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require "matrix"
require "set"

# Flood fill from min (up until max) stopping when we hit a cube.
def count_outward_faces(cubes, min, max)
  # To avoid stack overflow, we aren't recursive, instead we have a list of squares to check.
  to_check = [min]
  to_check_set = Set[min]

  # Keep track of squares we have tested (as you can get to the same square multiple ways).

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import torch
from torch import nn
from tqdm import tqdm
from torch import Tensor
from typing import Optional, List
import time


def subsequent_mask(size):
    return torch.triu(torch.full((size, size), float('-inf')), diagonal=1)

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import struct
from pathlib import Path
import wave


def parse_data(data):
    # the first 24 bytes are the header
    header = data[:24]

    # the remaining bytes are the data

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import torch
from torch import nn
from tqdm import tqdm


def subsequent_mask(size):
    return torch.triu(torch.full((size, size), float('-inf')), diagonal=1)


if __name__ == "__main__":

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import torch
import math
from torch import Tensor
from typing import Optional


def get_relative_positional_encoding(length1:int, length2:int, d_model:int, device:torch.device):
    xs = torch.arange(length1, device=device).unsqueeze(1)
    ys = torch.arange(length2, device=device).unsqueeze(0)

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import torch
from math import log
import matplotlib.pyplot as plt


def get_positional_encoding(cycle_limit):
    max_len = 5000
    d_model = 256

    position = torch.arange(max_len).unsqueeze(1)

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import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from tqdm import tqdm


if __name__ == "__main__":
    output_dir = Path('frames')
    output_dir.mkdir(exist_ok=True)

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import torch.nn as nn
import torch


# NOTE: I've just put this here so that I don't have to import any other part of your code base
# to try out / run this model
control_signals_labels = ['rhand', 'lhand', 'head']

residual_block_linear = 1024

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import torch
from torch import nn


class MyModule(nn.Module):
    def __init__(self, num_input_features):
        super().__init__()

        num_hidden = 5
        num_layers = 2

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import torch
from scipy.special import sph_harm


# Converted to numpy arrays in sph_harm
def real_sph_harm(m, l, phi, theta):
    z = sph_harm(abs(m), l, phi, theta)

    if m < 0:
        z = 2**0.5 * (-1)**m * z.imag