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"tests/encoder_decoder/test_modeling_encoder_decoder.py" did not exist on "2e20c0f34ade1f0ec6fa1ed24fd1de0b8970f0da"
Unverified Commit 9947dd07 authored by Gunjan Chhablani's avatar Gunjan Chhablani Committed by GitHub
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Add VisualBert type hints (#16544)

parent 59a9c83e
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Showing with 92 additions and 92 deletions
src/transformers/models/visual_bert/modeling_visual_bert.py
View file @ 9947dd07
...@@ -17,7 +17,7 @@ ...@@ -17,7 +17,7 @@
import math import math
from dataclasses import dataclass from dataclasses import dataclass
from typing import Optional, Tuple from typing import Optional, Tuple, Union
import torch import torch
import torch.utils.checkpoint import torch.utils.checkpoint
...@@ -720,20 +720,20 @@ class VisualBertModel(VisualBertPreTrainedModel): ...@@ -720,20 +720,20 @@ class VisualBertModel(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds=None, visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask=None, visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids=None, visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment=None, image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
r""" r"""
Returns: Returns:
...@@ -893,22 +893,22 @@ class VisualBertForPreTraining(VisualBertPreTrainedModel): ...@@ -893,22 +893,22 @@ class VisualBertForPreTraining(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=VisualBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds=None, visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask=None, visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids=None, visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment=None, image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
sentence_image_labels=None, sentence_image_labels: Optional[torch.LongTensor] = None,
): ) -> Union[Tuple[torch.Tensor], VisualBertForPreTrainingOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
...@@ -1039,21 +1039,21 @@ class VisualBertForMultipleChoice(VisualBertPreTrainedModel): ...@@ -1039,21 +1039,21 @@ class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=MultipleChoiceModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds=None, visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask=None, visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids=None, visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment=None, image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
): ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
...@@ -1191,21 +1191,21 @@ class VisualBertForQuestionAnswering(VisualBertPreTrainedModel): ...@@ -1191,21 +1191,21 @@ class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds=None, visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask=None, visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids=None, visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment=None, image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
): ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
...@@ -1317,21 +1317,21 @@ class VisualBertForVisualReasoning(VisualBertPreTrainedModel): ...@@ -1317,21 +1317,21 @@ class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds=None, visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask=None, visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids=None, visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment=None, image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
): ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
...@@ -1477,22 +1477,22 @@ class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel): ...@@ -1477,22 +1477,22 @@ class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
visual_embeds=None, visual_embeds: Optional[torch.FloatTensor] = None,
visual_attention_mask=None, visual_attention_mask: Optional[torch.LongTensor] = None,
visual_token_type_ids=None, visual_token_type_ids: Optional[torch.LongTensor] = None,
image_text_alignment=None, image_text_alignment: Optional[torch.LongTensor] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
region_to_phrase_position=None, region_to_phrase_position: Optional[torch.LongTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
): ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
r""" r"""
region_to_phrase_position (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*): region_to_phrase_position (`torch.LongTensor` of shape `(batch_size, total_sequence_length)`, *optional*):
The positions depicting the position of the image embedding corresponding to the textual tokens. The positions depicting the position of the image embedding corresponding to the textual tokens.
......
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