Fix test examples on windows (openvinotoolkit#3045)

### Changes

- `setuptools<=72.1.0` to avoid issue `Could not compile CPU
quantization extensions. Falling back on torch native operations - CPU
quantization fine-tuning may be slower than expected.` on windows.
- Add catch `BackendCompilerFailed` exception for
`post_training_quantization_torch_fx_resnet18`
- Bump version of anomalib to 1.0.1
`quantization_aware_training_torch_resnet18` tests on windows

### Related tickets

155866

examples/post_training_quantization/torch/mobilenet_v2/requirements.txt

@@ -3,3 +3,4 @@ openvino==2024.4
 scikit-learn
 torch==2.4.0
 torchvision==0.19.0
+setuptools<=72.1.0

examples/post_training_quantization/torch/ssd300_vgg16/requirements.txt

@@ -6,3 +6,4 @@ torch==2.4.0
 torchmetrics==1.0.1
 torchvision==0.19.0
 numpy<2
+setuptools<=72.1.0

examples/post_training_quantization/torch_fx/resnet18/main.py

@@ -24,11 +24,13 @@
 import torchvision.models as models
 import torchvision.transforms as transforms
 from fastdownload import FastDownload
+from torch._dynamo.exc import BackendCompilerFailed
 
 import nncf
 import nncf.torch
 from nncf.common.logging.track_progress import track
 from nncf.common.utils.helpers import create_table
+from nncf.common.utils.os import is_windows
 from nncf.torch import disable_patching
 
 IMAGE_SIZE = 64
@@ -205,7 +207,16 @@ def transform_fn(data_item):
     print("Benchmark FP32 model compiled with default backend ...")
     with disable_patching():
         compiled_model = torch.compile(model)
-        fp32_latency = measure_latency(compiled_model, example_inputs=example_input)
+        try:
+            fp32_latency = measure_latency(compiled_model, example_inputs=example_input)
+        except BackendCompilerFailed as exp:
+            if not is_windows():
+                raise exp
+            print(
+                "WARNING: Torch Inductor is currently unavailable on Windows. "
+                "For more information, visit https://github.com/pytorch/pytorch/issues/135954"
+            )
+            fp32_latency = float("nan")
     print(f"{fp32_latency:.3f} ms")
 
     print("Benchmark FP32 model compiled with openvino backend ...")

examples/quantization_aware_training/torch/anomalib/requirements.txt

@@ -1 +1,2 @@
-anomalib[core,openvino]==1.0.0
+anomalib[core,openvino]==1.0.1
+setuptools<=72.1.0

examples/quantization_aware_training/torch/resnet18/requirements.txt

@@ -2,3 +2,4 @@ fastdownload==0.0.7
 openvino==2024.4
 torch==2.4.0
 torchvision==0.19.0
+setuptools<=72.1.0