bring in openai text tokenizer from another repo, as well as residual vq...

from musiclm_pytorch.musiclm_pytorch import MuLaN

from musiclm_pytorch.musiclm_pytorch import MuLaN, MusicLM

import torch.nn.functional as F

from torch import nn, einsum

from einops import rearrange, repeat, reduce

from x_clip.tokenizer import tokenizer

from vector_quantize_pytorch import ResidualVQ

from einops import rearrange, repeat, reduce, pack, unpack

# functions

def exists(val):

    return val is not None

# tensor functions

def log(t, eps = 1e-20):

    return torch.log(t.clamp(min = eps))

def l2norm(t):

    return F.normalize(t, p = 2, dim = -1)

# biasless layernorm

class LayerNorm(nn.Module):

    def __init__(self, dim):

        super().__init__()

        self.gamma = nn.Parameter(torch.ones(dim))

        self.register_buffer("beta", torch.zeros(dim))

        self.register_buffer('beta', torch.zeros(dim))

    def forward(self, x):

        return F.layer_norm(x, x.shape[-1:], self.gamma, self.beta)

    def forward(self, x):

        return x

# music lm

class MusicLM(nn.Module):

    def __init__(self):

        super().__init__()

    'audiolm-pytorch',

    'einops>=0.4',

    'vector-quantize-pytorch>=0.10.15',

    'x-clip',

    'torch>=1.6',

    'torchaudio'

  ],