diff --git a/src/transformers/models/conditional_detr/modeling_conditional_detr.py b/src/transformers/models/conditional_detr/modeling_conditional_detr.py index c6d25cf7524..276dbd1b1f9 100644 --- a/src/transformers/models/conditional_detr/modeling_conditional_detr.py +++ b/src/transformers/models/conditional_detr/modeling_conditional_detr.py @@ -1126,7 +1126,7 @@ CONDITIONAL_DETR_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`) @@ -1872,7 +1872,7 @@ class ConditionalDetrForSegmentation(ConditionalDetrPreTrainedModel): 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, diff --git a/src/transformers/models/cpmant/modeling_cpmant.py b/src/transformers/models/cpmant/modeling_cpmant.py index 808a341ac99..6d2dc596fa6 100755 --- a/src/transformers/models/cpmant/modeling_cpmant.py +++ b/src/transformers/models/cpmant/modeling_cpmant.py @@ -653,7 +653,7 @@ class CpmAntModel(CpmAntPreTrainedModel): 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 diff --git a/src/transformers/models/decision_transformer/modeling_decision_transformer.py b/src/transformers/models/decision_transformer/modeling_decision_transformer.py index 08f5d7f1106..ced14403630 100755 --- a/src/transformers/models/decision_transformer/modeling_decision_transformer.py +++ b/src/transformers/models/decision_transformer/modeling_decision_transformer.py @@ -787,7 +787,7 @@ DECISION_TRANSFORMER_INPUTS_DOCSTRING = r""" 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 @@ class DecisionTransformerModel(DecisionTransformerPreTrainedModel): @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: diff --git a/src/transformers/models/deformable_detr/modeling_deformable_detr.py b/src/transformers/models/deformable_detr/modeling_deformable_detr.py index 8760ac32c97..f541ca13054 100755 --- a/src/transformers/models/deformable_detr/modeling_deformable_detr.py +++ b/src/transformers/models/deformable_detr/modeling_deformable_detr.py @@ -19,7 +19,7 @@ import copy 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 @@ DEFORMABLE_DETR_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`) @@ -1625,16 +1625,16 @@ class DeformableDetrModel(DeformableDetrPreTrainedModel): @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 @@ class DeformableDetrForObjectDetection(DeformableDetrPreTrainedModel): @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 diff --git a/src/transformers/models/deta/modeling_deta.py b/src/transformers/models/deta/modeling_deta.py index db0fe24c4ab..9cd29e94088 100644 --- a/src/transformers/models/deta/modeling_deta.py +++ b/src/transformers/models/deta/modeling_deta.py @@ -19,7 +19,7 @@ import copy 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 @@ DETA_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`) @@ -1533,16 +1533,16 @@ class DetaModel(DetaPreTrainedModel): @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 @@ class DetaForObjectDetection(DetaPreTrainedModel): @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 diff --git a/src/transformers/models/detr/modeling_detr.py b/src/transformers/models/detr/modeling_detr.py index 95d5be37526..4be5405d817 100644 --- a/src/transformers/models/detr/modeling_detr.py +++ b/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 @@ DETR_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`) @@ -1245,16 +1245,16 @@ class DetrModel(DetrPreTrainedModel): @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 @@ class DetrForObjectDetection(DetrPreTrainedModel): @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 @@ class DetrForSegmentation(DetrPreTrainedModel): @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 diff --git a/src/transformers/models/dpr/modeling_dpr.py b/src/transformers/models/dpr/modeling_dpr.py index ce5ba24899f..944ce142b0a 100644 --- a/src/transformers/models/dpr/modeling_dpr.py +++ b/src/transformers/models/dpr/modeling_dpr.py @@ -454,9 +454,9 @@ class DPRContextEncoder(DPRPretrainedContextEncoder): 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 @@ class DPRQuestionEncoder(DPRPretrainedQuestionEncoder): 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 @@ class DPRReader(DPRPretrainedReader): 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: