mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
added type hints for blenderbot and blenderbot_small (#16307)
This commit is contained in:
parent
e226a24f84
commit
96cd5bcbb9
@ -1119,22 +1119,22 @@ class BlenderbotModel(BlenderbotPreTrainedModel):
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@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
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@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
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def forward(
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def forward(
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self,
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self,
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input_ids=None,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask=None,
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attention_mask: Optional[torch.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[torch.LongTensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[torch.LongTensor] = None,
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head_mask=None,
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head_mask: Optional[torch.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[torch.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[torch.Tensor] = None,
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encoder_outputs=None,
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encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[torch.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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):
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) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
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r"""
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r"""
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Returns:
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Returns:
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@ -1275,23 +1275,23 @@ class BlenderbotForConditionalGeneration(BlenderbotPreTrainedModel):
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@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
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@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
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def forward(
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def forward(
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self,
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self,
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input_ids=None,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask=None,
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attention_mask: Optional[torch.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[torch.LongTensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[torch.LongTensor] = None,
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head_mask=None,
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head_mask: Optional[torch.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[torch.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[torch.Tensor] = None,
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encoder_outputs=None,
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encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[torch.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
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labels=None,
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labels: Optional[torch.LongTensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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):
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) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
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r"""
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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@ -18,7 +18,7 @@
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import os
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import os
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import random
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import random
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import warnings
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import warnings
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from typing import Optional, Tuple, Union
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from typing import List, Optional, Tuple, Union
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import tensorflow as tf
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import tensorflow as tf
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@ -1137,24 +1137,24 @@ class TFBlenderbotModel(TFBlenderbotPreTrainedModel):
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)
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)
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def call(
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def call(
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self,
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self,
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input_ids=None,
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input_ids: Optional[tf.Tensor] = None,
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attention_mask=None,
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attention_mask: Optional[tf.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[tf.Tensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[tf.Tensor] = None,
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head_mask=None,
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head_mask: Optional[tf.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[tf.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[tf.Tensor] = None,
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encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
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encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[tf.Tensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[tf.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[tf.Tensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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training=False,
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training: Optional[bool] = False,
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**kwargs
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**kwargs
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):
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) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
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outputs = self.model(
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outputs = self.model(
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input_ids=input_ids,
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input_ids=input_ids,
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attention_mask=attention_mask,
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attention_mask=attention_mask,
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@ -1253,25 +1253,25 @@ class TFBlenderbotForConditionalGeneration(TFBlenderbotPreTrainedModel, TFCausal
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@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
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@add_end_docstrings(BLENDERBOT_GENERATION_EXAMPLE)
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def call(
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def call(
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self,
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self,
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input_ids=None,
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input_ids: Optional[tf.Tensor] = None,
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attention_mask=None,
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attention_mask: Optional[tf.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[tf.Tensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[tf.Tensor] = None,
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head_mask=None,
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head_mask: Optional[tf.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[tf.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[tf.Tensor] = None,
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encoder_outputs: Optional[TFBaseModelOutput] = None,
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encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[tf.Tensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[tf.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[tf.Tensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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labels=None,
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labels: Optional[tf.Tensor] = None,
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training=False,
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training: Optional[bool] = False,
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**kwargs,
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**kwargs,
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):
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) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
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r"""
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r"""
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labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
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labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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@ -1102,22 +1102,22 @@ class BlenderbotSmallModel(BlenderbotSmallPreTrainedModel):
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@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
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@replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
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def forward(
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def forward(
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self,
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self,
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input_ids=None,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask=None,
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attention_mask: Optional[torch.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[torch.LongTensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[torch.LongTensor] = None,
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head_mask=None,
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head_mask: Optional[torch.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[torch.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[torch.Tensor] = None,
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encoder_outputs=None,
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encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[torch.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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):
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) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
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r"""
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r"""
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Returns:
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Returns:
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@ -1246,23 +1246,23 @@ class BlenderbotSmallForConditionalGeneration(BlenderbotSmallPreTrainedModel):
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@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
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@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
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def forward(
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def forward(
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self,
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self,
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input_ids=None,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask=None,
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attention_mask: Optional[torch.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[torch.LongTensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[torch.LongTensor] = None,
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head_mask=None,
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head_mask: Optional[torch.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[torch.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[torch.Tensor] = None,
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encoder_outputs=None,
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encoder_outputs: Optional[Union[Tuple, BaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[torch.FloatTensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[torch.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
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labels=None,
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labels: Optional[torch.LongTensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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):
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) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
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r"""
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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@ -16,7 +16,7 @@
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import random
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import random
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from typing import Optional, Tuple, Union
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import numpy as np
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import tensorflow as tf
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import tensorflow as tf
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@ -1132,24 +1132,24 @@ class TFBlenderbotSmallModel(TFBlenderbotSmallPreTrainedModel):
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)
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)
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def call(
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def call(
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self,
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self,
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input_ids=None,
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input_ids: Optional[tf.Tensor] = None,
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attention_mask=None,
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attention_mask: Optional[tf.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[tf.Tensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[tf.Tensor] = None,
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head_mask=None,
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head_mask: Optional[tf.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[tf.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[tf.Tensor] = None,
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encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
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encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
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past_key_values=None,
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past_key_values: Optional[List[tf.Tensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[tf.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[tf.Tensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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training=False,
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training: Optional[bool] = False,
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**kwargs
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**kwargs
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):
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) -> Union[Tuple[tf.Tensor], TFSeq2SeqModelOutput]:
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outputs = self.model(
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outputs = self.model(
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input_ids=input_ids,
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input_ids=input_ids,
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@ -1236,25 +1236,25 @@ class TFBlenderbotSmallForConditionalGeneration(TFBlenderbotSmallPreTrainedModel
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@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
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@add_end_docstrings(BLENDERBOT_SMALL_GENERATION_EXAMPLE)
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def call(
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def call(
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self,
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self,
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input_ids=None,
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input_ids: Optional[tf.Tensor] = None,
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attention_mask=None,
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attention_mask: Optional[tf.Tensor] = None,
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decoder_input_ids=None,
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decoder_input_ids: Optional[tf.Tensor] = None,
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decoder_attention_mask=None,
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decoder_attention_mask: Optional[tf.Tensor] = None,
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head_mask=None,
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head_mask: Optional[tf.Tensor] = None,
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decoder_head_mask=None,
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decoder_head_mask: Optional[tf.Tensor] = None,
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cross_attn_head_mask=None,
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cross_attn_head_mask: Optional[tf.Tensor] = None,
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encoder_outputs: Optional[TFBaseModelOutput] = None,
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encoder_outputs: Optional[TFBaseModelOutput] = None,
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past_key_values=None,
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past_key_values: Optional[List[tf.Tensor]] = None,
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inputs_embeds=None,
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inputs_embeds: Optional[tf.Tensor] = None,
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decoder_inputs_embeds=None,
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decoder_inputs_embeds: Optional[tf.Tensor] = None,
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use_cache=None,
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use_cache: Optional[bool] = None,
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output_attentions=None,
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output_attentions: Optional[bool] = None,
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output_hidden_states=None,
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output_hidden_states: Optional[bool] = None,
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return_dict=None,
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return_dict: Optional[bool] = None,
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labels=None,
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labels: Optional[tf.Tensor] = None,
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training=False,
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training: Optional[bool] = False,
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**kwargs,
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**kwargs,
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):
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) -> Union[Tuple[tf.Tensor], TFSeq2SeqLMOutput]:
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r"""
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r"""
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labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
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labels (`tf.tensor` of shape `(batch_size, sequence_length)`, *optional*):
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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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