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Add type hints for ViLT models #18577

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merged 3 commits into from
Aug 12, 2022
Merged

Add type hints for ViLT models #18577

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e280dcd
Add type hints for Vilt models
donelianc Aug 11, 2022
cced660
Add missing return type for TokenClassification class
donelianc Aug 11, 2022
e434c76
Merge branch 'huggingface:main' into add-type-hints-vilt-model-v2
donelianc Aug 11, 2022
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158 changes: 79 additions & 79 deletions src/transformers/models/vilt/modeling_vilt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
import collections.abc
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
Expand Down Expand Up @@ -761,19 +761,19 @@ class PreTrainedModel
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
pixel_values=None,
pixel_mask=None,
head_mask=None,
inputs_embeds=None,
image_embeds=None,
image_token_type_idx=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: Optional[torch.FloatTensor] = None,
image_token_type_idx: Optional[int] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[BaseModelOutputWithPooling, Tuple[torch.FloatTensor]]:
r"""
Returns:

Expand Down Expand Up @@ -914,19 +914,19 @@ def set_output_embeddings(self, new_embeddings):
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
pixel_values=None,
pixel_mask=None,
head_mask=None,
inputs_embeds=None,
image_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: 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[MaskedLMOutput, Tuple[torch.FloatTensor]]:
r"""
labels (*torch.LongTensor* of shape *(batch_size, sequence_length)*, *optional*):
Labels for computing the masked language modeling loss. Indices should be in *[-100, 0, ...,
Expand Down Expand Up @@ -1088,19 +1088,19 @@ def __init__(self, config):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
pixel_values=None,
pixel_mask=None,
head_mask=None,
inputs_embeds=None,
image_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: 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[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.FloatTensor` of shape `(batch_size, num_labels)`, *optional*):
Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of
Expand Down Expand Up @@ -1193,19 +1193,19 @@ def __init__(self, config):
@replace_return_docstrings(output_type=SequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
pixel_values=None,
pixel_mask=None,
head_mask=None,
inputs_embeds=None,
image_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: 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[SequenceClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels are currently not supported.
Expand Down Expand Up @@ -1299,19 +1299,19 @@ def __init__(self, config):
@replace_return_docstrings(output_type=ViltForImagesAndTextClassificationOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
pixel_values=None,
pixel_mask=None,
head_mask=None,
inputs_embeds=None,
image_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: 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[ViltForImagesAndTextClassificationOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Binary classification labels.
Expand Down Expand Up @@ -1436,19 +1436,19 @@ def __init__(self, config):
@replace_return_docstrings(output_type=TokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
pixel_values=None,
pixel_mask=None,
head_mask=None,
inputs_embeds=None,
image_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
pixel_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
image_embeds: 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[TokenClassifierOutput, Tuple[torch.FloatTensor]]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, text_sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Expand Down