Avoid importing all models when instantiating a pipeline (#24960)

* Avoid importing all models when instantiating a pipeline

* Remove sums that don't work

src/transformers/models/auto/auto_factory.py

@@ -650,6 +650,7 @@ class _LazyAutoMapping(OrderedDict):
         self._config_mapping = config_mapping
         self._reverse_config_mapping = {v: k for k, v in config_mapping.items()}
         self._model_mapping = model_mapping
+        self._model_mapping._model_mapping = self
         self._extra_content = {}
         self._modules = {}
 

src/transformers/pipelines/__init__.py

@@ -88,11 +88,6 @@ if is_tf_available():
     import tensorflow as tf
 
     from ..models.auto.modeling_tf_auto import (
-        TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
-        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
-        TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
-        TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
-        TF_MODEL_WITH_LM_HEAD_MAPPING,
         TFAutoModel,
         TFAutoModelForCausalLM,
         TFAutoModelForImageClassification,
@@ -110,13 +105,6 @@ if is_torch_available():
     import torch
 
     from ..models.auto.modeling_auto import (
-        MODEL_FOR_MASKED_LM_MAPPING,
-        MODEL_FOR_QUESTION_ANSWERING_MAPPING,
-        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
-        MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
-        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
-        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
-        MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
         AutoModel,
         AutoModelForAudioClassification,
         AutoModelForCausalLM,

src/transformers/pipelines/audio_classification.py

@@ -22,7 +22,7 @@ from .base import PIPELINE_INIT_ARGS, Pipeline
 
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -98,7 +98,7 @@ class AudioClassificationPipeline(Pipeline):
         if self.framework != "pt":
             raise ValueError(f"The {self.__class__} is only available in PyTorch.")
 
-        self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING)
+        self.check_model_type(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES)
 
     def __call__(
         self,

src/transformers/pipelines/automatic_speech_recognition.py

@@ -30,7 +30,7 @@ if TYPE_CHECKING:
 logger = logging.get_logger(__name__)
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_CTC_MAPPING, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_CTC_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
 
 
 def rescale_stride(stride, ratio):
@@ -205,7 +205,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
 
         if self.model.config.model_type == "whisper":
             self.type = "seq2seq_whisper"
-        elif self.model.__class__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.values():
+        elif self.model.__class__.__name__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.values():
             self.type = "seq2seq"
         elif (
             feature_extractor._processor_class
@@ -220,7 +220,9 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         if self.framework == "tf":
             raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.")
 
-        self.check_model_type(dict(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items() + MODEL_FOR_CTC_MAPPING.items()))
+        mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.copy()
+        mapping.update(MODEL_FOR_CTC_MAPPING_NAMES)
+        self.check_model_type(mapping)
 
     def __call__(
         self,

src/transformers/pipelines/base.py

@@ -952,10 +952,16 @@ class Pipeline(_ScikitCompat):
         """
         if not isinstance(supported_models, list):  # Create from a model mapping
             supported_models_names = []
-            for config, model in supported_models.items():
+            for _, model_name in supported_models.items():
                 # Mapping can now contain tuples of models for the same configuration.
-                if isinstance(model, tuple):
-                    supported_models_names.extend([_model.__name__ for _model in model])
+                if isinstance(model_name, tuple):
+                    supported_models_names.extend(list(model_name))
+                else:
+                    supported_models_names.append(model_name)
+            if hasattr(supported_models, "_model_mapping"):
+                for _, model in supported_models._model_mapping._extra_content.items():
+                    if isinstance(model_name, tuple):
+                        supported_models_names.extend([m.__name__ for m in model])
                     else:
                         supported_models_names.append(model.__name__)
             supported_models = supported_models_names

src/transformers/pipelines/depth_estimation.py

@@ -14,7 +14,7 @@ if is_vision_available():
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -48,7 +48,7 @@ class DepthEstimationPipeline(Pipeline):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         requires_backends(self, "vision")
-        self.check_model_type(MODEL_FOR_DEPTH_ESTIMATION_MAPPING)
+        self.check_model_type(MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES)
 
     def __call__(self, images: Union[str, List[str], "Image.Image", List["Image.Image"]], **kwargs):
         """

src/transformers/pipelines/document_question_answering.py

@@ -37,7 +37,7 @@ if is_vision_available():
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES
 
 TESSERACT_LOADED = False
 if is_pytesseract_available():
@@ -142,7 +142,7 @@ class DocumentQuestionAnsweringPipeline(ChunkPipeline):
             if self.model.config.encoder.model_type != "donut-swin":
                 raise ValueError("Currently, the only supported VisionEncoderDecoder model is Donut")
         else:
-            self.check_model_type(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING)
+            self.check_model_type(MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES)
             if self.model.config.__class__.__name__ == "LayoutLMConfig":
                 self.model_type = ModelType.LayoutLM
             else:

src/transformers/pipelines/image_classification.py

@@ -19,11 +19,11 @@ if is_vision_available():
 if is_tf_available():
     import tensorflow as tf
 
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
     from ..tf_utils import stable_softmax
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -57,9 +57,9 @@ class ImageClassificationPipeline(Pipeline):
         super().__init__(*args, **kwargs)
         requires_backends(self, "vision")
         self.check_model_type(
-            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
             if self.framework == "tf"
-            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
+            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
         )
 
     def _sanitize_parameters(self, top_k=None):

src/transformers/pipelines/image_segmentation.py

@@ -13,10 +13,10 @@ if is_vision_available():
 
 if is_torch_available():
     from ..models.auto.modeling_auto import (
-        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
-        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
-        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
-        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
+        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES,
+        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES,
+        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
+        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES,
     )
 
 
@@ -71,14 +71,11 @@ class ImageSegmentationPipeline(Pipeline):
             raise ValueError(f"The {self.__class__} is only available in PyTorch.")
 
         requires_backends(self, "vision")
-        self.check_model_type(
-            dict(
-                MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
-                + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
-                + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
-                + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()
-            )
-        )
+        mapping = MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES.copy()
+        mapping.update(MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES)
+        mapping.update(MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING_NAMES)
+        mapping.update(MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING_NAMES)
+        self.check_model_type(mapping)
 
     def _sanitize_parameters(self, **kwargs):
         preprocess_kwargs = {}

src/transformers/pipelines/image_to_text.py

@@ -17,12 +17,12 @@ if is_vision_available():
     from ..image_utils import load_image
 
 if is_tf_available():
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES
 
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -55,7 +55,7 @@ class ImageToTextPipeline(Pipeline):
         super().__init__(*args, **kwargs)
         requires_backends(self, "vision")
         self.check_model_type(
-            TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING
+            TF_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES
         )
 
     def _sanitize_parameters(self, max_new_tokens=None, generate_kwargs=None, prompt=None):

src/transformers/pipelines/mask_generation.py

@@ -14,7 +14,7 @@ from .base import PIPELINE_INIT_ARGS, ChunkPipeline
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -96,7 +96,7 @@ class MaskGenerationPipeline(ChunkPipeline):
         if self.framework != "pt":
             raise ValueError(f"The {self.__class__} is only available in PyTorch.")
 
-        self.check_model_type(MODEL_FOR_MASK_GENERATION_MAPPING)
+        self.check_model_type(MODEL_FOR_MASK_GENERATION_MAPPING_NAMES)
 
     def _sanitize_parameters(self, **kwargs):
         preprocess_kwargs = {}

src/transformers/pipelines/object_detection.py

@@ -11,7 +11,10 @@ if is_vision_available():
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_auto import (
+        MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
+        MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
+    )
 
 logger = logging.get_logger(__name__)
 
@@ -53,9 +56,9 @@ class ObjectDetectionPipeline(Pipeline):
             raise ValueError(f"The {self.__class__} is only available in PyTorch.")
 
         requires_backends(self, "vision")
-        self.check_model_type(
-            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items())
-        )
+        mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES.copy()
+        mapping.update(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES)
+        self.check_model_type(mapping)
 
     def _sanitize_parameters(self, **kwargs):
         postprocess_kwargs = {}

src/transformers/pipelines/question_answering.py

@@ -32,7 +32,7 @@ if TYPE_CHECKING:
 if is_tf_available():
     import tensorflow as tf
 
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
 
     Dataset = None
 
@@ -40,7 +40,7 @@ if is_torch_available():
     import torch
     from torch.utils.data import Dataset
 
-    from ..models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
 
 
 def decode_spans(
@@ -270,7 +270,9 @@ class QuestionAnsweringPipeline(ChunkPipeline):
 
         self._args_parser = QuestionAnsweringArgumentHandler()
         self.check_model_type(
-            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING if self.framework == "tf" else MODEL_FOR_QUESTION_ANSWERING_MAPPING
+            TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
+            if self.framework == "tf"
+            else MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
         )
 
     @staticmethod

src/transformers/pipelines/table_question_answering.py

@@ -17,8 +17,8 @@ if is_torch_available():
     import torch
 
     from ..models.auto.modeling_auto import (
-        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
-        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
+        MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
     )
 
 if is_tf_available() and is_tensorflow_probability_available():
@@ -26,8 +26,8 @@ if is_tf_available() and is_tensorflow_probability_available():
     import tensorflow_probability as tfp
 
     from ..models.auto.modeling_tf_auto import (
-        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
-        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
+        TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
+        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES,
     )
 
 
@@ -122,16 +122,13 @@ class TableQuestionAnsweringPipeline(Pipeline):
         super().__init__(*args, **kwargs)
         self._args_parser = args_parser
 
-        self.check_model_type(
-            dict(
-                TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
-                + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()
-            )
-            if self.framework == "tf"
-            else dict(
-                MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()
-            )
-        )
+        if self.framework == "tf":
+            mapping = TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES.copy()
+            mapping.update(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES)
+        else:
+            mapping = MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES.copy()
+            mapping.update(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES)
+        self.check_model_type(mapping)
 
         self.aggregate = bool(getattr(self.model.config, "aggregation_labels", None)) and bool(
             getattr(self.model.config, "num_aggregation_labels", None)

src/transformers/pipelines/text2text_generation.py

@@ -9,10 +9,10 @@ from .base import PIPELINE_INIT_ARGS, Pipeline
 if is_tf_available():
     import tensorflow as tf
 
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -65,9 +65,9 @@ class Text2TextGenerationPipeline(Pipeline):
         super().__init__(*args, **kwargs)
 
         self.check_model_type(
-            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
             if self.framework == "tf"
-            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
+            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
         )
 
     def _sanitize_parameters(

src/transformers/pipelines/text_classification.py

@@ -9,10 +9,10 @@ from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
 
 
 if is_tf_available():
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
 
 
 def sigmoid(_outputs):
@@ -84,9 +84,9 @@ class TextClassificationPipeline(Pipeline):
         super().__init__(**kwargs)
 
         self.check_model_type(
-            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
+            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
             if self.framework == "tf"
-            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
+            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
         )
 
     def _sanitize_parameters(self, return_all_scores=None, function_to_apply=None, top_k="", **tokenizer_kwargs):

src/transformers/pipelines/text_generation.py

 import enum
 import warnings
 
-from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
-from ..utils import add_end_docstrings, is_tf_available
+from ..utils import add_end_docstrings, is_tf_available, is_torch_available
 from .base import PIPELINE_INIT_ARGS, Pipeline
 
 
+if is_torch_available():
+    from ..models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+
 if is_tf_available():
     import tensorflow as tf
 
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+
 
 class ReturnType(enum.Enum):
     TENSORS = 0
@@ -62,7 +66,7 @@ class TextGenerationPipeline(Pipeline):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.check_model_type(
-            TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING
+            TF_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         )
         if "prefix" not in self._preprocess_params:
             # This is very specific. The logic is quite complex and needs to be done

src/transformers/pipelines/token_classification.py

@@ -17,9 +17,9 @@ from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline, Dataset
 if is_tf_available():
     import tensorflow as tf
 
-    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
 
 
 class TokenClassificationArgumentHandler(ArgumentHandler):
@@ -135,9 +135,9 @@ class TokenClassificationPipeline(ChunkPipeline):
     def __init__(self, args_parser=TokenClassificationArgumentHandler(), *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.check_model_type(
-            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
+            TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
             if self.framework == "tf"
-            else MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
+            else MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
         )
 
         self._basic_tokenizer = BasicTokenizer(do_lower_case=False)

src/transformers/pipelines/video_classification.py

@@ -13,7 +13,7 @@ if is_decord_available():
 
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -34,7 +34,7 @@ class VideoClassificationPipeline(Pipeline):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         requires_backends(self, "decord")
-        self.check_model_type(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING)
+        self.check_model_type(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES)
 
     def _sanitize_parameters(self, top_k=None, num_frames=None, frame_sampling_rate=None):
         preprocess_params = {}

src/transformers/pipelines/visual_question_answering.py

@@ -10,7 +10,7 @@ if is_vision_available():
     from ..image_utils import load_image
 
 if is_torch_available():
-    from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
+    from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES
 
 logger = logging.get_logger(__name__)
 
@@ -53,7 +53,7 @@ class VisualQuestionAnsweringPipeline(Pipeline):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.check_model_type(MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING)
+        self.check_model_type(MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES)
 
     def _sanitize_parameters(self, top_k=None, padding=None, truncation=None, **kwargs):
         preprocess_params, postprocess_params = {}, {}