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added type hints for blenderbot and blenderbot_small (#16307)

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ivanllt 2022-03-22 03:13:58 +08:00 committed by GitHub
parent e226a24f84
commit 96cd5bcbb9
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 135 additions and 135 deletions
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66
src/transformers/models/blenderbot/modeling_blenderbot.py
View File

@ -1119,22 +1119,22 @@ class BlenderbotModel(BlenderbotPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=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,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r"""
Returns:
@ -1275,23 +1275,23 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

70
src/transformers/models/blenderbot/modeling_tf_blenderbot.py
View File

@ -18,7 +18,7 @@
import os
import random
import warnings
from typing import Optional, Tuple, Union
from typing import List, Optional, Tuple, Union
import tensorflow as tf
@ -1137,24 +1137,24 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
input_ids: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = 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,
training=False,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds: Optional[tf.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs
):
) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
@ -1253,25 +1253,25 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs: Optional[TFBaseModelOutput] = 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,
input_ids: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds: Optional[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: Optional[bool] = False,
**kwargs,
):
) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

66
src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
View File

@ -1102,22 +1102,22 @@ class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=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,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r"""
Returns:
@ -1246,23 +1246,23 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def forward(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
encoder_outputs=None,
past_key_values=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,

68
src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
View File

@ -16,7 +16,7 @@
import random
from typing import Optional, Tuple, Union
from typing import List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
@ -1132,24 +1132,24 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
input_ids: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = 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,
training=False,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds: Optional[tf.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
**kwargs
):
) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
outputs = self.model(
input_ids=input_ids,
@ -1236,25 +1236,25 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def call(
self,
input_ids=None,
attention_mask=None,
decoder_input_ids=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
input_ids: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[TFBaseModelOutput] = 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,
past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds: Optional[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: Optional[bool] = False,
**kwargs,
):
) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,