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 print_losses(train_losses: list[float], validation_losses: list[float], show=False):
    plt.plot(train_losses, label="Training loss")
    plt.plot(validation_losses, label="Validation loss")
    plt.xlabel("Epoch")
    plt.ylabel("Loss (cross entropy)")
    plt.legend()

    if show:
        plt.show()
    plt.savefig("losses.png")


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