Use HF_HUB_OFFLINE + fix has_file in offline mode (#31016)

* Fix has_file in offline mode

* harmonize env variable for offline mode

* Switch to HF_HUB_OFFLINE

* fix test

* revert test_offline to test TRANSFORMERS_OFFLINE

* Add new offline test

* merge conflicts

* docs

docs/source/de/installation.md

@@ -162,7 +162,7 @@ Transformers verwendet die Shell-Umgebungsvariablen `PYTORCH_TRANSFORMERS_CACHE`
 
 ## Offline Modus
 
-Transformers ist in der Lage, in einer Firewall- oder Offline-Umgebung zu laufen, indem es nur lokale Dateien verwendet. Setzen Sie die Umgebungsvariable `TRANSFORMERS_OFFLINE=1`, um dieses Verhalten zu aktivieren.
+Transformers ist in der Lage, in einer Firewall- oder Offline-Umgebung zu laufen, indem es nur lokale Dateien verwendet. Setzen Sie die Umgebungsvariable `HF_HUB_OFFLINE=1`, um dieses Verhalten zu aktivieren.
 
 <Tip>
 
@@ -179,7 +179,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 Führen Sie das gleiche Programm in einer Offline-Instanz mit aus:
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/en/installation.md

@@ -169,7 +169,7 @@ Pretrained models are downloaded and locally cached at: `~/.cache/huggingface/hu
 
 ## Offline mode
 
-Run 🤗 Transformers in a firewalled or offline environment with locally cached files by setting the environment variable `TRANSFORMERS_OFFLINE=1`.
+Run 🤗 Transformers in a firewalled or offline environment with locally cached files by setting the environment variable `HF_HUB_OFFLINE=1`.
 
 <Tip>
 
@@ -178,7 +178,7 @@ Add [🤗 Datasets](https://huggingface.co/docs/datasets/) to your offline train
 </Tip>
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/es/installation.md

@@ -154,7 +154,7 @@ Los modelos preentrenados se descargan y almacenan en caché localmente en: `~/.
 
 ## Modo Offline
 
-🤗 Transformers puede ejecutarse en un entorno con firewall o fuera de línea (offline) usando solo archivos locales. Configura la variable de entorno `TRANSFORMERS_OFFLINE=1` para habilitar este comportamiento.
+🤗 Transformers puede ejecutarse en un entorno con firewall o fuera de línea (offline) usando solo archivos locales. Configura la variable de entorno `HF_HUB_OFFLINE=1` para habilitar este comportamiento.
 
 <Tip>
 
@@ -171,7 +171,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 Ejecuta este mismo programa en una instancia offline con el siguiente comando:
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/fr/installation.md

@@ -171,7 +171,7 @@ Les modèles pré-entraînés sont téléchargés et mis en cache localement dan
 
 ## Mode hors ligne
 
-🤗 Transformers peut fonctionner dans un environnement cloisonné ou hors ligne en n'utilisant que des fichiers locaux. Définissez la variable d'environnement `TRANSFORMERS_OFFLINE=1` pour activer ce mode.
+🤗 Transformers peut fonctionner dans un environnement cloisonné ou hors ligne en n'utilisant que des fichiers locaux. Définissez la variable d'environnement `HF_HUB_OFFLINE=1` pour activer ce mode.
 
 <Tip>
 
@@ -180,7 +180,7 @@ Ajoutez [🤗 Datasets](https://huggingface.co/docs/datasets/) à votre processu
 </Tip>
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/it/installation.md

@@ -152,7 +152,7 @@ I modelli pre-allenati sono scaricati e memorizzati localmente nella cache in: `
 
 ## Modalità Offline
 
-🤗 Transformers può essere eseguita in un ambiente firewalled o offline utilizzando solo file locali. Imposta la variabile d'ambiente `TRANSFORMERS_OFFLINE=1` per abilitare questo comportamento.
+🤗 Transformers può essere eseguita in un ambiente firewalled o offline utilizzando solo file locali. Imposta la variabile d'ambiente `HF_HUB_OFFLINE=1` per abilitare questo comportamento.
 
 <Tip>
 
@@ -169,7 +169,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 Esegui lo stesso programma in un'istanza offline con:
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/ja/installation.md

@@ -157,7 +157,7 @@ conda install conda-forge::transformers
 
 ## オフラインモード
 
-🤗 Transformersはローカルファイルのみを使用することでファイアウォールやオフラインの環境でも動作させることができます。この動作を有効にするためには、環境変数`TRANSFORMERS_OFFLINE=1`を設定します。
+🤗 Transformersはローカルファイルのみを使用することでファイアウォールやオフラインの環境でも動作させることができます。この動作を有効にするためには、環境変数`HF_HUB_OFFLINE=1`を設定します。
 
 <Tip>
 
@@ -174,7 +174,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 オフラインインスタンスでこの同じプログラムを実行します:
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/ko/installation.md

@@ -157,7 +157,7 @@ conda install conda-forge::transformers
 
 ## 오프라인 모드[[offline-mode]]
 
-🤗 Transformers를 로컬 파일만 사용하도록 해서 방화벽 또는 오프라인 환경에서 실행할 수 있습니다. 활성화하려면 `TRANSFORMERS_OFFLINE=1` 환경 변수를 설정하세요.
+🤗 Transformers를 로컬 파일만 사용하도록 해서 방화벽 또는 오프라인 환경에서 실행할 수 있습니다. 활성화하려면 `HF_HUB_OFFLINE=1` 환경 변수를 설정하세요.
 
 <Tip>
 
@@ -174,7 +174,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 오프라인 기기에서 동일한 프로그램을 다음과 같이 실행할 수 있습니다.
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/pt/installation.md

@@ -173,7 +173,7 @@ No Windows, este diretório pré-definido é dado por `C:\Users\username\.cache\
 ## Modo Offline
 
 O 🤗 Transformers também pode ser executado num ambiente de firewall ou fora da rede (offline) usando arquivos locais.
-Para tal, configure a variável de ambiente de modo que `TRANSFORMERS_OFFLINE=1`.
+Para tal, configure a variável de ambiente de modo que `HF_HUB_OFFLINE=1`.
 
 <Tip>
 
@@ -191,7 +191,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 Execute esse mesmo programa numa instância offline com o seguinte comando:
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

docs/source/zh/installation.md

@@ -169,7 +169,7 @@ conda install conda-forge::transformers
 
 ## 离线模式
 
-🤗 Transformers 可以仅使用本地文件在防火墙或离线环境中运行。设置环境变量 `TRANSFORMERS_OFFLINE=1` 以启用该行为。
+🤗 Transformers 可以仅使用本地文件在防火墙或离线环境中运行。设置环境变量 `HF_HUB_OFFLINE=1` 以启用该行为。
 
 <Tip>
 
@@ -186,7 +186,7 @@ python examples/pytorch/translation/run_translation.py --model_name_or_path goog
 在离线环境中运行相同的程序：
 
 ```bash
-HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \
+HF_DATASETS_OFFLINE=1 HF_HUB_OFFLINE=1 \
 python examples/pytorch/translation/run_translation.py --model_name_or_path google-t5/t5-small --dataset_name wmt16 --dataset_config ro-en ...
 ```
 

src/transformers/modeling_flax_utils.py

@@ -823,6 +823,8 @@ class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
                             "revision": revision,
                             "proxies": proxies,
                             "token": token,
+                            "cache_dir": cache_dir,
+                            "local_files_only": local_files_only,
                         }
                         if has_file(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME, **has_file_kwargs):
                             is_sharded = True

src/transformers/modeling_tf_utils.py

@@ -2864,6 +2864,8 @@ class TFPreTrainedModel(keras.Model, TFModelUtilsMixin, TFGenerationMixin, PushT
                             "revision": revision,
                             "proxies": proxies,
                             "token": token,
+                            "cache_dir": cache_dir,
+                            "local_files_only": local_files_only,
                         }
                         if has_file(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME, **has_file_kwargs):
                             is_sharded = True

src/transformers/modeling_utils.py

@@ -3405,6 +3405,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                                     "revision": revision,
                                     "proxies": proxies,
                                     "token": token,
+                                    "cache_dir": cache_dir,
+                                    "local_files_only": local_files_only,
                                 }
                                 cached_file_kwargs = {
                                     "cache_dir": cache_dir,
@@ -3432,6 +3434,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                                 "revision": revision,
                                 "proxies": proxies,
                                 "token": token,
+                                "cache_dir": cache_dir,
+                                "local_files_only": local_files_only,
                             }
                             if has_file(pretrained_model_name_or_path, TF2_WEIGHTS_NAME, **has_file_kwargs):
                                 raise EnvironmentError(
@@ -3459,6 +3463,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                                     f" {_add_variant(WEIGHTS_NAME, variant)}, {_add_variant(SAFE_WEIGHTS_NAME, variant)},"
                                     f" {TF2_WEIGHTS_NAME}, {TF_WEIGHTS_NAME} or {FLAX_WEIGHTS_NAME}."
                                 )
+
                 except EnvironmentError:
                     # Raise any environment error raise by `cached_file`. It will have a helpful error message adapted
                     # to the original exception.

src/transformers/utils/hub.py

@@ -51,9 +51,11 @@ from huggingface_hub.utils import (
     GatedRepoError,
     HFValidationError,
     LocalEntryNotFoundError,
+    OfflineModeIsEnabled,
     RepositoryNotFoundError,
     RevisionNotFoundError,
     build_hf_headers,
+    get_session,
     hf_raise_for_status,
     send_telemetry,
 )
@@ -75,7 +77,7 @@ from .logging import tqdm
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
-_is_offline_mode = True if os.environ.get("TRANSFORMERS_OFFLINE", "0").upper() in ENV_VARS_TRUE_VALUES else False
+_is_offline_mode = huggingface_hub.constants.HF_HUB_OFFLINE
 
 
 def is_offline_mode():
@@ -599,11 +601,17 @@ def has_file(
     revision: Optional[str] = None,
     proxies: Optional[Dict[str, str]] = None,
     token: Optional[Union[bool, str]] = None,
+    *,
+    local_files_only: bool = False,
+    cache_dir: Union[str, Path, None] = None,
+    repo_type: Optional[str] = None,
     **deprecated_kwargs,
 ):
     """
     Checks if a repo contains a given file without downloading it. Works for remote repos and local folders.
 
+    If offline mode is enabled, checks if the file exists in the cache.
+
     <Tip warning={false}>
 
     This function will raise an error if the repository `path_or_repo` is not valid or if `revision` does not exist for
@@ -621,15 +629,41 @@ def has_file(
             raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
         token = use_auth_token
 
+    # If path to local directory, check if the file exists
     if os.path.isdir(path_or_repo):
         return os.path.isfile(os.path.join(path_or_repo, filename))
 
-    url = hf_hub_url(path_or_repo, filename=filename, revision=revision)
+    # Else it's a repo => let's check if the file exists in local cache or on the Hub
-    headers = build_hf_headers(token=token, user_agent=http_user_agent())
 
-    r = requests.head(url, headers=headers, allow_redirects=False, proxies=proxies, timeout=10)
+    # Check if file exists in cache
+    # This information might be outdated so it's best to also make a HEAD call (if allowed).
+    cached_path = try_to_load_from_cache(
+        repo_id=path_or_repo,
+        filename=filename,
+        revision=revision,
+        repo_type=repo_type,
+        cache_dir=cache_dir,
+    )
+    has_file_in_cache = isinstance(cached_path, str)
+
+    # If local_files_only, don't try the HEAD call
+    if local_files_only:
+        return has_file_in_cache
+
+    # Check if the file exists
     try:
-        hf_raise_for_status(r)
+        response = get_session().head(
+            hf_hub_url(path_or_repo, filename=filename, revision=revision, repo_type=repo_type),
+            headers=build_hf_headers(token=token, user_agent=http_user_agent()),
+            allow_redirects=False,
+            proxies=proxies,
+            timeout=10,
+        )
+    except OfflineModeIsEnabled:
+        return has_file_in_cache
+
+    try:
+        hf_raise_for_status(response)
         return True
     except GatedRepoError as e:
         logger.error(e)
@@ -640,16 +674,20 @@ def has_file(
         ) from e
     except RepositoryNotFoundError as e:
         logger.error(e)
-        raise EnvironmentError(f"{path_or_repo} is not a local folder or a valid repository name on 'https://hf.co'.")
+        raise EnvironmentError(
+            f"{path_or_repo} is not a local folder or a valid repository name on 'https://hf.co'."
+        ) from e
     except RevisionNotFoundError as e:
         logger.error(e)
         raise EnvironmentError(
             f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for this "
             f"model name. Check the model page at 'https://huggingface.co/{path_or_repo}' for available revisions."
-        )
+        ) from e
+    except EntryNotFoundError:
+        return False  # File does not exist
     except requests.HTTPError:
-        # We return false for EntryNotFoundError (logical) as well as any connection error.
+        # Any authentication/authorization error will be caught here => default to cache
-        return False
+        return has_file_in_cache
 
 
 class PushToHubMixin:

tests/test_configuration_utils.py

@@ -12,7 +12,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 import json
 import os
 import shutil

tests/utils/test_hub_utils.py

@@ -18,6 +18,7 @@ import unittest
 import unittest.mock as mock
 from pathlib import Path
 
+from huggingface_hub import hf_hub_download
 from requests.exceptions import HTTPError
 
 from transformers.utils import (
@@ -33,6 +34,7 @@ from transformers.utils import (
 
 
 RANDOM_BERT = "hf-internal-testing/tiny-random-bert"
+TINY_BERT_PT_ONLY = "hf-internal-testing/tiny-bert-pt-only"
 CACHE_DIR = os.path.join(TRANSFORMERS_CACHE, "models--hf-internal-testing--tiny-random-bert")
 FULL_COMMIT_HASH = "9b8c223d42b2188cb49d29af482996f9d0f3e5a6"
 
@@ -99,9 +101,20 @@ class GetFromCacheTests(unittest.TestCase):
             mock_head.assert_called()
 
     def test_has_file(self):
-        self.assertTrue(has_file("hf-internal-testing/tiny-bert-pt-only", WEIGHTS_NAME))
+        self.assertTrue(has_file(TINY_BERT_PT_ONLY, WEIGHTS_NAME))
-        self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only", TF2_WEIGHTS_NAME))
+        self.assertFalse(has_file(TINY_BERT_PT_ONLY, TF2_WEIGHTS_NAME))
-        self.assertFalse(has_file("hf-internal-testing/tiny-bert-pt-only", FLAX_WEIGHTS_NAME))
+        self.assertFalse(has_file(TINY_BERT_PT_ONLY, FLAX_WEIGHTS_NAME))
+
+    def test_has_file_in_cache(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            # Empty cache dir + offline mode => return False
+            assert not has_file(TINY_BERT_PT_ONLY, WEIGHTS_NAME, local_files_only=True, cache_dir=tmp_dir)
+
+            # Populate cache dir
+            hf_hub_download(TINY_BERT_PT_ONLY, WEIGHTS_NAME, cache_dir=tmp_dir)
+
+            # Cache dir + offline mode => return True
+            assert has_file(TINY_BERT_PT_ONLY, WEIGHTS_NAME, local_files_only=True, cache_dir=tmp_dir)
 
     def test_get_file_from_repo_distant(self):
         # `get_file_from_repo` returns None if the file does not exist

tests/utils/test_offline.py

@@ -14,6 +14,7 @@
 
 import subprocess
 import sys
+from typing import Tuple
 
 from transformers import BertConfig, BertModel, BertTokenizer, pipeline
 from transformers.testing_utils import TestCasePlus, require_torch
@@ -56,15 +57,9 @@ socket.socket = offline_socket
         pipeline(task="fill-mask", model=mname)
 
         # baseline - just load from_pretrained with normal network
-        cmd = [sys.executable, "-c", "\n".join([load, run, mock])]
-
-        # should succeed
-        env = self.get_env()
         # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
-        env["TRANSFORMERS_OFFLINE"] = "1"
+        stdout, _ = self._execute_with_env(load, run, mock, TRANSFORMERS_OFFLINE="1")
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
+        self.assertIn("success", stdout)
-        self.assertEqual(result.returncode, 0, result.stderr)
-        self.assertIn("success", result.stdout.decode())
 
     @require_torch
     def test_offline_mode_no_internet(self):
@@ -97,13 +92,9 @@ socket.socket = offline_socket
         pipeline(task="fill-mask", model=mname)
 
         # baseline - just load from_pretrained with normal network
-        cmd = [sys.executable, "-c", "\n".join([load, run, mock])]
-
         # should succeed
-        env = self.get_env()
+        stdout, _ = self._execute_with_env(load, run, mock)
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
+        self.assertIn("success", stdout)
-        self.assertEqual(result.returncode, 0, result.stderr)
-        self.assertIn("success", result.stdout.decode())
 
     @require_torch
     def test_offline_mode_sharded_checkpoint(self):
@@ -132,27 +123,17 @@ socket.socket = offline_socket
         """
 
         # baseline - just load from_pretrained with normal network
-        cmd = [sys.executable, "-c", "\n".join([load, run])]
-
         # should succeed
-        env = self.get_env()
+        stdout, _ = self._execute_with_env(load, run)
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
+        self.assertIn("success", stdout)
-        self.assertEqual(result.returncode, 0, result.stderr)
-        self.assertIn("success", result.stdout.decode())
 
         # next emulate no network
-        cmd = [sys.executable, "-c", "\n".join([load, mock, run])]
-
         # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
-        # env["TRANSFORMERS_OFFLINE"] = "0"
+        # self._execute_with_env(load, mock, run, should_fail=True, TRANSFORMERS_OFFLINE="0")
-        # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
-        # self.assertEqual(result.returncode, 1, result.stderr)
 
         # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
-        env["TRANSFORMERS_OFFLINE"] = "1"
+        stdout, _ = self._execute_with_env(load, mock, run, TRANSFORMERS_OFFLINE="1")
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
+        self.assertIn("success", stdout)
-        self.assertEqual(result.returncode, 0, result.stderr)
-        self.assertIn("success", result.stdout.decode())
 
     @require_torch
     def test_offline_mode_pipeline_exception(self):
@@ -169,14 +150,11 @@ import socket
 def offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")
 socket.socket = offline_socket
         """
-        env = self.get_env()
+
-        env["TRANSFORMERS_OFFLINE"] = "1"
+        _, stderr = self._execute_with_env(load, mock, run, should_fail=True, TRANSFORMERS_OFFLINE="1")
-        cmd = [sys.executable, "-c", "\n".join([load, mock, run])]
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
-        self.assertEqual(result.returncode, 1, result.stderr)
         self.assertIn(
             "You cannot infer task automatically within `pipeline` when using offline mode",
-            result.stderr.decode().replace("\n", ""),
+            stderr.replace("\n", ""),
         )
 
     @require_torch
@@ -191,16 +169,51 @@ print("success")
         """
 
         # baseline - just load from_pretrained with normal network
-        cmd = [sys.executable, "-c", "\n".join([load, run])]
-
         # should succeed
-        env = self.get_env()
+        stdout, _ = self._execute_with_env(load, run)
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
+        self.assertIn("success", stdout)
-        self.assertEqual(result.returncode, 0, result.stderr)
-        self.assertIn("success", result.stdout.decode())
 
         # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
-        env["TRANSFORMERS_OFFLINE"] = "1"
+        stdout, _ = self._execute_with_env(load, run, TRANSFORMERS_OFFLINE="1")
-        result = subprocess.run(cmd, env=env, check=False, capture_output=True)
+        self.assertIn("success", stdout)
+
+    def test_is_offline_mode(self):
+        """
+        Test `_is_offline_mode` helper (should respect both HF_HUB_OFFLINE and legacy TRANSFORMERS_OFFLINE env vars)
+        """
+        load = "from transformers.utils import is_offline_mode"
+        run = "print(is_offline_mode())"
+
+        stdout, _ = self._execute_with_env(load, run)
+        self.assertIn("False", stdout)
+
+        stdout, _ = self._execute_with_env(load, run, TRANSFORMERS_OFFLINE="1")
+        self.assertIn("True", stdout)
+
+        stdout, _ = self._execute_with_env(load, run, HF_HUB_OFFLINE="1")
+        self.assertIn("True", stdout)
+
+    def _execute_with_env(self, *commands: Tuple[str, ...], should_fail: bool = False, **env) -> Tuple[str, str]:
+        """Execute Python code with a given environment and return the stdout/stderr as strings.
+
+        If `should_fail=True`, the command is expected to fail. Otherwise, it should succeed.
+        Environment variables can be passed as keyword arguments.
+        """
+        # Build command
+        cmd = [sys.executable, "-c", "\n".join(commands)]
+
+        # Configure env
+        new_env = self.get_env()
+        new_env.update(env)
+
+        # Run command
+        result = subprocess.run(cmd, env=new_env, check=False, capture_output=True)
+
+        # Check execution
+        if should_fail:
+            self.assertNotEqual(result.returncode, 0, result.stderr)
+        else:
             self.assertEqual(result.returncode, 0, result.stderr)
-        self.assertIn("success", result.stdout.decode())
+
+        # Return output
+        return result.stdout.decode(), result.stderr.decode()