import torch
from torch import nn

from funasr.modules.layer_norm  import LayerNorm


class SpeakerAttributeSpkDecoderFirstLayer(nn.Module):

    def __init__(
        self,
        size,
        self_attn,
        src_attn,
        feed_forward,
        dropout_rate,
        normalize_before=True,
        concat_after=False,
    ):
        """Construct an DecoderLayer object."""
        super(SpeakerAttributeSpkDecoderFirstLayer, self).__init__()
        self.size = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)
        self.norm2 = 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, asr_memory, spk_memory, memory_mask, cache=None):
        
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)

        if cache is None:
            tgt_q = tgt
            tgt_q_mask = tgt_mask
        else:
            # compute only the last frame query keeping dim: max_time_out -> 1
            assert cache.shape == (
                tgt.shape[0],
                tgt.shape[1] - 1,
                self.size,
            ), f"{cache.shape} == {(tgt.shape[0], tgt.shape[1] - 1, self.size)}"
            tgt_q = tgt[:, -1:, :]
            residual = residual[:, -1:, :]
            tgt_q_mask = None
            if tgt_mask is not None:
                tgt_q_mask = tgt_mask[:, -1:, :]

        if self.concat_after:
            tgt_concat = torch.cat(
                (tgt_q, self.self_attn(tgt_q, tgt, tgt, tgt_q_mask)), dim=-1
            )
            x = residual + self.concat_linear1(tgt_concat)
        else:
            x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask))
        if not self.normalize_before:
            x = self.norm1(x)
        z = x
        
        residual = x
        if self.normalize_before:
            x = self.norm1(x)

        skip = self.src_attn(x, asr_memory, spk_memory, memory_mask)

        if self.concat_after:
            x_concat = torch.cat(
                (x, skip), dim=-1
            )
            x = residual + self.concat_linear2(x_concat)
        else:
            x = residual + self.dropout(skip)
        if not self.normalize_before:
            x = self.norm1(x)
        
        residual = x
        if self.normalize_before:
            x = self.norm2(x)
        x = residual + 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, tgt_mask, asr_memory, spk_memory, memory_mask, z

class SpeakerAttributeAsrDecoderFirstLayer(nn.Module):
    
    def __init__(
        self,
        size,
        d_size,
        src_attn,
        feed_forward,
        dropout_rate,
        normalize_before=True,
        concat_after=False,
    ):
        """Construct an DecoderLayer object."""
        super(SpeakerAttributeAsrDecoderFirstLayer, self).__init__()
        self.size = size
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.norm1 = LayerNorm(size)
        self.norm2 = LayerNorm(size)
        self.norm3 = LayerNorm(size)
        self.dropout = nn.Dropout(dropout_rate)
        self.normalize_before = normalize_before
        self.concat_after = concat_after
        self.spk_linear = nn.Linear(d_size, size, bias=False)
        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, dn, cache=None):
        
        residual = tgt
        if self.normalize_before:
            tgt = self.norm1(tgt)

        if cache is None:
            tgt_q = tgt
            tgt_q_mask = tgt_mask
        else:
            
            tgt_q = tgt[:, -1:, :]
            residual = residual[:, -1:, :]
            tgt_q_mask = None
            if tgt_mask is not None:
                tgt_q_mask = tgt_mask[:, -1:, :]

        x = tgt_q
        if self.normalize_before:
            x = self.norm2(x)
        if self.concat_after:
            x_concat = torch.cat(
                (x, self.src_attn(x, memory, memory, memory_mask)), dim=-1
            )
            x = residual + self.concat_linear2(x_concat)
        else:
            x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask))
        if not self.normalize_before:
            x = self.norm2(x)
        residual = x

        if dn!=None:
            x = x + self.spk_linear(dn)
        if self.normalize_before:
            x = self.norm3(x)
        
        x = residual + self.dropout(self.feed_forward(x))
        if not self.normalize_before:
            x = self.norm3(x)

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

        return x, tgt_mask, memory, memory_mask