# Copyright 2019 Shigeki Karita
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

"""Transformer encoder definition."""

from typing import List
from typing import Optional
from typing import Tuple

import torch
from torch import nn
import logging

from funasr_detach.models.transformer.attention import MultiHeadedAttention
from funasr_detach.models.transformer.embedding import PositionalEncoding
from funasr_detach.models.transformer.layer_norm import LayerNorm
from funasr_detach.models.transformer.utils.multi_layer_conv import Conv1dLinear
from funasr_detach.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d
from funasr_detach.models.transformer.utils.nets_utils import make_pad_mask
from funasr_detach.models.transformer.positionwise_feed_forward import (
    PositionwiseFeedForward,  # noqa: H301
)
from funasr_detach.models.transformer.utils.repeat import repeat
from funasr_detach.models.transformer.utils.dynamic_conv import DynamicConvolution
from funasr_detach.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D
from funasr_detach.models.transformer.utils.lightconv import LightweightConvolution
from funasr_detach.models.transformer.utils.lightconv2d import LightweightConvolution2D
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling2
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling6
from funasr_detach.models.transformer.utils.subsampling import Conv2dSubsampling8
from funasr_detach.models.transformer.utils.subsampling import TooShortUttError
from funasr_detach.models.transformer.utils.subsampling import check_short_utt


class EncoderLayer(nn.Module):
    """Encoder layer module.

    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
            can be used as the argument.
        feed_forward (torch.nn.Module): Feed-forward module instance.
            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
            can be used as the argument.
        dropout_rate (float): Dropout rate.
        normalize_before (bool): Whether to use layer_norm before the first block.
        concat_after (bool): Whether to concat attention layer's input and output.
            if True, additional linear will be applied.
            i.e. x -> x + linear(concat(x, att(x)))
            if False, no additional linear will be applied. i.e. x -> x + att(x)
        stochastic_depth_rate (float): Proability to skip this layer.
            During training, the layer may skip residual computation and return input
            as-is with given probability.
    """

    def __init__(
        self,
        size,
        self_attn,
        feed_forward,
        dropout_rate,
        normalize_before=True,
        concat_after=False,
        stochastic_depth_rate=0.0,
    ):
        """Construct an EncoderLayer object."""
        super(EncoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)
        self.norm2 = LayerNorm(size)
        self.dropout = nn.Dropout(dropout_rate)
        self.size = size
        self.normalize_before = normalize_before
        self.concat_after = concat_after
        if self.concat_after:
            self.concat_linear = nn.Linear(size + size, size)
        self.stochastic_depth_rate = stochastic_depth_rate

    def forward(self, x, mask, cache=None):
        """Compute encoded features.

        Args:
            x_input (torch.Tensor): Input tensor (#batch, time, size).
            mask (torch.Tensor): Mask tensor for the input (#batch, time).
            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).

        Returns:
            torch.Tensor: Output tensor (#batch, time, size).
            torch.Tensor: Mask tensor (#batch, time).

        """
        skip_layer = False
        # with stochastic depth, residual connection `x + f(x)` becomes
        # `x <- x + 1 / (1 - p) * f(x)` at training time.
        stoch_layer_coeff = 1.0
        if self.training and self.stochastic_depth_rate > 0:
            skip_layer = torch.rand(1).item() < self.stochastic_depth_rate
            stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate)

        if skip_layer:
            if cache is not None:
                x = torch.cat([cache, x], dim=1)
            return x, mask

        residual = x
        if self.normalize_before:
            x = self.norm1(x)

        if cache is None:
            x_q = x
        else:
            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
            x_q = x[:, -1:, :]
            residual = residual[:, -1:, :]
            mask = None if mask is None else mask[:, -1:, :]

        if self.concat_after:
            x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
            x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
        else:
            x = residual + stoch_layer_coeff * self.dropout(
                self.self_attn(x_q, x, x, mask)
            )
        if not self.normalize_before:
            x = self.norm1(x)

        residual = x
        if self.normalize_before:
            x = self.norm2(x)
        x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm2(x)

        if cache is not None:
            x = torch.cat([cache, x], dim=1)

        return x, mask


class TransformerEncoder_lm(nn.Module):
    """Transformer encoder module.

    Args:
        idim (int): Input dimension.
        attention_dim (int): Dimension of attention.
        attention_heads (int): The number of heads of multi head attention.
        conv_wshare (int): The number of kernel of convolution. Only used in
            selfattention_layer_type == "lightconv*" or "dynamiconv*".
        conv_kernel_length (Union[int, str]): Kernel size str of convolution
            (e.g. 71_71_71_71_71_71). Only used in selfattention_layer_type
            == "lightconv*" or "dynamiconv*".
        conv_usebias (bool): Whether to use bias in convolution. Only used in
            selfattention_layer_type == "lightconv*" or "dynamiconv*".
        linear_units (int): The number of units of position-wise feed forward.
        num_blocks (int): The number of decoder blocks.
        dropout_rate (float): Dropout rate.
        positional_dropout_rate (float): Dropout rate after adding positional encoding.
        attention_dropout_rate (float): Dropout rate in attention.
        input_layer (Union[str, torch.nn.Module]): Input layer type.
        pos_enc_class (torch.nn.Module): Positional encoding module class.
            `PositionalEncoding `or `ScaledPositionalEncoding`
        normalize_before (bool): Whether to use layer_norm before the first block.
        concat_after (bool): Whether to concat attention layer's input and output.
            if True, additional linear will be applied.
            i.e. x -> x + linear(concat(x, att(x)))
            if False, no additional linear will be applied. i.e. x -> x + att(x)
        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
        selfattention_layer_type (str): Encoder attention layer type.
        padding_idx (int): Padding idx for input_layer=embed.
        stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
        intermediate_layers (Union[List[int], None]): indices of intermediate CTC layer.
            indices start from 1.
            if not None, intermediate outputs are returned (which changes return type
            signature.)

    """

    def __init__(
        self,
        idim,
        attention_dim=256,
        attention_heads=4,
        conv_wshare=4,
        conv_kernel_length="11",
        conv_usebias=False,
        linear_units=2048,
        num_blocks=6,
        dropout_rate=0.1,
        positional_dropout_rate=0.1,
        attention_dropout_rate=0.0,
        input_layer="conv2d",
        pos_enc_class=PositionalEncoding,
        normalize_before=True,
        concat_after=False,
        positionwise_layer_type="linear",
        positionwise_conv_kernel_size=1,
        selfattention_layer_type="selfattn",
        padding_idx=-1,
        stochastic_depth_rate=0.0,
        intermediate_layers=None,
        ctc_softmax=None,
        conditioning_layer_dim=None,
    ):
        """Construct an Encoder object."""
        super().__init__()

        self.conv_subsampling_factor = 1
        if input_layer == "linear":
            self.embed = torch.nn.Sequential(
                torch.nn.Linear(idim, attention_dim),
                torch.nn.LayerNorm(attention_dim),
                torch.nn.Dropout(dropout_rate),
                torch.nn.ReLU(),
                pos_enc_class(attention_dim, positional_dropout_rate),
            )
        elif input_layer == "conv2d":
            self.embed = Conv2dSubsampling(idim, attention_dim, dropout_rate)
            self.conv_subsampling_factor = 4
        elif input_layer == "conv2d-scaled-pos-enc":
            self.embed = Conv2dSubsampling(
                idim,
                attention_dim,
                dropout_rate,
                pos_enc_class(attention_dim, positional_dropout_rate),
            )
            self.conv_subsampling_factor = 4
        elif input_layer == "conv2d6":
            self.embed = Conv2dSubsampling6(idim, attention_dim, dropout_rate)
            self.conv_subsampling_factor = 6
        elif input_layer == "conv2d8":
            self.embed = Conv2dSubsampling8(idim, attention_dim, dropout_rate)
            self.conv_subsampling_factor = 8
        elif input_layer == "embed":
            self.embed = torch.nn.Sequential(
                torch.nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
                pos_enc_class(attention_dim, positional_dropout_rate),
            )
        elif isinstance(input_layer, torch.nn.Module):
            self.embed = torch.nn.Sequential(
                input_layer,
                pos_enc_class(attention_dim, positional_dropout_rate),
            )
        elif input_layer is None:
            self.embed = torch.nn.Sequential(
                pos_enc_class(attention_dim, positional_dropout_rate)
            )
        else:
            raise ValueError("unknown input_layer: " + input_layer)
        self.normalize_before = normalize_before
        positionwise_layer, positionwise_layer_args = self.get_positionwise_layer(
            positionwise_layer_type,
            attention_dim,
            linear_units,
            dropout_rate,
            positionwise_conv_kernel_size,
        )
        if selfattention_layer_type in [
            "selfattn",
            "rel_selfattn",
            "legacy_rel_selfattn",
        ]:
            logging.info("encoder self-attention layer type = self-attention")
            encoder_selfattn_layer = MultiHeadedAttention
            encoder_selfattn_layer_args = [
                (
                    attention_heads,
                    attention_dim,
                    attention_dropout_rate,
                )
            ] * num_blocks
        elif selfattention_layer_type == "lightconv":
            logging.info("encoder self-attention layer type = lightweight convolution")
            encoder_selfattn_layer = LightweightConvolution
            encoder_selfattn_layer_args = [
                (
                    conv_wshare,
                    attention_dim,
                    attention_dropout_rate,
                    int(conv_kernel_length.split("_")[lnum]),
                    False,
                    conv_usebias,
                )
                for lnum in range(num_blocks)
            ]
        elif selfattention_layer_type == "lightconv2d":
            logging.info(
                "encoder self-attention layer "
                "type = lightweight convolution 2-dimensional"
            )
            encoder_selfattn_layer = LightweightConvolution2D
            encoder_selfattn_layer_args = [
                (
                    conv_wshare,
                    attention_dim,
                    attention_dropout_rate,
                    int(conv_kernel_length.split("_")[lnum]),
                    False,
                    conv_usebias,
                )
                for lnum in range(num_blocks)
            ]
        elif selfattention_layer_type == "dynamicconv":
            logging.info("encoder self-attention layer type = dynamic convolution")
            encoder_selfattn_layer = DynamicConvolution
            encoder_selfattn_layer_args = [
                (
                    conv_wshare,
                    attention_dim,
                    attention_dropout_rate,
                    int(conv_kernel_length.split("_")[lnum]),
                    False,
                    conv_usebias,
                )
                for lnum in range(num_blocks)
            ]
        elif selfattention_layer_type == "dynamicconv2d":
            logging.info(
                "encoder self-attention layer type = dynamic convolution 2-dimensional"
            )
            encoder_selfattn_layer = DynamicConvolution2D
            encoder_selfattn_layer_args = [
                (
                    conv_wshare,
                    attention_dim,
                    attention_dropout_rate,
                    int(conv_kernel_length.split("_")[lnum]),
                    False,
                    conv_usebias,
                )
                for lnum in range(num_blocks)
            ]
        else:
            raise NotImplementedError(selfattention_layer_type)

        self.encoders = repeat(
            num_blocks,
            lambda lnum: EncoderLayer(
                attention_dim,
                encoder_selfattn_layer(*encoder_selfattn_layer_args[lnum]),
                positionwise_layer(*positionwise_layer_args),
                dropout_rate,
                normalize_before,
                concat_after,
                stochastic_depth_rate * float(1 + lnum) / num_blocks,
            ),
        )
        if self.normalize_before:
            self.after_norm = LayerNorm(attention_dim)

        self.intermediate_layers = intermediate_layers
        self.use_conditioning = True if ctc_softmax is not None else False
        if self.use_conditioning:
            self.ctc_softmax = ctc_softmax
            self.conditioning_layer = torch.nn.Linear(
                conditioning_layer_dim, attention_dim
            )

    def get_positionwise_layer(
        self,
        positionwise_layer_type="linear",
        attention_dim=256,
        linear_units=2048,
        dropout_rate=0.1,
        positionwise_conv_kernel_size=1,
    ):
        """Define positionwise layer."""
        if positionwise_layer_type == "linear":
            positionwise_layer = PositionwiseFeedForward
            positionwise_layer_args = (attention_dim, linear_units, dropout_rate)
        elif positionwise_layer_type == "conv1d":
            positionwise_layer = MultiLayeredConv1d
            positionwise_layer_args = (
                attention_dim,
                linear_units,
                positionwise_conv_kernel_size,
                dropout_rate,
            )
        elif positionwise_layer_type == "conv1d-linear":
            positionwise_layer = Conv1dLinear
            positionwise_layer_args = (
                attention_dim,
                linear_units,
                positionwise_conv_kernel_size,
                dropout_rate,
            )
        else:
            raise NotImplementedError("Support only linear or conv1d.")
        return positionwise_layer, positionwise_layer_args

    def forward(self, xs, masks):
        """Encode input sequence.

        Args:
            xs (torch.Tensor): Input tensor (#batch, time, idim).
            masks (torch.Tensor): Mask tensor (#batch, time).

        Returns:
            torch.Tensor: Output tensor (#batch, time, attention_dim).
            torch.Tensor: Mask tensor (#batch, time).

        """
        if isinstance(
            self.embed,
            (Conv2dSubsampling, Conv2dSubsampling6, Conv2dSubsampling8),
        ):
            xs, masks = self.embed(xs, masks)
        else:
            xs = self.embed(xs)

        if self.intermediate_layers is None:
            xs, masks = self.encoders(xs, masks)
        else:
            intermediate_outputs = []
            for layer_idx, encoder_layer in enumerate(self.encoders):
                xs, masks = encoder_layer(xs, masks)

                if (
                    self.intermediate_layers is not None
                    and layer_idx + 1 in self.intermediate_layers
                ):
                    encoder_output = xs
                    # intermediate branches also require normalization.
                    if self.normalize_before:
                        encoder_output = self.after_norm(encoder_output)
                    intermediate_outputs.append(encoder_output)

                    if self.use_conditioning:
                        intermediate_result = self.ctc_softmax(encoder_output)
                        xs = xs + self.conditioning_layer(intermediate_result)

        if self.normalize_before:
            xs = self.after_norm(xs)

        if self.intermediate_layers is not None:
            return xs, masks, intermediate_outputs
        return xs, masks

    def forward_one_step(self, xs, masks, cache=None):
        """Encode input frame.

        Args:
            xs (torch.Tensor): Input tensor.
            masks (torch.Tensor): Mask tensor.
            cache (List[torch.Tensor]): List of cache tensors.

        Returns:
            torch.Tensor: Output tensor.
            torch.Tensor: Mask tensor.
            List[torch.Tensor]: List of new cache tensors.

        """
        if isinstance(self.embed, Conv2dSubsampling):
            xs, masks = self.embed(xs, masks)
        else:
            xs = self.embed(xs)
        if cache is None:
            cache = [None for _ in range(len(self.encoders))]
        new_cache = []
        for c, e in zip(cache, self.encoders):
            xs, masks = e(xs, masks, cache=c)
            new_cache.append(xs)
        if self.normalize_before:
            xs = self.after_norm(xs)
        return xs, masks, new_cache
