Refactor AutoModel classes and add Flax Auto classes (#11027)

* Refactor AutoModel classes and add Flax Auto classes

* Add new objects to the init

* Fix hubconf and sort models

* Fix TF tests

* Missing coma

* Update src/transformers/models/auto/auto_factory.py
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Fix init

* Fix dummies

* Other init to fix
Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

docs/source/model_doc/auto.rst

@@ -189,3 +189,52 @@ FlaxAutoModel
 
 .. autoclass:: transformers.FlaxAutoModel
     :members:
+
+
+FlaxAutoModelForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForPreTraining
+    :members:
+
+
+FlaxAutoModelForMaskedLM
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForMaskedLM
+    :members:
+
+
+FlaxAutoModelForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForSequenceClassification
+    :members:
+
+
+FlaxAutoModelForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForQuestionAnswering
+    :members:
+
+
+FlaxAutoModelForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForTokenClassification
+    :members:
+
+
+FlaxAutoModelForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForMultipleChoice
+    :members:
+
+
+FlaxAutoModelForNextSentencePrediction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FlaxAutoModelForNextSentencePrediction
+    :members:

hubconf.py

@@ -22,9 +22,10 @@ sys.path.append(SRC_DIR)
 from transformers import (
     AutoConfig,
     AutoModel,
+    AutoModelForCausalLM,
+    AutoModelForMaskedLM,
     AutoModelForQuestionAnswering,
     AutoModelForSequenceClassification,
-    AutoModelWithLMHead,
     AutoTokenizer,
     add_start_docstrings,
 )
@@ -86,22 +87,41 @@ def model(*args, **kwargs):
     return AutoModel.from_pretrained(*args, **kwargs)
 
 
-@add_start_docstrings(AutoModelWithLMHead.__doc__)
-def modelWithLMHead(*args, **kwargs):
+@add_start_docstrings(AutoModelForCausalLM.__doc__)
+def modelForCausalLM(*args, **kwargs):
     r"""
         # Using torch.hub !
         import torch
 
-        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
-        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
+        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'gpt2')    # Download model and configuration from huggingface.co and cache.
+        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './test/saved_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', 'gpt2', output_attentions=True)  # Update configuration during loading
+        assert model.config.output_attentions == True
+        # Loading from a TF checkpoint file instead of a PyTorch model (slower)
+        config = AutoConfig.from_pretrained('./tf_model/gpt_tf_model_config.json')
+        model = torch.hub.load('huggingface/transformers', 'modelForCausalLM', './tf_model/gpt_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+
+    """
+    return AutoModelForCausalLM.from_pretrained(*args, **kwargs)
+
+
+@add_start_docstrings(AutoModelForMaskedLM.__doc__)
+def modelForMaskedLM(*args, **kwargs):
+    r"""
+            # Using torch.hub !
+            import torch
+
+            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
+            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
+            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', 'bert-base-uncased', output_attentions=True)  # Update configuration during loading
             assert model.config.output_attentions == True
             # Loading from a TF checkpoint file instead of a PyTorch model (slower)
             config = AutoConfig.from_pretrained('./tf_model/bert_tf_model_config.json')
-        model = torch.hub.load('huggingface/transformers', 'modelWithLMHead', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
+            model = torch.hub.load('huggingface/transformers', 'modelForMaskedLM', './tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
 
         """
-    return AutoModelWithLMHead.from_pretrained(*args, **kwargs)
+
+    return AutoModelForMaskedLM.from_pretrained(*args, **kwargs)
 
 
 @add_start_docstrings(AutoModelForSequenceClassification.__doc__)

src/transformers/__init__.py

@@ -1300,7 +1300,26 @@ else:
 # FLAX-backed objects
 if is_flax_available():
     _import_structure["modeling_flax_utils"] = ["FlaxPreTrainedModel"]
-    _import_structure["models.auto"].extend(["FLAX_MODEL_MAPPING", "FlaxAutoModel"])
+    _import_structure["models.auto"].extend(
+        [
+            "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
+            "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
+            "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
+            "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
+            "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
+            "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
+            "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
+            "FLAX_MODEL_MAPPING",
+            "FlaxAutoModel",
+            "FlaxAutoModelForMaskedLM",
+            "FlaxAutoModelForMultipleChoice",
+            "FlaxAutoModelForNextSentencePrediction",
+            "FlaxAutoModelForPreTraining",
+            "FlaxAutoModelForQuestionAnswering",
+            "FlaxAutoModelForSequenceClassification",
+            "FlaxAutoModelForTokenClassification",
+        ]
+    )
     _import_structure["models.bert"].extend(
         [
             "FlaxBertForMaskedLM",
@@ -2410,7 +2429,24 @@ if TYPE_CHECKING:
 
     if is_flax_available():
         from .modeling_flax_utils import FlaxPreTrainedModel
-        from .models.auto import FLAX_MODEL_MAPPING, FlaxAutoModel
+        from .models.auto import (
+            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
+            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
+            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
+            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
+            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
+            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
+            FLAX_MODEL_MAPPING,
+            FlaxAutoModel,
+            FlaxAutoModelForMaskedLM,
+            FlaxAutoModelForMultipleChoice,
+            FlaxAutoModelForNextSentencePrediction,
+            FlaxAutoModelForPreTraining,
+            FlaxAutoModelForQuestionAnswering,
+            FlaxAutoModelForSequenceClassification,
+            FlaxAutoModelForTokenClassification,
+        )
         from .models.bert import (
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,

src/transformers/models/auto/__init__.py

@@ -82,7 +82,24 @@ if is_tf_available():
     ]
 
 if is_flax_available():
-    _import_structure["modeling_flax_auto"] = ["FLAX_MODEL_MAPPING", "FlaxAutoModel"]
+    _import_structure["modeling_flax_auto"] = [
+        "FLAX_MODEL_FOR_MASKED_LM_MAPPING",
+        "FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING",
+        "FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
+        "FLAX_MODEL_FOR_PRETRAINING_MAPPING",
+        "FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING",
+        "FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING",
+        "FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING",
+        "FLAX_MODEL_MAPPING",
+        "FlaxAutoModel",
+        "FlaxAutoModelForMaskedLM",
+        "FlaxAutoModelForMultipleChoice",
+        "FlaxAutoModelForNextSentencePrediction",
+        "FlaxAutoModelForPreTraining",
+        "FlaxAutoModelForQuestionAnswering",
+        "FlaxAutoModelForSequenceClassification",
+        "FlaxAutoModelForTokenClassification",
+    ]
 
 
 if TYPE_CHECKING:
@@ -145,7 +162,24 @@ if TYPE_CHECKING:
         )
 
     if is_flax_available():
-        from .modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel
+        from .modeling_flax_auto import (
+            FLAX_MODEL_FOR_MASKED_LM_MAPPING,
+            FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
+            FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
+            FLAX_MODEL_FOR_PRETRAINING_MAPPING,
+            FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
+            FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+            FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
+            FLAX_MODEL_MAPPING,
+            FlaxAutoModel,
+            FlaxAutoModelForMaskedLM,
+            FlaxAutoModelForMultipleChoice,
+            FlaxAutoModelForNextSentencePrediction,
+            FlaxAutoModelForPreTraining,
+            FlaxAutoModelForQuestionAnswering,
+            FlaxAutoModelForSequenceClassification,
+            FlaxAutoModelForTokenClassification,
+        )
 
 else:
     import importlib

src/transformers/models/auto/configuration_auto.py

@@ -256,8 +256,8 @@ def _list_model_options(indent, config_to_class=None, use_model_types=True):
                 if config in config_to_class
             }
         lines = [
-            f"{indent}- **{model_type}** -- :class:`~transformers.{cls_name}` ({MODEL_NAMES_MAPPING[model_type]} model)"
-            for model_type, cls_name in model_type_to_name.items()
+            f"{indent}- **{model_type}** -- :class:`~transformers.{model_type_to_name[model_type]}` ({MODEL_NAMES_MAPPING[model_type]} model)"
+            for model_type in sorted(model_type_to_name.keys())
         ]
     else:
         config_to_name = {config.__name__: clas.__name__ for config, clas in config_to_class.items()}
@@ -265,8 +265,8 @@ def _list_model_options(indent, config_to_class=None, use_model_types=True):
             config.__name__: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING.items()
         }
         lines = [
-            f"{indent}- :class:`~transformers.{config_name}` configuration class: :class:`~transformers.{cls_name}` ({config_to_model_name[config_name]} model)"
-            for config_name, cls_name in config_to_name.items()
+            f"{indent}- :class:`~transformers.{config_name}` configuration class: :class:`~transformers.{config_to_name[config_name]}` ({config_to_model_name[config_name]} model)"
+            for config_name in sorted(config_to_name.keys())
         ]
     return "\n".join(lines)
 

src/transformers/models/auto/modeling_flax_auto.py

@@ -17,11 +17,20 @@
 
 from collections import OrderedDict
 
-from ...configuration_utils import PretrainedConfig
 from ...utils import logging
-from ..bert.modeling_flax_bert import FlaxBertModel
+from ..bert.modeling_flax_bert import (
+    FlaxBertForMaskedLM,
+    FlaxBertForMultipleChoice,
+    FlaxBertForNextSentencePrediction,
+    FlaxBertForPreTraining,
+    FlaxBertForQuestionAnswering,
+    FlaxBertForSequenceClassification,
+    FlaxBertForTokenClassification,
+    FlaxBertModel,
+)
 from ..roberta.modeling_flax_roberta import FlaxRobertaModel
-from .configuration_auto import AutoConfig, BertConfig, RobertaConfig
+from .auto_factory import auto_class_factory
+from .configuration_auto import BertConfig, RobertaConfig
 
 
 logger = logging.get_logger(__name__)
@@ -29,140 +38,90 @@ logger = logging.get_logger(__name__)
 
 FLAX_MODEL_MAPPING = OrderedDict(
     [
+        # Base model mapping
         (RobertaConfig, FlaxRobertaModel),
         (BertConfig, FlaxBertModel),
     ]
 )
 
+FLAX_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
+    [
+        # Model for pre-training mapping
+        (BertConfig, FlaxBertForPreTraining),
+    ]
+)
 
-class FlaxAutoModel(object):
-    r"""
-    :class:`~transformers.FlaxAutoModel` is a generic model class that will be instantiated as one of the base model
-    classes of the library when created with the `FlaxAutoModel.from_pretrained(pretrained_model_name_or_path)` or the
-    `FlaxAutoModel.from_config(config)` class methods.
-
-    This class cannot be instantiated using `__init__()` (throws an error).
-    """
-
-    def __init__(self):
-        raise EnvironmentError(
-            "FlaxAutoModel is designed to be instantiated "
-            "using the `FlaxAutoModel.from_pretrained(pretrained_model_name_or_path)` or "
-            "`FlaxAutoModel.from_config(config)` methods."
-        )
+FLAX_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
+    [
+        # Model for Masked LM mapping
+        (BertConfig, FlaxBertForMaskedLM),
+    ]
+)
+
+FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
+    [
+        # Model for Sequence Classification mapping
+        (BertConfig, FlaxBertForSequenceClassification),
+    ]
+)
 
-    @classmethod
-    def from_config(cls, config):
-        r"""
-        Instantiates one of the base model classes of the library from a configuration.
-
-        Args:
-            config (:class:`~transformers.PretrainedConfig`):
-                The model class to instantiate is selected based on the configuration class:
-
-                - isInstance of `roberta` configuration class: :class:`~transformers.FlaxRobertaModel` (RoBERTa model)
-                - isInstance of `bert` configuration class: :class:`~transformers.FlaxBertModel` (Bert model
-
-        Examples::
-
-            config = BertConfig.from_pretrained('bert-base-uncased')
-            # Download configuration from huggingface.co and cache.
-            model = FlaxAutoModel.from_config(config)
-            # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-        """
-        for config_class, model_class in FLAX_MODEL_MAPPING.items():
-            if isinstance(config, config_class):
-                return model_class(config)
-        raise ValueError(
-            f"Unrecognized configuration class {config.__class__} "
-            f"for this kind of FlaxAutoModel: {cls.__name__}.\n"
-            f"Model type should be one of {', '.join(c.__name__ for c in FLAX_MODEL_MAPPING.keys())}."
-        )
-
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        r"""
-        Instantiates one of the base model classes of the library from a pre-trained model configuration.
-
-        The `from_pretrained()` method takes care of returning the correct model class instance based on the
-        `model_type` property of the config object, or when it's missing, falling back to using pattern matching on the
-        `pretrained_model_name_or_path` string.
-
-        The base model class to instantiate is selected as the first pattern matching in the
-        `pretrained_model_name_or_path` string (in the following order):
-
-            - contains `roberta`: :class:`~transformers.FlaxRobertaModel` (RoBERTa model)
-            - contains `bert`: :class:`~transformers.FlaxBertModel` (Bert model)
-
-            The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated) To
-            train the model, you should first set it back in training mode with `model.train()`
-
-        Args:
-            pretrained_model_name_or_path: either:
-
-                - a string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co. Valid
-                  model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a user or
-                  organization name, like ``dbmdz/bert-base-german-cased``.
-                - a path to a `directory` containing model weights saved using
-                  :func:`~transformers.FlaxPreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
-                - a path or url to a `pytorch index checkpoint file` (e.g. `./pt_model/pytorch_model.bin`). In this
-                  case, ``from_pt`` should be set to True and a configuration object should be provided as ``config``
-                  argument.
-
-            model_args: (`optional`) Sequence of positional arguments:
-                All remaining positional arguments will be passed to the underlying model's ``__init__`` method
-
-            config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
-                Configuration for the model to use instead of an automatically loaded configuration. Configuration can
-                be automatically loaded when:
-
-                - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a
-                  pretrained model), or
-                - the model was saved using :func:`~transformers.FlaxPreTrainedModel.save_pretrained` and is reloaded
-                  by supplying the save directory.
-                - the model is loaded by supplying a local directory as ``pretrained_model_name_or_path`` and a
-                  configuration JSON file named `config.json` is found in the directory.
-
-            cache_dir: (`optional`) string:
-                Path to a directory in which a downloaded pre-trained model configuration should be cached if the
-                standard cache should not be used.
-
-            force_download: (`optional`) boolean, default False:
-                Force to (re-)download the model weights and configuration files and override the cached versions if
-                they exists.
-
-            resume_download: (`optional`) boolean, default False:
-                Do not delete incompletely received file. Attempt to resume the download if such a file exists.
-
-            proxies: (`optional`) dict, default None:
-                A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128',
-                'http://hostname': 'foo.bar:4012'}. The proxies are used on each request.
-
-            output_loading_info: (`optional`) boolean:
-                Set to ``True`` to also return a dictionary containing missing keys, unexpected keys and error
-                messages.
-
-            kwargs: (`optional`) Remaining dictionary of keyword arguments:
-                These arguments will be passed to the configuration and the model.
-
-        Examples::
-
-            model = FlaxAutoModel.from_pretrained('bert-base-uncased')    # Download model and configuration from huggingface.co and cache.
-            model = FlaxAutoModel.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
-            assert model.config.output_attention == True
-
-        """
-        config = kwargs.pop("config", None)
-        if not isinstance(config, PretrainedConfig):
-            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-
-        for config_class, model_class in FLAX_MODEL_MAPPING.items():
-            if isinstance(config, config_class):
-                return model_class.from_pretrained(
-                    pretrained_model_name_or_path, *model_args, config=config, _from_auto=True, **kwargs
-                )
-        raise ValueError(
-            f"Unrecognized configuration class {config.__class__} "
-            f"for this kind of FlaxAutoModel: {cls.__name__}.\n"
-            f"Model type should be one of {', '.join(c.__name__ for c in FLAX_MODEL_MAPPING.keys())}"
-        )
+FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
+    [
+        # Model for Question Answering mapping
+        (BertConfig, FlaxBertForQuestionAnswering),
+    ]
+)
+
+FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
+    [
+        # Model for Token Classification mapping
+        (BertConfig, FlaxBertForTokenClassification),
+    ]
+)
+
+FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
+    [
+        # Model for Multiple Choice mapping
+        (BertConfig, FlaxBertForMultipleChoice),
+    ]
+)
+
+FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = OrderedDict(
+    [
+        (BertConfig, FlaxBertForNextSentencePrediction),
+    ]
+)
+
+FlaxAutoModel = auto_class_factory("FlaxAutoModel", FLAX_MODEL_MAPPING)
+
+FlaxAutoModelForPreTraining = auto_class_factory(
+    "FlaxAutoModelForPreTraining", FLAX_MODEL_FOR_PRETRAINING_MAPPING, head_doc="pretraining"
+)
+
+FlaxAutoModelForMaskedLM = auto_class_factory(
+    "FlaxAutoModelForMaskedLM", FLAX_MODEL_FOR_MASKED_LM_MAPPING, head_doc="masked language modeling"
+)
+
+FlaxAutoModelForSequenceClassification = auto_class_factory(
+    "AFlaxutoModelForSequenceClassification",
+    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
+    head_doc="sequence classification",
+)
+
+FlaxAutoModelForQuestionAnswering = auto_class_factory(
+    "FlaxAutoModelForQuestionAnswering", FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING, head_doc="question answering"
+)
+
+FlaxAutoModelForTokenClassification = auto_class_factory(
+    "FlaxAutoModelForTokenClassification", FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, head_doc="token classification"
+)
+
+FlaxAutoModelForMultipleChoice = auto_class_factory(
+    "AutoModelForMultipleChoice", FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, head_doc="multiple choice"
+)
+
+FlaxAutoModelForNextSentencePrediction = auto_class_factory(
+    "FlaxAutoModelForNextSentencePrediction",
+    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
+    head_doc="next sentence prediction",
+)

src/transformers/utils/dummy_flax_objects.py

@@ -11,6 +11,27 @@ class FlaxPreTrainedModel:
         requires_flax(self)
 
 
+FLAX_MODEL_FOR_MASKED_LM_MAPPING = None
+
+
+FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING = None
+
+
+FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING = None
+
+
+FLAX_MODEL_FOR_PRETRAINING_MAPPING = None
+
+
+FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING = None
+
+
+FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = None
+
+
+FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = None
+
+
 FLAX_MODEL_MAPPING = None
 
 
@@ -23,6 +44,69 @@ class FlaxAutoModel:
         requires_flax(self)
 
 
+class FlaxAutoModelForMaskedLM:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
+class FlaxAutoModelForMultipleChoice:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
+class FlaxAutoModelForNextSentencePrediction:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
+class FlaxAutoModelForPreTraining:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
+class FlaxAutoModelForQuestionAnswering:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
+class FlaxAutoModelForSequenceClassification:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
+class FlaxAutoModelForTokenClassification:
+    def __init__(self, *args, **kwargs):
+        requires_flax(self)
+
+    @classmethod
+    def from_pretrained(self, *args, **kwargs):
+        requires_flax(self)
+
+
 class FlaxBertForMaskedLM:
     def __init__(self, *args, **kwargs):
         requires_flax(self)