From 2c0e0c227877038a9a89363e71cb715199ce0041 Mon Sep 17 00:00:00 2001
From: Tibo De Peuter <tibo.depeuter@ugent.be>
Date: Thu, 11 Dec 2025 23:24:34 +0100
Subject: [PATCH] WIP: Attempt at switching

---
 src/process.py | 66 ++++++++++++++++++++++++++------------------------
 1 file changed, 35 insertions(+), 31 deletions(-)

diff --git a/src/process.py b/src/process.py
index dc5c479..e59defd 100644
--- a/src/process.py
+++ b/src/process.py
@@ -36,45 +36,49 @@ def compress(
 
     # Init AE
     print("Initializing AE")
-    AE = ArithmeticEncoding(frequency_table={0: 1})  # These are dummies because they are not used
-    stage_min, stage_max = Decimal(0), Decimal(1)
-    stage = None
 
-    # Compress
-    context = deque([0] * context_length, maxlen=context_length)
-    for byte in tqdm(tensor.tolist(), desc="Compressing"):
-        context_tensor = torch.tensor([list(context)], dtype=torch.long, device=device)
+    with contextlib.closing(reference_ae.BitOutputStream(open(output_file, "wb"))) as bitout:
+        enc = reference_ae.ArithmeticEncoder(len(byte_data), bitout)
 
-        with torch.inference_mode():
-            logits = model(context_tensor)
-            probabilities = torch.softmax(logits[0], dim=-1)
-        probabilities = probabilities.detach().cpu().numpy()
+        context = deque([0] * context_length, maxlen=context_length)
 
-        eps = 1e-10
-        frequency_table = {i: float(probabilities[i]) + eps for i in range(len(probabilities))}
-        probability_table = AE.get_probability_table(frequency_table)
+        stage_min, stage_max = Decimal(0), Decimal(1)
+        stage = None
 
-        stage = AE.process_stage(probability_table, stage_min, stage_max)
-        stage_min, stage_max = stage[byte]
+        # Compress
+        for byte in tqdm(tensor.tolist(), desc="Compressing"):
+            context_tensor = torch.tensor([list(context)], dtype=torch.long, device=device)
 
-        context.append(byte)
+            with torch.inference_mode():
+                logits = model(context_tensor)
+                probabilities = torch.softmax(logits[0], dim=-1)
+            probabilities = probabilities.detach().cpu().numpy()
 
-    print("Getting encoded value")
-    interval_min, interval_max, _ = AE.get_encoded_value(stage)
-    print("Encoding in binary")
-    binary_code, _ = AE.custom_binary_encoding(interval_min, interval_max)
+            eps = 1e-10
+            frequency_table = {i: float(probabilities[i]) + eps for i in range(len(probabilities))}
+            probability_table = reference_ae.SimpleFrequencyTable([0] * len(probabilities))
+            probability_table = AE.get_probability_table(frequency_table)
 
-    # Pack
-    val = int(binary_code, 2) if len(binary_code) else 0
-    out_bytes = val.to_bytes((len(binary_code) + 7) // 8, "big")
+            enc.write(frequency_table, byte)
 
-    if output_file:
-        print(f"Writing to {output_file}")
-        with open(output_file, "w") as file:
-            file.write(f"{len(byte_data)}\n")
-            file.write(binary_code)  # todo: temporary, decoding depends on binary string
-    else:
-        print(out_bytes)
+            context.append(byte)
+
+        # print("Getting encoded value")
+        # interval_min, interval_max, _ = AE.get_encoded_value(stage)
+        # print("Encoding in binary")
+        # binary_code, _ = AE.custom_binary_encoding(interval_min, interval_max)
+
+        # Pack
+        # val = int(binary_code, 2) if len(binary_code) else 0
+        # out_bytes = val.to_bytes((len(binary_code) + 7) // 8, "big")
+
+        # if output_file:
+        #     print(f"Writing to {output_file}")
+        #     with open(output_file, "w") as file:
+        #         file.write(f"{len(byte_data)}\n")
+        #         file.write(binary_code)  # todo: temporary, decoding depends on binary string
+        # else:
+        #     print(out_bytes)
 
 
 def bits_to_number(bits: str) -> float: