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import torch
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import torch.nn as nn
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import re
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class IdentityMap(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x, *args, **kwargs):
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return x
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@property
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def config(self):
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return {"mm_projector_type": 'identity'}
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class SimpleResBlock(nn.Module):
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def __init__(self, channels):
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super().__init__()
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self.pre_norm = nn.LayerNorm(channels)
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self.proj = nn.Sequential(
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nn.Linear(channels, channels),
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nn.GELU(),
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nn.Linear(channels, channels)
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)
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def forward(self, x):
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x = self.pre_norm(x)
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return x + self.proj(x)
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def build_vision_projector(config, delay_load=False, **kwargs):
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projector_type = getattr(config, 'mm_projector_type', 'linear')
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if projector_type == 'linear':
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return nn.Linear(config.mm_hidden_size, config.hidden_size)
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mlp_gelu_match = re.match(r'^mlp(\d+)x_gelu$', projector_type)
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if mlp_gelu_match:
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mlp_depth = int(mlp_gelu_match.group(1))
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modules = [nn.Linear(config.mm_hidden_size, config.hidden_size)]
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for _ in range(1, mlp_depth):
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modules.append(nn.GELU())
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modules.append(nn.Linear(config.hidden_size, config.hidden_size))
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return nn.Sequential(*modules)
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if projector_type == 'identity':
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return IdentityMap()
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raise ValueError(f'Unknown projector type: {projector_type}')
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