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from collections import Counter

files = ['train.to','train.from']

for name in files:
    words = []
    save_name = "vocab.{}".format(name.split('.')[1])
    print(save_name)
    with open(name, 'r') as f:
        data = f.read().split('\n')

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import random

hm_samples = 300000
max_val = 100000
options = ["add", "sub", "mul", "div"]


def generate_pair(action):
    x = random.randrange(1, max_val)
    y = random.randrange(1, max_val)

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from pylsl import StreamInlet, resolve_stream
import numpy as np
import time
import matplotlib.pyplot as plt
from matplotlib import style
from collections import deque


last_print = time.time()
fps_counter = deque(maxlen=150)

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#https://cs231n.github.io/neural-networks-case-study/
def spiral_data(points, classes):
    X = np.zeros((points*classes, 2))
    y = np.zeros(points*classes, dtype='uint8')
    for class_number in range(classes):
        ix = range(points*class_number, points*(class_number+1))
        r = np.linspace(0.0, 1, points)  # radius
        t = np.linspace(class_number*4, (class_number+1)*4, points) + np.random.randn(points)*0.2
        X[ix] = np.c_[r*np.sin(t*2.5), r*np.cos(t*2.5)]
        y[ix] = class_number

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import torch
from diffusers import StableDiffusionPipeline
from torch import autocast
import random
import matplotlib.pyplot as plt
import os


prompts = [
          "1965 Porsche 911",

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from stable_diffusion_videos.stable_diffusion_walk import walk

prompt_n_seed = {
    "1965 Porsche 911": 743,
    "1975 Porsche 911": 140,
    "1985 Porsche 911": 40,
    "1995 Porsche 911": 560,
    "2005 Porsche 911 directly facing camera": 996,
    "2015 Porsche 911": 283,
    "2020 Porsche 911": 116,

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