wip

Signed-off-by: Raphael Glon <oOraph@users.noreply.github.com>

tests/test_modeling_utils.py

@@ -33,6 +33,7 @@
 from transformers import (
     AutoConfig,
     AutoModel,
+    AutoModelForImageClassification,
     AutoModelForSequenceClassification,
     OwlViTForObjectDetection,
     PretrainedConfig,
@@ -76,7 +77,6 @@
 
 from test_module.custom_configuration import CustomConfig, NoSuperInitConfig  # noqa E402
 
-
 if is_torch_available():
     import torch
     from safetensors.torch import save_file as safe_save_file
@@ -194,6 +194,99 @@ def forward(self, mask, inputs_embeds):
             attention_mask = _prepare_4d_attention_mask(mask, dtype=inputs_embeds.dtype)
             return attention_mask
 
+    class TestOffline(unittest.TestCase):
+
+        def test_offline(self):
+
+            # Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported
+            from transformers.utils import hub
+            from huggingface_hub import constants
+            offlfine_env = hub._is_offline_mode
+            hub_cache_env = constants.HF_HUB_CACHE
+            hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE
+            default_cache = constants.default_cache_path
+            transformers_cache = hub.TRANSFORMERS_CACHE
+
+            try:
+                hub._is_offline_mode = True
+                with tempfile.TemporaryDirectory() as tmpdir:
+                    LOG.info("Temporary cache dir %s", tmpdir)
+                    constants.HF_HUB_CACHE = tmpdir
+                    constants.HUGGINGFACE_HUB_CACHE = tmpdir
+                    constants.default_cache_path = tmpdir
+                    hub.TRANSFORMERS_CACHE = tmpdir
+                    # First offline load should fail
+                    try:
+                        AutoModelForImageClassification.from_pretrained(TINY_IMAGE_CLASSIF, revision="main",
+                                                                        use_auth_token=None)
+                    except OSError:
+                        LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF)
+                    else:
+                        self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF))
+
+                    # Download model -> Huggingface Hub not concerned by our offline mode
+                    LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF)
+                    hub_api = HfApi()
+                    local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
+
+                    print("TMPDIR, %s" % tmpdir)
+                    print("LOCAL DIR %s", local_dir)
+
+                    LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir)
+
+                    import pdb
+                    pdb.set_trace()
+                    AutoModelForImageClassification.from_pretrained(TINY_IMAGE_CLASSIF, revision="main",
+                                                                    use_auth_token=None)
+            finally:
+                # Tear down: reset env as it was before calling this test
+                hub._is_offline_mode = offlfine_env
+                constants.HF_HUB_CACHE = hub_cache_env
+                constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1
+                constants.default_cache_path = default_cache
+                hub.TRANSFORMERS_CACHE = transformers_cache
+
+        def test_local_files_only(self):
+            # Ugly setup with monkeypatches, amending env vars here is too late as libs have already been imported
+            from transformers.utils import hub
+            from huggingface_hub import constants
+            hub_cache_env = constants.HF_HUB_CACHE
+            hub_cache_env1 = constants.HUGGINGFACE_HUB_CACHE
+            default_cache = constants.default_cache_path
+            transformers_cache = hub.TRANSFORMERS_CACHE
+            try:
+                with tempfile.TemporaryDirectory() as tmpdir:
+                    LOG.info("Temporary cache dir %s", tmpdir)
+                    constants.HF_HUB_CACHE = tmpdir
+                    constants.HUGGINGFACE_HUB_CACHE = tmpdir
+                    constants.default_cache_path = tmpdir
+                    hub.TRANSFORMERS_CACHE = tmpdir
+                    try:
+                        AutoModelForImageClassification.from_pretrained(TINY_IMAGE_CLASSIF, revision="main",
+                                                                        use_auth_token=None, local_files_only=True)
+                    except OSError:
+                        LOG.info("Loading model %s in offline mode failed as expected", TINY_IMAGE_CLASSIF)
+                    else:
+                        self.fail("Loading model {} in offline mode should fail".format(TINY_IMAGE_CLASSIF))
+
+                    LOG.info("Downloading %s for offline tests", TINY_IMAGE_CLASSIF)
+                    hub_api = HfApi()
+                    local_dir = hub_api.snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
+
+                    print("TMPDIR, %s" % tmpdir)
+                    print("LOCAL DIR %s", local_dir)
+
+                    LOG.info("Model %s downloaded in %s", TINY_IMAGE_CLASSIF, local_dir)
+
+                    AutoModelForImageClassification.from_pretrained(TINY_IMAGE_CLASSIF, revision="main",
+                                                                    use_auth_token=None, local_files_only=True)
+            finally:
+                # Tear down: reset env as it was before calling this test
+                constants.HF_HUB_CACHE = hub_cache_env
+                constants.HUGGINGFACE_HUB_CACHE = hub_cache_env1
+                constants.default_cache_path = default_cache
+                hub.TRANSFORMERS_CACHE = transformers_cache
+
 
 if is_flax_available():
     from transformers import FlaxBertModel
@@ -205,6 +298,9 @@ def forward(self, mask, inputs_embeds):
 TINY_T5 = "patrickvonplaten/t5-tiny-random"
 TINY_BERT_FOR_TOKEN_CLASSIFICATION = "hf-internal-testing/tiny-bert-for-token-classification"
 TINY_MISTRAL = "hf-internal-testing/tiny-random-MistralForCausalLM"
+TINY_IMAGE_CLASSIF = "hf-internal-testing/tiny-random-SiglipForImageClassification"
+
+LOG = logging.get_logger(__name__)
 
 
 def check_models_equal(model1, model2):