import torch
import torch.nn as nn
import re

class IdentityMap(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, *args, **kwargs):
        return x

    @property
    def config(self):
        return {"mm_projector_type": 'identity'}


class SimpleResBlock(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.pre_norm = nn.LayerNorm(channels)
        self.proj = nn.Sequential(
            nn.Linear(channels, channels),
            nn.GELU(),
            nn.Linear(channels, channels)
        )

    def forward(self, x):
        x = self.pre_norm(x)
        return x + self.proj(x)


class DualMLPProjector(nn.Module):
    def __init__(self, config, mlp_depth):
        super().__init__()

        self.encoder_mlp = nn.Sequential(
            nn.Linear(config.mm_hidden_size * 4, config.hidden_size),
            *[nn.Sequential(nn.GELU(), nn.Linear(config.hidden_size, config.hidden_size)) for _ in range(mlp_depth-1)]
        )


    def forward(self, image_features, encoder_last_hidden_state):
        encoder_last_hidden_state = torch.cat((image_features, encoder_last_hidden_state), dim=-1)
        concatenated = self.encoder_mlp(encoder_last_hidden_state)

        return concatenated


def build_vision_projector(config, delay_load=False, **kwargs):
    projector_type = getattr(config, 'mm_projector_type', 'linear')

    if projector_type == 'linear':
        return nn.Linear(config.mm_hidden_size, config.hidden_size)


    mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
    if mlp_gelu_match:
        mlp_depth = int(mlp_gelu_match.group(1))
        return DualMLPProjector(config, mlp_depth)

    if projector_type == 'identity':
        return IdentityMap()

    raise ValueError(f'Unknown projector type: {projector_type}')