Add type hints for several pytorch models (batch-2)

src/transformers/models/conditional_detr/modeling_conditional_detr.py

@@ -1126,7 +1126,7 @@ def _set_gradient_checkpointing(self, module, value=False):
 
             [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`)
@@ -1872,7 +1872,7 @@ def forward(
         self,
         pixel_values: torch.FloatTensor,
         pixel_mask: Optional[torch.LongTensor] = None,
-        decoder_attention_mask: Optional[torch.LongTensor] = 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,

src/transformers/models/cpmant/modeling_cpmant.py

@@ -653,7 +653,7 @@ def forward(
         use_cache: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         **kwargs,
-    ):
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]:
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

src/transformers/models/decision_transformer/modeling_decision_transformer.py

@@ -787,7 +787,7 @@ def _init_weights(self, module):
             The returns for each state in the trajectory
         timesteps (`torch.LongTensor` of shape `(batch_size, episode_length)`):
             The timestep for each step in the trajectory
-        attention_mask (`torch.LongTensor` of shape `(batch_size, episode_length)`):
+        attention_mask (`torch.FloatTensor` of shape `(batch_size, episode_length)`):
             Masking, used to mask the actions when performing autoregressive prediction
 """
 
@@ -830,16 +830,16 @@ def __init__(self, config):
     @replace_return_docstrings(output_type=DecisionTransformerOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
-        states=None,
-        actions=None,
-        rewards=None,
-        returns_to_go=None,
-        timesteps=None,
-        attention_mask=None,
-        output_hidden_states=None,
-        output_attentions=None,
-        return_dict=None,
-    ) -> Union[Tuple, DecisionTransformerOutput]:
+        states: Optional[torch.FloatTensor] = None,
+        actions: Optional[torch.FloatTensor] = None,
+        rewards: Optional[torch.FloatTensor] = None,
+        returns_to_go: Optional[torch.FloatTensor] = None,
+        timesteps: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.FloatTensor], DecisionTransformerOutput]:
         r"""
         Returns:
 

src/transformers/models/deformable_detr/modeling_deformable_detr.py

@@ -19,7 +19,7 @@
 import math
 import warnings
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -1123,7 +1123,7 @@ def _set_gradient_checkpointing(self, module, value=False):
 
             [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`)
@@ -1625,16 +1625,16 @@ def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes)
     @replace_return_docstrings(output_type=DeformableDetrModelOutput, 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.LongTensor] = 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], DeformableDetrModelOutput]:
         r"""
         Returns:
 
@@ -1885,17 +1885,17 @@ def _set_aux_loss(self, outputs_class, outputs_coord):
     @replace_return_docstrings(output_type=DeformableDetrObjectDetectionOutput, 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.LongTensor] = 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], DeformableDetrObjectDetectionOutput]:
         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/deta/modeling_deta.py

@@ -19,7 +19,7 @@
 import math
 import warnings
 from dataclasses import dataclass
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F
@@ -1013,7 +1013,7 @@ def _set_gradient_checkpointing(self, module, value=False):
 
             [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`)
@@ -1533,16 +1533,16 @@ def gen_encoder_output_proposals(self, enc_output, padding_mask, spatial_shapes)
     @replace_return_docstrings(output_type=DetaModelOutput, 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.LongTensor] = 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], DetaModelOutput]:
         r"""
         Returns:
 
@@ -1838,17 +1838,17 @@ def _set_aux_loss(self, outputs_class, outputs_coord):
     @replace_return_docstrings(output_type=DetaObjectDetectionOutput, 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.LongTensor] = 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], DetaObjectDetectionOutput]:
         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/detr/modeling_detr.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
@@ -881,7 +881,7 @@ def _set_gradient_checkpointing(self, module, value=False):
 
             [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`)
@@ -1245,16 +1245,16 @@ def unfreeze_backbone(self):
     @replace_return_docstrings(output_type=DetrModelOutput, 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.LongTensor] = 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], DetrModelOutput]:
         r"""
         Returns:
 
@@ -1405,17 +1405,17 @@ def _set_aux_loss(self, outputs_class, outputs_coord):
     @replace_return_docstrings(output_type=DetrObjectDetectionOutput, 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.LongTensor] = 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], DetrObjectDetectionOutput]:
         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
@@ -1575,17 +1575,17 @@ def __init__(self, config: DetrConfig):
     @replace_return_docstrings(output_type=DetrSegmentationOutput, 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.LongTensor] = 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], DetrSegmentationOutput]:
         r"""
         labels (`List[Dict]` of len `(batch_size,)`, *optional*):
             Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each

src/transformers/models/dpr/modeling_dpr.py

@@ -454,9 +454,9 @@ def forward(
         attention_mask: Optional[Tensor] = None,
         token_type_ids: Optional[Tensor] = None,
         inputs_embeds: Optional[Tensor] = None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
     ) -> Union[DPRContextEncoderOutput, Tuple[Tensor, ...]]:
         r"""
         Return:
@@ -535,9 +535,9 @@ def forward(
         attention_mask: Optional[Tensor] = None,
         token_type_ids: Optional[Tensor] = None,
         inputs_embeds: Optional[Tensor] = None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
     ) -> Union[DPRQuestionEncoderOutput, Tuple[Tensor, ...]]:
         r"""
         Return:
@@ -616,9 +616,9 @@ def forward(
         input_ids: Optional[Tensor] = None,
         attention_mask: Optional[Tensor] = None,
         inputs_embeds: Optional[Tensor] = None,
-        output_attentions: bool = None,
-        output_hidden_states: bool = None,
-        return_dict=None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
     ) -> Union[DPRReaderOutput, Tuple[Tensor, ...]]:
         r"""
         Return: