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Unverified Commit 96cd5bcb authored by ivanllt's avatar ivanllt Committed by GitHub
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added type hints for blenderbot and blenderbot_small (#16307)

parent e226a24f
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Showing with 135 additions and 135 deletions
src/transformers/models/blenderbot/modeling_blenderbot.py
View file @ 96cd5bcb
...@@ -1119,22 +1119,22 @@ class BlenderbotModel(BlenderbotPreTrainedModel): ...@@ -1119,22 +1119,22 @@ class BlenderbotModel(BlenderbotPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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,
): ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r""" r"""
Returns: Returns:
...@@ -1275,23 +1275,23 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel): ...@@ -1275,23 +1275,23 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE) @add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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,
): ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
......
src/transformers/models/blenderbot/modeling_tf_blenderbot.py
View file @ 96cd5bcb
...@@ -18,7 +18,7 @@ ...@@ -18,7 +18,7 @@
import os import os
import random import random
import warnings import warnings
from typing import Optional, Tuple, Union from typing import List, Optional, Tuple, Union
import tensorflow as tf import tensorflow as tf
...@@ -1137,24 +1137,24 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel): ...@@ -1137,24 +1137,24 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[tf.Tensor] = None,
attention_mask=None, attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask=None, head_mask: Optional[tf.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[tf.Tensor] = None,
use_cache=None, use_cache: Optional[bool] = 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: Optional[bool] = False,
**kwargs **kwargs
): ) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
outputs = self.model( outputs = self.model(
input_ids=input_ids, input_ids=input_ids,
attention_mask=attention_mask, attention_mask=attention_mask,
...@@ -1253,25 +1253,25 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal ...@@ -1253,25 +1253,25 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE) @add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
def call( def call(
self, self,
input_ids=None, input_ids: Optional[tf.Tensor] = None,
attention_mask=None, attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask=None, head_mask: Optional[tf.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[TFBaseModelOutput] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[tf.Tensor] = None,
use_cache=None, use_cache: Optional[bool] = 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[tf.Tensor] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r""" r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
......
src/transformers/models/blenderbot_small/modeling_blenderbot_small.py
View file @ 96cd5bcb
...@@ -1102,22 +1102,22 @@ class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel): ...@@ -1102,22 +1102,22 @@ class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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,
): ) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
r""" r"""
Returns: Returns:
...@@ -1246,23 +1246,23 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel): ...@@ -1246,23 +1246,23 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE) @add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.LongTensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs=None, encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels=None, labels: Optional[torch.LongTensor] = None,
use_cache=None, use_cache: Optional[bool] = 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,
): ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
r""" r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
......
src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
View file @ 96cd5bcb
...@@ -16,7 +16,7 @@ ...@@ -16,7 +16,7 @@
import random import random
from typing import Optional, Tuple, Union from typing import List, Optional, Tuple, Union
import numpy as np import numpy as np
import tensorflow as tf import tensorflow as tf
...@@ -1132,24 +1132,24 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel): ...@@ -1132,24 +1132,24 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
) )
def call( def call(
self, self,
input_ids=None, input_ids: Optional[tf.Tensor] = None,
attention_mask=None, attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask=None, head_mask: Optional[tf.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
past_key_values=None, past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[tf.Tensor] = None,
use_cache=None, use_cache: Optional[bool] = 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: Optional[bool] = False,
**kwargs **kwargs
): ) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
outputs = self.model( outputs = self.model(
input_ids=input_ids, input_ids=input_ids,
...@@ -1236,25 +1236,25 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel ...@@ -1236,25 +1236,25 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE) @add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
def call( def call(
self, self,
input_ids=None, input_ids: Optional[tf.Tensor] = None,
attention_mask=None, attention_mask: Optional[tf.Tensor] = None,
decoder_input_ids=None, decoder_input_ids: Optional[tf.Tensor] = None,
decoder_attention_mask=None, decoder_attention_mask: Optional[tf.Tensor] = None,
head_mask=None, head_mask: Optional[tf.Tensor] = None,
decoder_head_mask=None, decoder_head_mask: Optional[tf.Tensor] = None,
cross_attn_head_mask=None, cross_attn_head_mask: Optional[tf.Tensor] = None,
encoder_outputs: Optional[TFBaseModelOutput] = None, encoder_outputs: Optional[TFBaseModelOutput] = None,
past_key_values=None, past_key_values: Optional[List[tf.Tensor]] = None,
inputs_embeds=None, inputs_embeds: Optional[tf.Tensor] = None,
decoder_inputs_embeds=None, decoder_inputs_embeds: Optional[tf.Tensor] = None,
use_cache=None, use_cache: Optional[bool] = 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[tf.Tensor] = None,
training=False, training: Optional[bool] = False,
**kwargs, **kwargs,
): ) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
r""" r"""
labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*): labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
......
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