fix ci

Author: akuporos

src/bindings/python/src/openvino/frontend/pytorch/fx_decoder.py

@@ -88,9 +88,9 @@ def get_type_for_value(value):
             return OVAny(DecoderType.PyScalar(OVAny(OVType.boolean)))
         elif isinstance(value, list):
             if len(value) > 0:
-                return OVAny(DecoderType.list(BaseFXDecoder.get_type_for_value(value[0])))
+                return OVAny(DecoderType.List(BaseFXDecoder.get_type_for_value(value[0])))
             else:
-                return OVAny(DecoderType.list(OVAny(OVType.i32)))
+                return OVAny(DecoderType.List(OVAny(OVType.i32)))
         return OVAny(OVType.dynamic)
 
     def inputs(self):

src/bindings/python/src/openvino/frontend/pytorch/ts_decoder.py

@@ -269,7 +269,7 @@ def _get_known_type_for_value(self, pt_type):
             return OVAny(DecoderType.Tensor(self._get_known_type_for_value(pt_type.dtype())))
         elif isinstance(pt_type, torch.ListType):
             element_type = pt_type.getElementType()
-            return OVAny(DecoderType.list(self._get_known_type_for_value(element_type)))
+            return OVAny(DecoderType.List(self._get_known_type_for_value(element_type)))
         elif isinstance(pt_type, (torch.StringType, torch.DeviceObjType)):
             return OVAny(DecoderType.Str())
         elif isinstance(pt_type, torch.NoneType):

src/bindings/python/src/openvino/frontend/pytorch/utils.py

@@ -267,7 +267,9 @@ def prepare_example_inputs_and_model(inputs, input_params, model):
             inputs, input_params, model)
         return examples, ordered, wrapped, input_is_list
     if isinstance(inputs, list) and len(inputs) == 1 and isinstance(inputs[0], torch.Tensor):
+        print(str(input_params[input_signature[0]].annotation))
         if "list" in str(input_params[input_signature[0]].annotation):
+            print("no")
             inputs = inputs[0].unsqueeze(0)
             input_is_list = True
 

src/bindings/python/src/openvino/utils/decorators.py

@@ -79,6 +79,10 @@ def __init__(self, name: str):
     # Checks if actual_type is a subclass of any type in the union
     def matches_union(self, union_type, actual_type) -> bool:  # type: ignore
         for type_arg in get_args(union_type):
+            origin = get_origin(type_arg)
+            if origin is not None:
+                type_arg = origin
+
             if isinstance(type_arg, type) and issubclass(actual_type, type_arg):
                 return True
             elif get_origin(type_arg) == list:

tests/layer_tests/ovc_python_api_tests/test_pytorch.py

@@ -4,7 +4,6 @@
 import os
 import tempfile
 import unittest
-from typing import Tuple, List
 
 import numpy as np
 import pytest
@@ -736,7 +735,7 @@ def forward(self, x):
 def create_pytorch_module_with_nested_inputs(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, z: Tuple[torch.Tensor, torch.Tensor]):
+        def forward(self, z: tuple[torch.Tensor, torch.Tensor]):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -761,7 +760,7 @@ def forward(self, z: Tuple[torch.Tensor, torch.Tensor]):
 def create_pytorch_module_with_nested_inputs_compress_to_fp16_default(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, z: Tuple[torch.Tensor, torch.Tensor]):
+        def forward(self, z: tuple[torch.Tensor, torch.Tensor]):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -787,7 +786,7 @@ def forward(self, z: Tuple[torch.Tensor, torch.Tensor]):
 def create_pytorch_module_with_nested_inputs2(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor]):
+        def forward(self, x: torch.Tensor, z: tuple[torch.Tensor, torch.Tensor]):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -815,7 +814,7 @@ def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor]):
 def create_pytorch_module_with_nested_inputs3(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, z: Tuple[torch.Tensor, torch.Tensor], x: torch.Tensor):
+        def forward(self, z: tuple[torch.Tensor, torch.Tensor], x: torch.Tensor):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -843,7 +842,7 @@ def forward(self, z: Tuple[torch.Tensor, torch.Tensor], x: torch.Tensor):
 def create_pytorch_module_with_nested_inputs4(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor], y: torch.Tensor):
+        def forward(self, x: torch.Tensor, z: tuple[torch.Tensor, torch.Tensor], y: torch.Tensor):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -874,7 +873,7 @@ def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor], y: torc
 def create_pytorch_module_with_nested_inputs5(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor], y: torch.Tensor):
+        def forward(self, x: torch.Tensor, z: tuple[torch.Tensor, torch.Tensor], y: torch.Tensor):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -904,7 +903,7 @@ def forward(self, x: torch.Tensor, z: Tuple[torch.Tensor, torch.Tensor], y: torc
 def create_pytorch_module_with_nested_inputs6(tmp_dir):
     class PTModel(torch.nn.Module):
 
-        def forward(self, x: torch.Tensor, y: torch.Tensor = None, z: Tuple[torch.Tensor, torch.Tensor] = None):
+        def forward(self, x: torch.Tensor, y: torch.Tensor = None, z: tuple[torch.Tensor, torch.Tensor] = None):
             z1, z2 = z
             zeros1 = torch.zeros((1, 1))
             zeros2 = torch.zeros((1, 5, 1))
@@ -933,7 +932,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor = None, z: Tuple[torch.Tensor
 
 def create_pytorch_module_with_nested_list_and_single_input(tmp_dir):
     class PTModel(torch.nn.Module):
-        def forward(self, x: List[torch.Tensor]):
+        def forward(self, x: list[torch.Tensor]):
             x0 = x[0]
             x0 = torch.cat([x0, torch.zeros(1, 1)], 1)
             return x0 + torch.ones((1, 1))