Parameters

• image (np.ndarray) —The input image to be processed.
• grid_pinpoints (list[tuple[int, int]]) —A list of possible resolutions as tuples.
• patch_size (int) —Size of the patches to divide the image into.
• resample (PILImageResampling) —Resampling filter to use if resizing the image.
• data_format (ChannelDimension orstr) —The channel dimension format for the output image.
• input_data_format (ChannelDimension orstr) —The channel dimension format of the input image.

A list of NumPy arrays containing the processed image patches.

Parameters

• height (int) —Height of the input image.
• width (int) —Width of the input image.
• images_kwargs (dict,optional) —Any kwargs to override defaults of the image processor.

Number of patches per image.

Parameters

• image (np.ndarray) —The image to pad.
• padding (int ortuple[int, int] orIterable[tuple[int, int]]) —Padding to apply to the edges of the height, width axes. Can be one of three formats:
  • ((before_height, after_height), (before_width, after_width)) unique pad widths for each axis.
  • ((before, after),) yields same before and after pad for height and width.
  • (pad,) or int is a shortcut for before = after = pad width for all axes.
• mode (PaddingMode) —The padding mode to use. Can be one of:
  • "constant": pads with a constant value.
  • "reflect": pads with the reflection of the vector mirrored on the first and last values of thevector along each axis.
  • "replicate": pads with the replication of the last value on the edge of the array along each axis.
  • "symmetric": pads with the reflection of the vector mirrored along the edge of the array.
• constant_values (float orIterable[float],optional) —The value to use for the padding ifmode is"constant".
• data_format (str orChannelDimension,optional) —The channel dimension format for the output image. Can be one of:
  • "channels_first" orChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" orChannelDimension.LAST: image in (height, width, num_channels) format.If unset, will use same as the input image.
• input_data_format (str orChannelDimension,optional) —The channel dimension format for the input image. Can be one of:
  • "channels_first" orChannelDimension.FIRST: image in (num_channels, height, width) format.
  • "channels_last" orChannelDimension.LAST: image in (height, width, num_channels) format.If unset, will use the inferred format of the input image.

The padded image.

Parameters

• images (ImageInput or list of ImageInput) —The input image or a list of images.
• image_mean (list,optional, defaults to [0.5, 0.5, 0.5]) —Mean values for normalization.
• image_std (list,optional, defaults to [0.5, 0.5, 0.5]) —Standard deviation values for normalization.
• max_image_size (int,optional, defaults toself.max_image_size (980)) —Maximum image size.
• min_image_size (int,optional, defaults toself.min_image_size (336)) —Minimum image size.
• split_image (bool,optional, defaults toself.split_image (False)) —Whether to split the image.
• do_convert_rgb (bool,optional, defaults toself.do_convert_rgb (True)) —Whether to convert the image to RGB.
• do_rescale (bool,optional, defaults toself.do_rescale) —Whether to rescale the image.
• rescale_factor (float,optional, defaults toself.rescale_factor) —Rescale factor to rescale the image by ifdo_rescale is set toTrue.
• do_normalize (bool,optional, defaults toself.do_normalize (True)) —Whether to normalize the image.
• resample (PILImageResampling,optional, defaults toself.resample (BICUBIC)) —The resampling filter to use if resizing the image.
• return_tensors (str orTensorType,optional, defaults to “pt”) —The type of tensor to return.
• data_format (str orChannelDimension,optional) —The channel dimension format for the output image. Can be one of:
  • "channels_first" orChannelDimension.FIRST:image in (num_channels, height, width) format.
  • "channels_last" orChannelDimension.LAST:image in (height, width, num_channels) format.If unset, will use same as the input image.
• input_data_format (str orChannelDimension,optional) —The channel dimension format for the input image. Can be one of:
  • "channels_first" orChannelDimension.FIRST:image in (num_channels, height, width) format.
  • "channels_last" orChannelDimension.LAST:image in (height, width, num_channels) format.If unset, will use the inferred format of the input image.

A BatchFeature object containing:

• ‘pixel_values’:Tensor of processed image pixel values.
• ‘pixel_mask’:Boolean pixel mask. This mask is a 2D tensor of shape (max_image_size, max_image_size) where:
  • True (1) values indicate pixels that belong to the original resized image.
  • False (0) values indicate pixels that are part of the padding.The mask helps distinguish between actual image content and padded areas in subsequent processing steps.
• ‘num_crops’:The maximum number of crops across all images.

Parameters

• input_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.

  What are input IDs?

• attention_mask (torch.Tensor of shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:
  • 1 for tokens that arenot masked,
  • 0 for tokens that aremasked.

  What are attention masks?

• position_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1].

  What are position IDs?

• past_key_values (~cache_utils.Cache,optional) —Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attentionblocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=True orconfig.use_cache=True.

  OnlyCache instance is allowed as input, see ourkv cache guide.If nopast_key_values are passed,DynamicCache will be initialized by default.

  The model will output the same cache format that is fed as input.

  Ifpast_key_values are used, the user is expected to input only unprocessedinput_ids (those that don’thave their past key value states given to this model) of shape(batch_size, unprocessed_length) instead of allinput_idsof shape(batch_size, sequence_length).

• inputs_embeds (torch.FloatTensor of shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_ids you can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_ids indices into associated vectors than themodel’s internal embedding lookup matrix.
• cache_position (torch.LongTensor of shape(sequence_length),optional) —Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids,this tensor is not affected by padding. It is used to update the cache in the correct position and to inferthe complete sequence length.
• use_cache (bool,optional) —If set toTrue,past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).

Atransformers.modeling_outputs.BaseModelOutputWithPast or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (AriaConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape(batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  Ifpast_key_values is used only the last hidden-state of the sequences of shape(batch_size, 1, hidden_size) is output.

• past_key_values (Cache,optional, returned whenuse_cache=True is passed or whenconfig.use_cache=True) — It is aCache instance. For more details, see ourkv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally ifconfig.is_encoder_decoder=True in the cross-attention blocks) that can be used (seepast_key_valuesinput) to speed up sequential decoding.

• hidden_states (tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

• attentions (tuple(torch.FloatTensor),optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor (one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attentionheads.

Parameters

• input_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.

  What are input IDs?

• pixel_values (torch.FloatTensor of shape(batch_size, num_channels, image_size, image_size),optional) —The tensors corresponding to the input images. Pixel values can be obtained usingAriaImageProcessor. SeeAriaImageProcessor.call() for details (AriaProcessor usesAriaImageProcessor for processing images).
• pixel_mask (torch.LongTensor of shape(batch_size, height, width),optional) —Mask to avoid performing attention on padding pixel values. Mask values selected in[0, 1]:
  • 1 for pixels that are real (i.e.not masked),
  • 0 for pixels that are padding (i.e.masked).

  What are attention masks?

• attention_mask (torch.Tensor of shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:
  • 1 for tokens that arenot masked,
  • 0 for tokens that aremasked.

  What are attention masks?

• position_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1].

  What are position IDs?

• past_key_values (~cache_utils.Cache,optional) —Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attentionblocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=True orconfig.use_cache=True.

  OnlyCache instance is allowed as input, see ourkv cache guide.If nopast_key_values are passed,DynamicCache will be initialized by default.

  The model will output the same cache format that is fed as input.

  Ifpast_key_values are used, the user is expected to input only unprocessedinput_ids (those that don’thave their past key value states given to this model) of shape(batch_size, unprocessed_length) instead of allinput_idsof shape(batch_size, sequence_length).

• inputs_embeds (torch.FloatTensor of shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_ids you can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_ids indices into associated vectors than themodel’s internal embedding lookup matrix.
• use_cache (bool,optional) —If set toTrue,past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• cache_position (torch.LongTensor of shape(sequence_length),optional) —Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids,this tensor is not affected by padding. It is used to update the cache in the correct position and to inferthe complete sequence length.

Atransformers.models.aria.modeling_aria.AriaModelOutputWithPast or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (AriaConfig) and inputs.

• last_hidden_state (torch.FloatTensor of shape(batch_size, sequence_length, hidden_size),optional) — Sequence of hidden-states at the output of the last layer of the model.

• past_key_values (Cache,optional, returned whenuse_cache=True is passed or whenconfig.use_cache=True) — It is aCache instance. For more details, see ourkv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (seepast_key_values input) to speed up sequential decoding.

• hidden_states (tuple[torch.FloatTensor, ...],optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

• attentions (tuple[torch.FloatTensor, ...],optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor (one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attentionheads.

• image_hidden_states (torch.FloatTensor,optional) — Atorch.FloatTensor of size(batch_size, num_images, sequence_length, hidden_size).image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

Parameters

• pixel_values (torch.FloatTensor] of shape(batch_size, channels, height, width)) —The tensors corresponding to the input images.
• pixel_mask (torch.FloatTensor],optional) —The tensors corresponding to the input image mask.
• vision_feature_layer (Union[int, list[int]],optional) —The index of the layer to select the vision feature. If multiple indices are provided,the vision feature of the corresponding indices will be concatenated to form thevision features.

Image feature tensor of shape(num_images, image_length, embed_dim)).

Parameters

• input_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.

  What are input IDs?

• attention_mask (torch.Tensor of shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:
  • 1 for tokens that arenot masked,
  • 0 for tokens that aremasked.

  What are attention masks?

• position_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1].

  What are position IDs?

• past_key_values (~cache_utils.Cache,optional) —Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attentionblocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=True orconfig.use_cache=True.

  OnlyCache instance is allowed as input, see ourkv cache guide.If nopast_key_values are passed,DynamicCache will be initialized by default.

  The model will output the same cache format that is fed as input.

  Ifpast_key_values are used, the user is expected to input only unprocessedinput_ids (those that don’thave their past key value states given to this model) of shape(batch_size, unprocessed_length) instead of allinput_idsof shape(batch_size, sequence_length).

• inputs_embeds (torch.FloatTensor of shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_ids you can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_ids indices into associated vectors than themodel’s internal embedding lookup matrix.
• labels (torch.LongTensor of shape(batch_size, sequence_length),optional) —Labels for computing the masked language modeling loss. Indices should either be in[0, ..., config.vocab_size] or -100 (seeinput_ids docstring). Tokens with indices set to-100 are ignored(masked), the loss is only computed for the tokens with labels in[0, ..., config.vocab_size].
• use_cache (bool,optional) —If set toTrue,past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• cache_position (torch.LongTensor of shape(sequence_length),optional) —Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids,this tensor is not affected by padding. It is used to update the cache in the correct position and to inferthe complete sequence length.
• logits_to_keep (Union[int, torch.Tensor], defaults to0) —If anint, compute logits for the lastlogits_to_keep tokens. If0, calculate logits for allinput_ids (special case). Only last token logits are needed for generation, and calculating them only for thattoken can save memory, which becomes pretty significant for long sequences or large vocabulary size.If atorch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension.This is useful when using packed tensor format (single dimension for batch and sequence length).

Atransformers.modeling_outputs.CausalLMOutputWithPast or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (AriaConfig) and inputs.

• loss (torch.FloatTensor of shape(1,),optional, returned whenlabels is provided) — Language modeling loss (for next-token prediction).

• logits (torch.FloatTensor of shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

• past_key_values (Cache,optional, returned whenuse_cache=True is passed or whenconfig.use_cache=True) — It is aCache instance. For more details, see ourkv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (seepast_key_values input) to speed up sequential decoding.

• hidden_states (tuple(torch.FloatTensor),optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

• attentions (tuple(torch.FloatTensor),optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor (one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attentionheads.

>>>from transformersimport AutoTokenizer, AriaTextForCausalLM>>>model = AriaTextForCausalLM.from_pretrained("meta-aria_text/AriaText-2-7b-hf")>>>tokenizer = AutoTokenizer.from_pretrained("meta-aria_text/AriaText-2-7b-hf")>>>prompt ="Hey, are you conscious? Can you talk to me?">>>inputs = tokenizer(prompt, return_tensors="pt")>>># Generate>>>generate_ids = model.generate(inputs.input_ids, max_length=30)>>>tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."

Parameters

• input_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained usingAutoTokenizer. SeePreTrainedTokenizer.encode() andPreTrainedTokenizer.call() for details.

  What are input IDs?

• pixel_values (torch.FloatTensor of shape(batch_size, num_channels, image_size, image_size),optional) —The tensors corresponding to the input images. Pixel values can be obtained usingAriaImageProcessor. SeeAriaImageProcessor.call() for details (AriaProcessor usesAriaImageProcessor for processing images).
• pixel_mask (torch.LongTensor of shape(batch_size, height, width),optional) —Mask to avoid performing attention on padding pixel values. Mask values selected in[0, 1]:
  • 1 for pixels that are real (i.e.not masked),
  • 0 for pixels that are padding (i.e.masked).

  What are attention masks?

• attention_mask (torch.Tensor of shape(batch_size, sequence_length),optional) —Mask to avoid performing attention on padding token indices. Mask values selected in[0, 1]:
  • 1 for tokens that arenot masked,
  • 0 for tokens that aremasked.

  What are attention masks?

• position_ids (torch.LongTensor of shape(batch_size, sequence_length),optional) —Indices of positions of each input sequence tokens in the position embeddings. Selected in the range[0, config.n_positions - 1].

  What are position IDs?

• past_key_values (~cache_utils.Cache,optional) —Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attentionblocks) that can be used to speed up sequential decoding. This typically consists in thepast_key_valuesreturned by the model at a previous stage of decoding, whenuse_cache=True orconfig.use_cache=True.

  OnlyCache instance is allowed as input, see ourkv cache guide.If nopast_key_values are passed,DynamicCache will be initialized by default.

  The model will output the same cache format that is fed as input.

  Ifpast_key_values are used, the user is expected to input only unprocessedinput_ids (those that don’thave their past key value states given to this model) of shape(batch_size, unprocessed_length) instead of allinput_idsof shape(batch_size, sequence_length).

• inputs_embeds (torch.FloatTensor of shape(batch_size, sequence_length, hidden_size),optional) —Optionally, instead of passinginput_ids you can choose to directly pass an embedded representation. Thisis useful if you want more control over how to convertinput_ids indices into associated vectors than themodel’s internal embedding lookup matrix.
• labels (torch.LongTensor of shape(batch_size, sequence_length),optional) —Labels for computing the masked language modeling loss. Indices should either be in[0, ..., config.vocab_size] ormodel.image_token_id (wheremodel is your instance ofAriaForConditionalGeneration).Tokens with indices set tomodel.image_token_id are ignored (masked), the loss is onlycomputed for the tokens with labels in[0, ..., config.vocab_size].
• use_cache (bool,optional) —If set toTrue,past_key_values key value states are returned and can be used to speed up decoding (seepast_key_values).
• logits_to_keep (Union[int, torch.Tensor], defaults to0) —If anint, compute logits for the lastlogits_to_keep tokens. If0, calculate logits for allinput_ids (special case). Only last token logits are needed for generation, and calculating them only for thattoken can save memory, which becomes pretty significant for long sequences or large vocabulary size.If atorch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension.This is useful when using packed tensor format (single dimension for batch and sequence length).
• cache_position (torch.LongTensor of shape(sequence_length),optional) —Indices depicting the position of the input sequence tokens in the sequence. Contrarily toposition_ids,this tensor is not affected by padding. It is used to update the cache in the correct position and to inferthe complete sequence length.

Atransformers.models.aria.modeling_aria.AriaCausalLMOutputWithPast or a tuple oftorch.FloatTensor (ifreturn_dict=False is passed or whenconfig.return_dict=False) comprising variouselements depending on the configuration (AriaConfig) and inputs.

• loss (torch.FloatTensor of shape(1,),optional, returned whenlabels is provided) — Language modeling loss (for next-token prediction).

• logits (torch.FloatTensor of shape(batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

• past_key_values (Cache,optional, returned whenuse_cache=True is passed or whenconfig.use_cache=True) — It is aCache instance. For more details, see ourkv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (seepast_key_values input) to speed up sequential decoding.

• hidden_states (tuple[torch.FloatTensor],optional, returned whenoutput_hidden_states=True is passed or whenconfig.output_hidden_states=True) — Tuple oftorch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, +one for the output of each layer) of shape(batch_size, sequence_length, hidden_size).

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

• attentions (tuple[torch.FloatTensor],optional, returned whenoutput_attentions=True is passed or whenconfig.output_attentions=True) — Tuple oftorch.FloatTensor (one for each layer) of shape(batch_size, num_heads, sequence_length, sequence_length).

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attentionheads.

• image_hidden_states (torch.FloatTensor,optional) — Atorch.FloatTensor of size(batch_size, num_images, sequence_length, hidden_size).image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.

>>>import requests>>>import torch>>>from PILimport Image>>>from ioimport BytesIO>>>from transformersimport AutoProcessor, AutoModel>>>from transformers.image_utilsimport load_image>>># Note that passing the image urls (instead of the actual pil images) to the processor is also possible>>>image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")>>>image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")>>>image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")>>>processor = AutoProcessor.from_pretrained("Rhymes-AI/Aria")>>>model = AutoModel.from_pretrained("Rhymes-AI/Aria", dtype=torch.bfloat16, device_map="auto")>>># Create inputs>>>messages = [...    {..."role":"user",..."content": [...            {"type":"image"},...            {"type":"text","text":"In this image, we can see the city of New York, and more specifically the Statue of Liberty."},...            {"type":"image"},...            {"type":"text","text":"What can we see in this image?"},...        ]...    },...    {..."role":"user",..."content": [...            {"type":"image"},...            {"type":"text","text":"In which city is that bridge located?"},...        ]...    }...]>>>prompts = [processor.apply_chat_template([message], add_generation_prompt=True)for messagein messages]>>>images = [[image1, image2], [image3]]>>>inputs = processor(text=prompts, images=images, padding=True, return_tensors="pt").to(model.device)>>># Generate>>>generated_ids = model.generate(**inputs, max_new_tokens=256)>>>generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)>>>print(generated_texts[0])Assistant: There are buildings, trees, lights,and water visiblein this image.>>>print(generated_texts[1])Assistant: The bridgeisin San Francisco.