import torch
from torch.utils.data import TensorDataset
import matplotlib.pyplot as plt


def make_context_pairs(data: bytes, context_length: int) -> TensorDataset:
    data = torch.tensor(list(data), dtype=torch.long)
    sample_count = data.shape[0] - context_length
    x = data.unfold(0, context_length, 1)[:sample_count]
    y = data[context_length:]
    return TensorDataset(x, y)

def print_distribution(from_to: tuple[int, int], probabilities: list[float]):
    plt.hist(range(from_to[0], from_to[1]), weights=probabilities)
    plt.show()


def load_data(path: str) -> bytes:
    with open(path, "rb") as f:
        return f.read()