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Unverified Commit 16399d61 authored by Jack McDonald's avatar Jack McDonald Committed by GitHub
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Add type annotations for Perceiver (#16174)

parent 015de6f0
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Showing with 63 additions and 63 deletions
src/transformers/models/perceiver/modeling_perceiver.py
View file @ 16399d61
......@@ -19,7 +19,7 @@ import math
from dataclasses import dataclass
from functools import reduce
from operator import __add__
from typing import Any, Callable, Mapping, Optional, Tuple
from typing import Any, Callable, Dict, Mapping, Optional, Tuple, Union
import numpy as np
import torch
......@@ -986,15 +986,15 @@ class PerceiverForMaskedLM(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
input_ids=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
input_ids: Optional[torch.Tensor] = None,
) -> Union[Tuple, PerceiverMaskedLMOutput]:
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, ...,
......@@ -1103,15 +1103,15 @@ class PerceiverForSequenceClassification(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
input_ids=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
input_ids: Optional[torch.Tensor] = None,
) -> Union[Tuple, PerceiverClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the classification/regression loss. Indices should be in `[0, ..., config.num_labels -
......@@ -1236,15 +1236,15 @@ class PerceiverForImageClassificationLearned(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
pixel_values=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
) -> Union[Tuple, PerceiverClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
......@@ -1373,15 +1373,15 @@ class PerceiverForImageClassificationFourier(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
pixel_values=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
) -> Union[Tuple, PerceiverClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
......@@ -1510,15 +1510,15 @@ class PerceiverForImageClassificationConvProcessing(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
pixel_values=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
pixel_values: Optional[torch.Tensor] = None,
) -> Union[Tuple, PerceiverClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
......@@ -1664,14 +1664,14 @@ class PerceiverForOpticalFlow(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, PerceiverClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the optical flow loss. Indices should be in `[0, ..., config.num_labels - 1]`.
......@@ -1873,15 +1873,15 @@ class PerceiverForMultimodalAutoencoding(PerceiverPreTrainedModel):
@replace_return_docstrings(output_type=PerceiverClassifierOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
inputs=None,
attention_mask=None,
subsampled_output_points=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
labels=None,
return_dict=None,
):
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
subsampled_output_points: Optional[Dict[str, torch.tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, PerceiverClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
......
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