Kill model archive maps (#4636)

* Kill model archive maps

* Fixup

* Also kill model_archive_map for MaskedBertPreTrainedModel

* Unhook config_archive_map

* Tokenizers: align with model id changes

* make style && make quality

* Fix CI

templates/adding_a_new_example_script/run_xxx.py

@@ -63,8 +63,6 @@ logger = logging.getLogger(__name__)
 MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
 MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
 
-ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), (),)
-
 
 def set_seed(args):
     random.seed(args.seed)
@@ -411,7 +409,7 @@ def main():
         default=None,
         type=str,
         required=True,
-        help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS),
+        help="Path to pretrained model or model identifier from huggingface.co/models",
     )
     parser.add_argument(
         "--output_dir",

templates/adding_a_new_model/configuration_xxx.py

@@ -57,7 +57,6 @@ class XxxConfig(PretrainedConfig):
                 initializing all weight matrices.
             layer_norm_eps: The epsilon used by LayerNorm.
     """
-    pretrained_config_archive_map = XXX_PRETRAINED_CONFIG_ARCHIVE_MAP
     model_type = "xxx"
 
     def __init__(

templates/adding_a_new_model/modeling_tf_xxx.py

@@ -32,13 +32,13 @@ from .modeling_tf_utils import TFPreTrainedModel, get_initializer, shape_list
 logger = logging.getLogger(__name__)
 
 ####################################################
-# This dict contrains shortcut names and associated url
-# for the pretrained weights provided with the models
+# This list contrains shortcut names for some of
+# the pretrained weights provided with the models
 ####################################################
-TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    "xxx-base-uncased": "https://cdn.huggingface.co/xxx-base-uncased-tf_model.h5",
-    "xxx-large-uncased": "https://cdn.huggingface.co/xxx-large-uncased-tf_model.h5",
-}
+TF_XXX_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "xxx-base-uncased",
+    "xxx-large-uncased",
+]
 
 
 ####################################################
@@ -180,7 +180,6 @@ class TFXxxPreTrainedModel(TFPreTrainedModel):
     """
 
     config_class = XxxConfig
-    pretrained_model_archive_map = TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP
     base_model_prefix = "transformer"
 
 

templates/adding_a_new_model/modeling_xxx.py

@@ -34,13 +34,13 @@ from .modeling_utils import PreTrainedModel
 logger = logging.getLogger(__name__)
 
 ####################################################
-# This dict contrains shortcut names and associated url
-# for the pretrained weights provided with the models
+# This list contrains shortcut names for some of
+# the pretrained weights provided with the models
 ####################################################
-XXX_PRETRAINED_MODEL_ARCHIVE_MAP = {
-    "xxx-base-uncased": "https://cdn.huggingface.co/xxx-base-uncased-pytorch_model.bin",
-    "xxx-large-uncased": "https://cdn.huggingface.co/xxx-large-uncased-pytorch_model.bin",
-}
+XXX_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "xxx-base-uncased",
+    "xxx-large-uncased",
+]
 
 
 ####################################################
@@ -180,7 +180,6 @@ class XxxPreTrainedModel(PreTrainedModel):
     """
 
     config_class = XxxConfig
-    pretrained_model_archive_map = XXX_PRETRAINED_MODEL_ARCHIVE_MAP
     load_tf_weights = load_tf_weights_in_xxx
     base_model_prefix = "transformer"
 

templates/adding_a_new_model/tests/test_modeling_xxx.py

@@ -32,7 +32,7 @@ if is_torch_available():
         XxxForSequenceClassification,
         XxxForTokenClassification,
     )
-    from transformers.modeling_xxx import XXX_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers.modeling_xxx import XXX_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
 @require_torch
@@ -269,6 +269,6 @@ class XxxModelTest(ModelTesterMixin, unittest.TestCase):
 
     @slow
     def test_model_from_pretrained(self):
-        for model_name in list(XXX_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
+        for model_name in XXX_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             model = XxxModel.from_pretrained(model_name, cache_dir=CACHE_DIR)
             self.assertIsNotNone(model)