import math
import re
import torch
import torch.nn as nn

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'}

def mlp2x_gelu(projector_type):
    # mm_hidden_size = 1024
    mm_hidden_size = 1280
    hidden_size = 3584

    mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
    if mlp_gelu_match:
        mlp_depth = int(mlp_gelu_match.group(1))
        modules = [nn.Linear(mm_hidden_size, hidden_size)]
        for _ in range(1, mlp_depth):
            modules.append(nn.GELU())
            modules.append(nn.Linear(hidden_size, hidden_size))
        return nn.Sequential(*modules)

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

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