riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-08-01 18:51:14 +06:00
Code Issues Actions 3 Packages Projects Releases Wiki Activity

Pytorch type hints (#20112)

Browse Source 
* initial commit

* Update modeling_whisper.py

* Fixing Tests

* modeling_vision_text_dual_encoder

* modeling_vision_encoder_decoder

* Update modeling_vit.py

* Update modeling_vit_msn.py

* Update modeling_trajectory_transformer.py

* style

* Update modeling_time_series_transformer.py

* Update modeling_time_series_transformer.py

* Update modeling_segformer.py

* Update modeling_plbart.py

* Update modeling_dpt.py

* Update modeling_deit.py

* Update modeling_dpt.py

* Update modeling_esm.py

* Update modeling_fnet.py

* Update modeling_fnet.py

* Update modeling_fnet.py

* Update modeling_flava.py

* Update modeling_flava.py

* Update modeling_layoutlmv3.py

* Update modeling_levit.py
This commit is contained in:
IMvision12 2022-11-14 18:09:18 +05:30 committed by GitHub
parent 03bc6ece1b
commit d24e84d9ed
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
20 changed files with 233 additions and 233 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
src/transformers/models/deit/modeling_deit.py
View File

@ -494,7 +494,7 @@ class DeiTModel(DeiTPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

40
src/transformers/models/dpt/modeling_dpt.py
View File

@ -712,12 +712,12 @@ class DPTModel(DPTPreTrainedModel):
) )
def forward( def forward(
self, self,
pixel_values, pixel_values: torch.FloatTensor,
head_mask=None, head_mask: Optional[torch.FloatTensor] = 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, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -875,13 +875,13 @@ class DPTForDepthEstimation(DPTPreTrainedModel):
@replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=DepthEstimatorOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
pixel_values, pixel_values: torch.FloatTensor,
head_mask=None, head_mask: Optional[torch.FloatTensor] = None,
labels=None, labels: 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], DepthEstimatorOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth depth estimation maps for computing the loss. Ground truth depth estimation maps for computing the loss.
@ -1036,13 +1036,13 @@ class DPTForSemanticSegmentation(DPTPreTrainedModel):
@replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=SemanticSegmenterOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
pixel_values=None, pixel_values: Optional[torch.FloatTensor] = None,
head_mask=None, head_mask: Optional[torch.FloatTensor] = None,
labels=None, labels: 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], SemanticSegmenterOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ..., Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,

6
src/transformers/models/esm/modeling_esm.py
View File

@ -940,7 +940,7 @@ class EsmForMaskedLM(EsmPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, MaskedLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, 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, ...,
@ -1042,7 +1042,7 @@ class EsmForSequenceClassification(EsmPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, 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, ...,
@ -1138,7 +1138,7 @@ class EsmForTokenClassification(EsmPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, TokenClassifierOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

6
src/transformers/models/flava/modeling_flava.py
View File

@ -943,7 +943,7 @@ class FlavaImageModel(FlavaPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1039,7 +1039,7 @@ class FlavaTextModel(FlavaPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@ -1142,7 +1142,7 @@ class FlavaMultimodalModel(FlavaPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

14
src/transformers/models/fnet/modeling_fnet.py
View File

@ -548,13 +548,13 @@ class FNetModel(FNetPreTrainedModel):
) )
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[tuple, BaseModelOutput]:
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
) )

24
src/transformers/models/layoutlmv3/modeling_layoutlmv3.py
View File

@ -848,18 +848,18 @@ class LayoutLMv3Model(LayoutLMv3PreTrainedModel):
@replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=BaseModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
bbox=None, bbox: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.FloatTensor] = 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.FloatTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
pixel_values=None, pixel_values: Optional[torch.FloatTensor] = 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, BaseModelOutput]:
r""" r"""
Returns: Returns:

8
src/transformers/models/levit/modeling_levit.py
View File

@ -16,7 +16,7 @@
import itertools import itertools
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
@ -561,7 +561,7 @@ class LevitModel(LevitPreTrainedModel):
pixel_values: torch.FloatTensor = None, pixel_values: torch.FloatTensor = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, BaseModelOutputWithPoolingAndNoAttention]:
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
) )
@ -630,7 +630,7 @@ class LevitForImageClassification(LevitPreTrainedModel):
labels: Optional[torch.LongTensor] = None, labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ..., Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
@ -722,7 +722,7 @@ class LevitForImageClassificationWithTeacher(LevitPreTrainedModel):
pixel_values: torch.FloatTensor = None, pixel_values: torch.FloatTensor = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, LevitForImageClassificationWithTeacherOutput]:
return_dict = return_dict if return_dict is not None else self.config.use_return_dict return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.levit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict) outputs = self.levit(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)

2
src/transformers/models/plbart/modeling_plbart.py
View File

@ -1176,7 +1176,7 @@ class PLBartModel(PLBartPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple[torch.Tensor], Seq2SeqModelOutput]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

116
src/transformers/models/realm/modeling_realm.py
View File

@ -1159,16 +1159,16 @@ class RealmEmbedder(RealmPreTrainedModel):
@replace_return_docstrings(output_type=RealmEmbedderOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=RealmEmbedderOutput, 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.FloatTensor] = 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.FloatTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = 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, RealmEmbedderOutput]:
r""" r"""
Returns: Returns:
@ -1241,20 +1241,20 @@ class RealmScorer(RealmPreTrainedModel):
@replace_return_docstrings(output_type=RealmScorerOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=RealmScorerOutput, 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.FloatTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
candidate_input_ids=None, candidate_input_ids: Optional[torch.LongTensor] = None,
candidate_attention_mask=None, candidate_attention_mask: Optional[torch.FloatTensor] = None,
candidate_token_type_ids=None, candidate_token_type_ids: Optional[torch.LongTensor] = None,
candidate_inputs_embeds=None, candidate_inputs_embeds: Optional[torch.FloatTensor] = None,
head_mask=None, head_mask: Optional[torch.FloatTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = 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, RealmScorerOutput]:
r""" r"""
candidate_input_ids (`torch.LongTensor` of shape `(batch_size, num_candidates, sequence_length)`): candidate_input_ids (`torch.LongTensor` of shape `(batch_size, num_candidates, sequence_length)`):
Indices of candidate input sequence tokens in the vocabulary. Indices of candidate input sequence tokens in the vocabulary.
@ -1396,19 +1396,19 @@ class RealmKnowledgeAugEncoder(RealmPreTrainedModel):
@replace_return_docstrings(output_type=MaskedLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=MaskedLMOutput, 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.FloatTensor] = 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.FloatTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
relevance_score=None, relevance_score: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
mlm_mask=None, mlm_mask: 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, MaskedLMOutput]:
r""" r"""
relevance_score (`torch.FloatTensor` of shape `(batch_size, num_candidates)`, *optional*): relevance_score (`torch.FloatTensor` of shape `(batch_size, num_candidates)`, *optional*):
Relevance score derived from RealmScorer, must be specified if you want to compute the masked language Relevance score derived from RealmScorer, must be specified if you want to compute the masked language
@ -1537,21 +1537,21 @@ class RealmReader(RealmPreTrainedModel):
@replace_return_docstrings(output_type=RealmReaderOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=RealmReaderOutput, 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.FloatTensor] = 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.FloatTensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
relevance_score=None, relevance_score: Optional[torch.FloatTensor] = None,
block_mask=None, block_mask: Optional[torch.BoolTensor] = None,
start_positions=None, start_positions: Optional[torch.LongTensor] = None,
end_positions=None, end_positions: Optional[torch.LongTensor] = None,
has_answers=None, has_answers: Optional[torch.BoolTensor] = 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, RealmReaderOutput]:
r""" r"""
relevance_score (`torch.FloatTensor` of shape `(searcher_beam_size,)`, *optional*): relevance_score (`torch.FloatTensor` of shape `(searcher_beam_size,)`, *optional*):
Relevance score, which must be specified if you want to compute the logits and marginal log loss. Relevance score, which must be specified if you want to compute the logits and marginal log loss.
@ -1763,12 +1763,12 @@ class RealmForOpenQA(RealmPreTrainedModel):
@replace_return_docstrings(output_type=RealmForOpenQAOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=RealmForOpenQAOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids, input_ids: Optional[torch.LongTensor],
attention_mask=None, attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids=None, token_type_ids: Optional[torch.LongTensor] = None,
answer_ids=None, answer_ids: Optional[torch.LongTensor] = None,
return_dict=None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, RealmForOpenQAOutput]:
r""" r"""
Returns: Returns:

2
src/transformers/models/segformer/modeling_segformer.py
View File

@ -706,7 +706,7 @@ class SegformerDecodeHead(SegformerPreTrainedModel):
self.config = config self.config = config
def forward(self, encoder_hidden_states): def forward(self, encoder_hidden_states: torch.FloatTensor):
batch_size = encoder_hidden_states[-1].shape[0] batch_size = encoder_hidden_states[-1].shape[0]
all_hidden_states = () all_hidden_states = ()

32
src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
View File

@ -15,7 +15,7 @@
""" Classes to support Speech-Encoder-Text-Decoder architectures""" """ Classes to support Speech-Encoder-Text-Decoder architectures"""
from typing import Optional from typing import Optional, Tuple, Union
import torch import torch
from torch import nn from torch import nn
@ -443,22 +443,22 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
inputs=None, inputs: Optional[torch.FloatTensor] = None,
attention_mask=None, attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.BoolTensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
input_values=None, input_values: Optional[torch.FloatTensor] = None,
input_features=None, input_features: Optional[torch.FloatTensor] = None,
return_dict=None, return_dict: Optional[bool] = None,
**kwargs, **kwargs,
): ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r""" r"""
Returns: Returns:

64
src/transformers/models/speech_to_text/modeling_speech_to_text.py
View File

@ -17,7 +17,7 @@
import math import math
import random import random
from typing import Optional, Tuple from typing import Optional, Tuple, Union
import torch import torch
from torch import nn from torch import nn
@ -1144,21 +1144,21 @@ class Speech2TextModel(Speech2TextPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_features=None, input_features: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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.FloatTensor], Seq2SeqLMOutput]:
r""" r"""
Returns: Returns:
@ -1291,22 +1291,22 @@ class Speech2TextForConditionalGeneration(Speech2TextPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_features=None, input_features: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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.FloatTensor], Seq2SeqLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`

30
src/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py
View File

@ -18,7 +18,7 @@
import copy import copy
import math import math
import random import random
from typing import Optional, Tuple from typing import Optional, Tuple, Union
import torch import torch
from torch import nn from torch import nn
@ -780,20 +780,20 @@ class Speech2Text2ForCausalLM(Speech2Text2PreTrainedModel):
@replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=CausalLMOutputWithCrossAttentions, 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.Tensor] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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.FloatTensor], CausalLMOutputWithCrossAttentions]:
r""" r"""
Args: Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):

4
src/transformers/models/time_series_transformer/modeling_time_series_transformer.py
View File

@ -1584,7 +1584,7 @@ class TimeSeriesTransformerModel(TimeSeriesTransformerPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Seq2SeqTimeSeriesModelOutput, Tuple]:
r""" r"""
Returns: Returns:
@ -1747,7 +1747,7 @@ class TimeSeriesTransformerForPrediction(TimeSeriesTransformerPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
use_cache: Optional[bool] = None, use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Seq2SeqTimeSeriesModelOutput, Tuple]:
r""" r"""
Returns: Returns:

4
src/transformers/models/trajectory_transformer/modeling_trajectory_transformer.py
View File

@ -17,7 +17,7 @@
import math import math
import os import os
from dataclasses import dataclass from dataclasses import dataclass
from typing import Optional, Tuple from typing import Optional, Tuple, Union
import numpy as np import numpy as np
import torch import torch
@ -478,7 +478,7 @@ class TrajectoryTransformerModel(TrajectoryTransformerPreTrainedModel):
output_attentions: Optional[bool] = None, output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple[torch.Tensor], TrajectoryTransformerOutput]:
r""" r"""
Returns: Returns:

26
src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py
View File

@ -18,7 +18,7 @@
import gc import gc
import os import os
import tempfile import tempfile
from typing import Optional from typing import Optional, Tuple, Union
import torch import torch
from torch import nn from torch import nn
@ -520,19 +520,19 @@ class VisionEncoderDecoderModel(PreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
pixel_values=None, pixel_values: Optional[torch.FloatTensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.BoolTensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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,
**kwargs, **kwargs,
): ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r""" r"""
Returns: Returns:

22
src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py
View File

@ -15,7 +15,7 @@
""" PyTorch VisionTextDualEncoder model.""" """ PyTorch VisionTextDualEncoder model."""
from typing import Optional from typing import Optional, Tuple, Union
import torch import torch
from torch import nn from torch import nn
@ -295,16 +295,16 @@ class VisionTextDualEncoderModel(PreTrainedModel):
@replace_return_docstrings(output_type=CLIPOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=CLIPOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
pixel_values=None, pixel_values: Optional[torch.FloatTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.LongTensor] = None,
return_loss=None, return_loss: Optional[bool] = None,
token_type_ids=None, token_type_ids: 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], CLIPOutput]:
r""" r"""
Returns: Returns:

2
src/transformers/models/vit/modeling_vit.py
View File

@ -541,7 +541,7 @@ class ViTModel(ViTPreTrainedModel):
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[Tuple, BaseModelOutputWithPooling]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = ( output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states

2
src/transformers/models/vit_msn/modeling_vit_msn.py
View File

@ -525,7 +525,7 @@ class ViTMSNModel(ViTMSNPreTrainedModel):
output_hidden_states: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: Optional[bool] = None, interpolate_pos_encoding: Optional[bool] = None,
return_dict: Optional[bool] = None, return_dict: Optional[bool] = None,
): ) -> Union[tuple, BaseModelOutput]:
r""" r"""
Returns: Returns:

60
src/transformers/models/whisper/modeling_whisper.py
View File

@ -17,7 +17,7 @@
import math import math
import random import random
from typing import Optional, Tuple from typing import Optional, Tuple, Union
import torch import torch
import torch.utils.checkpoint import torch.utils.checkpoint
@ -1004,20 +1004,20 @@ class WhisperModel(WhisperPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_features=None, input_features: Optional[torch.LongTensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
use_cache=None, use_cache: Optional[bool] = 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], Seq2SeqModelOutput]:
r""" r"""
Returns: Returns:
@ -1140,21 +1140,21 @@ class WhisperForConditionalGeneration(WhisperPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_features=None, input_features: Optional[torch.LongTensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[Tuple[torch.FloatTensor]] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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], Seq2SeqLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` Labels for computing the language modeling loss. Indices should either be in `[0, ..., config.vocab_size]`