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from transformers.configuration_utils import PretrainedConfig
class MoonViTConfig(PretrainedConfig):
model_type = "moonvit"
def __init__(
self,
patch_size: int = 14,
init_pos_emb_height: int = 64,
init_pos_emb_width: int = 64,
num_attention_heads: int = 16,
num_hidden_layers: int = 27,
hidden_size: int = 1152,
intermediate_size: int = 4304,
merge_kernel_size: tuple[int, int] = (2, 2),
**kwargs,
):
super().__init__(**kwargs)
self.patch_size = patch_size
# Positional embedding config
self.init_pos_emb_height = init_pos_emb_height
self.init_pos_emb_width = init_pos_emb_width
# Transformer config
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
# Patch merger config
self.merge_kernel_size = merge_kernel_size
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