From 969534af4bf8049b674917c712dd9c1f9ae88242 Mon Sep 17 00:00:00 2001 From: IMvision12 <88665786+IMvision12@users.noreply.github.com> Date: Fri, 7 Oct 2022 18:14:50 +0530 Subject: [PATCH] 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 --- .../models/xlm/modeling_tf_xlm.py | 143 +++++++++--------- 1 file changed, 72 insertions(+), 71 deletions(-) diff --git a/src/transformers/models/xlm/modeling_tf_xlm.py b/src/transformers/models/xlm/modeling_tf_xlm.py index 8bc0925c2fd..3511e2f4496 100644 --- a/src/transformers/models/xlm/modeling_tf_xlm.py +++ b/src/transformers/models/xlm/modeling_tf_xlm.py @@ -19,7 +19,7 @@ import itertools import warnings from dataclasses import dataclass -from typing import Dict, Optional, Tuple +from typing import Dict, Optional, Tuple, Union import numpy as np import tensorflow as tf @@ -33,6 +33,7 @@ from ...modeling_tf_outputs import ( TFTokenClassifierOutput, ) from ...modeling_tf_utils import ( + TFModelInputType, TFMultipleChoiceLoss, TFPreTrainedModel, TFQuestionAnsweringLoss, @@ -844,19 +845,19 @@ class TFXLMWithLMHeadModel(TFXLMPreTrainedModel): ) def call( self, - input_ids=None, - attention_mask=None, - langs=None, - token_type_ids=None, - position_ids=None, - lengths=None, - cache=None, - head_mask=None, - inputs_embeds=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, + langs: Optional[Union[np.ndarray, tf.Tensor]] = None, + token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + lengths: Optional[Union[np.ndarray, tf.Tensor]] = None, + cache: Optional[Dict[str, tf.Tensor]] = None, + head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + training: bool = False, ): transformer_outputs = self.transformer( input_ids=input_ids, @@ -916,20 +917,20 @@ class TFXLMForSequenceClassification(TFXLMPreTrainedModel, TFSequenceClassificat ) def call( self, - input_ids=None, - attention_mask=None, - langs=None, - token_type_ids=None, - position_ids=None, - lengths=None, - cache=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - labels=None, - training=False, + input_ids: Optional[TFModelInputType] = None, + attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + langs: Optional[Union[np.ndarray, tf.Tensor]] = None, + token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + lengths: Optional[Union[np.ndarray, tf.Tensor]] = None, + cache: Optional[Dict[str, tf.Tensor]] = None, + head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[Union[np.ndarray, tf.Tensor]] = None, + training: bool = False, ): r""" labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): @@ -1023,20 +1024,20 @@ class TFXLMForMultipleChoice(TFXLMPreTrainedModel, TFMultipleChoiceLoss): ) def call( self, - input_ids=None, - attention_mask=None, - langs=None, - token_type_ids=None, - position_ids=None, - lengths=None, - cache=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - labels=None, - training=False, + input_ids: Optional[TFModelInputType] = None, + attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + langs: Optional[Union[np.ndarray, tf.Tensor]] = None, + token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + lengths: Optional[Union[np.ndarray, tf.Tensor]] = None, + cache: Optional[Dict[str, tf.Tensor]] = None, + head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[Union[np.ndarray, tf.Tensor]] = None, + training: bool = False, ): if input_ids is not None: num_choices = shape_list(input_ids)[1] @@ -1147,20 +1148,20 @@ class TFXLMForTokenClassification(TFXLMPreTrainedModel, TFTokenClassificationLos ) def call( self, - input_ids=None, - attention_mask=None, - langs=None, - token_type_ids=None, - position_ids=None, - lengths=None, - cache=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - labels=None, - training=False, + input_ids: Optional[TFModelInputType] = None, + attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + langs: Optional[Union[np.ndarray, tf.Tensor]] = None, + token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + lengths: Optional[Union[np.ndarray, tf.Tensor]] = None, + cache: Optional[Dict[str, tf.Tensor]] = None, + head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + labels: Optional[Union[np.ndarray, tf.Tensor]] = None, + training: bool = False, ): r""" labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): @@ -1232,21 +1233,21 @@ class TFXLMForQuestionAnsweringSimple(TFXLMPreTrainedModel, TFQuestionAnsweringL ) def call( self, - input_ids=None, - attention_mask=None, - langs=None, - token_type_ids=None, - position_ids=None, - lengths=None, - cache=None, - head_mask=None, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - start_positions=None, - end_positions=None, - training=False, + input_ids: Optional[TFModelInputType] = None, + attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + langs: Optional[Union[np.ndarray, tf.Tensor]] = None, + token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None, + lengths: Optional[Union[np.ndarray, tf.Tensor]] = None, + cache: Optional[Dict[str, tf.Tensor]] = None, + head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None, + inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + start_positions: Optional[Union[np.ndarray, tf.Tensor]] = None, + end_positions: Optional[Union[np.ndarray, tf.Tensor]] = None, + training: bool = False, ): r""" start_positions (`tf.Tensor` of shape `(batch_size,)`, *optional*):