thomasahle

import torch
from torch.autograd import Function
import torch.nn.functional as F


@torch.no_grad()
def _find_ts(xs, ks, binary_iter=16, newton_iter=1):
    n = xs.shape[-1]
    assert torch.all((0 < ks) & (ks < n)), "We don't support k=0 or k=n"
    # Lo should be small enough that all sigmoids are in the 0 area.

thomasahle

################################################################################
# NFA Implementation using greenery
################################################################################

import greenery
from greenery import rxelems as rx
from collections import defaultdict

class State:
    def __init__(self, is_accept=False):

thomasahle

import dspy
from pydantic import BaseModel
from typing import List

class State(BaseModel):
    name: str
    abbreviation: str
    capital: str

class States(BaseModel):

thomasahle

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import tqdm

# Generate random dataset
np.random.seed(0)

thomasahle

import networkx as nx
import matplotlib.pyplot as plt
import random
import matplotlib.animation as animation
import numpy as np

# Step 1: Create the graph
G = nx.MultiGraph()
edges = [
    (1, 2), (1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6),

thomasahle

from collections import Counter
from manim import *
import networkx as nx
import random
import numpy as np
import itertools


class UF:
    def __init__(self, ids):

thomasahle

from collections import Counter
from manim import *
import networkx as nx
import random
import numpy as np
import itertools


class UF:
    def __init__(self, ids):

thomasahle

from collections import Counter
from manim import *
import networkx as nx
import random
import numpy as np
import itertools


class UF:
    def __init__(self, ids):

thomasahle

import torch
import math
from torch import nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib.colors as mcolors
import numpy as np
import seaborn as sns

thomasahle

import instructor
from pydantic import BaseModel, Field
from typing import overload, Union, Literal, Generator
from tqdm.asyncio import tqdm
import asyncio
import numpy as np
import json
import os, sys
import diskcache, inspect, functools
import random