# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This source file is copied from https://github.com/facebookresearch/encodec

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

"""Normalization modules."""

import typing as tp

import einops
import torch
from torch import nn


class ConvLayerNorm(nn.LayerNorm):
    """
    Convolution-friendly LayerNorm that moves channels to last dimensions
    before running the normalization and moves them back to original position right after.
    """

    def __init__(
        self, normalized_shape: tp.Union[int, tp.List[int], torch.Size], **kwargs
    ):
        super().__init__(normalized_shape, **kwargs)

    def forward(self, x):
        x = einops.rearrange(x, "b ... t -> b t ...")
        x = super().forward(x)
        x = einops.rearrange(x, "b t ... -> b ... t")
        return