Better check for packages availability (#23163)

* Better check for packages availability

* amend _optimumneuron_available

* amend torch_version

* amend PIL detection and lint

* lint

* amend _faiss_available

* remove overloaded signatures of _is_package_available

* fix sklearn and decord detection

* remove unused checks

* revert

src/transformers/file_utils.py

@@ -72,6 +72,7 @@ from .utils import (
     get_cached_models,
     get_file_from_repo,
     get_full_repo_name,
+    get_torch_version,
     has_file,
     http_user_agent,
     is_apex_available,
@@ -125,5 +126,4 @@ from .utils import (
     to_numpy,
     to_py_obj,
     torch_only_method,
-    torch_version,
 )

src/transformers/onnx/config.py

@@ -232,9 +232,9 @@ class OnnxConfig(ABC):
             `bool`: Whether the installed version of PyTorch is compatible with the model.
         """
         if is_torch_available():
-            from transformers.utils import torch_version
+            from transformers.utils import get_torch_version
 
-            return torch_version >= self.torch_onnx_minimum_version
+            return get_torch_version() >= self.torch_onnx_minimum_version
         else:
             return False
 

src/transformers/onnx/convert.py

@@ -334,12 +334,12 @@ def export(
         preprocessor = tokenizer
 
     if is_torch_available():
-        from ..utils import torch_version
+        from ..utils import get_torch_version
 
         if not config.is_torch_support_available:
             logger.warning(
                 f"Unsupported PyTorch version for this model. Minimum required is {config.torch_onnx_minimum_version},"
-                f" got: {torch_version}"
+                f" got: {get_torch_version()}"
             )
 
     if is_torch_available() and issubclass(type(model), PreTrainedModel):

src/transformers/utils/__init__.py

@@ -99,6 +99,7 @@ from .import_utils import (
     _LazyModule,
     ccl_version,
     direct_transformers_import,
+    get_torch_version,
     is_accelerate_available,
     is_apex_available,
     is_bitsandbytes_available,
@@ -170,7 +171,6 @@ from .import_utils import (
     is_vision_available,
     requires_backends,
     torch_only_method,
-    torch_version,
 )
 
 

src/transformers/utils/fx.py

@@ -25,7 +25,6 @@ import warnings
 from typing import Any, Callable, Dict, List, Optional, Type, Union
 
 import torch
-from packaging import version
 from torch import nn
 from torch.fx import Graph, GraphModule, Proxy, Tracer
 from torch.fx._compatibility import compatibility
@@ -54,8 +53,13 @@ from ..models.auto.modeling_auto import (
     MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES,
     MODEL_MAPPING_NAMES,
 )
-from ..utils import ENV_VARS_TRUE_VALUES, TORCH_FX_REQUIRED_VERSION, is_peft_available, is_torch_fx_available
-from ..utils.versions import importlib_metadata
+from ..utils import (
+    ENV_VARS_TRUE_VALUES,
+    TORCH_FX_REQUIRED_VERSION,
+    get_torch_version,
+    is_peft_available,
+    is_torch_fx_available,
+)
 
 
 if is_peft_available():
@@ -737,9 +741,8 @@ class HFTracer(Tracer):
         super().__init__(autowrap_modules=autowrap_modules, autowrap_functions=autowrap_functions)
 
         if not is_torch_fx_available():
-            torch_version = version.parse(importlib_metadata.version("torch"))
             raise ImportError(
-                f"Found an incompatible version of torch. Found version {torch_version}, but only version "
+                f"Found an incompatible version of torch. Found version {get_torch_version()}, but only version "
                 f"{TORCH_FX_REQUIRED_VERSION} is supported."
             )
 

tests/onnx/test_onnx_v2.py

@@ -319,12 +319,12 @@ class OnnxExportTestCaseV2(TestCase):
         onnx_config = onnx_config_class_constructor(model.config)
 
         if is_torch_available():
-            from transformers.utils import torch_version
+            from transformers.utils import get_torch_version
 
-            if torch_version < onnx_config.torch_onnx_minimum_version:
+            if get_torch_version() < onnx_config.torch_onnx_minimum_version:
                 pytest.skip(
                     "Skipping due to incompatible PyTorch version. Minimum required is"
-                    f" {onnx_config.torch_onnx_minimum_version}, got: {torch_version}"
+                    f" {onnx_config.torch_onnx_minimum_version}, got: {get_torch_version()}"
                 )
 
         preprocessor = get_preprocessor(model_name)
@@ -362,12 +362,12 @@ class OnnxExportTestCaseV2(TestCase):
         onnx_config = onnx_config_class_constructor(model.config)
 
         if is_torch_available():
-            from transformers.utils import torch_version
+            from transformers.utils import get_torch_version
 
-            if torch_version < onnx_config.torch_onnx_minimum_version:
+            if get_torch_version() < onnx_config.torch_onnx_minimum_version:
                 pytest.skip(
                     "Skipping due to incompatible PyTorch version. Minimum required is"
-                    f" {onnx_config.torch_onnx_minimum_version}, got: {torch_version}"
+                    f" {onnx_config.torch_onnx_minimum_version}, got: {get_torch_version()}"
                 )
 
         encoder_model = model.get_encoder()