format

optimum/exporters/openvino/__main__.py

@@ -20,7 +20,6 @@
 from requests.exceptions import ConnectionError as RequestsConnectionError
 from transformers import AutoTokenizer
 
-import openvino
 from openvino import Core
 from optimum.exporters import TasksManager
 from optimum.exporters.onnx import __main__ as optimum_main
@@ -138,7 +137,6 @@ def main_export(
                 "Quantization of the weights to int8 requires nncf, please install it with `pip install nncf`"
             )
 
-        import nncf
 
     if model_kwargs is None:
         model_kwargs = {}
@@ -251,7 +249,9 @@ def main_export(
         models_and_onnx_configs = {"model": (model, onnx_config)}
 
     if int8 is None:
-        int8 = (model.num_parameters() if not is_stable_diffusion else model.unet.num_parameters()) >= _MAX_UNCOMPRESSED_SIZE
+        int8 = (
+            model.num_parameters() if not is_stable_diffusion else model.unet.num_parameters()
+        ) >= _MAX_UNCOMPRESSED_SIZE
 
     if not is_stable_diffusion:
         needs_pad_token_id = (

optimum/exporters/openvino/convert.py

@@ -325,7 +325,9 @@ def ts_patched_forward(*args, **kwargs):
                 ov_model = convert_model(model, example_input=dummy_inputs, input=input_info)
         except Exception as ex:
             logger.warning(f"Export model to OpenVINO directly failed with: \n{ex}.\nModel will be exported to ONNX")
-            return export_pytorch_via_onnx(model, config, opset, output, device, input_shapes, model_kwargs, fp16=fp16, int8=int8)
+            return export_pytorch_via_onnx(
+                model, config, opset, output, device, input_shapes, model_kwargs, fp16=fp16, int8=int8
+            )
         ordered_dummy_inputs = {param: dummy_inputs[param] for param in sig.parameters if param in dummy_inputs}
         ordered_input_names = list(inputs)
         flatten_inputs = flattenize_inputs(ordered_dummy_inputs.values())