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Added Type hints for LED TF (#19315)

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* Update modeling_tf_led.py

* Update modeling_tf_led.py
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IMvision12 2022-10-04 19:25:15 +05:30 committed by GitHub
parent 3a1a56a8fe
commit ac5ea74ee8
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1 changed files with 18 additions and 16 deletions
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34
src/transformers/models/led/modeling_tf_led.py
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@ -19,6 +19,7 @@ import random
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
@ -26,6 +27,7 @@ from ...modeling_tf_outputs import TFBaseModelOutputWithPastAndCrossAttentions
# Public API
from ...modeling_tf_utils import (
TFModelInputType,
TFPreTrainedModel,
TFSharedEmbeddings,
TFWrappedEmbeddings,
@ -2390,23 +2392,23 @@ class TFLEDForConditionalGeneration(TFLEDPreTrainedModel):
@replace_return_docstrings(output_type=TFLEDSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_outputs: Optional[TFLEDEncoderBaseModelOutput] = None,
global_attention_mask=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
global_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[tf.Tensor] = None,
training: bool = False,
):
"""
Returns: