feat: model --> ready to test train

CNN-model/utils.py

@@ -0,0 +1,20 @@
+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()