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"""Normalization modules."""
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import typing as tp
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import einops
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import torch
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from torch import nn
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class ConvLayerNorm(nn.LayerNorm):
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"""
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Convolution-friendly LayerNorm that moves channels to last dimensions
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before running the normalization and moves them back to original position right after.
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"""
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def __init__(
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self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs
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):
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super().__init__(normalized_shape, **kwargs)
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def forward(self, x):
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x = einops.rearrange(x, "b ... t -> b t ...")
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x = super().forward(x)
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x = einops.rearrange(x, "b t ... -> b ... t")
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return
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