feat: better serialization. Closes #57

05_parameters.md

@@ -499,20 +499,35 @@ load the model on a different machine and run the solver.
 ```python
 from ortools.sat.python import cp_model
 from google.protobuf import text_format
-
-
-def export_model(model: cp_model.CpModel, filename: str):
-    with open(filename, "w") as file:
-        file.write(text_format.MessageToString(model.Proto()))
-
-
-def import_model(filename: str) -> cp_model.CpModel:
+from pathlib import Path
+
+def _detect_binary_mode(filename: str) -> bool:
+    if filename.endswith((".txt", ".pbtxt", ".pb.txt")):
+        return False
+    if filename.endswith((".pb", ".bin", ".proto.bin", ".dat")):
+        return True
+    raise ValueError(f"Unknown extension for file: {filename}")
+
+def export_model(model: cp_model.CpModel, filename: str, binary: bool | None = None):
+    binary = _detect_binary_mode(filename) if binary is None else binary
+    if binary:
+        Path(filename).write_bytes(model.Proto().SerializeToString())
+    else:
+        Path(filename).write_text(text_format.MessageToString(model.Proto()))
+
+def import_model(filename: str, binary: bool | None = None) -> cp_model.CpModel:
+    binary = _detect_binary_mode(filename) if binary is None else binary
     model = cp_model.CpModel()
-    with open(filename, "r") as file:
-        text_format.Parse(file.read(), model.Proto())
+    if binary:
+        model.Proto().ParseFromString(Path(filename).read_bytes())
+    else:
+        text_format.Parse(Path(filename).read_text(), model.Proto())
     return model
 ```
 
+The binary mode is more efficient and should be used for large models. The text
+mode is human-readable and can be easier shared and compared.
+
 ### Hints
 
 If you have a good intuition about how the solution might look—perhaps from

README.md

@@ -3366,20 +3366,35 @@ load the model on a different machine and run the solver.
 ```python
 from ortools.sat.python import cp_model
 from google.protobuf import text_format
+from pathlib import Path
 
-
-def export_model(model: cp_model.CpModel, filename: str):
-    with open(filename, "w") as file:
-        file.write(text_format.MessageToString(model.Proto()))
-
-
-def import_model(filename: str) -> cp_model.CpModel:
+def _detect_binary_mode(filename: str) -> bool:
+    if filename.endswith((".txt", ".pbtxt", ".pb.txt")):
+        return False
+    if filename.endswith((".pb", ".bin", ".proto.bin", ".dat")):
+        return True
+    raise ValueError(f"Unknown extension for file: {filename}")
+
+def export_model(model: cp_model.CpModel, filename: str, binary: bool | None = None):
+    binary = _detect_binary_mode(filename) if binary is None else binary
+    if binary:
+        Path(filename).write_bytes(model.Proto().SerializeToString())
+    else:
+        Path(filename).write_text(text_format.MessageToString(model.Proto()))
+
+def import_model(filename: str, binary: bool | None = None) -> cp_model.CpModel:
+    binary = _detect_binary_mode(filename) if binary is None else binary
     model = cp_model.CpModel()
-    with open(filename, "r") as file:
-        text_format.Parse(file.read(), model.Proto())
+    if binary:
+        model.Proto().ParseFromString(Path(filename).read_bytes())
+    else:
+        text_format.Parse(Path(filename).read_text(), model.Proto())
     return model
 ```
 
+The binary mode is more efficient and should be used for large models. The text
+mode is human-readable and can be easier shared and compared.
+
 ### Hints
 
 If you have a good intuition about how the solution might look—perhaps from

tests/test_serialization.py

@@ -0,0 +1,49 @@
+import tempfile
+
+
+def test_serialization():
+    from ortools.sat.python import cp_model
+    from google.protobuf import text_format
+    from pathlib import Path
+
+    def _detect_binary_mode(filename: str) -> bool:
+        if filename.endswith((".txt", ".pbtxt", ".pb.txt")):
+            return False
+        if filename.endswith((".pb", ".bin", ".proto.bin", ".dat")):
+            return True
+        raise ValueError(f"Unknown extension for file: {filename}")
+
+    def export_model(model: cp_model.CpModel, filename: str, binary: bool | None = None):
+        binary = _detect_binary_mode(filename) if binary is None else binary
+        if binary:
+            Path(filename).write_bytes(model.Proto().SerializeToString())
+        else:
+            Path(filename).write_text(text_format.MessageToString(model.Proto()))
+
+    def import_model(filename: str, binary: bool | None = None) -> cp_model.CpModel:
+        binary = _detect_binary_mode(filename) if binary is None else binary
+        model = cp_model.CpModel()
+        if binary:
+            model.Proto().ParseFromString(Path(filename).read_bytes())
+        else:
+            text_format.Parse(Path(filename).read_text(), model.Proto())
+        return model
+
+    model = cp_model.CpModel()
+    x = [model.NewIntVar(0, 10, f"x{i}") for i in range(10)]
+    model.add(sum(x)<=20)
+    model.maximize(sum(i*x[i] for i in range(10)))
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        export_model(model, f"{tmpdir}/model.pb")
+        model2 = import_model(f"{tmpdir}/model.pb")
+        assert model.Proto() == model2.Proto()
+        export_model(model, f"{tmpdir}/model.txt", binary=False)
+        model3 = import_model(f"{tmpdir}/model.txt", binary=False)
+        assert model.Proto() == model3.Proto()
+        export_model(model, f"{tmpdir}/model.pbtxt", binary=False)
+        model4 = import_model(f"{tmpdir}/model.pbtxt", binary=False)
+        assert model.Proto() == model4.Proto()
+        export_model(model, f"{tmpdir}/model.pb.txt", binary=True)
+        model5 = import_model(f"{tmpdir}/model.pb.txt", binary=True)
+        assert model.Proto() == model5.Proto()