Fix: AttributeError: module 'packaging' has no attribute 'version' (h…

…uggingface#1660)

* fix: fix module 'packaging' has no attribute 'version'

* fix ci

---------

Co-authored-by: fxmarty <9808326+fxmarty@users.noreply.github.com>

optimum/utils/import_utils.py

@@ -21,7 +21,7 @@
 from typing import Tuple, Union
 
 import numpy as np
-import packaging
+from packaging import version
 from transformers.utils import is_torch_available
 
 
@@ -48,14 +48,14 @@ def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[
     import importlib.metadata as importlib_metadata
 
 
-TORCH_MINIMUM_VERSION = packaging.version.parse("1.11.0")
-TRANSFORMERS_MINIMUM_VERSION = packaging.version.parse("4.25.0")
-DIFFUSERS_MINIMUM_VERSION = packaging.version.parse("0.18.0")
-AUTOGPTQ_MINIMUM_VERSION = packaging.version.parse("0.4.99")  # Allows 0.5.0.dev0
+TORCH_MINIMUM_VERSION = version.parse("1.11.0")
+TRANSFORMERS_MINIMUM_VERSION = version.parse("4.25.0")
+DIFFUSERS_MINIMUM_VERSION = version.parse("0.18.0")
+AUTOGPTQ_MINIMUM_VERSION = version.parse("0.4.99")  # Allows 0.5.0.dev0
 
 
 # This is the minimal required version to support some ONNX Runtime features
-ORT_QUANTIZE_MINIMUM_VERSION = packaging.version.parse("1.4.0")
+ORT_QUANTIZE_MINIMUM_VERSION = version.parse("1.4.0")
 
 
 _onnx_available = _is_package_available("onnx")
@@ -72,7 +72,7 @@ def _is_package_available(pkg_name: str, return_version: bool = False) -> Union[
 
 torch_version = None
 if is_torch_available():
-    torch_version = packaging.version.parse(importlib_metadata.version("torch"))
+    torch_version = version.parse(importlib_metadata.version("torch"))
 
 _is_torch_onnx_support_available = is_torch_available() and (
     TORCH_MINIMUM_VERSION.major,
@@ -133,7 +133,7 @@ def is_sentence_transformers_available():
 
 def is_auto_gptq_available():
     if _auto_gptq_available:
-        version_autogptq = packaging.version.parse(importlib_metadata.version("auto_gptq"))
+        version_autogptq = version.parse(importlib_metadata.version("auto_gptq"))
         if AUTOGPTQ_MINIMUM_VERSION < version_autogptq:
             return True
         else:
@@ -149,7 +149,7 @@ def check_if_pytorch_greater(target_version: str, message: str):
     """
     import torch
 
-    if not packaging.version.parse(torch.__version__) >= packaging.version.parse(target_version):
+    if not version.parse(torch.__version__) >= version.parse(target_version):
         raise ImportError(
             f"Found an incompatible version of PyTorch. Found version {torch.__version__}, but only {target_version} and above are supported. {message}"
         )
@@ -159,7 +159,7 @@ def check_if_pytorch_greater(target_version: str, message: str):
         pass
 
 
-def check_if_transformers_greater(target_version: Union[str, packaging.version.Version]) -> bool:
+def check_if_transformers_greater(target_version: Union[str, version.Version]) -> bool:
     """
     Checks whether the current install of transformers is greater than or equal to the target version.
 
@@ -172,9 +172,9 @@ def check_if_transformers_greater(target_version: Union[str, packaging.version.V
     import transformers
 
     if isinstance(target_version, str):
-        target_version = packaging.version.parse(target_version)
+        target_version = version.parse(target_version)
 
-    return packaging.version.parse(transformers.__version__) >= target_version
+    return version.parse(transformers.__version__) >= target_version
 
 
 def check_if_diffusers_greater(target_version: str) -> bool:
@@ -190,12 +190,12 @@ def check_if_diffusers_greater(target_version: str) -> bool:
     if not _diffusers_available:
         return False
 
-    return packaging.version.parse(_diffusers_version) >= packaging.version.parse(target_version)
+    return version.parse(_diffusers_version) >= version.parse(target_version)
 
 
 @contextmanager
-def require_numpy_strictly_lower(version: str, message: str):
-    if not packaging.version.parse(np.__version__) < packaging.version.parse(version):
+def require_numpy_strictly_lower(package_version: str, message: str):
+    if not version.parse(np.__version__) < version.parse(package_version):
         raise ImportError(
             f"Found an incompatible version of numpy. Found version {np.__version__}, but expected numpy<{version}. {message}"
         )

tests/exporters/onnx/test_onnx_config_loss.py

@@ -48,8 +48,9 @@ def test_onnx_config_with_loss(self):
         model_checkpoint = "hf-internal-testing/tiny-random-bert"
         models = {
             AutoModelForSequenceClassification.from_pretrained(model_checkpoint),
-            TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint),
         }
+        if is_tf_available():
+            models.add(TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint))
 
         for model in models:
             with self.subTest(model=model):