Add type hints for pytorch models (final batch) (#25750)

* Add type hints for table_transformer

* Add type hints to Timesformer model

* Add type hints to Timm Backbone model

* Add type hints to TVLT family models

* Add type hints to Vivit family models

* Use the typing instance instead of the python builtin.

* Fix the `replace_return_docstrings` decorator for Vivit model
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

---------
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

src/transformers/models/table_transformer/modeling_table_transformer.py

@@ -17,7 +17,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 from torch import Tensor, nn
@@ -814,7 +814,7 @@ TABLE_TRANSFORMER_INPUTS_DOCSTRING = r"""
 
             Pixel values can be obtained using [`DetrImageProcessor`]. See [`DetrImageProcessor.__call__`] for details.
 
-        pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
+        pixel_mask (`torch.FloatTensor` of shape `(batch_size, height, width)`, *optional*):
             Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
 
             - 1 for pixels that are real (i.e. **not masked**),
@@ -822,7 +822,7 @@ TABLE_TRANSFORMER_INPUTS_DOCSTRING = r"""
 
             [What are attention masks?](../glossary#attention-mask)
 
-        decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, num_queries)`, *optional*):
+        decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*):
             Not used by default. Can be used to mask object queries.
         encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
             Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
@@ -1190,16 +1190,16 @@ class TableTransformerModel(TableTransformerPreTrainedModel):
     @replace_return_docstrings(output_type=TableTransformerModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values,
-        pixel_mask=None,
-        decoder_attention_mask=None,
-        encoder_outputs=None,
-        inputs_embeds=None,
-        decoder_inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
+        pixel_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        decoder_attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_outputs: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], TableTransformerModelOutput]:
         r"""
         Returns:
 
@@ -1351,17 +1351,17 @@ class TableTransformerForObjectDetection(TableTransformerPreTrainedModel):
     @replace_return_docstrings(output_type=TableTransformerObjectDetectionOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values,
-        pixel_mask=None,
-        decoder_attention_mask=None,
-        encoder_outputs=None,
-        inputs_embeds=None,
-        decoder_inputs_embeds=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
+        pixel_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        decoder_attention_mask: Optional[torch.FloatTensor] = None,
+        encoder_outputs: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[List[Dict]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], TableTransformerObjectDetectionOutput]:
         r"""
         labels (`List[Dict]` of len `(batch_size,)`, *optional*):
             Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the

src/transformers/models/timesformer/modeling_timesformer.py

@@ -559,11 +559,11 @@ class TimesformerModel(TimesformerPreTrainedModel):
     @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
+        pixel_values: torch.FloatTensor,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutput]:
         r"""
         Returns:
 

src/transformers/models/timm_backbone/modeling_timm_backbone.py

@@ -13,7 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Tuple, Union
+from typing import Optional, Tuple, Union
+
+import torch
 
 from ...modeling_outputs import BackboneOutput
 from ...modeling_utils import PreTrainedModel
@@ -107,7 +109,12 @@ class TimmBackbone(PreTrainedModel, BackboneMixin):
         pass
 
     def forward(
-        self, pixel_values, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs
+        self,
+        pixel_values: torch.FloatTensor,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
     ) -> Union[BackboneOutput, Tuple[Tensor, ...]]:
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
         output_hidden_states = (

src/transformers/models/tvlt/modeling_tvlt.py

@@ -715,16 +715,16 @@ class TvltModel(TvltPreTrainedModel):
     @replace_return_docstrings(output_type=TvltModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values,
-        audio_values,
-        pixel_mask=None,
-        audio_mask=None,
-        mask_pixel=False,
-        mask_audio=False,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ) -> Union[tuple, TvltModelOutput]:
+        pixel_values: torch.FloatTensor,
+        audio_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        audio_mask: Optional[torch.FloatTensor] = None,
+        mask_pixel: bool = False,
+        mask_audio: bool = False,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], TvltModelOutput]:
         r"""
         Returns:
 
@@ -1049,17 +1049,17 @@ class TvltForPreTraining(TvltPreTrainedModel):
     @replace_return_docstrings(output_type=TvltForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values,
-        audio_values,
-        pixel_mask=None,
-        audio_mask=None,
-        labels=None,
-        pixel_values_mixed=None,
-        pixel_mask_mixed=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ) -> Union[tuple, TvltForPreTrainingOutput]:
+        pixel_values: torch.FloatTensor,
+        audio_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        audio_mask: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        pixel_values_mixed: Optional[torch.FloatTensor] = None,
+        pixel_mask_mixed: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], TvltForPreTrainingOutput]:
         r"""
         pixel_values_mixed (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`):
             Pixel values that mix positive and negative samples in Tvlt vision-audio matching. Audio values can be
@@ -1250,15 +1250,15 @@ class TvltForAudioVisualClassification(TvltPreTrainedModel):
     @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values,
-        audio_values,
-        pixel_mask=None,
-        audio_mask=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        labels=None,
-    ) -> Union[tuple, SequenceClassifierOutput]:
+        pixel_values: torch.FloatTensor,
+        audio_values: torch.FloatTensor,
+        pixel_mask: Optional[torch.FloatTensor] = None,
+        audio_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple[torch.FloatTensor], SequenceClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, num_labels)`, *optional*):
             Labels for computing the audiovisual loss. Indices should be in `[0, ..., num_classes-1]` where num_classes

src/transformers/models/vivit/modeling_vivit.py

@@ -486,15 +486,15 @@ class VivitModel(VivitPreTrainedModel):
             self.encoder.layer[layer].attention.prune_heads(heads)
 
     @add_start_docstrings_to_model_forward(VIVIT_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
+    @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values=None,
-        head_mask=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
+        pixel_values: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], BaseModelOutputWithPooling]:
         r"""
         Returns:
 
@@ -628,13 +628,13 @@ class VivitForVideoClassification(VivitPreTrainedModel):
     @replace_return_docstrings(output_type=ImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        pixel_values=None,
-        head_mask=None,
-        labels=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-    ):
+        pixel_values: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], ImageClassifierOutput]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,