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Unverified Commit 10cf1ffd authored by Kamal Raj's avatar Kamal Raj Committed by GitHub
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Added missing type hints - ELECTRA TF (#16104) 

* Add missing type hints - ELECTRA TF

* bool -> Optional[bool]
parent 6db86930
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Inline Side-by-side
Showing with 104 additions and 102 deletions
src/transformers/models/electra/modeling_tf_electra.py
View file @ 10cf1ffd
...@@ -19,6 +19,7 @@ import warnings ...@@ -19,6 +19,7 @@ import warnings
from dataclasses import dataclass from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf import tensorflow as tf
from ...activations_tf import get_tf_activation from ...activations_tf import get_tf_activation
...@@ -41,6 +42,7 @@ from ...modeling_tf_outputs import ( ...@@ -41,6 +42,7 @@ from ...modeling_tf_outputs import (
) )
from ...modeling_tf_utils import ( from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss, TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss, TFMultipleChoiceLoss,
TFPreTrainedModel, TFPreTrainedModel,
TFQuestionAnsweringLoss, TFQuestionAnsweringLoss,
...@@ -702,22 +704,22 @@ class TFElectraMainLayer(tf.keras.layers.Layer): ...@@ -702,22 +704,22 @@ class TFElectraMainLayer(tf.keras.layers.Layer):
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = 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,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
inputs = input_processing( inputs = input_processing(
func=self.call, func=self.call,
config=self.config, config=self.config,
...@@ -957,22 +959,22 @@ class TFElectraModel(TFElectraPreTrainedModel): ...@@ -957,22 +959,22 @@ class TFElectraModel(TFElectraPreTrainedModel):
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = 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,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
r""" r"""
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
...@@ -1069,18 +1071,18 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel): ...@@ -1069,18 +1071,18 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
@replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = 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,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFElectraForPreTrainingOutput, Tuple[tf.Tensor]]:
r""" r"""
Returns: Returns:
...@@ -1219,19 +1221,19 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos ...@@ -1219,19 +1221,19 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = 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[Union[np.ndarray, tf.Tensor]] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]:
r""" r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`tf.Tensor` 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, ...,
...@@ -1344,19 +1346,19 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla ...@@ -1344,19 +1346,19 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = 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[Union[np.ndarray, tf.Tensor]] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r""" r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): labels (`tf.Tensor` 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, ...,
...@@ -1452,19 +1454,19 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss) ...@@ -1452,19 +1454,19 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss)
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = 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[Union[np.ndarray, tf.Tensor]] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]:
r""" r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]`
...@@ -1591,19 +1593,19 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific ...@@ -1591,19 +1593,19 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = 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[Union[np.ndarray, tf.Tensor]] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]:
r""" r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`tf.Tensor` 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]`.
...@@ -1687,20 +1689,20 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin ...@@ -1687,20 +1689,20 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[TFModelInputType] = None,
attention_mask=None, attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids=None, token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids=None, position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask=None, head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = 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,
start_positions=None, start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
end_positions=None, end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]:
r""" r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss. Labels for position (index) of the start of the labelled span for computing the token classification loss.
......
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