#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

from typing import List
from typing import Tuple
import logging
import torch
import torch.nn as nn
import numpy as np

from funasr_detach.models.scama import utils as myutils
from funasr_detach.models.transformer.decoder import BaseTransformerDecoder

from funasr_detach.models.sanm.attention import (
    MultiHeadedAttentionSANMDecoder,
    MultiHeadedAttentionCrossAtt,
)
from funasr_detach.models.transformer.embedding import PositionalEncoding
from funasr_detach.models.transformer.layer_norm import LayerNorm
from funasr_detach.models.sanm.positionwise_feed_forward import (
    PositionwiseFeedForwardDecoderSANM,
)
from funasr_detach.models.transformer.utils.repeat import repeat

from funasr_detach.register import tables


class DecoderLayerSANM(nn.Module):
    """Single decoder layer module.

    Args:
        size (int): Input dimension.
        self_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` instance can be used as the argument.
        src_attn (torch.nn.Module): Self-attention module instance.
            `MultiHeadedAttention` 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)


    """

    def __init__(
        self,
        size,
        self_attn,
        src_attn,
        feed_forward,
        dropout_rate,
        normalize_before=True,
        concat_after=False,
    ):
        """Construct an DecoderLayer object."""
        super(DecoderLayerSANM, self).__init__()
        self.size = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)
        if self_attn is not None:
            self.norm2 = LayerNorm(size)
        if src_attn is not None:
            self.norm3 = LayerNorm(size)
        self.dropout = nn.Dropout(dropout_rate)
        self.normalize_before = normalize_before
        self.concat_after = concat_after
        if self.concat_after:
            self.concat_linear1 = nn.Linear(size + size, size)
            self.concat_linear2 = nn.Linear(size + size, size)

    def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
        """Compute decoded features.

        Args:
            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
            cache (List[torch.Tensor]): List of cached tensors.
                Each tensor shape should be (#batch, maxlen_out - 1, size).

        Returns:
            torch.Tensor: Output tensor(#batch, maxlen_out, size).
            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
            torch.Tensor: Encoded memory mask (#batch, maxlen_in).

        """
        # tgt = self.dropout(tgt)
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)
        tgt = self.feed_forward(tgt)

        x = tgt
        if self.self_attn:
            if self.normalize_before:
                tgt = self.norm2(tgt)
            x, _ = self.self_attn(tgt, tgt_mask)
            x = residual + self.dropout(x)

        if self.src_attn is not None:
            residual = x
            if self.normalize_before:
                x = self.norm3(x)

            x = residual + self.dropout(self.src_attn(x, memory, memory_mask))

        return x, tgt_mask, memory, memory_mask, cache

    def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
        """Compute decoded features.

        Args:
            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
            cache (List[torch.Tensor]): List of cached tensors.
                Each tensor shape should be (#batch, maxlen_out - 1, size).

        Returns:
            torch.Tensor: Output tensor(#batch, maxlen_out, size).
            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
            torch.Tensor: Encoded memory mask (#batch, maxlen_in).

        """
        # tgt = self.dropout(tgt)
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)
        tgt = self.feed_forward(tgt)

        x = tgt
        if self.self_attn:
            if self.normalize_before:
                tgt = self.norm2(tgt)
            if self.training:
                cache = None
            x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
            x = residual + self.dropout(x)

        if self.src_attn is not None:
            residual = x
            if self.normalize_before:
                x = self.norm3(x)

            x = residual + self.dropout(self.src_attn(x, memory, memory_mask))

        return x, tgt_mask, memory, memory_mask, cache

    def forward_chunk(
        self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0
    ):
        """Compute decoded features.

        Args:
            tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
            tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
            memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
            memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
            cache (List[torch.Tensor]): List of cached tensors.
                Each tensor shape should be (#batch, maxlen_out - 1, size).

        Returns:
            torch.Tensor: Output tensor(#batch, maxlen_out, size).
            torch.Tensor: Mask for output tensor (#batch, maxlen_out).
            torch.Tensor: Encoded memory (#batch, maxlen_in, size).
            torch.Tensor: Encoded memory mask (#batch, maxlen_in).

        """
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)
        tgt = self.feed_forward(tgt)

        x = tgt
        if self.self_attn:
            if self.normalize_before:
                tgt = self.norm2(tgt)
            x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache)
            x = residual + self.dropout(x)

        if self.src_attn is not None:
            residual = x
            if self.normalize_before:
                x = self.norm3(x)

            x, opt_cache = self.src_attn.forward_chunk(
                x, memory, opt_cache, chunk_size, look_back
            )
            x = residual + x

        return x, memory, fsmn_cache, opt_cache


@tables.register("decoder_classes", "FsmnDecoderSCAMAOpt")
class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
    """
    Author: Shiliang Zhang, Zhifu Gao, Haoneng Luo, Ming Lei, Jie Gao, Zhijie Yan, Lei Xie
    SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition
    https://arxiv.org/abs/2006.01712
    """

    def __init__(
        self,
        vocab_size: int,
        encoder_output_size: int,
        attention_heads: int = 4,
        linear_units: int = 2048,
        num_blocks: int = 6,
        dropout_rate: float = 0.1,
        positional_dropout_rate: float = 0.1,
        self_attention_dropout_rate: float = 0.0,
        src_attention_dropout_rate: float = 0.0,
        input_layer: str = "embed",
        use_output_layer: bool = True,
        pos_enc_class=PositionalEncoding,
        normalize_before: bool = True,
        concat_after: bool = False,
        att_layer_num: int = 6,
        kernel_size: int = 21,
        sanm_shfit: int = None,
        concat_embeds: bool = False,
        attention_dim: int = None,
        tf2torch_tensor_name_prefix_torch: str = "decoder",
        tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
        embed_tensor_name_prefix_tf: str = None,
    ):
        super().__init__(
            vocab_size=vocab_size,
            encoder_output_size=encoder_output_size,
            dropout_rate=dropout_rate,
            positional_dropout_rate=positional_dropout_rate,
            input_layer=input_layer,
            use_output_layer=use_output_layer,
            pos_enc_class=pos_enc_class,
            normalize_before=normalize_before,
        )
        if attention_dim is None:
            attention_dim = encoder_output_size

        if input_layer == "embed":
            self.embed = torch.nn.Sequential(
                torch.nn.Embedding(vocab_size, attention_dim),
            )
        elif input_layer == "linear":
            self.embed = torch.nn.Sequential(
                torch.nn.Linear(vocab_size, attention_dim),
                torch.nn.LayerNorm(attention_dim),
                torch.nn.Dropout(dropout_rate),
                torch.nn.ReLU(),
                pos_enc_class(attention_dim, positional_dropout_rate),
            )
        else:
            raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")

        self.normalize_before = normalize_before
        if self.normalize_before:
            self.after_norm = LayerNorm(attention_dim)
        if use_output_layer:
            self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
        else:
            self.output_layer = None

        self.att_layer_num = att_layer_num
        self.num_blocks = num_blocks
        if sanm_shfit is None:
            sanm_shfit = (kernel_size - 1) // 2
        self.decoders = repeat(
            att_layer_num,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                MultiHeadedAttentionSANMDecoder(
                    attention_dim,
                    self_attention_dropout_rate,
                    kernel_size,
                    sanm_shfit=sanm_shfit,
                ),
                MultiHeadedAttentionCrossAtt(
                    attention_heads,
                    attention_dim,
                    src_attention_dropout_rate,
                    encoder_output_size=encoder_output_size,
                ),
                PositionwiseFeedForwardDecoderSANM(
                    attention_dim, linear_units, dropout_rate
                ),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )
        if num_blocks - att_layer_num <= 0:
            self.decoders2 = None
        else:
            self.decoders2 = repeat(
                num_blocks - att_layer_num,
                lambda lnum: DecoderLayerSANM(
                    attention_dim,
                    MultiHeadedAttentionSANMDecoder(
                        attention_dim,
                        self_attention_dropout_rate,
                        kernel_size,
                        sanm_shfit=sanm_shfit,
                    ),
                    None,
                    PositionwiseFeedForwardDecoderSANM(
                        attention_dim, linear_units, dropout_rate
                    ),
                    dropout_rate,
                    normalize_before,
                    concat_after,
                ),
            )

        self.decoders3 = repeat(
            1,
            lambda lnum: DecoderLayerSANM(
                attention_dim,
                None,
                None,
                PositionwiseFeedForwardDecoderSANM(
                    attention_dim, linear_units, dropout_rate
                ),
                dropout_rate,
                normalize_before,
                concat_after,
            ),
        )
        if concat_embeds:
            self.embed_concat_ffn = repeat(
                1,
                lambda lnum: DecoderLayerSANM(
                    attention_dim + encoder_output_size,
                    None,
                    None,
                    PositionwiseFeedForwardDecoderSANM(
                        attention_dim + encoder_output_size,
                        linear_units,
                        dropout_rate,
                        adim=attention_dim,
                    ),
                    dropout_rate,
                    normalize_before,
                    concat_after,
                ),
            )
        else:
            self.embed_concat_ffn = None
        self.concat_embeds = concat_embeds
        self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
        self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
        self.embed_tensor_name_prefix_tf = embed_tensor_name_prefix_tf

    def forward(
        self,
        hs_pad: torch.Tensor,
        hlens: torch.Tensor,
        ys_in_pad: torch.Tensor,
        ys_in_lens: torch.Tensor,
        chunk_mask: torch.Tensor = None,
        pre_acoustic_embeds: torch.Tensor = None,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        """Forward decoder.

        Args:
            hs_pad: encoded memory, float32  (batch, maxlen_in, feat)
            hlens: (batch)
            ys_in_pad:
                input token ids, int64 (batch, maxlen_out)
                if input_layer == "embed"
                input tensor (batch, maxlen_out, #mels) in the other cases
            ys_in_lens: (batch)
        Returns:
            (tuple): tuple containing:

            x: decoded token score before softmax (batch, maxlen_out, token)
                if use_output_layer is True,
            olens: (batch, )
        """
        tgt = ys_in_pad
        tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]

        memory = hs_pad
        memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
        if chunk_mask is not None:
            memory_mask = memory_mask * chunk_mask
            if tgt_mask.size(1) != memory_mask.size(1):
                memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)

        x = self.embed(tgt)

        if pre_acoustic_embeds is not None and self.concat_embeds:
            x = torch.cat((x, pre_acoustic_embeds), dim=-1)
            x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)

        x, tgt_mask, memory, memory_mask, _ = self.decoders(
            x, tgt_mask, memory, memory_mask
        )
        if self.decoders2 is not None:
            x, tgt_mask, memory, memory_mask, _ = self.decoders2(
                x, tgt_mask, memory, memory_mask
            )
        x, tgt_mask, memory, memory_mask, _ = self.decoders3(
            x, tgt_mask, memory, memory_mask
        )
        if self.normalize_before:
            x = self.after_norm(x)
        if self.output_layer is not None:
            x = self.output_layer(x)

        olens = tgt_mask.sum(1)
        return x, olens

    def score(
        self,
        ys,
        state,
        x,
        x_mask=None,
        pre_acoustic_embeds: torch.Tensor = None,
    ):
        """Score."""
        ys_mask = myutils.sequence_mask(
            torch.tensor([len(ys)], dtype=torch.int32), device=x.device
        )[:, :, None]
        logp, state = self.forward_one_step(
            ys.unsqueeze(0),
            ys_mask,
            x.unsqueeze(0),
            memory_mask=x_mask,
            pre_acoustic_embeds=pre_acoustic_embeds,
            cache=state,
        )
        return logp.squeeze(0), state

    def forward_one_step(
        self,
        tgt: torch.Tensor,
        tgt_mask: torch.Tensor,
        memory: torch.Tensor,
        memory_mask: torch.Tensor = None,
        pre_acoustic_embeds: torch.Tensor = None,
        cache: List[torch.Tensor] = None,
    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
        """Forward one step.

        Args:
            tgt: input token ids, int64 (batch, maxlen_out)
            tgt_mask: input token mask,  (batch, maxlen_out)
                      dtype=torch.uint8 in PyTorch 1.2-
                      dtype=torch.bool in PyTorch 1.2+ (include 1.2)
            memory: encoded memory, float32  (batch, maxlen_in, feat)
            cache: cached output list of (batch, max_time_out-1, size)
        Returns:
            y, cache: NN output value and cache per `self.decoders`.
            y.shape` is (batch, maxlen_out, token)
        """

        x = tgt[:, -1:]
        tgt_mask = None
        x = self.embed(x)

        if pre_acoustic_embeds is not None and self.concat_embeds:
            x = torch.cat((x, pre_acoustic_embeds), dim=-1)
            x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)

        if cache is None:
            cache_layer_num = len(self.decoders)
            if self.decoders2 is not None:
                cache_layer_num += len(self.decoders2)
            cache = [None] * cache_layer_num
        new_cache = []
        # for c, decoder in zip(cache, self.decoders):
        for i in range(self.att_layer_num):
            decoder = self.decoders[i]
            c = cache[i]
            x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
                x, tgt_mask, memory, memory_mask, cache=c
            )
            new_cache.append(c_ret)

        if self.num_blocks - self.att_layer_num >= 1:
            for i in range(self.num_blocks - self.att_layer_num):
                j = i + self.att_layer_num
                decoder = self.decoders2[i]
                c = cache[j]
                x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
                    x, tgt_mask, memory, memory_mask, cache=c
                )
                new_cache.append(c_ret)

        for decoder in self.decoders3:
            x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step(
                x, tgt_mask, memory, None, cache=None
            )

        if self.normalize_before:
            y = self.after_norm(x[:, -1])
        else:
            y = x[:, -1]
        if self.output_layer is not None:
            y = self.output_layer(y)
            y = torch.log_softmax(y, dim=-1)

        return y, new_cache

    def gen_tf2torch_map_dict(self):

        tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
        tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
        embed_tensor_name_prefix_tf = (
            self.embed_tensor_name_prefix_tf
            if self.embed_tensor_name_prefix_tf is not None
            else tensor_name_prefix_tf
        )
        map_dict_local = {
            ## decoder
            # ffn
            "{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/gamma".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.norm1.bias".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/beta".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.feed_forward.w_1.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (1024,256),(1,256,1024)
            "{}.decoders.layeridx.feed_forward.w_1.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/bias".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders.layeridx.feed_forward.norm.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/gamma".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders.layeridx.feed_forward.norm.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/beta".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders.layeridx.feed_forward.w_2.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d_1/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (256,1024),(1,1024,256)
            # fsmn
            "{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.norm2.bias".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/beta".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.self_attn.fsmn_block.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/depth_conv_w".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 2, 0),
            },  # (256,1,31),(1,31,256,1)
            # src att
            "{}.decoders.layeridx.norm3.weight".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/gamma".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.norm3.bias".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/beta".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.src_attn.linear_q.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (256,256),(1,256,256)
            "{}.decoders.layeridx.src_attn.linear_q.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/bias".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders.layeridx.src_attn.linear_k_v.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (1024,256),(1,256,1024)
            "{}.decoders.layeridx.src_attn.linear_k_v.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/bias".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders.layeridx.src_attn.linear_out.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (256,256),(1,256,256)
            "{}.decoders.layeridx.src_attn.linear_out.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/bias".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            # dnn
            "{}.decoders3.layeridx.norm1.weight".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_dnn_layer_layeridx/LayerNorm/gamma".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders3.layeridx.norm1.bias".format(tensor_name_prefix_torch): {
                "name": "{}/decoder_dnn_layer_layeridx/LayerNorm/beta".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.decoders3.layeridx.feed_forward.w_1.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_dnn_layer_layeridx/conv1d/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (1024,256),(1,256,1024)
            "{}.decoders3.layeridx.feed_forward.w_1.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_dnn_layer_layeridx/conv1d/bias".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders3.layeridx.feed_forward.norm.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/gamma".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders3.layeridx.feed_forward.norm.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/beta".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.decoders3.layeridx.feed_forward.w_2.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/decoder_dnn_layer_layeridx/conv1d_1/kernel".format(
                    tensor_name_prefix_tf
                ),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (256,1024),(1,1024,256)
            # embed_concat_ffn
            "{}.embed_concat_ffn.layeridx.norm1.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.embed_concat_ffn.layeridx.norm1.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/LayerNorm/beta".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.embed_concat_ffn.layeridx.feed_forward.w_1.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/conv1d/kernel".format(tensor_name_prefix_tf),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (1024,256),(1,256,1024)
            "{}.embed_concat_ffn.layeridx.feed_forward.w_1.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/conv1d/bias".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.embed_concat_ffn.layeridx.feed_forward.norm.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.embed_concat_ffn.layeridx.feed_forward.norm.bias".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/LayerNorm_1/beta".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (1024,),(1024,)
            "{}.embed_concat_ffn.layeridx.feed_forward.w_2.weight".format(
                tensor_name_prefix_torch
            ): {
                "name": "{}/cif_concat/conv1d_1/kernel".format(tensor_name_prefix_tf),
                "squeeze": 0,
                "transpose": (1, 0),
            },  # (256,1024),(1,1024,256)
            # out norm
            "{}.after_norm.weight".format(tensor_name_prefix_torch): {
                "name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            "{}.after_norm.bias".format(tensor_name_prefix_torch): {
                "name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (256,),(256,)
            # in embed
            "{}.embed.0.weight".format(tensor_name_prefix_torch): {
                "name": "{}/w_embs".format(embed_tensor_name_prefix_tf),
                "squeeze": None,
                "transpose": None,
            },  # (4235,256),(4235,256)
            # out layer
            "{}.output_layer.weight".format(tensor_name_prefix_torch): {
                "name": [
                    "{}/dense/kernel".format(tensor_name_prefix_tf),
                    "{}/w_embs".format(embed_tensor_name_prefix_tf),
                ],
                "squeeze": [None, None],
                "transpose": [(1, 0), None],
            },  # (4235,256),(256,4235)
            "{}.output_layer.bias".format(tensor_name_prefix_torch): {
                "name": [
                    "{}/dense/bias".format(tensor_name_prefix_tf),
                    (
                        "seq2seq/2bias"
                        if tensor_name_prefix_tf == "seq2seq/decoder/inputter_1"
                        else "seq2seq/bias"
                    ),
                ],
                "squeeze": [None, None],
                "transpose": [None, None],
            },  # (4235,),(4235,)
        }
        return map_dict_local

    def convert_tf2torch(
        self,
        var_dict_tf,
        var_dict_torch,
    ):

        map_dict = self.gen_tf2torch_map_dict()
        var_dict_torch_update = dict()
        decoder_layeridx_sets = set()
        for name in sorted(var_dict_torch.keys(), reverse=False):
            names = name.split(".")
            if names[0] == self.tf2torch_tensor_name_prefix_torch:
                if names[1] == "decoders":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(
                                data_tf, axis=map_dict[name_q]["squeeze"]
                            )
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(
                                data_tf, map_dict[name_q]["transpose"]
                            )
                        data_tf = (
                            torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        )
                        assert (
                            var_dict_torch[name].size() == data_tf.size()
                        ), "{}, {}, {} != {}".format(
                            name, name_tf, var_dict_torch[name].size(), data_tf.size()
                        )
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                                name, data_tf.size(), name_v, var_dict_tf[name_tf].shape
                            )
                        )

                elif names[1] == "decoders2":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
                    name_q = name_q.replace("decoders2", "decoders")
                    layeridx_bias = len(decoder_layeridx_sets)

                    layeridx += layeridx_bias
                    if "decoders." in name:
                        decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(
                                data_tf, axis=map_dict[name_q]["squeeze"]
                            )
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(
                                data_tf, map_dict[name_q]["transpose"]
                            )
                        data_tf = (
                            torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        )
                        assert (
                            var_dict_torch[name].size() == data_tf.size()
                        ), "{}, {}, {} != {}".format(
                            name, name_tf, var_dict_torch[name].size(), data_tf.size()
                        )
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                                name, data_tf.size(), name_v, var_dict_tf[name_tf].shape
                            )
                        )

                elif names[1] == "decoders3":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")

                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    if "decoders." in name:
                        decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(
                                data_tf, axis=map_dict[name_q]["squeeze"]
                            )
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(
                                data_tf, map_dict[name_q]["transpose"]
                            )
                        data_tf = (
                            torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        )
                        assert (
                            var_dict_torch[name].size() == data_tf.size()
                        ), "{}, {}, {} != {}".format(
                            name, name_tf, var_dict_torch[name].size(), data_tf.size()
                        )
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                                name, data_tf.size(), name_v, var_dict_tf[name_tf].shape
                            )
                        )

                elif names[1] == "embed" or names[1] == "output_layer":
                    name_tf = map_dict[name]["name"]
                    if isinstance(name_tf, list):
                        idx_list = 0
                        if name_tf[idx_list] in var_dict_tf.keys():
                            pass
                        else:
                            idx_list = 1
                        data_tf = var_dict_tf[name_tf[idx_list]]
                        if map_dict[name]["squeeze"][idx_list] is not None:
                            data_tf = np.squeeze(
                                data_tf, axis=map_dict[name]["squeeze"][idx_list]
                            )
                        if map_dict[name]["transpose"][idx_list] is not None:
                            data_tf = np.transpose(
                                data_tf, map_dict[name]["transpose"][idx_list]
                            )
                        data_tf = (
                            torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        )
                        assert (
                            var_dict_torch[name].size() == data_tf.size()
                        ), "{}, {}, {} != {}".format(
                            name, name_tf, var_dict_torch[name].size(), data_tf.size()
                        )
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                                name,
                                data_tf.size(),
                                name_tf[idx_list],
                                var_dict_tf[name_tf[idx_list]].shape,
                            )
                        )

                    else:
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name]["squeeze"] is not None:
                            data_tf = np.squeeze(
                                data_tf, axis=map_dict[name]["squeeze"]
                            )
                        if map_dict[name]["transpose"] is not None:
                            data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
                        data_tf = (
                            torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        )
                        assert (
                            var_dict_torch[name].size() == data_tf.size()
                        ), "{}, {}, {} != {}".format(
                            name, name_tf, var_dict_torch[name].size(), data_tf.size()
                        )
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                                name,
                                data_tf.size(),
                                name_tf,
                                var_dict_tf[name_tf].shape,
                            )
                        )

                elif names[1] == "after_norm":
                    name_tf = map_dict[name]["name"]
                    data_tf = var_dict_tf[name_tf]
                    data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                    var_dict_torch_update[name] = data_tf
                    logging.info(
                        "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                            name, data_tf.size(), name_tf, var_dict_tf[name_tf].shape
                        )
                    )

                elif names[1] == "embed_concat_ffn":
                    layeridx = int(names[2])
                    name_q = name.replace(".{}.".format(layeridx), ".layeridx.")

                    layeridx_bias = 0
                    layeridx += layeridx_bias
                    if "decoders." in name:
                        decoder_layeridx_sets.add(layeridx)
                    if name_q in map_dict.keys():
                        name_v = map_dict[name_q]["name"]
                        name_tf = name_v.replace("layeridx", "{}".format(layeridx))
                        data_tf = var_dict_tf[name_tf]
                        if map_dict[name_q]["squeeze"] is not None:
                            data_tf = np.squeeze(
                                data_tf, axis=map_dict[name_q]["squeeze"]
                            )
                        if map_dict[name_q]["transpose"] is not None:
                            data_tf = np.transpose(
                                data_tf, map_dict[name_q]["transpose"]
                            )
                        data_tf = (
                            torch.from_numpy(data_tf).type(torch.float32).to("cpu")
                        )
                        assert (
                            var_dict_torch[name].size() == data_tf.size()
                        ), "{}, {}, {} != {}".format(
                            name, name_tf, var_dict_torch[name].size(), data_tf.size()
                        )
                        var_dict_torch_update[name] = data_tf
                        logging.info(
                            "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(
                                name, data_tf.size(), name_v, var_dict_tf[name_tf].shape
                            )
                        )

        return var_dict_torch_update
