import torch
import torch.nn as nn

from funasr.register import tables


@tables.register("adaptor_classes", "Linear")
class Linear(nn.Module):
    def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
        super().__init__()
        self.k = downsample_rate
        self.encoder_dim = encoder_dim
        self.llm_dim = llm_dim
        self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(ffn_dim, self.llm_dim)

    def forward(self, x):
        batch_size, seq_len, dim = x.size()
        num_frames_to_discard = seq_len % self.k
        if num_frames_to_discard > 0:
            x = x[:, :-num_frames_to_discard, :]
        seq_len = x.size(1)

        x = x.contiguous()
        x = x.view(batch_size, seq_len // self.k, dim * self.k)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        return x


@tables.register("adaptor_classes", "QFormer")
class EncoderProjectorQFormer(nn.Module):
    def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs):
        super().__init__()
        self.encoder_dim = encoder_dim
        self.llm_dim = llm_dim
        from transformers import Blip2QFormerConfig, Blip2QFormerModel

        configuration = Blip2QFormerConfig()
        configuration.encoder_hidden_size = self.encoder_dim
        configuration.num_hidden_layers = 2

        self.query_len = 64
        self.query = nn.Parameter(torch.zeros(1, self.query_len, configuration.hidden_size))
        self.query.data.normal_(mean=0.0, std=1.0)
        self.qformer = Blip2QFormerModel(configuration)

        self.linear = nn.Linear(configuration.hidden_size, self.llm_dim)
        self.norm = nn.LayerNorm(self.llm_dim, eps=1e-5)

    def forward(self, x, atts):
        query = self.query.expand(x.shape[0], -1, -1)

        query_output = self.qformer(
            query_embeds=query,
            encoder_hidden_states=x,
            encoder_attention_mask=atts,
            return_dict=True,
        )

        query_proj = self.norm(self.linear(query_output.last_hidden_state))

        return query_proj