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Fix quality with ruff==0.0.253 (#21828)

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fix quality with ruff 0.0.253

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar 2023-02-27 19:38:44 +01:00 committed by GitHub
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1 changed files with 3 additions and 3 deletions
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src/transformers/models/blip_2/modeling_blip_2.py
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@ -347,7 +347,7 @@ BLIP_2_INPUTS_DOCSTRING = r"""
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for Pixel values. Pixel values can be obtained using [`Blip2Processor`]. See [`Blip2Processor.__call__`] for
details. details.
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be Indices of input sequence tokens in the vocabulary of the language model. Input tokens can optionally be
provided to serve as text prompt, which the language model can continue. provided to serve as text prompt, which the language model can continue.
@ -366,10 +366,10 @@ BLIP_2_INPUTS_DOCSTRING = r"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an Indices of decoder input sequence tokens in the vocabulary of the language model. Only relevant in case an
encoder-decoder language model (like T5) is used. encoder-decoder language model (like T5) is used.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids)
decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*): decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default. be used by default.