Add missing ModelOutput subclass return type hints

src/transformers/models/dac/modeling_dac.py

@@ -16,7 +16,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import Optional
+from typing import Optional, Union
 
 import numpy as np
 import torch
@@ -583,7 +583,7 @@ def encode(
         input_values: torch.Tensor,
         n_quantizers: Optional[int] = None,
         return_dict: Optional[bool] = None,
-    ):
+    ) -> Union[tuple, DacEncoderOutput]:
         r"""
         input_values (`torch.Tensor of shape `(batch_size, 1, time_steps)`):
             Input audio data to encode,
@@ -610,7 +610,7 @@ def decode(
         quantized_representation: Optional[torch.Tensor] = None,
         audio_codes: Optional[torch.Tensor] = None,
         return_dict: Optional[bool] = None,
-    ):
+    ) -> Union[tuple, DacDecoderOutput]:
         r"""
         quantized_representation (torch.Tensor of shape `(batch_size, dimension, time_steps)`, *optional*):
             Quantized continuous representation of input.
@@ -643,7 +643,7 @@ def forward(
         input_values: torch.Tensor,
         n_quantizers: Optional[int] = None,
         return_dict: Optional[bool] = None,
-    ):
+    ) -> Union[tuple, DacOutput]:
         r"""
         input_values (`torch.Tensor` of shape `(batch_size, 1, time_steps)`):
             Audio data to encode.

src/transformers/models/deepseek_vl/modeling_deepseek_vl.py

@@ -196,7 +196,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
-    ):
+    ) -> DeepseekVLBaseModelOutputWithPast:
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
                 "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
@@ -268,7 +268,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
-    ):
+    ) -> DeepseekVLCausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

src/transformers/models/deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py

@@ -314,7 +314,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
-    ):
+    ) -> DeepseekVLHybridBaseModelOutputWithPast:
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
                 "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
@@ -424,7 +424,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
-    ):
+    ) -> DeepseekVLHybridCausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py

@@ -297,7 +297,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
-    ):
+    ) -> DeepseekVLHybridBaseModelOutputWithPast:
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
                 "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
@@ -361,7 +361,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
-    ):
+    ) -> DeepseekVLHybridCausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

src/transformers/models/flava/modeling_flava.py

@@ -1107,7 +1107,7 @@ def forward(
         output_hidden_states: bool = True,
         return_dict: Optional[bool] = None,
         **kwargs,
-    ) -> Union[tuple, FlavaOutput]:
+    ) -> Union[tuple, FlavaModelOutput]:
         r"""
         input_ids (`torch.LongTensor` of shape `(batch_size, image_num_patches + text_seq_len)`):
             Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See

src/transformers/models/grounding_dino/modeling_grounding_dino.py

@@ -1511,7 +1511,7 @@ def forward(
         output_hidden_states=None,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, GroundingDinoEncoderOutput]:
         r"""
         Args:
             vision_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -1666,7 +1666,7 @@ def forward(
         output_hidden_states=None,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, GroundingDinoDecoderOutput]:
         r"""
         Args:
             inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -2059,7 +2059,7 @@ def forward(
         output_hidden_states=None,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, GroundingDinoModelOutput]:
         r"""
         input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
             Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide

src/transformers/models/janus/modeling_janus.py

@@ -1126,7 +1126,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
-    ):
+    ) -> JanusBaseModelOutputWithPast:
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
                 "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
@@ -1203,7 +1203,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
-    ):
+    ) -> JanusCausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

src/transformers/models/janus/modular_janus.py

@@ -942,7 +942,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs,
-    ):
+    ) -> JanusBaseModelOutputWithPast:
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError(
                 "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
@@ -1019,7 +1019,7 @@ def forward(
         use_cache: Optional[bool] = None,
         logits_to_keep: Union[int, torch.Tensor] = 0,
         **kwargs: Unpack[TransformersKwargs],
-    ):
+    ) -> JanusCausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

src/transformers/models/maskformer/modeling_maskformer_swin.py

@@ -19,7 +19,7 @@
 import collections.abc
 import math
 from dataclasses import dataclass
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 from torch import Tensor, nn
@@ -656,7 +656,7 @@ def forward(
         output_attentions=False,
         output_hidden_states=False,
         return_dict=True,
-    ):
+    ) -> Union[tuple, MaskFormerSwinBaseModelOutput]:
         all_hidden_states = () if output_hidden_states else None
         all_input_dimensions = ()
         all_self_attentions = () if output_attentions else None
@@ -739,7 +739,7 @@ def forward(
         interpolate_pos_encoding=False,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, MaskFormerSwinModelOutputWithPooling]:
         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/mm_grounding_dino/modeling_mm_grounding_dino.py

@@ -1181,7 +1181,7 @@ def forward(
         output_hidden_states=None,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, MMGroundingDinoEncoderOutput]:
         r"""
         Args:
             vision_features (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -1478,7 +1478,7 @@ def forward(
         output_hidden_states=None,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, MMGroundingDinoDecoderOutput]:
         r"""
         Args:
             inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -1954,7 +1954,7 @@ def forward(
         output_hidden_states=None,
         return_dict=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, MMGroundingDinoModelOutput]:
         r"""
         input_ids (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`):
             Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide

src/transformers/models/tvp/modeling_tvp.py

@@ -16,7 +16,7 @@
 
 import math
 from dataclasses import dataclass
-from typing import Optional
+from typing import Optional, Union
 
 import torch
 from torch import nn
@@ -462,7 +462,7 @@ def forward(
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
-    ):
+    ) -> Union[tuple, BaseModelOutput]:
         return_dict = return_dict if return_dict is not None else self.config.return_dict
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -722,7 +722,7 @@ def forward(
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
         **kwargs,
-    ):
+    ) -> Union[tuple, BaseModelOutputWithPooling]:
         r"""
         Examples:
         ```python
@@ -824,7 +824,7 @@ def forward(
         return_dict: Optional[bool] = None,
         interpolate_pos_encoding: bool = False,
         **kwargs,
-    ):
+    ) -> Union[tuple, TvpVideoGroundingOutput]:
         r"""
         labels (`torch.FloatTensor` of shape `(batch_size, 3)`, *optional*):
             The labels contains duration, start time, and end time of the video corresponding to the text.

src/transformers/models/udop/modeling_udop.py

@@ -1106,7 +1106,7 @@ def forward(
         return_dict=None,
         cache_position=None,
         **kwargs,
-    ):
+    ) -> Union[tuple, BaseModelOutputWithAttentionMask]:
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
@@ -1476,7 +1476,7 @@ def forward(
         return_dict: Optional[bool] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
-    ) -> tuple[Tensor, ...]:
+    ) -> Union[tuple, Seq2SeqModelOutput]:
         r"""
         bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
             Bounding boxes of each input sequence tokens. Selected in the range `[0,
@@ -1655,7 +1655,7 @@ def forward(
         labels: Optional[Tensor] = None,
         cache_position: Optional[torch.LongTensor] = None,
         **kwargs,
-    ) -> tuple[Tensor, ...]:
+    ) -> Union[tuple, Seq2SeqLMOutput]:
         r"""
         bbox (`torch.LongTensor` of shape `({0}, 4)`, *optional*):
             Bounding boxes of each input sequence tokens. Selected in the range `[0,