chunk_threshold``, the
            chunk size will be sampled uniformly from 1 to short_chunk_size.
            This also will be used only when dynamic_chunk_training is True.
        num_left_chunks (int): the left context (in chunks) attention can see, the
            chunk size is decided by short_chunk_threshold and short_chunk_size.
            A minus value means seeing full left context.
            This also will be used only when dynamic_chunk_training is True.
        causal (bool): Whether to use causal convolution in conformer encoder
            layer. This MUST be True when using dynamic_chunk_training.
    """

    def __init__(
        self,
        num_features: int,
        subsampling_factor: int = 4,
        d_model: int = 256,
        nhead: int = 4,
        dim_feedforward: int = 2048,
        num_encoder_layers: int = 12,
        dropout: float = 0.1,
        layer_dropout: float = 0.075,
        cnn_module_kernel: int = 31,
        aux_layer_period: int = 3,
        dynamic_chunk_training: bool = False,
        short_chunk_threshold: float = 0.75,
        short_chunk_size: int = 25,
        num_left_chunks: int = -1,
        causal: bool = False,
    ) -> None:
        super(Tempformer, self).__init__()

        self.num_features = num_features
        self.subsampling_factor = subsampling_factor
        if subsampling_factor != 4:
            raise NotImplementedError("Support only 'subsampling_factor=4'.")

        self.encoder_layers = num_encoder_layers
        self.d_model = d_model
        self.cnn_module_kernel = cnn_module_kernel
        self.causal = causal
        self.dynamic_chunk_training = dynamic_chunk_training
        self.short_chunk_threshold = short_chunk_threshold
        self.short_chunk_size = short_chunk_size
        self.num_left_chunks = num_left_chunks

        def build_conformer(d_model, nhead, dim_feedforward, dropout, layer_dropout, cnn_module_kernel, causal):
            encoder_layer = ConformerEncoderLayer(
                d_model=d_model,
                nhead=nhead,
                dim_feedforward=dim_feedforward,
                dropout=dropout,
                layer_dropout=layer_dropout,
                cnn_module_kernel=cnn_module_kernel,
                causal=causal,
            )
            return encoder_layer


        self.encoder_layers = nn.ModuleList([
                                                build_conformer(d_model,
                                                                nhead,
                                                                dim_feedforward,
                                                                dropout,
                                                                layer_dropout,
                                                                cnn_module_kernel,
                                                                causal
                                                )
                                                for i in range(num_encoder_layers)
                                            ])

        self._init_state: List[torch.Tensor] = [torch.empty(0)]

    def forward(
        self, x
    ):
        layer_outputs = []
        pos_emb = x[-1]

        for enum, encoder in enumerate(self.encoder_layers):
            x_ = x[enum].permute(1, 0, 2)
            encoder_output = encoder(x_, pos_emb)
            encoder_output = encoder_output.permute(1, 0, 2)
            layer_outputs.append(encoder_output)

        return layer_outputs


class ConformerEncoderLayer(nn.Module):
    """
    ConformerEncoderLayer is made up of self-attn, feedforward and convolution networks.
    See: "Conformer: Convolution-augmented Transformer for Speech Recognition"

    Args:
        d_model: the number of expected features in the input (required).
        nhead: the number of heads in the multiheadattention models (required).
        dim_feedforward: the dimension of the feedforward network model (default=2048).
        dropout: the dropout value (default=0.1).
        cnn_module_kernel (int): Kernel size of convolution module.
        causal (bool): Whether to use causal convolution in conformer encoder
            layer. This MUST be True when using dynamic_chunk_training and streaming decoding.

    Examples::
        >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
        >>> src = torch.rand(10, 32, 512)
        >>> pos_emb = torch.rand(32, 19, 512)
        >>> out = encoder_layer(src, pos_emb)
    """

    def __init__(
        self,
        d_model: int,
        nhead: int,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        layer_dropout: float = 0.075,
        cnn_module_kernel: int = 31,
        causal: bool = False,
    ) -> None:
        super(ConformerEncoderLayer, self).__init__()

        self.layer_dropout = layer_dropout

        self.d_model = d_model

        self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0)

        self.feed_forward = nn.Sequential(
            ScaledLinear(d_model, dim_feedforward),
            ActivationBalancer(channel_dim=-1),
            DoubleSwish(),
            nn.Dropout(dropout),
            ScaledLinear(dim_feedforward, d_model, initial_scale=0.25),
        )

        self.feed_forward_macaron = nn.Sequential(
            ScaledLinear(d_model, dim_feedforward),
            ActivationBalancer(channel_dim=-1),
            DoubleSwish(),
            nn.Dropout(dropout),
            ScaledLinear(dim_feedforward, d_model, initial_scale=0.25),
        )

        self.conv_module = ConvolutionModule(d_model, cnn_module_kernel, causal=causal)

        self.norm_final = BasicNorm(d_model)

        # try to ensure the output is close to zero-mean (or at least, zero-median).
        self.balancer = ActivationBalancer(
            channel_dim=-1, min_positive=0.45, max_positive=0.55, max_abs=6.0
        )

        self.dropout = nn.Dropout(dropout)

    def forward(
        self,
        src: Tensor,
        pos_emb: Tensor,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        warmup: float = 1.0,
    ) -> Tensor:
        """
        Pass the input through the encoder layer.

        Args:
            src: the sequence to the encoder layer (required).
            pos_emb: Positional embedding tensor (required).
            src_mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).
            warmup: controls selective bypass of of layers; if < 1.0, we will
              bypass layers more frequently.

        Shape:
            src: (S, N, E).
            pos_emb: (N, 2*S-1, E)
            src_mask: (S, S).
            src_key_padding_mask: (N, S).
            S is the source sequence length, N is the batch size, E is the feature number
        """
        src_orig = src

        warmup_scale = min(0.1 + warmup, 1.0)
        # alpha = 1.0 means fully use this encoder layer, 0.0 would mean
        # completely bypass it.
        if self.training:
            alpha = (
                warmup_scale
                if torch.rand(()).item() <= (1.0 - self.layer_dropout)
                else 0.1
            )
        else:
            alpha = 1.0

        # macaron style feed forward module
        src = src + self.dropout(self.feed_forward_macaron(src))

        # multi-headed self-attention module
        src_att = self.self_attn(
            src,
            src,
            src,
            pos_emb=pos_emb,
            attn_mask=src_mask,
            key_padding_mask=src_key_padding_mask,
        )[0]
        src = src + self.dropout(src_att)

        # convolution module
        conv, _ = self.conv_module(src, src_key_padding_mask=src_key_padding_mask)
        src = src + self.dropout(conv)

        # feed forward module
        src = src + self.dropout(self.feed_forward(src))

        src = self.norm_final(self.balancer(src))

        if alpha != 1.0:
            src = alpha * src + (1 - alpha) * src_orig

        return src

    @torch.jit.export
    def chunk_forward(
        self,
        src: Tensor,
        pos_emb: Tensor,
        states: List[Tensor],
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        warmup: float = 1.0,
        left_context: int = 0,
        right_context: int = 0,
    ) -> Tuple[Tensor, List[Tensor]]:
        """
        Pass the input through the encoder layer.
        Args:
            src: the sequence to the encoder layer (required).
            pos_emb: Positional embedding tensor (required).
            states:
              The decode states for previous frames which contains the cached data.
              It has two elements, the first element is the attn_cache which has
              a shape of (left_context, batch, attention_dim),
              the second element is the conv_cache which has a shape of
              (cnn_module_kernel-1, batch, conv_dim).
              Note: states will be modified in this function.
            src_mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).
            warmup: controls selective bypass of of layers; if < 1.0, we will
              bypass layers more frequently.
            left_context:
              How many previous frames the attention can see in current chunk.
              Note: It's not that each individual frame has `left_context` frames
              of left context, some have more.
            right_context:
              How many future frames the attention can see in current chunk.
              Note: It's not that each individual frame has `right_context` frames
              of right context, some have more.
        Shape:
            src: (S, N, E).
            pos_emb: (N, 2*(S+left_context)-1, E).
            src_mask: (S, S).
            src_key_padding_mask: (N, S).
            S is the source sequence length, N is the batch size, E is the feature number
        """

        assert not self.training
        assert len(states) == 2
        assert states[0].shape == (left_context, src.size(1), src.size(2))

        # macaron style feed forward module
        src = src + self.dropout(self.feed_forward_macaron(src))

        # We put the attention cache this level (i.e. before linear transformation)
        # to save memory consumption, when decoding in streaming fashion, the
        # batch size would be thousands (for 32GB machine), if we cache key & val
        # separately, it needs extra several GB memory.
        # TODO(WeiKang): Move cache to self_attn level (i.e. cache key & val
        # separately) if needed.
        key = torch.cat([states[0], src], dim=0)
        val = key
        if right_context > 0:
            states[0] = key[
                -(left_context + right_context) : -right_context, ...  # noqa
            ]
        else:
            states[0] = key[-left_context:, ...]

        # multi-headed self-attention module
        src_att = self.self_attn(
            src,
            key,
            val,
            pos_emb=pos_emb,
            attn_mask=src_mask,
            key_padding_mask=src_key_padding_mask,
            left_context=left_context,
        )[0]

        src = src + self.dropout(src_att)

        # convolution module
        conv, conv_cache = self.conv_module(src, states[1], right_context)
        states[1] = conv_cache

        src = src + self.dropout(conv)

        # feed forward module
        src = src + self.dropout(self.feed_forward(src))

        src = self.norm_final(self.balancer(src))

        return src, states


class ConformerEncoder(nn.Module):
    r"""ConformerEncoder is a stack of N encoder layers

    Args:
        encoder_layer: an instance of the ConformerEncoderLayer() class (required).
        num_layers: the number of sub-encoder-layers in the encoder (required).

    Examples::
        >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
        >>> conformer_encoder = ConformerEncoder(encoder_layer, num_layers=6)
        >>> src = torch.rand(10, 32, 512)
        >>> pos_emb = torch.rand(32, 19, 512)
        >>> out = conformer_encoder(src, pos_emb)
    """

    def __init__(
        self,
        encoder_layer: nn.Module,
        num_layers: int,
        aux_layers: List[int],
    ) -> None:
        super().__init__()
        self.layers = nn.ModuleList(
            [copy.deepcopy(encoder_layer) for i in range(num_layers)]
        )
        self.num_layers = num_layers

        assert len(set(aux_layers)) == len(aux_layers)

        assert num_layers - 1 not in aux_layers
        self.aux_layers = aux_layers + [num_layers - 1]

        self.combiner = RandomCombine(
            num_inputs=len(self.aux_layers),
            final_weight=0.5,
            pure_prob=0.333,
            stddev=2.0,
        )

    def forward(
        self,
        src: Tensor,
        pos_emb: Tensor,
        mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        warmup: float = 1.0,
    ) -> Tensor:
        r"""Pass the input through the encoder layers in turn.

        Args:
            src: the sequence to the encoder (required).
            pos_emb: Positional embedding tensor (required).
            mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).

        Shape:
            src: (S, N, E).
            pos_emb: (N, 2*S-1, E)
            mask: (S, S).
            src_key_padding_mask: (N, S).
            S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number

        """
        output = src

        outputs = []
        layer_outputs = []
        
        for i, mod in enumerate(self.layers):
            output = mod(
                output,
                pos_emb,
                src_mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                warmup=warmup,
            )
            layer_outputs.append(output)

            if i in self.aux_layers:
                outputs.append(output)

        output = self.combiner(outputs)

        return output, layer_outputs

    @torch.jit.export
    def chunk_forward(
        self,
        src: Tensor,
        pos_emb: Tensor,
        states: List[Tensor],
        mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        warmup: float = 1.0,
        left_context: int = 0,
        right_context: int = 0,
    ) -> Tuple[Tensor, List[Tensor]]:
        r"""Pass the input through the encoder layers in turn.
        Args:
            src: the sequence to the encoder (required).
            pos_emb: Positional embedding tensor (required).
            states:
              The decode states for previous frames which contains the cached data.
              It has two elements, the first element is the attn_cache which has
              a shape of (encoder_layers, left_context, batch, attention_dim),
              the second element is the conv_cache which has a shape of
              (encoder_layers, cnn_module_kernel-1, batch, conv_dim).
              Note: states will be modified in this function.
            mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).
            warmup: controls selective bypass of of layers; if < 1.0, we will
              bypass layers more frequently.
            left_context:
              How many previous frames the attention can see in current chunk.
              Note: It's not that each individual frame has `left_context` frames
              of left context, some have more.
            right_context:
              How many future frames the attention can see in current chunk.
              Note: It's not that each individual frame has `right_context` frames
              of right context, some have more.
        Shape:
            src: (S, N, E).
            pos_emb: (N, 2*(S+left_context)-1, E).
            mask: (S, S).
            src_key_padding_mask: (N, S).
            S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number
        """
        assert not self.training
        assert len(states) == 2
        assert states[0].shape == (
            self.num_layers,
            left_context,
            src.size(1),
            src.size(2),
        )
        assert states[1].size(0) == self.num_layers

        output = src

        for layer_index, mod in enumerate(self.layers):
            cache = [states[0][layer_index], states[1][layer_index]]
            output, cache = mod.chunk_forward(
                output,
                pos_emb,
                states=cache,
                src_mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                warmup=warmup,
                left_context=left_context,
                right_context=right_context,
            )
            states[0][layer_index] = cache[0]
            states[1][layer_index] = cache[1]

        return output, states


class RelPositionalEncoding(torch.nn.Module):
    """Relative positional encoding module.

    See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
    Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py

    Args:
        d_model: Embedding dimension.
        dropout_rate: Dropout rate.
        max_len: Maximum input length.

    """

    def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None:
        """Construct an PositionalEncoding object."""
        super(RelPositionalEncoding, self).__init__()
        self.d_model = d_model
        self.dropout = torch.nn.Dropout(p=dropout_rate)
        self.pe = None
        self.extend_pe(torch.tensor(0.0).expand(1, max_len))

    def extend_pe(self, x: Tensor, left_context: int = 0) -> None:
        """Reset the positional encodings."""
        x_size_1 = x.size(1) + left_context
        if self.pe is not None:
            # self.pe contains both positive and negative parts
            # the length of self.pe is 2 * input_len - 1
            if self.pe.size(1) >= x_size_1 * 2 - 1:
                # Note: TorchScript doesn't implement operator== for torch.Device
                if self.pe.dtype != x.dtype or str(self.pe.device) != str(x.device):
                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                return
        # Suppose `i` means to the position of query vector and `j` means the
        # position of key vector. We use position relative positions when keys
        # are to the left (i>j) and negative relative positions otherwise (i<j).
        pe_positive = torch.zeros(x_size_1, self.d_model)
        pe_negative = torch.zeros(x_size_1, self.d_model)
        position = torch.arange(0, x_size_1, dtype=torch.float32).unsqueeze(1)
        div_term = torch.exp(
            torch.arange(0, self.d_model, 2, dtype=torch.float32)
            * -(math.log(10000.0) / self.d_model)
        )
        pe_positive[:, 0::2] = torch.sin(position * div_term)
        pe_positive[:, 1::2] = torch.cos(position * div_term)
        pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
        pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)

        # Reserve the order of positive indices and concat both positive and
        # negative indices. This is used to support the shifting trick
        # as in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
        pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
        pe_negative = pe_negative[1:].unsqueeze(0)
        pe = torch.cat([pe_positive, pe_negative], dim=1)
        self.pe = pe.to(device=x.device, dtype=x.dtype)

    def forward(self, x: torch.Tensor, left_context: int = 0) -> Tuple[Tensor, Tensor]:
        """Add positional encoding.

        Args:
            x (torch.Tensor): Input tensor (batch, time, `*`).
            left_context (int): left context (in frames) used during streaming decoding.
                this is used only in real streaming decoding, in other circumstances,
                it MUST be 0.

        Returns:
            torch.Tensor: Encoded tensor (batch, time, `*`).
            torch.Tensor: Encoded tensor (batch, 2*time-1, `*`).

        """
        self.extend_pe(x, left_context)
        x_size_1 = x.size(1) + left_context
        pos_emb = self.pe[
            :,
            self.pe.size(1) // 2
            - x_size_1
            + 1 : self.pe.size(1) // 2  # noqa E203
            + x.size(1),
        ]
        return self.dropout(x), self.dropout(pos_emb)


class RelPositionMultiheadAttention(nn.Module):
    r"""Multi-Head Attention layer with relative position encoding

    See reference: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"

    Args:
        embed_dim: total dimension of the model.
        num_heads: parallel attention heads.
        dropout: a Dropout layer on attn_output_weights. Default: 0.0.

    Examples::

        >>> rel_pos_multihead_attn = RelPositionMultiheadAttention(embed_dim, num_heads)
        >>> attn_output, attn_output_weights = multihead_attn(query, key, value, pos_emb)
    """

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
    ) -> None:
        super(RelPositionMultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads
        assert (
            self.head_dim * num_heads == self.embed_dim
        ), "embed_dim must be divisible by num_heads"

        self.in_proj = ScaledLinear(embed_dim, 3 * embed_dim, bias=True)
        self.out_proj = ScaledLinear(
            embed_dim, embed_dim, bias=True, initial_scale=0.25
        )

        # linear transformation for positional encoding.
        self.linear_pos = ScaledLinear(embed_dim, embed_dim, bias=False)
        # these two learnable bias are used in matrix c and matrix d
        # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
        self.pos_bias_u = nn.Parameter(torch.Tensor(num_heads, self.head_dim))
        self.pos_bias_v = nn.Parameter(torch.Tensor(num_heads, self.head_dim))
        self.pos_bias_u_scale = nn.Parameter(torch.zeros(()).detach())
        self.pos_bias_v_scale = nn.Parameter(torch.zeros(()).detach())
        self._reset_parameters()

    def _pos_bias_u(self):
        return self.pos_bias_u * self.pos_bias_u_scale.exp()

    def _pos_bias_v(self):
        return self.pos_bias_v * self.pos_bias_v_scale.exp()

    def _reset_parameters(self) -> None:
        nn.init.normal_(self.pos_bias_u, std=0.01)
        nn.init.normal_(self.pos_bias_v, std=0.01)

    def forward(
        self,
        query: Tensor,
        key: Tensor,
        value: Tensor,
        pos_emb: Tensor,
        key_padding_mask: Optional[Tensor] = None,
        need_weights: bool = True,
        attn_mask: Optional[Tensor] = None,
        left_context: int = 0,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query, key, value: map a query and a set of key-value pairs to an output.
            pos_emb: Positional embedding tensor
            key_padding_mask: if provided, specified padding elements in the key will
                be ignored by the attention. When given a binary mask and a value is True,
                the corresponding value on the attention layer will be ignored. When given
                a byte mask and a value is non-zero, the corresponding value on the attention
                layer will be ignored
            need_weights: output attn_output_weights.
            attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
                the batches while a 3D mask allows to specify a different mask for the entries of each batch.
            left_context (int): left context (in frames) used during streaming decoding.
                this is used only in real streaming decoding, in other circumstances,
                it MUST be 0.

        Shape:
            - Inputs:
            - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
            the embedding dimension.
            - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
            the embedding dimension.
            - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
            the embedding dimension.
            - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is
            the embedding dimension.
            - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
            If a ByteTensor is provided, the non-zero positions will be ignored while the position
            with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
            value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
            - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
            3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
            S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
            positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
            while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
            is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
            is provided, it will be added to the attention weight.

            - Outputs:
            - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
            E is the embedding dimension.
            - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
            L is the target sequence length, S is the source sequence length.
        """
        return self.multi_head_attention_forward(
            query,
            key,
            value,
            pos_emb,
            self.embed_dim,
            self.num_heads,
            self.in_proj.get_weight(),
            self.in_proj.get_bias(),
            self.dropout,
            self.out_proj.get_weight(),
            self.out_proj.get_bias(),
            training=self.training,
            key_padding_mask=key_padding_mask,
            need_weights=need_weights,
            attn_mask=attn_mask,
            left_context=left_context,
        )

    def rel_shift(self, x: Tensor, left_context: int = 0) -> Tensor:
        """Compute relative positional encoding.

        Args:
            x: Input tensor (batch, head, time1, 2*time1-1).
                time1 means the length of query vector.
            left_context (int): left context (in frames) used during streaming decoding.
                this is used only in real streaming decoding, in other circumstances,
                it MUST be 0.

        Returns:
            Tensor: tensor of shape (batch, head, time1, time2)
          (note: time2 has the same value as time1, but it is for
          the key, while time1 is for the query).
        """
        (batch_size, num_heads, time1, n) = x.shape
        time2 = time1 + left_context
        assert (
            n == left_context + 2 * time1 - 1
        ), f"{n} == {left_context} + 2 * {time1} - 1"
        # Note: TorchScript requires explicit arg for stride()
        batch_stride = x.stride(0)
        head_stride = x.stride(1)
        time1_stride = x.stride(2)
        n_stride = x.stride(3)
        return x.as_strided(
            (batch_size, num_heads, time1, time2),
            (batch_stride, head_stride, time1_stride - n_stride, n_stride),
            storage_offset=n_stride * (time1 - 1),
        )

    def multi_head_attention_forward(
        self,
        query: Tensor,
        key: Tensor,
        value: Tensor,
        pos_emb: Tensor,
        embed_dim_to_check: int,
        num_heads: int,
        in_proj_weight: Tensor,
        in_proj_bias: Tensor,
        dropout_p: float,
        out_proj_weight: Tensor,
        out_proj_bias: Tensor,
        training: bool = True,
        key_padding_mask: Optional[Tensor] = None,
        need_weights: bool = True,
        attn_mask: Optional[Tensor] = None,
        left_context: int = 0,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query, key, value: map a query and a set of key-value pairs to an output.
            pos_emb: Positional embedding tensor
            embed_dim_to_check: total dimension of the model.
            num_heads: parallel attention heads.
            in_proj_weight, in_proj_bias: input projection weight and bias.
            dropout_p: probability of an element to be zeroed.
            out_proj_weight, out_proj_bias: the output projection weight and bias.
            training: apply dropout if is ``True``.
            key_padding_mask: if provided, specified padding elements in the key will
                be ignored by the attention. This is an binary mask. When the value is True,
                the corresponding value on the attention layer will be filled with -inf.
            need_weights: output attn_output_weights.
            attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
                the batches while a 3D mask allows to specify a different mask for the entries of each batch.
            left_context (int): left context (in frames) used during streaming decoding.
                this is used only in real streaming decoding, in other circumstances,
                it MUST be 0.

        Shape:
            Inputs:
            - query: :math:`(L, N, E)` where L is the target sequence length, N is the batch size, E is
            the embedding dimension.
            - key: :math:`(S, N, E)`, where S is the source sequence length, N is the batch size, E is
            the embedding dimension.
            - value: :math:`(S, N, E)` where S is the source sequence length, N is the batch size, E is
            the embedding dimension.
            - pos_emb: :math:`(N, 2*L-1, E)` or :math:`(1, 2*L-1, E)` where L is the target sequence
            length, N is the batch size, E is the embedding dimension.
            - key_padding_mask: :math:`(N, S)` where N is the batch size, S is the source sequence length.
            If a ByteTensor is provided, the non-zero positions will be ignored while the zero positions
            will be unchanged. If a BoolTensor is provided, the positions with the
            value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged.
            - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
            3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
            S is the source sequence length. attn_mask ensures that position i is allowed to attend the unmasked
            positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
            while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
            are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
            is provided, it will be added to the attention weight.

            Outputs:
            - attn_output: :math:`(L, N, E)` where L is the target sequence length, N is the batch size,
            E is the embedding dimension.
            - attn_output_weights: :math:`(N, L, S)` where N is the batch size,
            L is the target sequence length, S is the source sequence length.
        """

        tgt_len, bsz, embed_dim = query.size()
        assert embed_dim == embed_dim_to_check
        assert key.size(0) == value.size(0) and key.size(1) == value.size(1)

        head_dim = embed_dim // num_heads
        assert (
            head_dim * num_heads == embed_dim
        ), "embed_dim must be divisible by num_heads"

        scaling = float(head_dim) ** -0.5

        if torch.equal(query, key) and torch.equal(key, value):
            # self-attention
            q, k, v = nn.functional.linear(query, in_proj_weight, in_proj_bias).chunk(
                3, dim=-1
            )

        elif torch.equal(key, value):
            # encoder-decoder attention
            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = 0
            _end = embed_dim
            _w = in_proj_weight[_start:_end, :]
            if _b is not None:
                _b = _b[_start:_end]
            q = nn.functional.linear(query, _w, _b)

            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = embed_dim
            _end = None
            _w = in_proj_weight[_start:, :]
            if _b is not None:
                _b = _b[_start:]
            k, v = nn.functional.linear(key, _w, _b).chunk(2, dim=-1)

        else:
            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = 0
            _end = embed_dim
            _w = in_proj_weight[_start:_end, :]
            if _b is not None:
                _b = _b[_start:_end]
            q = nn.functional.linear(query, _w, _b)

            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = embed_dim
            _end = embed_dim * 2
            _w = in_proj_weight[_start:_end, :]
            if _b is not None:
                _b = _b[_start:_end]
            k = nn.functional.linear(key, _w, _b)

            # This is inline in_proj function with in_proj_weight and in_proj_bias
            _b = in_proj_bias
            _start = embed_dim * 2
            _end = None
            _w = in_proj_weight[_start:, :]
            if _b is not None:
                _b = _b[_start:]
            v = nn.functional.linear(value, _w, _b)

        if attn_mask is not None:
            assert (
                attn_mask.dtype == torch.float32
                or attn_mask.dtype == torch.float64
                or attn_mask.dtype == torch.float16
                or attn_mask.dtype == torch.uint8
                or attn_mask.dtype == torch.bool
            ), "Only float, byte, and bool types are supported for attn_mask, not {}".format(
                attn_mask.dtype
            )
            if attn_mask.dtype == torch.uint8:
                warnings.warn(
                    "Byte tensor for attn_mask is deprecated. Use bool tensor instead."
                )
                attn_mask = attn_mask.to(torch.bool)

            if attn_mask.dim() == 2:
                attn_mask = attn_mask.unsqueeze(0)
                if list(attn_mask.size()) != [1, query.size(0), key.size(0)]:
                    raise RuntimeError("The size of the 2D attn_mask is not correct.")
            elif attn_mask.dim() == 3:
                if list(attn_mask.size()) != [
                    bsz * num_heads,
                    query.size(0),
                    key.size(0),
                ]:
                    raise RuntimeError("The size of the 3D attn_mask is not correct.")
            else:
                raise RuntimeError(
                    "attn_mask's dimension {} is not supported".format(attn_mask.dim())
                )
            # attn_mask's dim is 3 now.

        # convert ByteTensor key_padding_mask to bool
        if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
            warnings.warn(
                "Byte tensor for key_padding_mask is deprecated. Use bool tensor instead."
            )
            key_padding_mask = key_padding_mask.to(torch.bool)

        q = (q * scaling).contiguous().view(tgt_len, bsz, num_heads, head_dim)
        k = k.contiguous().view(-1, bsz, num_heads, head_dim)
        v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)

        src_len = k.size(0)

        if key_padding_mask is not None:
            assert key_padding_mask.size(0) == bsz, "{} == {}".format(
                key_padding_mask.size(0), bsz
            )
            assert key_padding_mask.size(1) == src_len, "{} == {}".format(
                key_padding_mask.size(1), src_len
            )

        q = q.transpose(0, 1)  # (batch, time1, head, d_k)

        pos_emb_bsz = pos_emb.size(0)
        assert pos_emb_bsz in (1, bsz)  # actually it is 1
        p = self.linear_pos(pos_emb).view(pos_emb_bsz, -1, num_heads, head_dim)
        # (batch, 2*time1, head, d_k) --> (batch, head, d_k, 2*time -1)
        p = p.permute(0, 2, 3, 1)

        q_with_bias_u = (q + self._pos_bias_u()).transpose(
            1, 2
        )  # (batch, head, time1, d_k)

        q_with_bias_v = (q + self._pos_bias_v()).transpose(
            1, 2
        )  # (batch, head, time1, d_k)

        # compute attention score
        # first compute matrix a and matrix c
        # as described in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context" Section 3.3
        k = k.permute(1, 2, 3, 0)  # (batch, head, d_k, time2)
        matrix_ac = torch.matmul(q_with_bias_u, k)  # (batch, head, time1, time2)

        # compute matrix b and matrix d
        matrix_bd = torch.matmul(q_with_bias_v, p)  # (batch, head, time1, 2*time1-1)
        matrix_bd = self.rel_shift(matrix_bd, left_context)

        attn_output_weights = matrix_ac + matrix_bd  # (batch, head, time1, time2)

        attn_output_weights = attn_output_weights.view(bsz * num_heads, tgt_len, -1)

        assert list(attn_output_weights.size()) == [
            bsz * num_heads,
            tgt_len,
            src_len,
        ]

        if attn_mask is not None:
            if attn_mask.dtype == torch.bool:
                attn_output_weights.masked_fill_(attn_mask, float("-inf"))
            else:
                attn_output_weights += attn_mask

        if key_padding_mask is not None:
            attn_output_weights = attn_output_weights.view(
                bsz, num_heads, tgt_len, src_len
            )
            attn_output_weights = attn_output_weights.masked_fill(
                key_padding_mask.unsqueeze(1).unsqueeze(2),
                float("-inf"),
            )
            attn_output_weights = attn_output_weights.view(
                bsz * num_heads, tgt_len, src_len
            )

        attn_output_weights = nn.functional.softmax(attn_output_weights, dim=-1)

        # If we are using dynamic_chunk_training and setting a limited
        # num_left_chunks, the attention may only see the padding values which
        # will also be masked out by `key_padding_mask`, at this circumstances,
        # the whole column of `attn_output_weights` will be `-inf`
        # (i.e. be `nan` after softmax), so, we fill `0.0` at the masking
        # positions to avoid invalid loss value below.
        if (
            attn_mask is not None
            and attn_mask.dtype == torch.bool
            and key_padding_mask is not None
        ):
            if attn_mask.size(0) != 1:
                attn_mask = attn_mask.view(bsz, num_heads, tgt_len, src_len)
                combined_mask = attn_mask | key_padding_mask.unsqueeze(1).unsqueeze(2)
            else:
                # attn_mask.shape == (1, tgt_len, src_len)
                combined_mask = attn_mask.unsqueeze(0) | key_padding_mask.unsqueeze(
                    1
                ).unsqueeze(2)

            attn_output_weights = attn_output_weights.view(
                bsz, num_heads, tgt_len, src_len
            )
            attn_output_weights = attn_output_weights.masked_fill(combined_mask, 0.0)
            attn_output_weights = attn_output_weights.view(
                bsz * num_heads, tgt_len, src_len
            )

        attn_output_weights = nn.functional.dropout(
            attn_output_weights, p=dropout_p, training=training
        )

        attn_output = torch.bmm(attn_output_weights, v)
        assert list(attn_output.size()) == [bsz * num_heads, tgt_len, head_dim]
        attn_output = (
            attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        )
        attn_output = nn.functional.linear(attn_output, out_proj_weight, out_proj_bias)

        if need_weights:
            # average attention weights over heads
            attn_output_weights = attn_output_weights.view(
                bsz, num_heads, tgt_len, src_len
            )
            return attn_output, attn_output_weights.sum(dim=1) / num_heads
        else:
            return attn_output, None


class ConvolutionModule(nn.Module):
    """ConvolutionModule in Conformer model.
    Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py

    Args:
        channels (int): The number of channels of conv layers.
        kernel_size (int): Kernerl size of conv layers.
        bias (bool): Whether to use bias in conv layers (default=True).
        causal (bool): Whether to use causal convolution.

    """

    def __init__(
        self,
        channels: int,
        kernel_size: int,
        bias: bool = True,
        causal: bool = False,
    ) -> None:
        """Construct an ConvolutionModule object."""
        super(ConvolutionModule, self).__init__()
        # kernerl_size should be a odd number for 'SAME' padding
        assert (kernel_size - 1) % 2 == 0

        self.causal = causal

        self.pointwise_conv1 = ScaledConv1d(
            channels,
            2 * channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=bias,
        )

        # after pointwise_conv1 we put x through a gated linear unit (nn.functional.glu).
        # For most layers the normal rms value of channels of x seems to be in the range 1 to 4,
        # but sometimes, for some reason, for layer 0 the rms ends up being very large,
        # between 50 and 100 for different channels.  This will cause very peaky and
        # sparse derivatives for the sigmoid gating function, which will tend to make
        # the loss function not learn effectively.  (for most layers the average absolute values
        # are in the range 0.5..9.0, and the average p(x>0), i.e. positive proportion,
        # at the output of pointwise_conv1.output is around 0.35 to 0.45 for different
        # layers, which likely breaks down as 0.5 for the "linear" half and
        # 0.2 to 0.3 for the part that goes into the sigmoid.  The idea is that if we
        # constrain the rms values to a reasonable range via a constraint of max_abs=10.0,
        # it will be in a better position to start learning something, i.e. to latch onto
        # the correct range.
        self.deriv_balancer1 = ActivationBalancer(
            channel_dim=1, max_abs=10.0, min_positive=0.05, max_positive=1.0
        )

        self.lorder = kernel_size - 1
        padding = (kernel_size - 1) // 2
        if self.causal:
            padding = 0

        self.depthwise_conv = ScaledConv1d(
            channels,
            channels,
            kernel_size,
            stride=1,
            padding=padding,
            groups=channels,
            bias=bias,
        )

        self.deriv_balancer2 = ActivationBalancer(
            channel_dim=1, min_positive=0.05, max_positive=1.0
        )

        self.activation = DoubleSwish()

        self.pointwise_conv2 = ScaledConv1d(
            channels,
            channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=bias,
            initial_scale=0.25,
        )

    def forward(
        self,
        x: Tensor,
        cache: Optional[Tensor] = None,
        right_context: int = 0,
        src_key_padding_mask: Optional[Tensor] = None,
    ) -> Tuple[Tensor, Tensor]:
        """Compute convolution module.

        Args:
            x: Input tensor (#time, batch, channels).
            cache: The cache of depthwise_conv, only used in real streaming
                decoding.
            right_context:
              How many future frames the attention can see in current chunk.
              Note: It's not that each individual frame has `right_context` frames
              of right context, some have more.
            src_key_padding_mask: the mask for the src keys per batch (optional).

        Returns:
            Tensor: Output tensor (#time, batch, channels).
            If cache is None return the output tensor (#time, batch, channels).
            If cache is not None, return a tuple of Tensor, the first one is
            the output tensor (#time, batch, channels), the second one is the
            new cache for next chunk (#kernel_size - 1, batch, channels).
        """
        # exchange the temporal dimension and the feature dimension
        x = x.permute(1, 2, 0)  # (#batch, channels, time).

        # GLU mechanism
        x = self.pointwise_conv1(x)  # (batch, 2*channels, time)

        x = self.deriv_balancer1(x)
        x = nn.functional.glu(x, dim=1)  # (batch, channels, time)

        # 1D Depthwise Conv
        if src_key_padding_mask is not None:
            x.masked_fill_(src_key_padding_mask.unsqueeze(1).expand_as(x), 0.0)
        if self.causal and self.lorder > 0:
            if cache is None:
                # Make depthwise_conv causal by
                # manualy padding self.lorder zeros to the left
                x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
            else:
                assert not self.training, "Cache should be None in training time"
                assert cache.size(0) == self.lorder
                x = torch.cat([cache.permute(1, 2, 0), x], dim=2)
                if right_context > 0:
                    cache = x.permute(2, 0, 1)[
                        -(self.lorder + right_context) : (-right_context),  # noqa
                        ...,
                    ]
                else:
                    cache = x.permute(2, 0, 1)[-self.lorder :, ...]  # noqa

        x = self.depthwise_conv(x)

        x = self.deriv_balancer2(x)
        x = self.activation(x)

        x = self.pointwise_conv2(x)  # (batch, channel, time)

        # torch.jit.script requires return types be the same as annotated above
        if cache is None:
            cache = torch.empty(0)

        return x.permute(2, 0, 1), cache


class Conv2dSubsampling(nn.Module):
    """Convolutional 2D subsampling (to 1/4 length).

    Convert an input of shape (N, T, idim) to an output
    with shape (N, T', odim), where
    T' = ((T-1)//2 - 1)//2, which approximates T' == T//4

    It is based on
    https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/subsampling.py  # noqa
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        layer1_channels: int = 8,
        layer2_channels: int = 32,
        layer3_channels: int = 128,
    ) -> None:
        """
        Args:
          in_channels:
            Number of channels in. The input shape is (N, T, in_channels).
            Caution: It requires: T >=7, in_channels >=7
          out_channels
            Output dim. The output shape is (N, ((T-1)//2 - 1)//2, out_channels)
          layer1_channels:
            Number of channels in layer1
          layer1_channels:
            Number of channels in layer2
        """
        assert in_channels >= 7
        super().__init__()

        self.conv = nn.Sequential(
            ScaledConv2d(
                in_channels=1,
                out_channels=layer1_channels,
                kernel_size=3,
                padding=1,
            ),
            ActivationBalancer(channel_dim=1),
            DoubleSwish(),
            ScaledConv2d(
                in_channels=layer1_channels,
                out_channels=layer2_channels,
                kernel_size=3,
                stride=2,
            ),
            ActivationBalancer(channel_dim=1),
            DoubleSwish(),
            ScaledConv2d(
                in_channels=layer2_channels,
                out_channels=layer3_channels,
                kernel_size=3,
                stride=2,
            ),
            ActivationBalancer(channel_dim=1),
            DoubleSwish(),
        )
        self.out = ScaledLinear(
            layer3_channels * (((in_channels - 1) // 2 - 1) // 2), out_channels
        )
        # set learn_eps=False because out_norm is preceded by `out`, and `out`
        # itself has learned scale, so the extra degree of freedom is not
        # needed.
        self.out_norm = BasicNorm(out_channels, learn_eps=False)
        # constrain median of output to be close to zero.
        self.out_balancer = ActivationBalancer(
            channel_dim=-1, min_positive=0.45, max_positive=0.55
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Subsample x.

        Args:
          x:
            Its shape is (N, T, idim).

        Returns:
          Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
        """
        # On entry, x is (N, T, idim)
        x = x.unsqueeze(1)  # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
        x = self.conv(x)
        # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2)
        b, c, t, f = x.size()
        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
        # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
        x = self.out_norm(x)
        x = self.out_balancer(x)
        return x


class RandomCombine(nn.Module):
    """
    This module combines a list of Tensors, all with the same shape, to
    produce a single output of that same shape which, in training time,
    is a random combination of all the inputs; but which in test time
    will be just the last input.

    The idea is that the list of Tensors will be a list of outputs of multiple
    conformer layers.  This has a similar effect as iterated loss. (See:
    DEJA-VU: DOUBLE FEATURE PRESENTATION AND ITERATED LOSS IN DEEP TRANSFORMER
    NETWORKS).
    """

    def __init__(
        self,
        num_inputs: int,
        final_weight: float = 0.5,
        pure_prob: float = 0.5,
        stddev: float = 2.0,
    ) -> None:
        """
        Args:
          num_inputs:
            The number of tensor inputs, which equals the number of layers'
            outputs that are fed into this module.  E.g. in an 18-layer neural
            net if we output layers 16, 12, 18, num_inputs would be 3.
          final_weight:
            The amount of weight or probability we assign to the
            final layer when randomly choosing layers or when choosing
            continuous layer weights.
          pure_prob:
            The probability, on each frame, with which we choose
            only a single layer to output (rather than an interpolation)
          stddev:
            A standard deviation that we add to log-probs for computing
            randomized weights.

        The method of choosing which layers, or combinations of layers, to use,
        is conceptually as follows::

            With probability `pure_prob`::
               With probability `final_weight`: choose final layer,
               Else: choose random non-final layer.
            Else::
               Choose initial log-weights that correspond to assigning
               weight `final_weight` to the final layer and equal
               weights to other layers; then add Gaussian noise
               with variance `stddev` to these log-weights, and normalize
               to weights (note: the average weight assigned to the
               final layer here will not be `final_weight` if stddev>0).
        """
        super().__init__()
        assert 0 <= pure_prob <= 1, pure_prob
        assert 0 < final_weight < 1, final_weight
        assert num_inputs >= 1

        self.num_inputs = num_inputs
        self.final_weight = final_weight
        self.pure_prob = pure_prob
        self.stddev = stddev

        self.final_log_weight = (
            torch.tensor((final_weight / (1 - final_weight)) * (self.num_inputs - 1))
            .log()
            .item()
        )

    def forward(self, inputs: List[Tensor]) -> Tensor:
        """Forward function.
        Args:
          inputs:
            A list of Tensor, e.g. from various layers of a transformer.
            All must be the same shape, of (*, num_channels)
        Returns:
          A Tensor of shape (*, num_channels).  In test mode
          this is just the final input.
        """
        num_inputs = self.num_inputs
        assert len(inputs) == num_inputs
        if not self.training or torch.jit.is_scripting():
            return inputs[-1]

        # Shape of weights: (*, num_inputs)
        num_channels = inputs[0].shape[-1]
        num_frames = inputs[0].numel() // num_channels

        ndim = inputs[0].ndim
        # stacked_inputs: (num_frames, num_channels, num_inputs)
        stacked_inputs = torch.stack(inputs, dim=ndim).reshape(
            (num_frames, num_channels, num_inputs)
        )

        # weights: (num_frames, num_inputs)
        weights = self._get_random_weights(
            inputs[0].dtype, inputs[0].device, num_frames
        )

        weights = weights.reshape(num_frames, num_inputs, 1)
        # ans: (num_frames, num_channels, 1)
        ans = torch.matmul(stacked_inputs, weights)
        # ans: (*, num_channels)

        ans = ans.reshape(inputs[0].shape[:-1] + (num_channels,))

        # The following if causes errors for torch script in torch 1.6.0
        #  if __name__ == "__main__":
        #      # for testing only...
        #      print("Weights = ", weights.reshape(num_frames, num_inputs))
        return ans

    def _get_random_weights(
        self, dtype: torch.dtype, device: torch.device, num_frames: int
    ) -> Tensor:
        """Return a tensor of random weights, of shape
        `(num_frames, self.num_inputs)`,
        Args:
          dtype:
            The data-type desired for the answer, e.g. float, double.
          device:
            The device needed for the answer.
          num_frames:
            The number of sets of weights desired
        Returns:
          A tensor of shape (num_frames, self.num_inputs), such that
          `ans.sum(dim=1)` is all ones.
        """
        pure_prob = self.pure_prob
        if pure_prob == 0.0:
            return self._get_random_mixed_weights(dtype, device, num_frames)
        elif pure_prob == 1.0:
            return self._get_random_pure_weights(dtype, device, num_frames)
        else:
            p = self._get_random_pure_weights(dtype, device, num_frames)
            m = self._get_random_mixed_weights(dtype, device, num_frames)
            return torch.where(
                torch.rand(num_frames, 1, device=device) < self.pure_prob, p, m
            )

    def _get_random_pure_weights(
        self, dtype: torch.dtype, device: torch.device, num_frames: int
    ):
        """Return a tensor of random one-hot weights, of shape
        `(num_frames, self.num_inputs)`,
        Args:
          dtype:
            The data-type desired for the answer, e.g. float, double.
          device:
            The device needed for the answer.
          num_frames:
            The number of sets of weights desired.
        Returns:
          A one-hot tensor of shape `(num_frames, self.num_inputs)`, with
          exactly one weight equal to 1.0 on each frame.
        """
        final_prob = self.final_weight

        # final contains self.num_inputs - 1 in all elements
        final = torch.full((num_frames,), self.num_inputs - 1, device=device)
        # nonfinal contains random integers in [0..num_inputs - 2], these are for non-final weights.
        nonfinal = torch.randint(self.num_inputs - 1, (num_frames,), device=device)

        indexes = torch.where(
            torch.rand(num_frames, device=device) < final_prob, final, nonfinal
        )
        ans = torch.nn.functional.one_hot(indexes, num_classes=self.num_inputs).to(
            dtype=dtype
        )
        return ans

    def _get_random_mixed_weights(
        self, dtype: torch.dtype, device: torch.device, num_frames: int
    ):
        """Return a tensor of random one-hot weights, of shape
        `(num_frames, self.num_inputs)`,
        Args:
          dtype:
            The data-type desired for the answer, e.g. float, double.
          device:
            The device needed for the answer.
          num_frames:
            The number of sets of weights desired.
        Returns:
          A tensor of shape (num_frames, self.num_inputs), which elements
          in [0..1] that sum to one over the second axis, i.e.
          `ans.sum(dim=1)` is all ones.
        """
        logprobs = (
            torch.randn(num_frames, self.num_inputs, dtype=dtype, device=device)
            * self.stddev
        )
        logprobs[:, -1] += self.final_log_weight
        return logprobs.softmax(dim=1)


def _test_random_combine(final_weight: float, pure_prob: float, stddev: float):
    print(
        f"_test_random_combine: final_weight={final_weight}, pure_prob={pure_prob}, stddev={stddev}"
    )
    num_inputs = 3
    num_channels = 50
    m = RandomCombine(
        num_inputs=num_inputs,
        final_weight=final_weight,
        pure_prob=pure_prob,
        stddev=stddev,
    )

    x = [torch.ones(3, 4, num_channels) for _ in range(num_inputs)]

    y = m(x)
    assert y.shape == x[0].shape
    assert torch.allclose(y, x[0])  # .. since actually all ones.


def _test_random_combine_main():
    _test_random_combine(0.999, 0, 0.0)
    _test_random_combine(0.5, 0, 0.0)
    _test_random_combine(0.999, 0, 0.0)
    _test_random_combine(0.5, 0, 0.3)
    _test_random_combine(0.5, 1, 0.3)
    _test_random_combine(0.5, 0.5, 0.3)

    feature_dim = 50
    c = Conformer(num_features=feature_dim, d_model=128, nhead=4)
    batch_size = 5
    seq_len = 20
    # Just make sure the forward pass runs.
    f = c(
        torch.randn(batch_size, seq_len, feature_dim),
        torch.full((batch_size,), seq_len, dtype=torch.int64),
    )
    f  # to remove flake8 warnings


if __name__ == "__main__":
    feature_dim = 50
    c = Conformer(num_features=feature_dim, d_model=128, nhead=4)
    batch_size = 5
    seq_len = 20
    # Just make sure the forward pass runs.
    f = c(
        torch.randn(batch_size, seq_len, feature_dim),
        torch.full((batch_size,), seq_len, dtype=torch.int64),
        warmup=0.5,
    )

    _test_random_combine_main()
