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Added Type hints for XLM TF (#19333)
* 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
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@ -19,7 +19,7 @@
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import itertools
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import warnings
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from dataclasses import dataclass
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from typing import Dict, Optional, Tuple
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from typing import Dict, Optional, Tuple, Union
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import numpy as np
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import tensorflow as tf
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@ -33,6 +33,7 @@ from ...modeling_tf_outputs import (
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TFTokenClassifierOutput,
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)
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from ...modeling_tf_utils import (
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TFModelInputType,
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TFMultipleChoiceLoss,
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TFPreTrainedModel,
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TFQuestionAnsweringLoss,
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@ -844,19 +845,19 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel):
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)
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def call(
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self,
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input_ids=None,
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attention_mask=None,
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langs=None,
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token_type_ids=None,
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position_ids=None,
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lengths=None,
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cache=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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training=False,
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input_ids: Optional[TFModelInputType] = None,
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attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
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token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
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cache: Optional[Dict[str, tf.Tensor]] = None,
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head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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training: bool = False,
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):
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transformer_outputs = self.transformer(
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input_ids=input_ids,
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@ -916,20 +917,20 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat
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)
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def call(
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self,
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input_ids=None,
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attention_mask=None,
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langs=None,
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token_type_ids=None,
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position_ids=None,
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lengths=None,
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cache=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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labels=None,
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training=False,
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input_ids: Optional[TFModelInputType] = None,
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attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
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token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
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cache: Optional[Dict[str, tf.Tensor]] = None,
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head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
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training: bool = False,
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):
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r"""
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labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
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@ -1023,20 +1024,20 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss):
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)
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def call(
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self,
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input_ids=None,
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attention_mask=None,
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langs=None,
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token_type_ids=None,
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position_ids=None,
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lengths=None,
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cache=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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labels=None,
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training=False,
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input_ids: Optional[TFModelInputType] = None,
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attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
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token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
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cache: Optional[Dict[str, tf.Tensor]] = None,
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head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
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training: bool = False,
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):
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if input_ids is not None:
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num_choices = shape_list(input_ids)[1]
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@ -1147,20 +1148,20 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos
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)
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def call(
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self,
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input_ids=None,
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attention_mask=None,
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langs=None,
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token_type_ids=None,
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position_ids=None,
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lengths=None,
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cache=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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labels=None,
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training=False,
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input_ids: Optional[TFModelInputType] = None,
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attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
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token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
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cache: Optional[Dict[str, tf.Tensor]] = None,
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head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
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training: bool = False,
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):
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r"""
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labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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@ -1232,21 +1233,21 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL
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)
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def call(
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self,
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input_ids=None,
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attention_mask=None,
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langs=None,
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token_type_ids=None,
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position_ids=None,
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lengths=None,
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cache=None,
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head_mask=None,
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inputs_embeds=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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start_positions=None,
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end_positions=None,
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training=False,
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input_ids: Optional[TFModelInputType] = None,
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attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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langs: Optional[Union[np.ndarray, tf.Tensor]] = None,
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token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
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lengths: Optional[Union[np.ndarray, tf.Tensor]] = None,
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cache: Optional[Dict[str, tf.Tensor]] = None,
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head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
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inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
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end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None,
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training: bool = False,
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):
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r"""
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start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):
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