From e11e8ede1b24f90338482c7befc0a893024257e9 Mon Sep 17 00:00:00 2001
From: Maxim Vafin <maxim.vafin@intel.com>
Date: Wed, 23 Aug 2023 21:16:26 +0200
Subject: [PATCH] [MO] Fix issue in nncf version verification (#19347)

* Return deleted nncf import

* Remove try-except, it hides exception

* Get version visout importing nncf module
---
 .../mo/moc_frontend/pytorch_frontend_utils.py | 21 +++++++++++--------
 .../moc_frontend/pytorch_frontend_utils.py    | 21 +++++++++++--------
 2 files changed, 24 insertions(+), 18 deletions(-)

diff --git a/tools/mo/openvino/tools/mo/moc_frontend/pytorch_frontend_utils.py b/tools/mo/openvino/tools/mo/moc_frontend/pytorch_frontend_utils.py
index aaf8b9fd89efcc..5b11f8c6998b66 100644
--- a/tools/mo/openvino/tools/mo/moc_frontend/pytorch_frontend_utils.py
+++ b/tools/mo/openvino/tools/mo/moc_frontend/pytorch_frontend_utils.py
@@ -17,17 +17,20 @@ def get_pytorch_decoder(model, input_shape, example_inputs, args):
     except Exception as e:
         log.error("PyTorch frontend loading failed")
         raise e
-    try:
-        if 'nncf' in sys.modules:
-            from nncf.torch.nncf_network import NNCFNetwork # pylint: disable=undefined-variable
-            from packaging import version
+    if 'nncf' in sys.modules:
+        is_good_version = True
+        try:
+            from nncf.torch.nncf_network import NNCFNetwork
 
             if isinstance(model, NNCFNetwork):
-                if version.parse(nncf.__version__) < version.parse("2.6"): # pylint: disable=undefined-variable
-                    raise RuntimeError(
-                        "NNCF models produced by nncf<2.6 are not supported directly. Please upgrade nncf or export to ONNX first.")
-    except:
-        pass
+                from packaging import version
+                if version.parse(sys.modules['nncf'].__version__) < version.parse("2.6"):
+                    is_good_version = False
+        except:
+            pass
+        if not is_good_version:
+            raise RuntimeError(
+                    "NNCF models produced by nncf<2.6 are not supported directly. Please upgrade nncf or export to ONNX first.")
     inputs = prepare_torch_inputs(example_inputs)
     decoder = TorchScriptPythonDecoder(model, example_input=inputs, shared_memory=args.get("share_weights", True))
     args['input_model'] = decoder
diff --git a/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py b/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
index 9004bacc4bf7a3..2703af43d8b192 100644
--- a/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
+++ b/tools/ovc/openvino/tools/ovc/moc_frontend/pytorch_frontend_utils.py
@@ -17,17 +17,20 @@ def get_pytorch_decoder(model, example_inputs, args):
     except Exception as e:
         log.error("PyTorch frontend loading failed")
         raise e
-    try:
-        if 'nncf' in sys.modules:
-            from nncf.torch.nncf_network import NNCFNetwork # pylint: disable=undefined-variable
-            from packaging import version
+    if 'nncf' in sys.modules:
+        is_good_version = True
+        try:
+            from nncf.torch.nncf_network import NNCFNetwork
 
             if isinstance(model, NNCFNetwork):
-                if version.parse(nncf.__version__) <= version.parse("2.6"): # pylint: disable=undefined-variable
-                    raise RuntimeError(
-                        "NNCF models produced by nncf<2.6 are not supported directly. Please upgrade nncf or export to ONNX first.")
-    except:
-        pass
+                from packaging import version
+                if version.parse(sys.modules['nncf'].__version__) < version.parse("2.6"):
+                    is_good_version = False
+        except:
+            pass
+        if not is_good_version:
+            raise RuntimeError(
+                    "NNCF models produced by nncf<2.6 are not supported directly. Please upgrade nncf or export to ONNX first.")
     inputs = prepare_torch_inputs(example_inputs)
     decoder = TorchScriptPythonDecoder(model, example_input=inputs, shared_memory=args.get("share_weights", True))
     args['input_model'] = decoder