Fix various imports (#22281)

* Fix various imports

* Fix copies

* Fix import

src/transformers/__init__.py

@@ -6009,16 +6009,6 @@ if TYPE_CHECKING:
             tf_top_k_top_p_filtering,
         )
         from .keras_callbacks import KerasMetricCallback, PushToHubCallback
-        from .modeling_tf_layoutlm import (
-            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
-            TFLayoutLMForMaskedLM,
-            TFLayoutLMForQuestionAnswering,
-            TFLayoutLMForSequenceClassification,
-            TFLayoutLMForTokenClassification,
-            TFLayoutLMMainLayer,
-            TFLayoutLMModel,
-            TFLayoutLMPreTrainedModel,
-        )
         from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list
 
         # TensorFlow model imports
@@ -6272,6 +6262,16 @@ if TYPE_CHECKING:
             TFHubertModel,
             TFHubertPreTrainedModel,
         )
+        from .models.layoutlm import (
+            TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFLayoutLMForMaskedLM,
+            TFLayoutLMForQuestionAnswering,
+            TFLayoutLMForSequenceClassification,
+            TFLayoutLMForTokenClassification,
+            TFLayoutLMMainLayer,
+            TFLayoutLMModel,
+            TFLayoutLMPreTrainedModel,
+        )
         from .models.layoutlmv3 import (
             TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFLayoutLMv3ForQuestionAnswering,

src/transformers/commands/convert.py

@@ -167,7 +167,7 @@ class ConvertCommand(BaseTransformersCLICommand):
 
             convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint, self._pytorch_dump_output)
         elif self._model_type == "lxmert":
-            from ..models.lxmert.convert_lxmert_original_pytorch_checkpoint_to_pytorch import (
+            from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
                 convert_lxmert_checkpoint_to_pytorch,
             )
 

src/transformers/models/data2vec/convert_data2vec_text_original_pytorch_checkpoint_to_pytorch.py

@@ -24,7 +24,12 @@ import torch
 from fairseq.modules import TransformerSentenceEncoderLayer
 from packaging import version
 
-from transformers import Data2VecTextConfig, Data2VecTextForMaskedLM, Data2VecTextForSequenceClassification
+from transformers import (
+    Data2VecTextConfig,
+    Data2VecTextForMaskedLM,
+    Data2VecTextForSequenceClassification,
+    Data2VecTextModel,
+)
 from transformers.models.bert.modeling_bert import (
     BertIntermediate,
     BertLayer,
@@ -35,7 +40,6 @@ from transformers.models.bert.modeling_bert import (
 
 # IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
 # File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
-from transformers.models.data2vec.data2vec_text import Data2VecTextModel
 from transformers.utils import logging
 
 

src/transformers/models/dpr/convert_dpr_original_checkpoint_to_pytorch.py

@@ -19,7 +19,7 @@ from pathlib import Path
 import torch
 from torch.serialization import default_restore_location
 
-from .transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
+from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
 
 
 CheckpointState = collections.namedtuple(

src/transformers/models/mgp_str/__init__.py

@@ -41,7 +41,7 @@ else:
 
 if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
-    from .processing_mgp_str.py import MgpstrProcessor
+    from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer
 
     try:

src/transformers/pipelines/automatic_speech_recognition.py

@@ -25,7 +25,7 @@ from .base import ChunkPipeline
 if TYPE_CHECKING:
     from pyctcdecode import BeamSearchDecoderCTC
 
-    from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+    from ..feature_extraction_sequence_utils import SequenceFeatureExtractor
 
 logger = logging.get_logger(__name__)
 

src/transformers/utils/dummy_tf_objects.py

@@ -125,58 +125,6 @@ class PushToHubCallback(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
-TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
-
-
-class TFLayoutLMForMaskedLM(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
-class TFLayoutLMForQuestionAnswering(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
-class TFLayoutLMForSequenceClassification(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
-class TFLayoutLMForTokenClassification(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
-class TFLayoutLMMainLayer(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
-class TFLayoutLMModel(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
-class TFLayoutLMPreTrainedModel(metaclass=DummyObject):
-    _backends = ["tf"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["tf"])
-
-
 class TFPreTrainedModel(metaclass=DummyObject):
     _backends = ["tf"]
 
@@ -1456,6 +1404,58 @@ class TFHubertPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class TFLayoutLMForMaskedLM(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMForQuestionAnswering(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMForSequenceClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMForTokenClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMMainLayer(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFLayoutLMPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST = None