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Unverified Commit 6deac5c8 authored by Thomas's avatar Thomas Committed by GitHub
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Adding type hints for TFXLnet (#19344) 

* Added type hints for TF: XLNet

* Added type hints for TF: XLNet

* Added type hints for TF: XLNet

* Added type hints for TF: XLNet

* Added type hints for TF: XLNet

* Added type hints for TF: XLNet

* Add type hints for XLnet (TF)
* Added type hints for XLnet (TF)

* Update src/transformers/models/xlnet/modeling_tf_xlnet.py
parent 7036c956
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Showing with 43 additions and 43 deletions
src/transformers/models/xlnet/modeling_tf_xlnet.py
View file @ 6deac5c8
......@@ -196,11 +196,11 @@ class TFXLNetRelativeAttention(tf.keras.layers.Layer):
attn_mask_g,
r,
seg_mat,
mems,
target_mapping,
head_mask,
output_attentions,
training=False,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = False,
training: bool = False,
):
if g is not None:
# Two-stream attention with relative positional encoding.
......@@ -370,11 +370,11 @@ class TFXLNetLayer(tf.keras.layers.Layer):
attn_mask,
pos_emb,
seg_mat,
mems,
target_mapping,
head_mask,
output_attentions,
training=False,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = False,
training: bool = False,
):
outputs = self.rel_attn(
output_h,
......@@ -583,20 +583,20 @@ class TFXLNetMainLayer(tf.keras.layers.Layer):
@unpack_inputs
def call(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
):
if training and use_mems is None:
......@@ -1152,20 +1152,20 @@ class TFXLNetModel(TFXLNetPreTrainedModel):
)
def call(
self,
input_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
token_type_ids=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
mems: Optional[Union[np.ndarray, tf.Tensor]] = None,
perm_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
target_mapping: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
input_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_mems: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
):
outputs = self.transformer(
input_ids=input_ids,
......@@ -1275,7 +1275,7 @@ class TFXLNetLMHeadModel(TFXLNetPreTrainedModel, TFCausalLanguageModelingLoss):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
training: bool = False,
) -> Union[TFXLNetLMHeadModelOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
......@@ -1406,7 +1406,7 @@ class TFXLNetForSequenceClassification(TFXLNetPreTrainedModel, TFSequenceClassif
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
training: bool = False,
) -> Union[TFXLNetForSequenceClassificationOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
......@@ -1512,7 +1512,7 @@ class TFXLNetForMultipleChoice(TFXLNetPreTrainedModel, TFMultipleChoiceLoss):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
training: bool = False,
) -> Union[TFXLNetForMultipleChoiceOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
......@@ -1633,7 +1633,7 @@ class TFXLNetForTokenClassification(TFXLNetPreTrainedModel, TFTokenClassificatio
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
training: bool = False,
) -> Union[TFXLNetForTokenClassificationOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
......@@ -1720,7 +1720,7 @@ class TFXLNetForQuestionAnsweringSimple(TFXLNetPreTrainedModel, TFQuestionAnswer
return_dict: Optional[bool] = None,
start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
training: bool = False,
) -> Union[TFXLNetForQuestionAnsweringSimpleOutput, Tuple[tf.Tensor]]:
r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
......
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