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Added Type hints for XLM TF (#19333)

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* Update modeling_tf_xlm.py

* Updates

* Update src/transformers/models/xlm/modeling_tf_xlm.py

* Update src/transformers/models/xlm/modeling_tf_xlm.py

* Update src/transformers/models/xlm/modeling_tf_xlm.py

* Update src/transformers/models/xlm/modeling_tf_xlm.py

* Update src/transformers/models/xlm/modeling_tf_xlm.py

Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>
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IMvision12 2022-10-07 18:14:50 +05:30 committed by GitHub
parent 46fd04b481
commit 969534af4b
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1 changed files with 72 additions and 71 deletions
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143
src/transformers/models/xlm/modeling_tf_xlm.py
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@ -19,7 +19,7 @@
import itertools import itertools
import warnings import warnings
from dataclasses import dataclass from dataclasses import dataclass
from typing import Dict, Optional, Tuple from typing import Dict, Optional, Tuple, Union
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
@ -33,6 +33,7 @@ from ...modeling_tf_outputs import (
TFTokenClassifierOutput, TFTokenClassifierOutput,
) )
from ...modeling_tf_utils import ( from ...modeling_tf_utils import (
TFModelInputType,
TFMultipleChoiceLoss, TFMultipleChoiceLoss,
TFPreTrainedModel, TFPreTrainedModel,
TFQuestionAnsweringLoss, TFQuestionAnsweringLoss,
@ -844,19 +845,19 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
) )
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,
langs=None, langs: 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,
lengths=None, lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
cache=None, cache: Optional[Dict[str, 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: bool = False,
): ):
transformer_outputs = self.transformer( transformer_outputs = self.transformer(
input_ids=input_ids, input_ids=input_ids,
@ -916,20 +917,20 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
) )
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,
langs=None, langs: 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,
lengths=None, lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
cache=None, cache: Optional[Dict[str, 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: bool = False,
): ):
r""" r"""
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
@ -1023,20 +1024,20 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, 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,
langs=None, langs: 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,
lengths=None, lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
cache=None, cache: Optional[Dict[str, 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: bool = False,
): ):
if input_ids is not None: if input_ids is not None:
num_choices = shape_list(input_ids)[1] num_choices = shape_list(input_ids)[1]
@ -1147,20 +1148,20 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos
) )
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,
langs=None, langs: 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,
lengths=None, lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
cache=None, cache: Optional[Dict[str, 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: bool = False,
): ):
r""" r"""
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
@ -1232,21 +1233,21 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
) )
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,
langs=None, langs: 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,
lengths=None, lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
cache=None, cache: Optional[Dict[str, 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: bool = False,
): ):
r""" r"""
start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*): start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):